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Wood C, Speed V, Fisher L, Curtis HJ, Schaffer AL, Walker AJ, Croker R, Brown AD, Cunningham C, Hulme WJ, Andrews CD, Butler-Cole BFC, Evans D, Inglesby P, Dillingham I, Bacon SCJ, Davy S, Ward T, Hickman G, Bridges L, O'Dwyer T, Maude S, Smith RM, Mehrkar A, Bates C, Cockburn J, Parry J, Hester F, Harper S, Goldacre B, MacKenna B. The impact of COVID-19 on medication reviews in English primary care. An OpenSAFELY-TPP analysis of 20 million adult electronic health records. Br J Clin Pharmacol 2024. [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.
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Affiliation(s)
- Christopher Wood
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Victoria Speed
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
- Department of Thrombosis and Haemostasis, King's College Hospital, London, UK
| | - Louis Fisher
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Helen J Curtis
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Andrea L Schaffer
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Alex J Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Richard Croker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Andrew D Brown
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Christine Cunningham
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - William J Hulme
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Colm D Andrews
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Ben F C Butler-Cole
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - David Evans
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Peter Inglesby
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Iain Dillingham
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Sebastian C J Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Simon Davy
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Tom Ward
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - George Hickman
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Lucy Bridges
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Thomas O'Dwyer
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Steven Maude
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Rebecca M Smith
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | | | | | | | | | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
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Noble SC, Woods E, Ward T, Ringwood JV. Accelerating P300-based neurofeedback training for attention enhancement using iterative learning control: a randomised controlled trial. J Neural Eng 2024; 21:026006. [PMID: 38394680 DOI: 10.1088/1741-2552/ad2c9e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 02/23/2024] [Indexed: 02/25/2024]
Abstract
Objective. Neurofeedback (NFB) training through brain-computer interfacing has demonstrated efficacy in treating neurological deficits and diseases, and enhancing cognitive abilities in healthy individuals. It was previously shown that event-related potential (ERP)-based NFB training using a P300 speller can improve attention in healthy adults by incrementally increasing the difficulty of the spelling task. This study aims to assess the impact of task difficulty adaptation on ERP-based attention training in healthy adults. To achieve this, we introduce a novel adaptation employing iterative learning control (ILC) and compare it against an existing method and a control group with random task difficulty variation.Approach. The study involved 45 healthy participants in a single-blind, three-arm randomised controlled trial. Each group underwent one NFB training session, using different methods to adapt task difficulty in a P300 spelling task: two groups with personalised difficulty adjustments (our proposed ILC and an existing approach) and one group with random difficulty. Cognitive performance was evaluated before and after the training session using a visual spatial attention task and we gathered participant feedback through questionnaires.Main results. All groups demonstrated a significant performance improvement in the spatial attention task post-training, with an average increase of 12.63%. Notably, the group using the proposed iterative learning controller achieved a 22% increase in P300 amplitude during training and a 17% reduction in post-training alpha power, all while significantly accelerating the training process compared to other groups.Significance. Our results suggest that ERP-based NFB training using a P300 speller effectively enhances attention in healthy adults, with significant improvements observed after a single session. Personalised task difficulty adaptation using ILC not only accelerates the training but also enhances ERPs during the training. Accelerating NFB training, while maintaining its effectiveness, is vital for its acceptability by both end-users and clinicians.
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Affiliation(s)
- S-C Noble
- Department of Electronic Engineering, Maynooth University, Maynooth, Ireland
| | - E Woods
- Discipline of Physiology, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - T Ward
- Insight SFI Research Centre for Data Analytics, Dublin City University, Dublin, Ireland
| | - J V Ringwood
- Department of Electronic Engineering, Maynooth University, Maynooth, Ireland
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Overton CE, Abbey R, Baird T, Christie R, Daniel O, Day J, Gittins M, Jones O, Paton R, Tang M, Ward T, Wilkinson J, Woodrow-Hill C, Aldridge T, Chen Y. Identifying employee, workplace and population characteristics associated with COVID-19 outbreaks in the workplace: a population-based study. Occup Environ Med 2024; 81:92-100. [PMID: 38191477 DOI: 10.1136/oemed-2023-109032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 11/15/2023] [Indexed: 01/10/2024]
Abstract
OBJECTIVES To identify risk factors that contribute to outbreaks of COVID-19 in the workplace and quantify their effect on outbreak risk. METHODS We identified outbreaks of COVID-19 cases in the workplace and investigated the characteristics of the individuals, the workplaces, the areas they work and the mode of commute to work, through data linkages based on Middle Layer Super Output Areas in England between 20 June 2021 and 20 February 2022. We estimated population-level associations between potential risk factors and workplace outbreaks, adjusting for plausible confounders identified using a directed acyclic graph. RESULTS For most industries, increased physical proximity in the workplace was associated with increased risk of COVID-19 outbreaks, while increased vaccination was associated with reduced risk. Employee demographic risk factors varied across industry, but for the majority of industries, a higher proportion of black/African/Caribbean ethnicities and living in deprived areas, was associated with increased outbreak risk. A higher proportion of employees in the 60-64 age group was associated with reduced outbreak risk. There were significant associations between gender, work commute modes and staff contract type with outbreak risk, but these were highly variable across industries. CONCLUSIONS This study has used novel national data linkages to identify potential risk factors of workplace COVID-19 outbreaks, including possible protective effects of vaccination and increased physical distance at work. The same methodological approach can be applied to wider occupational and environmental health research.
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Affiliation(s)
- Christopher E Overton
- UK Health Security Agency, London, UK
- Department of Mathematical Sciences, University of Liverpool, Liverpool, UK
| | | | - Tarrion Baird
- UK Health Security Agency, London, UK
- Department of Pathology, University of Cambridge, Cambridge, UK
| | | | | | - Julie Day
- UK Health Security Agency, London, UK
| | - Matthew Gittins
- Centre for Biostatistics, The University of Manchester, Manchester, UK
| | | | | | | | - Tom Ward
- UK Health Security Agency, London, UK
| | - Jack Wilkinson
- Centre for Biostatistics, The University of Manchester, Manchester, UK
| | | | | | - Yiqun Chen
- Science Division, Health and Safety Executive, Buxton, UK
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4
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Macdonald O, Green A, Walker A, Curtis H, Croker R, Brown A, Butler-Cole B, Andrews C, Massey J, Inglesby P, Morton C, Fisher L, Morley J, Mehrkar A, Bacon S, Davy S, Evans D, Dillingham I, Ward T, Hulme W, Bates C, Cockburn J, Parry J, Hester F, Harper S, O'Hanlon S, Eavis A, Jarvis R, Avramov D, Parkes N, Wood I, Goldacre B, Mackenna B. Impact of the COVID-19 pandemic on antipsychotic prescribing in individuals with autism, dementia, learning disability, serious mental illness or living in a care home: a federated analysis of 59 million patients' primary care records in situ using OpenSAFELY. BMJ 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.
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Affiliation(s)
- Orla Macdonald
- Pharmacy, Oxford Health NHS Foundation Trust, Oxford, UK
| | - Amelia Green
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Alex Walker
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Helen Curtis
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Richard Croker
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Andrew Brown
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Ben Butler-Cole
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Colm Andrews
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Jon Massey
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Peter Inglesby
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Caroline Morton
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Louis Fisher
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Jessica Morley
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Amir Mehrkar
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Sebastian Bacon
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Simon Davy
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - David Evans
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Iain Dillingham
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Tom Ward
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - William Hulme
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | | | | | | | | | | | | | | | | | | | | | | | - Ben Goldacre
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
| | - Brian Mackenna
- Nuffield Department of Primary Care, Oxford University, Oxford, UK
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5
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Fisher L, Curtis HJ, Croker R, Wiedemann M, Speed V, Wood C, Brown A, Hopcroft LEM, Higgins R, Massey J, Inglesby P, Morton CE, Walker AJ, Morley J, Mehrkar A, Bacon S, Hickman G, Macdonald O, Lewis T, Wood M, Myers M, Samuel M, Conibere R, Baqir W, Sood H, Drury C, Collison K, Bates C, Evans D, Dillingham I, Ward T, Davy S, Smith RM, Hulme W, Green A, Parry J, Hester F, Harper S, Cockburn J, O'Hanlon S, Eavis A, Jarvis R, Avramov D, Griffiths P, Fowles A, Parkes N, MacKenna B, Goldacre B. Eleven key measures for monitoring general practice clinical activity during COVID-19: A retrospective cohort study using 48 million adults' primary care records in England through OpenSAFELY. eLife 2023; 12:e84673. [PMID: 37498081 PMCID: PMC10374277 DOI: 10.7554/elife.84673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [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).
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Affiliation(s)
- Louis Fisher
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Helen J Curtis
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Richard Croker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Milan Wiedemann
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Victoria Speed
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Christopher Wood
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Andrew Brown
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Lisa E M Hopcroft
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rose Higgins
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jon Massey
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Peter Inglesby
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Caroline E Morton
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Alex J Walker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jessica Morley
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Amir Mehrkar
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Seb Bacon
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - George Hickman
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Orla Macdonald
- Oxford Health Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Tom Lewis
- Royal Devon University Healthcare NHS Foundation Trust, Barnstaple, United Kingdom
| | | | - Martin Myers
- Lancashire Teaching Hospitals NHS Foundation Trust, Chorley, United Kingdom
| | - Miriam Samuel
- Queen Mary University of London, London, United Kingdom
| | | | | | | | - Charles Drury
- Herefordshire and Worcestershire Health and Care NHS Trust, Worcester, United Kingdom
| | | | | | - David Evans
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Iain Dillingham
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Tom Ward
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Simon Davy
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rebecca M Smith
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - William Hulme
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Amelia Green
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | | | | | | | | | | | | | | | | | | | | | - Brian MacKenna
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- NHS England, London, United Kingdom
| | - Ben Goldacre
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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6
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Fisher L, Hopcroft LEM, Rodgers S, Barrett J, Oliver K, Avery AJ, Evans D, Curtis H, Croker R, Macdonald O, Morley J, Mehrkar A, Bacon S, Davy S, Dillingham I, Evans D, Hickman G, Inglesby P, Morton CE, Smith B, Ward T, Hulme W, Green A, Massey J, Walker AJ, Bates C, Cockburn J, Parry J, Hester F, Harper S, O’Hanlon S, Eavis A, Jarvis R, Avramov D, Griffiths P, Fowles A, Parkes N, Goldacre B, MacKenna B. Changes in medication safety indicators in England throughout the covid-19 pandemic using OpenSAFELY: population based, retrospective cohort study of 57 million patients using federated analytics. BMJ Med 2023; 2:e000392. [PMID: 37303488 PMCID: PMC10254692 DOI: 10.1136/bmjmed-2022-000392] [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] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 03/16/2023] [Indexed: 06/13/2023]
Abstract
Objective To implement complex, PINCER (pharmacist led information technology intervention) prescribing indicators, on a national scale with general practice data to describe the impact of the covid-19 pandemic on safe prescribing. Design Population based, retrospective cohort study using federated analytics. Setting Electronic general practice health record data from 56.8 million NHS patients by use of the OpenSAFELY platform, with the approval of the National Health Service (NHS) England. Participants NHS patients (aged 18-120 years) who were alive and registered at a general practice that used TPP or EMIS computer systems and were recorded as at risk of at least one potentially hazardous PINCER indicator. Main outcome measure Between 1 September 2019 and 1 September 2021, monthly trends and between practice variation for compliance with 13 PINCER indicators, as calculated on the first of every month, were reported. Prescriptions that do not adhere to these indicators are potentially hazardous and can cause gastrointestinal bleeds; are cautioned against in specific conditions (specifically heart failure, asthma, and chronic renal failure); or require blood test monitoring. The percentage for each indicator is formed of a numerator of patients deemed to be at risk of a potentially hazardous prescribing event and the denominator is of patients for which assessment of the indicator is clinically meaningful. Higher indicator percentages represent potentially poorer performance on medication safety. Results The PINCER indicators were successfully implemented across general practice data for 56.8 million patient records from 6367 practices in OpenSAFELY. Hazardous prescribing remained largely unchanged during the covid-19 pandemic, with no evidence of increases in indicators of harm as captured by the PINCER indicators. The percentage of patients at risk of potentially hazardous prescribing, as defined by each PINCER indicator, at mean quarter 1 (Q1) 2020 (representing before the pandemic) ranged from 1.11% (age ≥65 years and non-steroidal anti-inflammatory drugs) to 36.20% (amiodarone and no thyroid function test), while Q1 2021 (representing after the pandemic) percentages ranged from 0.75% (age ≥65 years and non-steroidal anti-inflammatory drugs) to 39.23% (amiodarone and no thyroid function test). Transient delays occurred in blood test monitoring for some medications, particularly angiotensin-converting enzyme inhibitors (where blood monitoring worsened from a mean of 5.16% in Q1 2020 to 12.14% in Q1 2021, and began to recover in June 2021). All indicators substantially recovered by September 2021. We identified 1 813 058 patients (3.1%) at risk of at least one potentially hazardous prescribing event. Conclusion NHS data from general practices can be analysed at national scale to generate insights into service delivery. Potentially hazardous prescribing was largely unaffected by the covid-19 pandemic in primary care health records in England.
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Affiliation(s)
- Louis Fisher
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Lisa EM Hopcroft
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Sarah Rodgers
- PRIMIS, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - James Barrett
- PRIMIS, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - Kerry Oliver
- PRIMIS, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - Anthony J Avery
- Centre for Academic Primary Care, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - Dai Evans
- PRIMIS, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - Helen Curtis
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Richard Croker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Orla Macdonald
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Jessica Morley
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Sebastian Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Simon Davy
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Iain Dillingham
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - David Evans
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - George Hickman
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Peter Inglesby
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Caroline E Morton
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Becky Smith
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Tom Ward
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - William Hulme
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Amelia Green
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Jon Massey
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Alex J Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
| | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
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Nab L, Parker EPK, Andrews CD, Hulme WJ, Fisher L, Morley J, Mehrkar A, MacKenna B, Inglesby P, Morton CE, Bacon SCJ, Hickman G, Evans D, Ward T, Smith RM, Davy S, Dillingham I, Maude S, Butler-Cole BFC, O'Dwyer T, Stables CL, Bridges L, Bates C, Cockburn J, Parry J, Hester F, Harper S, Zheng B, Williamson EJ, Eggo RM, Evans SJW, Goldacre B, Tomlinson LA, Walker AJ. Changes in COVID-19-related mortality across key demographic and clinical subgroups in England from 2020 to 2022: a retrospective cohort study using the OpenSAFELY platform. Lancet Public Health 2023; 8:e364-e377. [PMID: 37120260 PMCID: PMC10139026 DOI: 10.1016/s2468-2667(23)00079-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/01/2023] [Accepted: 03/22/2023] [Indexed: 05/01/2023]
Abstract
BACKGROUND COVID-19 has been shown to differently affect various demographic and clinical population subgroups. We aimed to describe trends in absolute and relative COVID-19-related mortality risks across clinical and demographic population subgroups during successive SARS-CoV-2 pandemic waves. METHODS We did a retrospective cohort study in England using the OpenSAFELY platform with the approval of National Health Service England, covering the first five SARS-CoV-2 pandemic waves (wave one [wild-type] from March 23 to May 30, 2020; wave two [alpha (B.1.1.7)] from Sept 7, 2020, to April 24, 2021; wave three [delta (B.1.617.2)] from May 28 to Dec 14, 2021; wave four [omicron (B.1.1.529)] from Dec 15, 2021, to April 29, 2022; and wave five [omicron] from June 24 to Aug 3, 2022). In each wave, we included people aged 18-110 years who were registered with a general practice on the first day of the wave and who had at least 3 months of continuous general practice registration up to this date. We estimated crude and sex-standardised and age-standardised wave-specific COVID-19-related death rates and relative risks of COVID-19-related death in population subgroups. FINDINGS 18 895 870 adults were included in wave one, 19 014 720 in wave two, 18 932 050 in wave three, 19 097 970 in wave four, and 19 226 475 in wave five. Crude COVID-19-related death rates per 1000 person-years decreased from 4·48 deaths (95% CI 4·41-4·55) in wave one to 2·69 (2·66-2·72) in wave two, 0·64 (0·63-0·66) in wave three, 1·01 (0·99-1·03) in wave four, and 0·67 (0·64-0·71) in wave five. In wave one, the standardised COVID-19-related death rates were highest in people aged 80 years or older, people with chronic kidney disease stage 5 or 4, people receiving dialysis, people with dementia or learning disability, and people who had received a kidney transplant (ranging from 19·85 deaths per 1000 person-years to 44·41 deaths per 1000 person-years, compared with from 0·05 deaths per 1000 person-years to 15·93 deaths per 1000 person-years in other subgroups). In wave two compared with wave one, in a largely unvaccinated population, the decrease in COVID-19-related mortality was evenly distributed across population subgroups. In wave three compared with wave one, larger decreases in COVID-19-related death rates were seen in groups prioritised for primary SARS-CoV-2 vaccination, including people aged 80 years or older and people with neurological disease, learning disability, or severe mental illness (90-91% decrease). Conversely, smaller decreases in COVID-19-related death rates were observed in younger age groups, people who had received organ transplants, and people with chronic kidney disease, haematological malignancies, or immunosuppressive conditions (0-25% decrease). In wave four compared with wave one, the decrease in COVID-19-related death rates was smaller in groups with lower vaccination coverage (including younger age groups) and conditions associated with impaired vaccine response, including people who had received organ transplants and people with immunosuppressive conditions (26-61% decrease). INTERPRETATION There was a substantial decrease in absolute COVID-19-related death rates over time in the overall population, but demographic and clinical relative risk profiles persisted and worsened for people with lower vaccination coverage or impaired immune response. Our findings provide an evidence base to inform UK public health policy for protecting these vulnerable population subgroups. FUNDING UK Research and Innovation, Wellcome Trust, UK Medical Research Council, National Institute for Health and Care Research, and Health Data Research UK.
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Affiliation(s)
- Linda Nab
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Colm D Andrews
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - William J Hulme
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Louis Fisher
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Jessica Morley
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Peter Inglesby
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Caroline E Morton
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Sebastian C J Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - George Hickman
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - David Evans
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Tom Ward
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Rebecca M Smith
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Simon Davy
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Iain Dillingham
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Steven Maude
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Ben F C Butler-Cole
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Thomas O'Dwyer
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Catherine L Stables
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Lucy Bridges
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | | | | | | | | | - Bang Zheng
- London School of Hygiene & Tropical Medicine, London, UK
| | | | | | | | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Alex J Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
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8
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Curtis HJ, MacKenna B, Wiedemann M, Fisher L, Croker R, Morton CE, Inglesby P, Walker AJ, Morley J, Mehrkar A, Bacon SC, Hickman G, Evans D, Ward T, Davy S, Hulme WJ, Macdonald O, Conibere R, Lewis T, Myers M, Wanninayake S, Collison K, Drury C, Samuel M, Sood H, Cipriani A, Fazel S, Sharma M, Baqir W, Bates C, Parry J, Goldacre B. OpenSAFELY NHS Service Restoration Observatory 2: changes in primary care clinical activity in England during the COVID-19 pandemic. Br J Gen Pract 2023; 73:e318-e331. [PMID: 37068964 PMCID: PMC10131234 DOI: 10.3399/bjgp.2022.0301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [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.
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Affiliation(s)
- Helen J Curtis
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Brian MacKenna
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Milan Wiedemann
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Louis Fisher
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Richard Croker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Caroline E Morton
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Peter Inglesby
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Alex J Walker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Jessica Morley
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Amir Mehrkar
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Sebastian Cj Bacon
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - George Hickman
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - David Evans
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Tom Ward
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Simon Davy
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - William J Hulme
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Orla Macdonald
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | | | - Tom Lewis
- Royal Devon University Healthcare NHS Foundation Trust, Barnstaple
| | - Martin Myers
- Lancashire Teaching Hospitals NHS Foundation Trust, Preston
| | | | | | - Charles Drury
- Herefordshire and Worcestershire Health and Care NHS Trust, Worcester
| | - Miriam Samuel
- Wolfson Institute of Population Health, Queen Mary University of London, London
| | - Harpreet Sood
- University College London Hospitals NHS Foundation Trust, London
| | | | - Seena Fazel
- Department of Psychiatry, University of Oxford, Oxford
| | - Manuj Sharma
- Department of Primary Care and Population Health, University College London, London
| | | | | | | | - Ben Goldacre
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
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Ward T, Jha A, Daynes E, Ackland J, Chalmers JD. Review of the British Thoracic Society Winter Meeting 23 November 2022 23-25 November 2022. Thorax 2023; 78:e1. [PMID: 36717241 DOI: 10.1136/thorax-2022-219941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 01/06/2023] [Indexed: 02/01/2023]
Abstract
The British Thoracic Society Winter Meeting at the QEII Centre in London provided the first opportunity for the respiratory community to meet and disseminate research findings face to face since the start of the COVID-19 pandemic. World-leading researchers from the UK and abroad presented their latest findings across a range of respiratory diseases. This article aims to represent the range of the conference and as such is written from the perspective of a basic scientist, a physiotherapist and two doctors. The authors reviewed showcase sessions plus a selection of symposia based on their personal highlights. Content ranged from exciting new developments in basic science to new and unpublished results from clinical trials, delivered by leading scientists from their fields including former deputy chief medical officer Professor Sir Jonathan Van-Tam and former WHO chief scientist Dr Soumya Swaminathan.
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Affiliation(s)
- Tom Ward
- Department Respiratory Sciences, College of Life Sciences, University of Leicester, Leicester, UK
| | - Akhilesh Jha
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Enya Daynes
- Department of Respiratory Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Jodie Ackland
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - James D Chalmers
- Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
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10
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Green ACA, Curtis HJ, Higgins R, Nab L, Mahalingasivam V, Smith RM, Mehrkar A, Inglesby P, Drysdale H, DeVito NJ, Croker R, Rentsch CT, Bhaskaran K, Tazare J, Zheng B, Andrews CD, Bacon SCJ, Davy S, Dillingham I, Evans D, Fisher L, Hickman G, Hopcroft LEM, Hulme WJ, Massey J, MacDonald O, Morley J, Morton CE, Park RY, Walker AJ, Ward T, Wiedemann M, Bates C, Cockburn J, Parry J, Hester F, Harper S, Douglas IJ, Evans SJW, Goldacre B, Tomlinson LA, MacKenna B. Trends, variation, and clinical characteristics of recipients of antiviral drugs and neutralising monoclonal antibodies for covid-19 in community settings: retrospective, descriptive cohort study of 23.4 million people in OpenSAFELY. BMJ Med 2023; 2:e000276. [PMID: 36936265 PMCID: PMC9951378 DOI: 10.1136/bmjmed-2022-000276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 11/25/2022] [Indexed: 01/15/2023]
Abstract
Objective To ascertain patient eligibility status and describe coverage of antiviral drugs and neutralising monoclonal antibodies (nMAB) as treatment for covid-19 in community settings in England. Design Retrospective, descriptive cohort study, approved by NHS England. Setting Routine clinical data from 23.4 million people linked to data on covid-19 infection and treatment, within the OpenSAFELY-TPP database. Participants Outpatients with covid-19 at high risk of severe outcomes. Interventions Nirmatrelvir/ritonavir (paxlovid), sotrovimab, molnupiravir, casirivimab/imdevimab, or remdesivir, used in the community by covid-19 medicine delivery units. Results 93 870 outpatients with covid-19 were identified between 11 December 2021 and 28 April 2022 to be at high risk of severe outcomes and therefore potentially eligible for antiviral or nMAB treatment (or both). Of these patients, 19 040 (20%) received treatment (sotrovimab, 9660 (51%); molnupiravir, 4620 (24%); paxlovid, 4680 (25%); casirivimab/imdevimab, 50 (<1%); and remdesivir, 30 (<1%)). The proportion of patients treated increased from 9% (190/2220) in the first week of treatment availability to 29% (460/1600) in the latest week. The proportion treated varied by high risk group, being lowest in those with liver disease (16%; 95% confidence interval 15% to 17%); by treatment type, with sotrovimab favoured over molnupiravir and paxlovid in all but three high risk groups (Down's syndrome (35%; 30% to 39%), rare neurological conditions (45%; 43% to 47%), and immune deficiencies (48%; 47% to 50%)); by age, ranging from ≥80 years (13%; 12% to 14%) to 50-59 years (23%; 22% to 23%); by ethnic group, ranging from black (11%; 10% to 12%) to white (21%; 21% to 21%); by NHS region, ranging from 13% (12% to 14%) in Yorkshire and the Humber to 25% (24% to 25%) in the East of England); and by deprivation level, ranging from 15% (14% to 15%) in the most deprived areas to 23% (23% to 24%) in the least deprived areas. Groups that also had lower coverage included unvaccinated patients (7%; 6% to 9%), those with dementia (6%; 5% to 7%), and care home residents (6%; 6% to 7%). Conclusions Using the OpenSAFELY platform, we were able to identify patients with covid-19 at high risk of severe outcomes who were potentially eligible to receive treatment and assess the coverage of these new treatments among these patients. In the context of a rapid deployment of a new service, the NHS analytical code used to determine eligibility could have been over-inclusive and some of the eligibility criteria not fully captured in healthcare data. However targeted activity might be needed to resolve apparent lower treatment coverage observed among certain groups, in particular (at present): different NHS regions, ethnic groups, people aged ≥80 years, those living in socioeconomically deprived areas, and care home residents.
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Affiliation(s)
- Amelia C A Green
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Helen J Curtis
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Rose Higgins
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Linda Nab
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Rebecca M Smith
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Amir Mehrkar
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Peter Inglesby
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Henry Drysdale
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Nicholas J DeVito
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Richard Croker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | | | - John Tazare
- London School of Hygiene and Tropical Medicine, London, UK
| | - Bang Zheng
- London School of Hygiene and Tropical Medicine, London, UK
| | - Colm D Andrews
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Sebastian C J Bacon
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Simon Davy
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Iain Dillingham
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - David Evans
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Louis Fisher
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - George Hickman
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Lisa E M Hopcroft
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - William J Hulme
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Jon Massey
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Jessica Morley
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Caroline E Morton
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Robin Y Park
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Alex J Walker
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Tom Ward
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Milan Wiedemann
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | | | | | | | | | - Ian J Douglas
- London School of Hygiene and Tropical Medicine, London, UK
| | | | - Ben Goldacre
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Brian MacKenna
- Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
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Nadarajah R, Ludman P, Appelman Y, Brugaletta S, Budaj A, Bueno H, Huber K, Kunadian V, Leonardi S, Lettino M, Milasinovic D, Gale CP, Budaj A, Dagres N, Danchin N, Delgado V, Emberson J, Friberg O, Gale CP, Heyndrickx G, Iung B, James S, Kappetein AP, Maggioni AP, Maniadakis N, Nagy KV, Parati G, Petronio AS, Pietila M, Prescott E, Ruschitzka F, Van de Werf F, Weidinger F, Zeymer U, Gale CP, Beleslin B, Budaj A, Chioncel O, Dagres N, Danchin N, Emberson J, Erlinge D, Glikson M, Gray A, Kayikcioglu M, Maggioni AP, Nagy KV, Nedoshivin A, Petronio AP, Roos-Hesselink JW, Wallentin L, Zeymer U, Popescu BA, Adlam D, Caforio ALP, Capodanno D, Dweck M, Erlinge D, Glikson M, Hausleiter J, Iung B, Kayikcioglu M, Ludman P, Lund L, Maggioni AP, Matskeplishvili S, Meder B, Nagy KV, Nedoshivin A, Neglia D, Pasquet AA, Roos-Hesselink JW, Rossello FJ, Shaheen SM, Torbica A, Gale CP, Ludman PF, Lettino M, Bueno H, Huber K, Leonardi S, Budaj A, Milasinovic (Serbia) D, Brugaletta S, Appelman Y, Kunadian V, Al Mahmeed WAR, Kzhdryan H, Dumont C, Geppert A, Bajramovic NS, Cader FA, Beauloye C, Quesada D, Hlinomaz O, Liebetrau C, Marandi T, Shokry K, Bueno H, Kovacevic M, Crnomarkovic B, Cankovic M, Dabovic D, Jarakovic M, Pantic T, Trajkovic M, Pupic L, Ruzicic D, Cvetanovic D, Mansourati J, Obradovic I, Stankovic M, Loh PH, Kong W, Poh KK, Sia CH, Saw K, Liška D, Brozmannová D, Gbur M, Gale CP, Maxian R, Kovacic D, Poznic NG, Keric T, Kotnik G, Cercek M, Steblovnik K, Sustersic M, Cercek AC, Djokic I, Maisuradze D, Drnovsek B, Lipar L, Mocilnik M, Pleskovic A, Lainscak M, Crncic D, Nikojajevic I, Tibaut M, Cigut M, Leskovar B, Sinanis T, Furlan T, Grilj V, Rezun M, Mateo VM, Anguita MJF, Bustinza ICM, Quintana RB, Cimadevilla OCF, Fuertes J, Lopez F, Dharma S, Martin MD, Martinez L, Barrabes JA, Bañeras J, Belahnech Y, Ferreira-Gonzalez I, Jordan P, Lidon RM, Mila L, Sambola A, Orvin K, Sionis A, Bragagnini W, Cambra AD, Simon C, Burdeus MV, Ariza-Solé A, Alegre O, Alsina M, Ferrando JIL, Bosch X, Sinha A, Vidal P, Izquierdo M, Marin F, Esteve-Pastor MA, Tello-Montoliu A, Lopez-Garcia C, Rivera-Caravaca JM, Gil-Pérez P, Nicolas-Franco S, Keituqwa I, Farhan HA, Silva L, Blasco A, Escudier JM, Ortega J, Zamorano JL, Sanmartin M, Pereda DC, Rincon LM, Gonzalez P, Casado T, Sadeghipour P, Lopez-Sendon JL, Manjavacas AMI, Marin LAM, Sotelo LR, Rodriguez SOR, Bueno H, Martin R, Maruri R, Moreno G, Moris C, Gudmundsdottir I, Avanzas P, Ayesta A, Junco-Vicente A, Cubero-Gallego H, Pascual I, Sola NB, Rodriguez OA, Malagon L, Martinez-Basterra J, Arizcuren AM, Indolfi C, Romero J, Calleja AG, Fuertes DG, Crespín Crespín M, Bernal FJC, Ojeda FB, Padron AL, Cabeza MM, Vargas CM, Yanes G, Kitai T, Gonzalez MJG, Gonzalez Gonzalez J, Jorge P, De La Fuente B, Bermúdez MG, Perez-Lopez CMB, Basiero AB, Ruiz AC, Pamias RF, Chamero PS, Mirrakhimov E, Hidalgo-Urbano R, Garcia-Rubira JC, Seoane-Garcia T, Arroyo-Monino DF, Ruiz AB, Sanz-Girgas E, Bonet G, Rodríguez-López J, Scardino C, De Sousa D, Gustiene O, Elbasheer E, Humida A, Mahmoud H, Mohamed A, Hamid E, Hussein S, Abdelhameed M, Ali T, Ali Y, Eltayeb M, Philippe F, Ali M, Almubarak E, Badri M, Altaher S, Alla MD, Dellborg M, Dellborg H, Hultsberg-Olsson G, Marjeh YB, Abdin A, Erglis A, Alhussein F, Mgazeel F, Hammami R, Abid L, Bahloul A, Charfeddine S, Ellouze T, Canpolat U, Oksul M, Muderrisoglu H, Popovici M, Karacaglar E, Akgun A, Ari H, Ari S, Can V, Tuncay B, Kaya H, Dursun L, Kalenderoglu K, Tasar O, Kalpak O, Kilic S, Kucukosmanoglu M, Aytekin V, Baydar O, Demirci Y, Gürsoy E, Kilic A, Yildiz Ö, Arat-Ozkan A, Sinan UY, Dagva M, Gungor B, Sekerci SS, Zeren G, Erturk M, Demir AR, Yildirim C, Can C, Kayikcioglu M, Yagmur B, Oney S, Xuereb RG, Sabanoglu C, Inanc IH, Ziyrek M, Sen T, Astarcioglu MA, Kahraman F, Utku O, Celik A, Surmeli AO, Basaran O, Ahmad WAW, Demirbag R, Besli F, Gungoren F, Ingabire P, Mondo C, Ssemanda S, Semu T, Mulla AA, Atos JS, Wajid I, Appelman Y, Al Mahmeed WAR, Atallah B, Bakr 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Potpara T, Marinkovic M, Mihajlovic M, Mujovic N, Kocijancic A, Mijatovic Z, Radovanovic M, Matic D, Milosevic A, Savic L, Subotic I, Uscumlic A, Zlatic N, Antonijevic J, Vesic O, Vucic R, Martinovic SS, Kostic T, Atanaskovic V, Mitic V, Stanojevic D, Petrovic M. Cohort profile: the ESC EURObservational Research Programme Non-ST-segment elevation myocardial infraction (NSTEMI) Registry. Eur Heart J Qual Care Clin Outcomes 2022; 9:8-15. [PMID: 36259751 DOI: 10.1093/ehjqcco/qcac067] [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] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 11/12/2022]
Abstract
AIMS The European Society of Cardiology (ESC) EURObservational Research Programme (EORP) Non-ST-segment elevation myocardial infarction (NSTEMI) Registry aims to identify international patterns in NSTEMI management in clinical practice and outcomes against the 2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without ST-segment-elevation. METHODS AND RESULTS Consecutively hospitalised adult NSTEMI patients (n = 3620) were enrolled between 11 March 2019 and 6 March 2021, and individual patient data prospectively collected at 287 centres in 59 participating countries during a two-week enrolment period per centre. The registry collected data relating to baseline characteristics, major outcomes (in-hospital death, acute heart failure, cardiogenic shock, bleeding, stroke/transient ischaemic attack, and 30-day mortality) and guideline-recommended NSTEMI care interventions: electrocardiogram pre- or in-hospital, pre-hospitalization receipt of aspirin, echocardiography, coronary angiography, referral to cardiac rehabilitation, smoking cessation advice, dietary advice, and prescription on discharge of aspirin, P2Y12 inhibition, angiotensin converting enzyme inhibitor (ACEi)/angiotensin receptor blocker (ARB), beta-blocker, and statin. CONCLUSION The EORP NSTEMI Registry is an international, prospective registry of care and outcomes of patients treated for NSTEMI, which will provide unique insights into the contemporary management of hospitalised NSTEMI patients, compliance with ESC 2015 NSTEMI Guidelines, and identify potential barriers to optimal management of this common clinical presentation associated with significant morbidity and mortality.
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Affiliation(s)
- Ramesh Nadarajah
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, LS2 9JT Leeds, UK.,Leeds Institute of Data Analytics, University of Leeds, LS2 9JT Leeds, UK.,Department of Cardiology, Leeds Teaching Hospitals NHS Trust, LS1 3EX Leeds, UK
| | - Peter Ludman
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
| | - Yolande Appelman
- Department of Cardiology, Amsterdam UMC-Vrije Universiteit, Amsterdam Cardiovascular Sciences, Amsterdam, Netherlands
| | - Salvatore Brugaletta
- Hospital Clinic de Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - Andrzej Budaj
- Department of Cardiology, Center of Postgraduate Medical Education, Grochowski Hospital, Warsaw, Poland
| | - Hector Bueno
- Cardiology Department, Hospital Universitario 12 de Octubre and Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain.,Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.,Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - Kurt Huber
- 3rd Medical Department, Cardiology and Intensive Care Medicine, Clinic Ottakring (Wilhelminenhospital), Vienna, Austria.,Medical Faculty, Sigmund Freud University, Vienna, Austria
| | - Vijay Kunadian
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.,Cardiothoracic Centre, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Sergio Leonardi
- University of Pavia, Pavia, Italy.,Fondazione IRCCS Policlinico S.Matteo, Pavia, Italy
| | - Maddalena Lettino
- Cardio-Thoracic and Vascular Department, San Gerardo Hospital, ASST-Monza, Monza, Italy
| | - Dejan Milasinovic
- Department of Cardiology, University Clinical Center of Serbia and Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Chris P Gale
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, LS2 9JT Leeds, UK.,Leeds Institute of Data Analytics, University of Leeds, LS2 9JT Leeds, UK.,Department of Cardiology, Leeds Teaching Hospitals NHS Trust, LS1 3EX Leeds, UK
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| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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Zheng B, Green ACA, Tazare J, Curtis HJ, Fisher L, Nab L, Schultze A, Mahalingasivam V, Parker EPK, Hulme WJ, Bacon SCJ, DeVito NJ, Bates C, Evans D, Inglesby P, Drysdale H, Davy S, Cockburn J, Morton CE, Hickman G, Ward T, Smith RM, Parry J, Hester F, Harper S, Mehrkar A, Eggo RM, Walker AJ, Evans SJW, Douglas IJ, MacKenna B, Goldacre B, Tomlinson LA. Comparative effectiveness of sotrovimab and molnupiravir for prevention of severe covid-19 outcomes in patients in the community: observational cohort study with the OpenSAFELY platform. BMJ 2022; 379:e071932. [PMID: 36384890 PMCID: PMC9667468 DOI: 10.1136/bmj-2022-071932] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/06/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To compare the effectiveness of sotrovimab (a neutralising monoclonal antibody) with molnupiravir (an antiviral) in preventing severe outcomes of covid-19 in adult patients infected with SARS-CoV-2 in the community and at high risk of severe outcomes from covid-19. DESIGN Observational cohort study with the OpenSAFELY platform. SETTING With the approval of NHS England, a real world cohort study was conducted with the OpenSAFELY-TPP platform (a secure, transparent, open source software platform for analysis of NHS electronic health records), and patient level electronic health record data were obtained from 24 million people registered with a general practice in England that uses TPP software. The primary care data were securely linked with data on SARS-CoV-2 infection and treatments, hospital admission, and death, over a period when both drug treatments were frequently prescribed in community settings. PARTICIPANTS Adult patients with covid-19 in the community at high risk of severe outcomes from covid-19, treated with sotrovimab or molnupiravir from 16 December 2021. INTERVENTIONS Sotrovimab or molnupiravir given in the community by covid-19 medicine delivery units. MAIN OUTCOME MEASURES Admission to hospital with covid-19 (ie, with covid-19 as the primary diagnosis) or death from covid-19 (ie, with covid-19 as the underlying or contributing cause of death) within 28 days of the start of treatment. RESULTS Between 16 December 2021 and 10 February 2022, 3331 and 2689 patients were treated with sotrovimab and molnupiravir, respectively, with no substantial differences in baseline characteristics. Mean age of all 6020 patients was 52 (standard deviation 16) years; 59% were women, 89% were white, and 88% had received three or more covid-19 vaccinations. Within 28 days of the start of treatment, 87 (1.4%) patients were admitted to hospital or died of infection from SARS-CoV-2 (32 treated with sotrovimab and 55 with molnupiravir). Cox proportional hazards models stratified by area showed that after adjusting for demographic information, high risk cohort categories, vaccination status, calendar time, body mass index, and other comorbidities, treatment with sotrovimab was associated with a substantially lower risk than treatment with molnupiravir (hazard ratio 0.54, 95% confidence interval 0.33 to 0.88, P=0.01). Consistent results were found from propensity score weighted Cox models (0.50, 0.31 to 0.81, P=0.005) and when restricted to people who were fully vaccinated (0.53, 0.31 to 0.90, P=0.02). No substantial effect modifications by other characteristics were detected (all P values for interaction >0.10). The findings were similar in an exploratory analysis of patients treated between 16 February and 1 May 2022 when omicron BA.2 was the predominant variant in England. CONCLUSIONS In routine care of adult patients in England with covid-19 in the community, at high risk of severe outcomes from covid-19, those who received sotrovimab were at lower risk of severe outcomes of covid-19 than those treated with molnupiravir.
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Affiliation(s)
- Bang Zheng
- London School of Hygiene and Tropical Medicine, London, UK
| | - Amelia C A Green
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - John Tazare
- London School of Hygiene and Tropical Medicine, London, UK
| | - Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Louis Fisher
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Linda Nab
- 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
| | | | | | - William J Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Sebastian C J Bacon
- 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
| | | | - 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
| | - Henry Drysdale
- 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
| | | | - Caroline E Morton
- 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
| | - Tom Ward
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Rebecca M Smith
- 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
| | | | - Alex J Walker
- 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
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
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Cast K, Shipman S, Gilbertson C, Owens T, Ward T. 50 Hospital Admission Rates and Mortality Among Emergency Department Patients With COVID-19 Discharged With Remote Patient Monitoring With or Without HO2ME (home oxygen) – A Value-Based Approach. Ann Emerg Med 2022. [PMCID: PMC9519226 DOI: 10.1016/j.annemergmed.2022.08.444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Greenwood KE, Gurnani M, Ward T, Vogel E, Vella C, McGourty A, Robertson S, Sacadura C, Hardy A, Rus‐Calafell M, Collett N, Emsley R, Freeman D, Fowler D, Kuipers E, Bebbington P, Dunn G, Michelson D, Garety P. The service user experience of SlowMo therapy: A co-produced thematic analysis of service users' subjective experience. Psychol Psychother 2022; 95:680-700. [PMID: 35445520 PMCID: PMC9873386 DOI: 10.1111/papt.12393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 03/18/2022] [Indexed: 01/27/2023]
Abstract
OBJECTIVES SlowMo is the first blended digital therapy for paranoia, showing significant small-moderate reductions in paranoia in a recent large-scale randomized controlled trial (RCT). This study explored the subjective service-user experience of the SlowMo therapy content and design; the experience of the blended therapy approach, including the triangle of the therapeutic alliance; and the experience of the digital aspects of the intervention. DESIGN Qualitative co-produced sub-study of an RCT. METHODS Participants were 22 adult service users with schizophrenia-spectrum psychosis and persistent distressing paranoia, who completed at least one SlowMo therapy session and a 24-week follow-up, at one of 3 sites in Oxford, London, and Sussex, UK. They were interviewed by peer researchers, using a topic guide co-produced by the Patient and Public Involvement (PPI) team. The transcribed data were analysed thematically. Multiple coding and triangulation, and lay peer researcher validation were used to reach a consensus on the final theme structure. RESULTS Six core themes were identified: (i) starting the SlowMo journey; (ii) the central role of the supportive therapist; (iii) slowing things down; (iv) value and learning from social connections; (v) approaches and challenges of technology; and (vi) improvements in paranoia and well-being. CONCLUSIONS For these service users, slowing down for a moment was helpful, and integrated into thinking over time. Learning from social connections reflected reduced isolation, and enhanced learning through videos, vignettes, and peers. The central role of the supportive therapist and the triangle of alliance between service user, therapist, and digital platform were effective in promoting positive therapeutic outcomes.
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Affiliation(s)
- Kathryn E. Greenwood
- School of PsychologyUniversity of SussexBrightonUK,Sussex Partnership NHS Foundation TrustWorthingUK
| | | | - Tom Ward
- Department of PsychologyInstitute of Psychiatry, Psychology and NeuroscienceKing’s College LondonLondonUK,South London and Maudsley NHS Foundation TrustLondonUK
| | - Evelin Vogel
- Sussex Partnership NHS Foundation TrustWorthingUK
| | - Claire Vella
- Sussex Partnership NHS Foundation TrustWorthingUK
| | | | | | | | - Amy Hardy
- Department of PsychologyInstitute of Psychiatry, Psychology and NeuroscienceKing’s College LondonLondonUK,South London and Maudsley NHS Foundation TrustLondonUK
| | | | | | - Richard Emsley
- Department of Biostatistics and Health InformaticsInstitute of Psychiatry, Psychology and NeuroscienceKing’s College LondonLondonUK
| | - Daniel Freeman
- Oxford Health NHS Foundation TrustOxfordUK,Department of PsychiatryOxford UniversityOxfordUK
| | - David Fowler
- School of PsychologyUniversity of SussexBrightonUK,Sussex Partnership NHS Foundation TrustWorthingUK
| | - Elizabeth Kuipers
- Department of PsychologyInstitute of Psychiatry, Psychology and NeuroscienceKing’s College LondonLondonUK,South London and Maudsley NHS Foundation TrustLondonUK
| | | | - Graham Dunn
- Centre for BiostatisticsSchool of Health SciencesManchester Academic Health Science CentreThe University of ManchesterManchesterUK
| | | | - Philippa Garety
- Department of PsychologyInstitute of Psychiatry, Psychology and NeuroscienceKing’s College LondonLondonUK,South London and Maudsley NHS Foundation TrustLondonUK
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Curtis HJ, Inglesby P, MacKenna B, Croker R, Hulme WJ, Rentsch CT, Bhaskaran K, Mathur R, Morton CE, Bacon SC, Smith RM, Evans D, Mehrkar A, Tomlinson L, Walker AJ, Bates C, Hickman G, Ward T, Morley J, Cockburn J, Davy S, Williamson EJ, Eggo RM, Parry J, Hester F, Harper S, O'Hanlon S, Eavis A, Jarvis R, Avramov D, Griffiths P, Fowles A, Parkes N, Evans SJ, Douglas IJ, Smeeth L, Goldacre B. Recording of 'COVID-19 vaccine declined': a cohort study on 57.9 million National Health Service patients' records in situ using OpenSAFELY, England, 8 December 2020 to 25 May 2021. Euro Surveill 2022; 27:2100885. [PMID: 35983770 PMCID: PMC9389857 DOI: 10.2807/1560-7917.es.2022.27.33.2100885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
BackgroundPriority patients in England were offered COVID-19 vaccination by mid-April 2021. Codes in clinical record systems can denote the vaccine being declined.AimWe describe records of COVID-19 vaccines being declined, according to clinical and demographic factors.MethodsWith the approval of NHS England, we conducted a retrospective cohort study between 8 December 2020 and 25 May 2021 with primary care records for 57.9 million patients using OpenSAFELY, a secure health analytics platform. COVID-19 vaccination priority patients were those aged ≥ 50 years or ≥ 16 years clinically extremely vulnerable (CEV) or 'at risk'. We describe the proportion recorded as declining vaccination for each group and stratified by clinical and demographic subgroups, subsequent vaccination and distribution of clinical code usage across general practices.ResultsOf 24.5 million priority patients, 663,033 (2.7%) had a decline recorded, while 2,155,076 (8.8%) had neither a vaccine nor decline recorded. Those recorded as declining, who were subsequently vaccinated (n = 125,587; 18.9%) were overrepresented in the South Asian population (32.3% vs 22.8% for other ethnicities aged ≥ 65 years). The proportion of declining unvaccinated patients was highest in CEV (3.3%), varied strongly with ethnicity (black 15.3%, South Asian 5.6%, white 1.5% for ≥ 80 years) and correlated positively with increasing deprivation.ConclusionsClinical codes indicative of COVID-19 vaccinations being declined are commonly used in England, but substantially more common among black and South Asian people, and in more deprived areas. Qualitative research is needed to determine typical reasons for recorded declines, including to what extent they reflect patients actively declining.
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Affiliation(s)
- Helen J Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - William J Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | | | - Rohini Mathur
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Caroline E Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Sebastian Cj Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rebecca M Smith
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - David Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Laurie Tomlinson
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | - George Hickman
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Tom Ward
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jessica Morley
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | - Simon Davy
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | - Rosalind M Eggo
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | | | | | | | | | | | | | | | | | | | - Stephen Jw Evans
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Ian J Douglas
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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Jha A, Ward T, Walker S, Goodwin AT, Chalmers JD. Review of the British Thoracic Society Winter Meeting 2021, 24-26 November 2021. Thorax 2022; 77:1030-1035. [PMID: 35907640 DOI: 10.1136/thorax-2022-219150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 07/13/2022] [Indexed: 11/04/2022]
Abstract
The Winter Meeting of the British Thoracic Society (BTS) is a platform for the latest clinical and scientific research in respiratory medicine. This review summarises the key symposia and presentations from the BTS Winter Meeting 2021 held online due to the COVID-19 pandemic.
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Affiliation(s)
- Akhilesh Jha
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Tom Ward
- Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Steven Walker
- School of Clinical Sciences, University of Bristol Academic Respiratory Unit, Westbury on Trym, UK
| | - Amanda T Goodwin
- Nottingham NIHR Respiratory Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - James D Chalmers
- Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
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Hulme WJ, Williamson EJ, Green ACA, Bhaskaran K, McDonald HI, Rentsch CT, Schultze A, Tazare J, Curtis HJ, Walker AJ, Tomlinson LA, Palmer T, Horne EMF, MacKenna B, Morton CE, Mehrkar A, Morley J, Fisher L, Bacon SCJ, Evans D, Inglesby P, Hickman G, Davy S, Ward T, Croker R, Eggo RM, Wong AYS, Mathur R, Wing K, Forbes H, Grint DJ, Douglas IJ, Evans SJW, Smeeth L, Bates C, Cockburn J, Parry J, Hester F, Harper S, Sterne JAC, Hernán MA, Goldacre B. Comparative effectiveness of ChAdOx1 versus BNT162b2 covid-19 vaccines in health and social care workers in England: cohort study using OpenSAFELY. BMJ 2022; 378:e068946. [PMID: 35858680 PMCID: PMC9295078 DOI: 10.1136/bmj-2021-068946] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [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.
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Affiliation(s)
- William J Hulme
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | | | - Amelia C A Green
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | | | - Helen I McDonald
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | - Anna Schultze
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - John Tazare
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Helen J Curtis
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Alex J Walker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | | | - Tom Palmer
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Elsie M F Horne
- Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
- NIHR Bristol, Biomedical Research Centre, Bristol BS8 2BN, UK
| | - Brian MacKenna
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Caroline E Morton
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Amir Mehrkar
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Jessica Morley
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Louis Fisher
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Sebastian C J Bacon
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - David Evans
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Peter Inglesby
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - George Hickman
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Simon Davy
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Tom Ward
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Richard Croker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
| | - Rosalind M Eggo
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Angel Y S Wong
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Rohini Mathur
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Kevin Wing
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Harriet Forbes
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Daniel J Grint
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Ian J Douglas
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Chris Bates
- TPP, TPP House, Horsforth, Leeds LS18 5PX, UK
| | | | - John Parry
- TPP, TPP House, Horsforth, Leeds LS18 5PX, UK
| | | | - Sam Harper
- TPP, TPP House, Horsforth, Leeds LS18 5PX, UK
| | - Jonathan A C Sterne
- Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
- NIHR Bristol, Biomedical Research Centre, Bristol BS8 2BN, UK
- Health Data Research UK South West
| | - Miguel A Hernán
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Ben Goldacre
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX2 6GG, UK
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Horne EMF, Hulme WJ, Keogh RH, Palmer TM, Williamson EJ, Parker EPK, Green A, Walker V, Walker AJ, Curtis H, Fisher L, MacKenna B, Croker R, Hopcroft L, Park RY, Massey J, Morley J, Mehrkar A, Bacon S, Evans D, Inglesby P, Morton CE, Hickman G, Davy S, Ward T, Dillingham I, Goldacre B, Hernán MA, Sterne JAC. Waning effectiveness of BNT162b2 and ChAdOx1 covid-19 vaccines over six months since second dose: OpenSAFELY cohort study using linked electronic health records. BMJ 2022; 378:e071249. [PMID: 35858698 PMCID: PMC10441183 DOI: 10.1136/bmj-2022-071249] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 12/22/2022]
Abstract
OBJECTIVE To estimate waning of covid-19 vaccine effectiveness over six months after second dose. DESIGN Cohort study, approved by NHS England. SETTING Linked primary care, hospital, and covid-19 records within the OpenSAFELY-TPP database. PARTICIPANTS Adults without previous SARS-CoV-2 infection were eligible, excluding care home residents and healthcare professionals. EXPOSURES People who had received two doses of BNT162b2 or ChAdOx1 (administered during the national vaccine rollout) were compared with unvaccinated people during six consecutive comparison periods, each of four weeks. MAIN OUTCOME MEASURES Adjusted hazard ratios for covid-19 related hospital admission, covid-19 related death, positive SARS-CoV-2 test, and non-covid-19 related death comparing vaccinated with unvaccinated people. Waning vaccine effectiveness was quantified as ratios of adjusted hazard ratios per four week period, separately for subgroups aged ≥65 years, 18-64 years and clinically vulnerable, 40-64 years, and 18-39 years. RESULTS 1 951 866 and 3 219 349 eligible adults received two doses of BNT162b2 and ChAdOx1, respectively, and 2 422 980 remained unvaccinated. Waning of vaccine effectiveness was estimated to be similar across outcomes and vaccine brands. In the ≥65 years subgroup, ratios of adjusted hazard ratios for covid-19 related hospital admission, covid-19 related death, and positive SARS-CoV-2 test ranged from 1.19 (95% confidence interval 1.14 to 1.24)to 1.34 (1.09 to 1.64) per four weeks. Despite waning vaccine effectiveness, rates of covid-19 related hospital admission and death were substantially lower among vaccinated than unvaccinated adults up to 26 weeks after the second dose, with estimated vaccine effectiveness ≥80% for BNT162b2, and ≥75% for ChAdOx1. By weeks 23-26, rates of positive SARS-CoV-2 test in vaccinated people were similar to or higher than in unvaccinated people (adjusted hazard ratios up to 1.72 (1.11 to 2.68) for BNT162b2 and 1.86 (1.79 to 1.93) for ChAdOx1). CONCLUSIONS The rate at which estimated vaccine effectiveness waned was consistent for covid-19 related hospital admission, covid-19 related death, and positive SARS-CoV-2 test and was similar across subgroups defined by age and clinical vulnerability. If sustained to outcomes of infection with the omicron variant and to booster vaccination, these findings will facilitate scheduling of booster vaccination.
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Affiliation(s)
- Elsie M F Horne
- Population Health Sciences, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - William J Hulme
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Ruth H Keogh
- London School of Hygiene and Tropical Medicine, London, UK
| | - Tom M Palmer
- Population Health Sciences, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | | | | | - Amelia Green
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Venexia Walker
- Population Health Sciences, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Alex J Walker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Helen Curtis
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Louis Fisher
- 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
| | - Richard Croker
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Lisa Hopcroft
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Robin Y Park
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Jon Massey
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Jessica Morley
- 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
| | - Sebastian Bacon
- 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
| | - Caroline E Morton
- 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
| | - Simon Davy
- 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
| | - Iain Dillingham
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
- Contributed equally
| | - Miguel A Hernán
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, USA
- Contributed equally
| | - Jonathan A C Sterne
- Population Health Sciences, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Health Data Research UK South-West, Bristol, UK
- Contributed equally
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Andrews C, Schultze A, Curtis H, Hulme W, Tazare J, Evans S, Mehrkar A, Bacon S, Hickman G, Bates C, Parry J, Hester F, Harper S, Cockburn J, Evans D, Ward T, Davy S, Inglesby P, Goldacre B, MacKenna B, Tomlinson L, Walker A. OpenSAFELY: Representativeness of electronic health record platform OpenSAFELY-TPP data compared to the population of England. Wellcome Open Res 2022; 7:191. [PMID: 35966958 PMCID: PMC9346309 DOI: 10.12688/wellcomeopenres.18010.1] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [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: 07/08/2022] [Indexed: 12/11/2022] Open
Abstract
Background: Since its inception in March 2020, data from the OpenSAFELY-TPP electronic health record platform has been used for more than 20 studies relating to the global COVID-19 emergency. OpenSAFELY-TPP data is derived from practices in England using SystmOne software, and has been used for the majority of these studies. We set out to investigate the representativeness of OpenSAFELY-TPP data by comparing it to national population estimates. Methods: With the approval of NHS England, we describe the age, sex, Index of Multiple Deprivation and ethnicity of the OpenSAFELY-TPP population compared to national estimates from the Office for National Statistics. The five leading causes of death occurring between the 1st January 2020 and the 31st December 2020 were also compared to deaths registered in England during the same period. Results: Despite regional variations, TPP is largely representative of the general population of England in terms of IMD (all within 1.1 percentage points), age, sex (within 0.1 percentage points), ethnicity and causes of death. The proportion of the five leading causes of death is broadly similar to those reported by ONS (all within 1 percentage point). Conclusions: Data made available via OpenSAFELY-TPP is broadly representative of the English population. Users of OpenSAFELY must consider the issues of representativeness, generalisability and external validity associated with using TPP data for health research. Although the coverage of TPP practices varies regionally across England, TPP registered patients are generally representative of the English population as a whole in terms of key demographic characteristics.
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Affiliation(s)
- Colm Andrews
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Anna Schultze
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Helen Curtis
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - William Hulme
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - John Tazare
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Stephen Evans
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Amir Mehrkar
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Sebastian Bacon
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - George Hickman
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | | | - John Parry
- TPP, TPP House, Leeds, Yorkshire, LS18 5PX, UK
| | | | - Sam Harper
- TPP, TPP House, Leeds, Yorkshire, LS18 5PX, UK
| | | | - David Evans
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Tom Ward
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Simon Davy
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Peter Inglesby
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Ben Goldacre
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Brian MacKenna
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
| | - Laurie Tomlinson
- London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK
| | - Alex Walker
- Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Oxon, OX26GG,, UK
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20
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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.
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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.
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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.
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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
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22
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Craig T, Garety P, Ward T, Edwards C, Rus-Calafell M, Huckvale M, Emsley R. The UK AVATAR 1 and 2 Trials for People with Distressing Voices – Findings and Learning from AVATAR1, and AVATAR2 Developments in Theory and Therapy. Eur Psychiatry 2022. [PMCID: PMC9566973 DOI: 10.1192/j.eurpsy.2022.84] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Introduction Many people suffering from psychotic disorders report persistent auditory verbal hallucinations (‘voices’) despite pharmacological and psychological therapy. Interest is growing in approaches that emphasise the personal relationship between the patient and their voice(s). AVATAR therapy is one such approach that uses a digital representation (avatar) of a selected voice to facilitate a three-way discussion between patient, therapist and voice, the therapist speaking either as him/herself or in the digitally transformed voice of the avatar. Objectives: To describe AVATAR therapy and an ongoing multi-centre clinical trial. Methods: Encouraging findings from an earlier controlled trial (AVATAR1) comparing AVATAR therapy and supportive counselling informed our current multi-site cost-effectiveness trial of brief and extended versions of the therapy compared to treatment as usual (AVATAR2). Results: AVATAR1 delivered in 7 weekly sessions resulted in a reduction in the frequency, distress and power of voices that was significantly superior to supportive counselling. Clinical experience suggested that some participants improved in response to the early focus on anxiety while others seemed more responsive to later more formulation-driven approach. These findings led us to the current ongoing three arm clinical trial comprising a brief (6 session) focus on anxiety/assertiveness, an extended (12 session) formulation-driven approach both approaches compared to treatment as usual. Conclusion: Previous AVATAR studies suggest this is a therapy with considerable promise. It can be delivered through widely available laptop computers, usually in clinic but also remotely via existing commercial platforms. The current trial will address questions about dissemination, training and cost-effectiveness in NHS settings. Disclosure The digital technology employed in AVATAR therapy is provided by licence for the trial from Avatar Therapy Ltd
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23
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Garety P, Ward T, Emsley R, Greenwood K, Hardy A. Psychotherapy of Biases in Cognition in Schizophrenia: the SlowMo Randomised Controlled Trial for Paranoia, Outcomes and Mechanisms. Eur Psychiatry 2022. [PMCID: PMC9564150 DOI: 10.1192/j.eurpsy.2022.75] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Reasoning biases, specifically jumping to conclusions and belief inflexibility, may play a causal role in persistent paranoia. SlowMo, a new digitally supported blended cognitive-behavioural therapy, targets these biases. Adopting the terms ‘fast’ and ‘slow thinking’ as a heuristic to support therapy, SlowMo encourages people to notice a tendency to fast thinking, and to slow down for a moment to reduce paranoia. SlowMo therapy is the first digital blended therapy for paranoia, employing face to face therapy sessions with interactive digital content, and using mobile technology to promote generalisation to daily life. We report a randomised controlled trial with N=362 participants with distressing and persistent (3+months) paranoia, comparing 8 sessions of SlowMo plus Treatment as Usual (TAU) with TAU alone. We examined SlowMo’s effectiveness in reducing paranoia and improving reasoning biases; its mechanisms of action; usability; and acceptability (Garety et al., 2021). Outcomes: SlowMo was beneficial for paranoia: 10 /11 paranoia measures at 12 weeks and 8/11 at 24 weeks demonstrated significant effects, and sustained moderate effects were observed on all observer-rated measures of persecutory delusions. Improvements in self-esteem, worry, wellbeing and quality of life were also reported. Mediation: Consistent with the theory-driven design and treatment rationale, improvements in slower thinking were found to mediate change in paranoia at 12-
and 24-week follow-ups. However contrary to hypothesis, reduced fast thinking did not mediate change in paranoia, whereas worry did. These findings highlight the potential therapeutic mechanisms of action of SlowMo which which are discussed further. Garety P, Ward T, Emsley R, et al. Effects of SlowMo, a Blended Digital Therapy Targeting Reasoning, on Paranoia Among People With Psychosis: A Randomized Clinical Trial. JAMA Psychiatry. 2021;78(7):714–725. doi:10.1001/jamapsychiatry.2021.0326
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24
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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.
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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
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25
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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.
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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.
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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
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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.
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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
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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.
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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
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Jombart T, Ghozzi S, Schumacher D, Taylor TJ, Leclerc QJ, Jit M, Flasche S, Greaves F, Ward T, Eggo RM, Nightingale E, Meakin S, Brady OJ, Medley GF, Höhle M, Edmunds WJ. Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200266. [PMID: 34053271 PMCID: PMC8165581 DOI: 10.1098/rstb.2020.0266] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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] [Accepted: 02/15/2021] [Indexed: 01/21/2023] Open
Abstract
As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggests ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. As such, our method could be of wider use for infectious disease surveillance. We illustrate ASMODEE using publicly available data of National Health Service (NHS) Pathways reporting potential COVID-19 cases in England at a fine spatial scale, showing that the method would have enabled the early detection of the flare-ups in Leicester and Blackburn with Darwen, two to three weeks before their respective lockdown. ASMODEE is implemented in the free R package trendbreaker. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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Affiliation(s)
- Thibaut Jombart
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
- UK Public Health Rapid Support Team, London WC1E 7HT, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London SW7 2DD, UK
| | - Stéphane Ghozzi
- Department of Epidemiology, Helmholtz Centre for Infection Research, Brunswick, 38124, Braunschweig, Lower Saxony, Germany
| | - Dirk Schumacher
- Department of Infectious Disease Epidemiology, Robert Koch-Institute, DE-13353 Berlin, Germany
- Unit for Medical Biometry and Statistics, Federal Institute for Quality Assurance and Transparency in Healthcare, Berlin, Germany
| | - Timothy J. Taylor
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Quentin J. Leclerc
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Mark Jit
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Stefan Flasche
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Felix Greaves
- Department of Health and Social Care, Joint Biosecurity Centre, London SW1H 0EU, UK
- Department of Primary Care and Public Health, Imperial College London, London W6 8RP, UK
| | - Tom Ward
- Department of Health and Social Care, Joint Biosecurity Centre, London SW1H 0EU, UK
| | - Rosalind M. Eggo
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Emily Nightingale
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Sophie Meakin
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Oliver J. Brady
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Centre for Mathematical Modelling of Infectious Diseases COVID-19 Working Group
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
- UK Public Health Rapid Support Team, London WC1E 7HT, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London SW7 2DD, UK
- Department of Epidemiology, Helmholtz Centre for Infection Research, Brunswick, 38124, Braunschweig, Lower Saxony, Germany
- Department of Infectious Disease Epidemiology, Robert Koch-Institute, DE-13353 Berlin, Germany
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Health and Social Care, Joint Biosecurity Centre, London SW1H 0EU, UK
- Department of Primary Care and Public Health, Imperial College London, London W6 8RP, UK
- Department of Mathematics, Stockholm University, 114 19 Stockholm, Sweden
- Unit for Medical Biometry and Statistics, Federal Institute for Quality Assurance and Transparency in Healthcare, Berlin, Germany
| | - Graham F. Medley
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Michael Höhle
- Department of Mathematics, Stockholm University, 114 19 Stockholm, Sweden
| | - W. John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
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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.
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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
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Parker B, Ward T, Hayward O, Jacob I, Arthurs E, Becker D, Anderson SJ, Chounta V, Van de Velde N. Cost-effectiveness of the long-acting regimen cabotegravir plus rilpivirine for the treatment of HIV-1 and its potential impact on adherence and viral transmission: A modelling study. PLoS One 2021; 16:e0245955. [PMID: 33529201 PMCID: PMC7853524 DOI: 10.1371/journal.pone.0245955] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 01/11/2021] [Indexed: 11/23/2022] Open
Abstract
Introduction Combination antiretroviral therapy (cART) improves outcomes for people living with HIV (PLWH) but requires adherence to daily dosing. Suboptimal adherence results in reduced treatment effectiveness, increased costs, and greater risk of resistance and onwards transmission. Treatment with long-acting (LA), injection-based ART administered by healthcare professionals (directly observed therapy (DOT)) eliminates the need for adherence to daily dosing and may improve clinical outcomes. This study reports the cost-effectiveness of the cabotegravir plus rilpivirine LA regimen (CAB+RPV LA) and models the potential impact of LA DOT therapies. Methods Parameterisation was performed using pooled data from recent CAB+RPV LA Phase III trials. The analysis was conducted using a cohort-level hybrid decision-tree and state-transition model, with states defined by viral load and CD4 cell count. The efficacy of oral cART was adjusted to reflect adherence to daily regimens from published data. A Canadian health service perspective was adopted. Results CAB+RPV LA is predicted to be the dominant intervention when compared to oral cART, generating, per 1,000 patients treated, lifetime cost-savings of $1.5 million, QALY and life-year gains of 107 and 138 respectively with three new HIV cases averted. Conclusions Economic evaluations of LA DOTs need to account for the impact of adherence and HIV transmission. This study adds to the existing literature by incorporating transmission and using clinical data from the first LA DOT regimen. Providing PLWH and healthcare providers with novel modes of ART administration, enhancing individualisation of treatment, may facilitate the achievement of UNAIDS 95-95-95 objectives.
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Affiliation(s)
- Ben Parker
- Health Economics and Outcomes Research Ltd, Pontprennau, Cardiff, United Kingdom
| | - Tom Ward
- Health Economics and Outcomes Research Ltd, Pontprennau, Cardiff, United Kingdom
| | - Olivia Hayward
- Health Economics and Outcomes Research Ltd, Pontprennau, Cardiff, United Kingdom
| | - Ian Jacob
- Health Economics and Outcomes Research Ltd, Pontprennau, Cardiff, United Kingdom
- * E-mail: (IJ); (NVdV)
| | - Erin Arthurs
- Health Economics & Outcomes Research, GlaxoSmithKline, Toronto, Ontario, Canada
| | - Debbie Becker
- Quadrant Health Economics Inc, Cambridge, Ontario, Canada
| | - Sarah-Jane Anderson
- Value Evidence and Outcomes, GlaxoSmithKline, Brentford, Middlesex, United Kingdom
| | - Vasiliki Chounta
- Global Health Outcomes, ViiV Healthcare Ltd, Brentford, Middlesex, United Kingdom
| | - Nicolas Van de Velde
- Global Health Outcomes, ViiV Healthcare Ltd, Brentford, Middlesex, United Kingdom
- * E-mail: (IJ); (NVdV)
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McLean KA, Ahmed WUR, Akhbari M, Claireaux HA, English C, Frost J, Henshall DE, Khan M, Kwek I, Nicola M, Rehman S, Varghese S, Drake TM, Bell S, Nepogodiev D, McLean KA, Drake TM, Glasbey JC, Borakati A, Drake TM, Kamarajah S, McLean KA, Bath MF, Claireaux HA, Gundogan B, Mohan M, Deekonda P, Kong C, Joyce H, Mcnamee L, Woin E, Burke J, Khatri C, Fitzgerald JE, Harrison EM, Bhangu A, Nepogodiev D, Arulkumaran N, Bell S, Duthie F, Hughes J, Pinkney TD, Prowle J, Richards T, Thomas M, Dynes K, Patel M, Patel P, Wigley C, Suresh R, Shaw A, Klimach S, Jull P, Evans D, Preece R, Ibrahim I, Manikavasagar V, Smith R, Brown FS, Deekonda P, Teo R, Sim DPY, Borakati A, Logan AE, Barai I, Amin H, Suresh S, Sethi R, Bolton W, Corbridge O, Horne L, Attalla M, Morley R, Robinson C, Hoskins T, McAllister R, Lee S, Dennis Y, Nixon G, Heywood E, Wilson H, Ng L, Samaraweera S, Mills A, Doherty C, Woin E, Belchos J, Phan V, Chouari T, Gardner T, Goergen N, Hayes JDB, MacLeod CS, McCormack R, McKinley A, McKinstry S, Milligan W, Ooi L, Rafiq NM, Sammut T, Sinclair E, Smith M, Baker C, Boulton APR, Collins J, Copley HC, Fearnhead N, Fox H, Mah T, McKenna J, Naruka V, Nigam N, Nourallah B, Perera S, Qureshi A, Saggar S, Sun L, Wang X, Yang DD, Caroll P, Doyle C, Elangovan S, Falamarzi A, Perai KG, Greenan E, Jain D, Lang-Orsini M, Lim S, O'Byrne L, Ridgway P, Van der Laan S, Wong J, Arthur J, Barclay J, Bradley P, Edwin C, Finch E, Hayashi E, Hopkins M, Kelly D, Kelly M, McCartan N, Ormrod A, Pakenham A, Hayward J, Hitchen C, Kishore A, Martins T, Philomen J, Rao R, Rickards C, Burns N, Copeland M, Durand C, Dyal A, Ghaffar A, Gidwani A, Grant M, Gribbon C, Gruhn A, Leer M, Ahmad K, Beattie G, Beatty M, Campbell G, Donaldson G, Graham S, Holmes D, Kanabar S, Liu H, McCann C, Stewart R, Vara S, Ajibola-Taylor O, Andah EJE, Ani C, Cabdi NMO, Ito G, Jones M, Komoriyama A, Patel P, Titu L, Basra M, Gallogly P, Harinath G, Leong SH, Pradhan A, Siddiqui I, Zaat S, Ali A, Galea M, Looi WL, Ng JCK, Atkin G, Azizi A, Cargill Z, China Z, Elliot J, Jebakumar R, Lam J, Mudalige G, Onyerindu C, Renju M, Babu VS, Hussain M, Joji N, Lovett B, Mownah H, Ali B, Cresswell B, Dhillon AK, Dupaguntla YS, Hungwe C, Lowe-Zinola JD, Tsang JCH, Bevan K, Cardus C, Duggal A, Hossain S, McHugh M, Scott M, Chan F, Evans R, Gurung E, Haughey B, Jacob-Ramsdale B, Kerr M, Lee J, McCann E, O'Boyle K, Reid N, Hayat F, Hodgson S, Johnston R, Jones W, Khan M, Linn T, Long S, Seetharam P, Shaman S, Smart B, Anilkumar A, Davies J, Griffith J, Hughes B, Islam Y, Kidanu D, Mushaini N, Qamar I, Robinson H, Schramm M, Tan CY, Apperley H, Billyard C, Blazeby JM, Cannon SP, Carse S, Göpfert A, Loizidou A, Parkin J, Sanders E, Sharma S, Slade G, Telfer R, Huppatz IW, Worley E, Chandramoorthy L, Friend C, Harris L, Jain P, Karim MJ, Killington K, McGillicuddy J, Rafferty C, Rahunathan N, Rayne T, Varathan Y, Verma N, Zanichelli D, Arneill M, Brown F, Campbell B, Crozier L, Henry J, McCusker C, Prabakaran P, Wilson R, Asif U, Connor M, Dindyal S, Math N, Pagarkar A, Saleem H, Seth I, Sharma S, Standfield N, Swartbol T, Adamson R, Choi JE, El Tokhy O, Ho W, Javaid NR, Kelly M, Mehdi AS, Menon D, Plumptre I, Sturrock S, Turner J, Warren O, Crane E, Ferris B, Gadsby C, Smallwood J, Vipond M, Wilson V, Amarnath T, Doshi A, Gregory C, Kandiah K, Powell B, Spoor H, Toh C, Vizor R, Common M, Dunleavy K, Harris S, Luo C, Mesbah Z, Kumar AP, Redmond A, Skulsky S, Walsh T, Daly D, Deery L, Epanomeritakis E, Harty M, Kane D, Khan K, Mackey R, McConville J, McGinnity K, Nixon G, Ang A, Kee JY, Leung E, Norman S, Palaniappan SV, Sarathy PP, Yeoh T, Frost J, Hazeldine P, Jones L, Karbowiak M, Macdonald C, Mutarambirwa A, Omotade A, Runkel M, Ryan G, Sawers N, Searle C, Suresh S, Vig S, Ahmad A, McGartland R, Sim R, Song A, Wayman J, Brown R, Chang LH, Concannon K, Crilly C, Arnold TJ, Burgin A, Cadden F, Choy CH, Coleman M, Lim D, Luk J, Mahankali-Rao P, Prudence-Taylor AJ, Ramakrishnan D, Russell J, Fawole A, Gohil J, Green B, Hussain A, McMenamin L, McMenamin L, Tang M, Azmi F, Benchetrit S, Cope T, Haque A, Harlinska A, Holdsworth R, Ivo T, Martin J, Nisar T, Patel A, Sasapu K, Trevett J, Vernet G, Aamir A, Bird C, Durham-Hall A, Gibson W, Hartley J, May N, Maynard V, Johnson S, Wood CM, O'Brien M, Orbell J, Stringfellow TD, Tenters F, Tresidder S, Cheung W, Grant A, Tod N, Bews-Hair M, Lim ZH, Lim SW, Vella-Baldacchino M, Auckburally S, Chopada A, Easdon S, Goodson R, McCurdie F, Narouz M, Radford A, Rea E, Taylor O, Yu T, Alfa-Wali M, Amani L, Auluck I, Bruce P, Emberton J, Kumar R, Lagzouli N, Mehta A, Murtaza A, Raja M, Dennahy IS, Frew K, Given A, He YY, Karim MA, MacDonald E, McDonald E, McVinnie D, Ng SK, Pettit A, Sim DPY, Berthaume-Hawkins SD, Charnley R, Fenton K, Jones D, Murphy C, Ng JQ, Reehal R, Robinson H, Seraj SS, Shang E, Tonks A, White P, Yeo A, Chong P, Gabriel R, Patel N, Richardson E, Symons L, Aubrey-Jones D, Dawood S, Dobrzynska M, Faulkner S, Griffiths H, Mahmood F, Patel P, Perry M, Power A, Simpson R, Ali A, Brobbey P, Burrows A, Elder P, Ganyani R, Horseman C, Hurst P, Mann H, Marimuthu K, McBride S, Pilsworth E, Powers N, Stanier P, Innes R, Kersey T, Kopczynska M, Langasco N, Patel N, Rajagopal R, Atkins B, Beasley W, Lim ZC, Gill A, Ang HL, Williams H, Yogeswara T, Carter R, Fam M, Fong J, Latter J, Long M, Mackinnon S, McKenzie C, Osmanska J, Raghuvir V, Shafi A, Tsang K, Walker L, Bountra K, Coldicutt O, Fletcher D, Hudson S, Iqbal S, Bernal TL, Martin JWB, Moss-Lawton F, Smallwood J, Vipond M, Cardwell A, Edgerton K, Laws J, Rai A, Robinson K, Waite K, Ward J, Youssef H, Knight C, Koo PY, Lazarou A, Stanger S, Thorn C, Triniman MC, Botha A, Boyles L, Cumming S, Deepak S, Ezzat A, Fowler AJ, Gwozdz AM, Hussain SF, Khan S, Li H, Morrell BL, Neville J, Nitiahpapand R, Pickering O, Sagoo H, Sharma E, Welsh K, Denley S, Khan S, Agarwal M, Al-Saadi N, Bhambra R, Gupta A, Jawad ZAR, Jiao LR, Khan K, Mahir G, Singagireson S, Thoms BL, Tseu B, Wei R, Yang N, Britton N, Leinhardt D, Mahfooz M, Palkhi A, Price M, Sheikh S, Barker M, Bowley D, Cant M, Datta U, Farooqi M, Lee A, Morley G, Amin MN, Parry A, Patel S, Strang S, Yoganayagam N, Adlan A, Chandramoorthy S, Choudhary Y, Das K, Feldman M, France B, Grace R, Puddy H, Soor P, Ali M, Dhillon P, Faraj A, Gerard L, Glover M, Imran H, Kim S, Patrick Y, Peto J, Prabhudesai A, Smith R, Tang A, Vadgama N, Dhaliwal R, Ecclestone T, Harris A, Ong D, Patel D, Philp C, Stewart E, Wang L, Wong E, Xu Y, Ashaye T, Fozard T, Galloway F, Kaptanis S, Mistry P, Nguyen T, Olagbaiye F, Osman M, Philip Z, Rembacken R, Tayeh S, Theodoropoulou K, Herman A, Lau J, Saha A, Trotter M, Adeleye O, Cave D, Gunwa T, Magalhães J, Makwana S, Mason R, Parish M, Regan H, Renwick P, Roberts G, Salekin D, Sivakumar C, Tariq A, Liew I, McDade A, Stewart D, Hague M, Hudson-Peacock N, Jackson CES, James F, Pitt J, Walker EY, Aftab R, Ang JJ, Anwar S, Battle J, Budd E, Chui J, Crook H, Davies P, Easby S, Hackney E, Ho B, Imam SZ, Rammell J, Andrews H, Perry C, Schinle P, Ahmed P, Aquilina T, Balai E, Church M, Cumber E, Curtis A, Davies G, Dennis Y, Dumann E, Greenhalgh S, Kim P, King S, Metcalfe KHM, Passby L, Redgrave N, Soonawalla Z, Waters S, Zornoza A, Gulzar I, Hole J, Hull K, Ishaq H, Karaj J, Kelkar A, Love E, Patel S, Thakrar D, Vine M, Waterman A, Dib NP, Francis N, Hanson M, Ingleton R, Sadanand KS, Sukirthan N, Arnell S, Ball M, Bassam N, Beghal G, Chang A, Dawe V, George A, Huq T, Hussain A, Ikram B, Kanapeckaite L, Khan M, Ramjas D, Rushd A, Sait S, Serry M, Yardimci E, Capella S, Chenciner L, Episkopos C, Karam E, McCarthy C, Moore-Kelly W, Watson N, Ahluwalia V, Barnfield J, Ben-Gal O, Bloom I, Gharatya A, Khodatars K, Merchant N, Moonan A, Moore M, Patel K, Spiers H, Sundaram K, Turner J, Bath MF, Black J, Chadwick H, Huisman L, Ingram H, Khan S, Martin L, Metcalfe M, Sangal P, Seehra J, Thatcher A, Venturini S, Whitcroft I, Afzal Z, Brown S, Gani A, Gomaa A, Hussein N, Oh SY, Pazhaniappan N, Sharkey E, Sivagnanasithiyar T, Williams C, Yeung J, Cruddas L, Gurjar S, Pau A, Prakash R, Randhawa R, Chen L, Eiben I, Naylor M, Osei-Bordom D, Trenear R, Bannard-Smith J, Griffiths N, Patel BY, Saeed F, Abdikadir H, Bennett M, Church R, Clements SE, Court J, Delvi A, Hubert J, Macdonald B, Mansour F, Patel RR, Perris R, Small S, Betts A, Brown N, Chong A, Croitoru C, Grey A, Hickland P, Ho C, Hollington D, McKie L, Nelson AR, Stewart H, Eiben P, Nedham M, Ali I, Brown T, Cumming S, Hunt C, Joyner C, McAlinden C, Roberts J, Rogers D, Thachettu A, Tyson N, Vaughan R, Verma N, Yasin T, Andrew K, Bhamra N, Leong S, Mistry R, Noble H, Rashed F, Walker NR, Watson L, Worsfold M, Yarham E, Abdikadir H, Arshad A, Barmayehvar B, Cato L, Chan-lam N, Do V, Leong A, Sheikh Z, Zheleniakova T, Coppel J, Hussain ST, Mahmood R, Nourzaie R, Prowle J, Sheik-Ali S, Thomas A, Alagappan A, Ashour R, Bains H, Diamond J, Gordon J, Ibrahim B, Khalil M, Mittapalli D, Neo YN, Patil P, Peck FS, Reza N, Swan I, Whyte M, Chaudhry S, Hernon J, Khawar H, O'Brien J, Pullinger M, Rothnie K, Ujjal S, Bhatte S, Curtis J, Green S, Mayer A, Watkinson G, Chapple K, Hawthorne T, Khaliq M, Majkowski L, Malik TAM, Mclauchlan K, En BNW, Parton S, Robinson SD, Saat MI, Shurovi BN, Varatharasasingam K, Ward AE, Behranwala K, Bertelli M, Cohen J, Duff F, Fafemi O, Gupta R, Manimaran M, Mayhew J, Peprah D, Wong MHY, Farmer N, Houghton C, Kandhari N, Khan K, Ladha D, Mayes J, McLennan F, Panahi P, Seehra H, Agrawal R, Ahmed I, Ali S, Birkinshaw F, Choudhry M, Gokani S, Harrogate S, Jamal S, Nawrozzadeh F, Swaray A, Szczap A, Warusavitarne J, Abdalla M, Asemota N, Cullum R, Hartley M, Maxwell-Armstrong C, Mulvenna C, Phillips J, Yule A, Ahmed L, Clement KD, Craig N, Elseedawy E, Gorman D, Kane L, Livie J, Livie V, Moss E, Naasan A, Ravi F, Shields P, Zhu Y, Archer M, Cobley H, Dennis R, Downes C, Guevel B, Lamptey E, Murray H, Radhakrishnan A, Saravanabavan S, Sardar M, Shaw C, Tilliridou V, Wright R, Ye W, Alturki N, Helliwell R, Jones E, Kelly D, Lambotharan S, Scott K, Sivakumar R, Victor L, Boraluwe-Rallage H, Froggatt P, Haynes S, Hung YMA, Keyte A, Matthews L, Evans E, Haray P, John I, Mathivanan A, Morgan L, Oji O, Okorocha C, Rutherford A, Spiers H, Stageman N, Tsui A, Whitham R, Amoah-Arko A, Cecil E, Dietrich A, Fitzpatrick H, Guy C, Hair J, Hilton J, Jawad L, McAleer E, Taylor Z, Yap J, Akhbari M, Debnath D, Dhir T, Elbuzidi M, Elsaddig M, Glace S, Khawaja H, Koshy R, Lal K, Lobo L, McDermott A, Meredith J, Qamar MA, Vaidya A, Acquaah F, Barfi L, Carter N, Gnanappiragasam D, Ji C, Kaminski F, Lawday S, Mackay K, Sulaiman SK, Webb R, Ananthavarathan P, Dalal F, Farrar E, Hashemi R, Hossain M, Jiang J, Kiandee M, Lex J, Mason L, Matthews JH, McGeorge E, Modhwadia S, Pinkney T, Radotra A, Rickard L, Rodman L, Sales A, Tan KL, Bachi A, Bajwa DS, Battle J, Brown LR, Butler A, Calciu A, Davies E, Gardner I, Girdlestone T, Ikogho O, Keelan G, O'Loughlin P, Tam J, Elias J, Ngaage M, Thompson J, Bristow S, Brock E, Davis H, Pantelidou M, Sathiyakeerthy A, Singh K, Chaudhry A, Dickson G, Glen P, Gregoriou K, Hamid H, Mclean A, Mehtaji P, Neophytou G, Potts S, Belgaid DR, Burke J, Durno J, Ghailan N, Hanson M, Henshaw V, Nazir UR, Omar I, Riley BJ, Roberts J, Smart G, Van Winsen K, Bhatti A, Chan M, D'Auria M, Green S, Keshvala C, Li H, Maxwell-Armstrong C, Michaelidou M, Simmonds L, Smith C, Wimalathasan A, Abbas J, Cairns C, Chin YR, Connelly A, Moug S, Nair A, Svolkinas D, Coe P, Subar D, Wang H, Zaver V, Brayley J, Cookson P, Cunningham L, Gaukroger A, Ho M, Hough A, King J, O'Hagan D, Widdison A, Brown R, Brown B, Chavan A, Francis S, Hare L, Lund J, Malone N, Mavi B, McIlwaine A, Rangarajan S, Abuhussein N, Campbell HS, Daniels J, Fitzgerald I, Mansfield S, Pendrill A, Robertson D, Smart YW, Teng T, Yates J, Belgaumkar A, Katira A, Kossoff J, Kukran S, Laing C, Mathew B, Mohamed T, Myers S, Novell R, Phillips BL, Thomas M, Turlejski T, Turner S, Varcada M, Warren L, Wynell-Mayow W, Church R, Linley-Adams L, Osborn G, Saunders M, Spencer R, Srikanthan M, Tailor S, Tullett A, Ali M, Al-Masri S, Carr G, Ebhogiaye O, Heng S, Manivannan S, Manley J, McMillan LE, Peat C, Phillips B, Thomas S, Whewell H, Williams G, Bienias A, Cope EA, Courquin GR, Day L, Garner C, Gimson A, Harris C, Markham K, Moore T, Nadin T, Phillips C, Subratty SM, Brown K, Dada J, Durbacz M, Filipescu T, Harrison E, Kennedy ED, Khoo E, Kremel D, Lyell I, Pronin S, Tummon R, Ventre C, Walls L, Wootton E, Akhtar A, Davies E, El-Sawy D, Farooq M, Gaddah M, Griffiths H, Katsaiti I, Khadem N, Leong K, Williams I, Chean CS, Chudek D, Desai H, Ellerby N, Hammad A, Malla S, Murphy B, Oshin O, Popova P, Rana S, Ward T, Abbott TEF, Akpenyi O, Edozie F, El Matary R, English W, Jeyabaladevan S, Morgan C, Naidu V, Nicholls K, Peroos S, Prowle J, Sansome S, Torrance HD, Townsend D, Brecher J, Fung H, Kazmi Z, Outlaw P, Pursnani K, Ramanujam N, Razaq A, Sattar M, Sukumar S, Tan TSE, Chohan K, Dhuna S, Haq T, Kirby S, Lacy-Colson J, Logan P, Malik Q, McCann J, Mughal Z, Sadiq S, Sharif I, Shingles C, Simon A, Burnage S, Chan SSN, Craig ARJ, Duffield J, Dutta A, Eastwood M, Iqbal F, Mahmood F, Mahmood W, Patel C, Qadeer A, Robinson A, Rotundo A, Schade A, Slade RD, De Freitas M, Kinnersley H, McDowell E, Moens-Lecumberri S, Ramsden J, Rockall T, Wiffen L, Wright S, Bruce C, Francois V, Hamdan K, Limb C, Lunt AJ, Manley L, Marks M, Phillips CFE, Agnew CJF, Barr CJ, Benons N, Hart SJ, Kandage D, Krysztopik R, Mahalingam P, Mock J, Rajendran S, Stoddart MT, Clements B, Gillespie H, Lee S, McDougall R, Murray C, O'Loane R, Periketi S, Tan S, Amoah R, Bhudia R, Dudley B, Gilbert A, Griffiths B, Khan H, McKigney N, Roberts B, Samuel R, Seelarbokus A, Stubbing-Moore A, Thompson G, Williams P, Ahmed N, Akhtar R, Chandler E, Chappelow I, Gil H, Gower T, Kale A, Lingam G, Rutler L, Sellahewa C, Sheikh A, Stringer H, Taylor R, Aglan H, Ashraf MR, Choo S, Das E, Epstein J, Gentry R, Mills D, Poolovadoo Y, Ward N, Bull K, Cole A, Hack J, Khawari S, Lake C, Mandishona T, Perry R, Sleight S, Sultan S, Thornton T, Williams S, Arif T, Castle A, Chauhan P, Chesner R, Eilon T, Kamarajah S, Kambasha C, Lock L, Loka T, Mohammad F, Motahariasl S, Roper L, Sadhra SS, Sheikh A, Toma T, Wadood Q, Yip J, Ainger E, Busti S, Cunliffe L, Flamini T, Gaffing S, Moorcroft C, Peter M, Simpson L, Stokes E, Stott G, Wilson J, York J, Yousaf A, Borakati A, Brown M, Goaman A, Hodgson B, Ijeomah A, Iroegbu U, Kaur G, Lowe C, Mahmood S, Sattar Z, Sen P, Szuman A, Abbas N, Al-Ausi M, Anto N, Bhome R, Eccles L, Elliott J, Hughes EJ, Jones A, Karunatilleke AS, Knight JS, Manson CCF, Mekhail I, Michaels L, Noton TM, Okenyi E, Reeves T, Yasin IH, Banfield DA, Harris R, Lim D, Mason-Apps C, Roe T, Sandhu J, Shafiq N, Stickler E, Tam JP, Williams LM, Ainsworth P, Boualbanat Y, Doull C, Egan E, Evans L, Hassanin K, Ninkovic-Hall G, Odunlami W, Shergill M, Traish M, Cummings D, Kershaw S, Ong J, Reid F, Toellner H, Alwandi A, Amer M, George D, Haynes K, Hughes K, Peakall L, Premakumar Y, Punjabi N, Ramwell A, Sawkins H, Ashwood J, Baker A, Baron C, Bhide I, Blake E, De Cates C, Esmail R, Hosamuddin H, Kapp J, Nguru N, Raja M, Thomson F, Ahmed H, Aishwarya G, Al-Huneidi R, Ali S, Aziz R, Burke D, Clarke B, Kausar A, Maskill D, Mecia L, Myers L, Smith ACD, Walker G, Wroe N, Donohoe C, Gibbons D, Jordan P, Keogh C, Kiely A, Lalor P, McCrohan M, Powell C, Foley MP, Reynolds J, Silke E, Thorpe O, Kong JTH, White C, Ali Q, Dalrymple J, Ge Y, Khan H, Luo RS, Paine H, Paraskeva B, Parker L, Pillai K, Salciccioli J, Selvadurai S, Sonagara V, Springford LR, Tan L, Appleton S, Leadholm N, Zhang Y, Ahern D, Cotter M, Cremen S, Durrigan T, Flack V, Hrvacic N, Jones H, Jong B, Keane K, O'Connell PR, O'sullivan J, Pek G, Shirazi S, Barker C, Brown A, Carr W, Chen Y, Guillotte C, Harte J, Kokayi A, Lau K, McFarlane S, Morrison S, Broad J, Kenefick N, Makanji D, Printz V, Saito R, Thomas O, Breen H, Kirk S, Kong CH, O'Kane A, Eddama M, Engledow A, Freeman SK, Frost A, Goh C, Lee G, Poonawala R, Suri A, Taribagil P, Brown H, Christie S, Dean S, Gravell R, Haywood E, Holt F, Pilsworth E, Rabiu R, Roscoe HW, Shergill S, Sriram A, Sureshkumar A, Tan LC, Tanna A, Vakharia A, Bhullar S, Brannick S, Dunne E, Frere M, Kerin M, Kumar KM, Pratumsuwan T, Quek R, Salman M, Van Den Berg N, Wong C, Ahluwalia J, Bagga R, Borg CM, Calabria C, Draper A, Farwana M, Joyce H, Khan A, Mazza M, Pankin G, Sait MS, Sandhu N, Virani N, Wong J, Woodhams K, Croghan N, Ghag S, Hogg G, Ismail O, John N, Nadeem K, Naqi M, Noe SM, Sharma A, Tan S, Begum F, Best R, Collishaw A, Glasbey J, Golding D, Gwilym B, Harrison P, Jackman T, Lewis N, Luk YL, Porter T, Potluri S, Stechman M, Tate S, Thomas D, Walford B, Auld F, Bleakley A, Johnston S, Jones C, Khaw J, Milne S, O'Neill S, Singh KKR, Smith R, Swan A, Thorley N, Yalamarthi S, Yin ZD, Ali A, Balian V, Bana R, Clark K, Livesey C, McLachlan G, Mohammad M, Pranesh N, Richards C, Ross F, Sajid M, Brooke M, Francombe J, Gresly J, Hutchinson S, Kerrigan K, Matthews E, Nur S, Parsons L, Sandhu A, Vyas M, White F, Zulkifli A, Zuzarte L, Al-Mousawi A, Arya J, Azam S, Yahaya AA, Gill K, Hallan R, Hathaway C, Leptidis I, McDonagh L, Mitrasinovic S, Mushtaq N, Pang N, Peiris GB, Rinkoff S, Chan L, Christopher E, Farhan-Alanie MMH, Gonzalez-Ciscar A, Graham CJ, Lim H, McLean KA, Paterson HM, Rogers A, Roy C, Rutherford D, Smith F, Zubikarai G, Al-Khudairi R, Bamford M, Chang M, Cheng J, Hedley C, Joseph R, Mitchell B, Perera S, Rothwell L, Siddiqui A, Smith J, Taylor K, Wright OW, Baryan HK, Boyd G, Conchie H, Cox L, Davies J, Gardner S, Hill N, Krishna K, Lakin F, Scotcher S, Alberts J, Asad M, Barraclough J, Campbell A, Marshall D, Wakeford W, Cronbach P, D'Souza F, Gammeri E, Houlton J, Hall M, Kethees A, Patel R, Perera M, Prowle J, Shaid M, Webb E, Beattie S, Chadwick M, El-Taji O, Haddad S, Mann M, Patel M, Popat K, Rimmer L, Riyat H, Smith H, Anandarajah C, Cipparrone M, Desai K, Gao C, Goh ET, Howlader M, Jeffreys N, Karmarkar A, Mathew G, Mukhtar H, Ozcan E, Renukanthan A, Sarens N, Sinha C, Woolley A, Bogle R, Komolafe O, Loo F, Waugh D, Zeng R, Crewe A, Mathias J, Mills A, Owen A, Prior A, Saunders I, Baker A, Crilly L, McKeon J, Ubhi HK, Adeogun A, Carr R, Davison C, Devalia S, Hayat A, Karsan RB, Osborne C, Scott K, Weegenaar C, Wijeyaratne M, Babatunde F, Barnor-Ahiaku E, Beattie G, Chitsabesan P, Dixon O, Hall N, Ilenkovan N, Mackrell T, Nithianandasivam N, Orr J, Palazzo F, Saad M, Sandland-Taylor L, Sherlock J, Ashdown T, Chandler S, Garsaa T, Lloyd J, Loh SY, Ng S, Perkins C, Powell-Chandler A, Smith F, Underhill R. Perioperative intravenous contrast administration and the incidence of acute kidney injury after major gastrointestinal surgery: prospective, multicentre cohort study. Br J Surg 2020; 107:1023-1032. [PMID: 32026470 DOI: 10.1002/bjs.11453] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 09/21/2019] [Accepted: 11/08/2019] [Indexed: 01/14/2023]
Abstract
BACKGROUND This study aimed to determine the impact of preoperative exposure to intravenous contrast for CT and the risk of developing postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. METHODS This prospective, multicentre cohort study included adults undergoing gastrointestinal resection, stoma reversal or liver resection. Both elective and emergency procedures were included. Preoperative exposure to intravenous contrast was defined as exposure to contrast administered for the purposes of CT up to 7 days before surgery. The primary endpoint was the rate of AKI within 7 days. Propensity score-matched models were adjusted for patient, disease and operative variables. In a sensitivity analysis, a propensity score-matched model explored the association between preoperative exposure to contrast and AKI in the first 48 h after surgery. RESULTS A total of 5378 patients were included across 173 centres. Overall, 1249 patients (23·2 per cent) received intravenous contrast. The overall rate of AKI within 7 days of surgery was 13·4 per cent (718 of 5378). In the propensity score-matched model, preoperative exposure to contrast was not associated with AKI within 7 days (odds ratio (OR) 0·95, 95 per cent c.i. 0·73 to 1·21; P = 0·669). The sensitivity analysis showed no association between preoperative contrast administration and AKI within 48 h after operation (OR 1·09, 0·84 to 1·41; P = 0·498). CONCLUSION There was no association between preoperative intravenous contrast administered for CT up to 7 days before surgery and postoperative AKI. Risk of contrast-induced nephropathy should not be used as a reason to avoid contrast-enhanced CT.
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Beck AJ, Duffett-Leger L, Raffin Bouchal S, Ferber R, Ward T. 0917 Designing a Wearable Technology-Based Sleep Intervention To Support Sleep Health Among Adolescents: Using a Participatory Design Approach. Sleep 2020. [DOI: 10.1093/sleep/zsaa056.913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Introduction
Sleep problems during adolescence are increasingly common and have been associated with adverse physical and psychological health outcomes. Efforts to improve insufficient sleep among adolescents have resulted in increased sleep knowledge and temporary enhancements in sleep hygiene. Good sleep hygiene is established through the development of daily routines that support healthy sleep. Wearable technology offers a potential solution whereby adolescents can acquire and manage healthy sleep habits. In this study, we are co-designing with adolescents a prototype intervention using wearable technology to promote sustained improvements in their sleep hygiene.
Methods
Guided by participatory design approaches, the ongoing multi-phase mixed methods study is currently being conducted in a metropolitan area in western Canada. In phase 1, sleep data is being collected from a sample of 30 adolescent-parent dyads using wearable sensors (Actigraphy watches) and self-report sleep measures (questionnaires about sleep quality, hygiene, and beliefs and attitudes, as well as their general health) over a 10-day period. In phases 2 and 3, individual interviews and iterative user interface design sessions will be conducted with 25 adolescents.
Results
To date, thirteen adolescents-parent dyads (13-17 years, 9 females; 39-56 years, 11 females) have completed phase 1 of our study. Data analysis is currently being conducted to evaluate sleep onset/offset, total sleep time, wake after sleep onset, sleep efficiency, and sleep schedule differences between adolescents and their parents. Ten adolescents have completed individual interviews in phase 2 of the study. Preliminary qualitative data suggests that youth are aware of the importance of sleep to their overall health. However, they struggle with identifying credible information to act on from the various and sometimes conflicting sources (e.g. online, friends, family).
Conclusion
We anticipate that co-designing a wearable solution with adolescents will lead to a sleep intervention that is more relevant, persuasive, and useful in supporting their sleep health.
Support
This work is supported by the Sensor Technology in Monitoring Movement STiMM Program.
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Affiliation(s)
- A J Beck
- Faculty of Nursing, University of Calgary, Calgary, AB, CANADA
| | - L Duffett-Leger
- Faculty of Nursing, University of Calgary, Calgary, AB, CANADA
| | | | - R Ferber
- Faculty of Nursing, University of Calgary, Calgary, AB, CANADA
- Faculty of Kinesiology, University of Calgary, Calgary, AB, CANADA
| | - T Ward
- University of Washington, School of Nursing, Seattle, WA
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Noor M, Manchec B, Tran T, Coyne C, Feranec N, Ward T. 4:03 PM Abstract No. 262 Systemic anticoagulation versus catheter-directed thrombolysis in high-risk submassive pulmonary embolism. J Vasc Interv Radiol 2020. [DOI: 10.1016/j.jvir.2019.12.309] [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] Open
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Bruning-Richardson A, Sanganee H, Barry S, Tams D, Brend T, King H, Morton R, Ward T, Steele L, Shaw G, Esteves F, Droop A, Lawler S, Short S. PL3.6 Targeting GSK-3 activity promotes mitotic catastrophe via centrosome destabilisation and enhances the effect of radiotherapy in glioma models. Neuro Oncol 2019. [DOI: 10.1093/neuonc/noz126.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
BACKGROUND
Targeting kinases as regulators of cellular processes that drive cancer progression is a promising approach to improve patient outcome in GBM management. The glycogen synthase kinase 3 (GSK-3) plays a role in cancer progression and is known for its pro-proliferative activity in gliomas. The anti-proliferative and cytotoxic effects of the GSK-3 inhibitor AZD2858 were assessed in relevant in vitro and in vivo glioma models to confirm GSK-3 as a suitable target for improved single agent or combination treatments.
MATERIAL AND METHODS
The immortalised cell line U251 and the patient derived cell lines GBM1 and GBM4 were used in in vitro studies including MTT, clonogenic survival, live cell imaging, immunofluorescence microscopy and flow cytometry to assess the cytotoxic and anti-proliferative effects of AZD2858. Observed anti-proliferative effects were investigated by microarray technology for the identification of target genes with known roles in cell proliferation. Clinical relevance of targeting GSK-3 with the inhibitor either for single agent or combination treatment strategies was determined by subcutaneous and orthotopic in vivo modelling. Whole mount mass spectroscopy was used to confirm drug penetration in orthotopic tumour models.
RESULTS
AZD2858 was cytotoxic at low micromolar concentrations and at sub-micromolar concentrations (0.01 - 1.0 μM) induced mitotic defects in all cell lines examined. Prolonged mitosis, centrosome disruption/duplication and cytokinetic failure leading to cell death featured prominently among the cell lines concomitant with an observed S-phase arrest. No cytotoxic or anti-proliferative effect was observed in normal human astrocytes. Analysis of the RNA microarray screen of AZD2858 treated glioma cells revealed the dysregulation of mitosis-associated genes including ASPM and PRC1, encoding proteins with known roles in cytokinesis. The anti-proliferative and cytotoxic effect of AZD2858 was also confirmed in both subcutaneous and orthotopic in vivo models. In addition, combination treatment with AZD2858 enhanced clinically relevant radiation doses leading to reduced tumour volume and improved survival in orthotopic in vivo models.
CONCLUSION
GSK-3 inhibition with the small molecule inhibitor AZD2858 led to cell death in glioma stem cells preventing normal centrosome function and promoting mitotic failure. Normal human astrocytes were not affected by treatment with the inhibitor at submicromolar concentrations. Drug penetration was observed alongside an enhanced effect of clinical radiotherapy doses in vivo. The reported aberrant centrosomal duplication may be a direct consequence of failed cytokinesis suggesting a role of GSK-3 in regulation of mitosis in glioma. GSK-3 is a promising target for combination treatment with radiation in GBM management and plays a role in mitosis-associated events in glioma biology.
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Affiliation(s)
| | | | - S Barry
- Astra Zeneca, Cambridge, United Kingdom
| | - D Tams
- University of Leeds, Leeds, United Kingdom
| | - T Brend
- University of Leeds, Leeds, United Kingdom
| | - H King
- University of Leeds, Leeds, United Kingdom
| | - R Morton
- University of Leeds, Leeds, United Kingdom
| | - T Ward
- University of Leeds, Leeds, United Kingdom
| | - L Steele
- University of Leeds, Leeds, United Kingdom
| | - G Shaw
- University of Leeds, Leeds, United Kingdom
| | - F Esteves
- University of Leeds, Leeds, United Kingdom
| | - A Droop
- University of Leeds, Leeds, United Kingdom
| | - S Lawler
- Harvard University, Boston, MA, United States
| | - S Short
- University of Leeds, Leeds, United Kingdom
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Woodhouse C, Ward T, Gaskill-Shipley M, Chaudhary R. Feasibility of a modified Atkins diet in glioma patients during radiation and its effect on radiation sensitization. ACTA ACUST UNITED AC 2019; 26:e433-e438. [PMID: 31548811 DOI: 10.3747/co.26.4889] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background Gliomas are the most dreaded primary brain tumour because of their dismal cure rates. Ketogenic-type diets (kds) are high-fat, low-protein, and low-carbohydrate diets; the modified Atkins diet (mad) is a less-stringent version of a kd that still generates serum ketones in patients. The purpose of the present study was to retrospectively examine the feasibility of attaining ketosis and the safety of the mad in patients undergoing radiation and chemotherapy treatment for glioma. The rate of pseudoprogression (psp) after treatment was also assessed as a marker of radiation sensitization. To our knowledge, this dataset is the largest published relating to patients with glioma undergoing kd during radiation and chemotherapy. Methods We retrospectively studied 29 patients with grades ii-iv astrocytoma following the mad during standard radiation and chemotherapy. Feasibility of attaining ketosis was assessed though levels of beta hydroxybutyrate in blood. Pre- and post-radiation magnetic resonance images were evaluated for psp by a neuroradiologist blinded to patient data. Results In the 29 patients who started the mad during radiation, ketosis was achieved in all 29 (100%). No serious adverse events occurred secondary to the mad. Of those 29 patients, 19 had glioblastoma multiforme. Of the latter 19 patients, 11 (58%) showed psp after mad and radiation and temozolomide therapy. Conclusions A modified Atkins diet is feasible and safe for glioma patients during radiation and chemotherapy treatment. The mad and resulting ketosis could play a role as a radiation sensitizer.
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Affiliation(s)
- C Woodhouse
- University of Cincinnati Medical Center, Cincinnati, OH, U.S.A
| | - T Ward
- Division of Hematology/Oncology, Department of Medicine, University of Cincinnati Medical Center, Cincinnati, OH, U.S.A
| | - M Gaskill-Shipley
- Department of Radiology, University of Cincinnati Medical Center, Cincinnati, OH, U.S.A
| | - R Chaudhary
- Division of Hematology/Oncology, Department of Medicine, University of Cincinnati Medical Center, Cincinnati, OH, U.S.A
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Manchec B, Pepe J, Pham E, Noor M, Liu B, Seale T, Ward T. 03:54 PM Abstract No. 274 Contrast-enhanced CT may identify high-risk esophageal varices in cirrhotic patients. J Vasc Interv Radiol 2019. [DOI: 10.1016/j.jvir.2018.12.337] [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: 11/29/2022] Open
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McLean K, Glasbey J, Borakati A, Brooks T, Chang H, Choi S, Goodson R, Nielsen M, Pronin S, Salloum N, Sewart E, Vanniasegaram D, Drake T, Gillies M, Harrison E, Chapman S, Khatri C, Kong C, Claireaux H, Bath M, Mohan M, McNamee L, Kelly M, Mitchell H, Fitzgerald J, Bhangu A, Nepogodiev D, Antoniou I, Dean R, Davies N, Trecarten S, Henderson I, Holmes C, Wylie J, Shuttleworth R, Jindal A, Hughes F, Gouda P, Fleck R, Hanrahan M, Karunakaran P, Chen J, Sykes M, Sethi R, Suresh S, Patel P, Patel M, Varma R, Mushtaq J, Gundogan B, Bolton W, Khan T, Burke J, Morley R, Favero N, Adams R, Thirumal V, Kennedy E, Ong K, Tan Y, Gabriel J, Bakhsh A, Low J, Yener A, Paraoan V, Preece R, Tilston T, Cumber E, Dean S, Ross T, McCance E, Amin H, Satterthwaite L, Clement K, Gratton R, Mills E, Chiu S, Hung G, Rafiq N, Hayes J, Robertson K, Dynes K, Huang H, Assadullah S, Duncumb J, Moon R, Poo S, Mehta J, Joshi K, Callan R, Norris J, Chilvers N, Keevil H, Jull P, Mallick S, Elf D, Carr L, Player C, Barton E, Martin A, Ratu S, Roberts E, Phan P, Dyal A, Rogers J, Henson A, Reid N, Burke D, Culleton G, Lynne S, Mansoor S, Brennan C, Blessed R, Holloway C, Hill A, Goldsmith T, Mackin S, Kim S, Woin E, Brent G, Coffin J, Ziff O, Momoh Z, Debenham R, Ahmed M, Yong C, Wan J, Copley H, Raut P, Chaudhry F, Nixon G, Dorman C, Tan R, Kanabar S, Canning N, Dolaghan M, Bell N, McMenamin M, Chhabra A, Duke K, Turner L, Patel T, Chew L, Mirza M, Lunawat S, Oremule B, Ward N, Khan M, Tan E, Maclennan D, McGregor R, Chisholm E, Griffin E, Bell L, Hughes B, Davies J, Haq H, Ahmed H, Ungcharoen N, Whacha C, Thethi R, Markham R, Lee A, Batt E, Bullock N, Francescon C, Davies J, Shafiq N, Zhao J, Vivekanantham S, Barai I, Allen J, Marshall D, McIntyre C, Wilson H, Ashton A, Lek C, Behar N, Davis-Hall M, Seneviratne N, Esteve L, Sirakaya M, Ali S, Pope S, Ahn J, Craig-McQuaide A, Gatfield W, Leong S, Demetri A, Kerr A, Rees C, Loveday J, Liu S, Wijesekera M, Maru D, Attalla M, Smith N, Brown D, Sritharan P, Shah A, Charavanamuttu V, Heppenstall-Harris G, Ng K, Raghvani T, Rajan N, Hulley K, Moody N, Williams M, Cotton A, Sharifpour M, Lwin K, Bright M, Chitnis A, Abdelhadi M, Semana A, Morgan F, Reid R, Dickson J, Anderson L, McMullan R, Ahern N, Asmadi A, Anderson L, Boon Xuan JL, Crozier L, McAleer S, Lees D, Adebayo A, Das M, Amphlett A, Al-Robeye A, Valli A, Khangura J, Winarski A, Ali A, Woodward H, Gouldthrope C, Turner M, Sasapu K, Tonkins M, Wild J, Robinson M, Hardie J, Heminway R, Narramore R, Ramjeeawon N, Hibberd A, Winslow F, Ho W, Chong B, Lim K, Ho S, Crewdson J, Singagireson S, Kalra N, Koumpa F, Jhala H, Soon W, Karia M, Rasiah M, Xylas D, Gilbert H, Sundar-Singh M, Wills J, Akhtar S, Patel S, Hu L, Brathwaite-Shirley C, Nayee H, Amin O, Rangan T, Turner E, McCrann C, Shepherd R, Patel N, Prest-Smith J, Auyoung E, Murtaza A, Coates A, Prys-Jones O, King M, Gaffney S, Dewdney C, Nehikhare I, Lavery J, Bassett J, Davies K, Ahmad K, Collins A, Acres M, Egerton C, Cheng K, Chen X, Chan N, Sheldon A, Khan S, Empey J, Ingram E, Malik A, Johnstone M, Goodier R, Shah J, Giles J, Sanders J, McLure S, Pal S, Rangedara A, Baker A, Asbjoernsen C, Girling C, Gray L, Gauntlett L, Joyner C, Qureshi S, Mogan Y, Ng J, Kumar A, Park J, Tan D, Choo K, Raman K, Buakuma P, Xiao C, Govinden S, Thompson O, Charalambos M, Brown E, Karsan R, Dogra T, Bullman L, Dawson P, Frank A, Abid H, Tung L, Qureshi U, Tahmina A, Matthews B, Harris R, O'Connor A, Mazan K, Iqbal S, Stanger S, Thompson J, Sullivan J, Uppal E, MacAskill A, Bamgbose F, Neophytou C, Carroll A, Rookes C, Datta U, Dhutia A, Rashid S, Ahmed N, Lo T, Bhanderi S, Blore C, Ahmed S, Shaheen H, Abburu S, Majid S, Abbas Z, Talukdar S, Burney L, Patel J, Al-Obaedi O, Roberts A, Mahboob S, Singh B, Sheth S, Karia P, Prabhudesai A, Kow K, Koysombat K, Wang S, Morrison P, Maheswaran Y, Keane P, Copley P, Brewster O, Xu G, Harries P, Wall C, Al-Mousawi A, Bonsu S, Cunha P, Ward T, Paul J, Nadanakumaran K, Tayeh S, Holyoak H, Remedios J, Theodoropoulou K, Luhishi A, Jacob L, Long F, Atayi A, Sarwar S, Parker O, Harvey J, Ross H, Rampal R, Thomas G, Vanmali P, McGowan C, Stein J, Robertson V, Carthew L, Teng V, Fong J, Street A, Thakker C, O'Reilly D, Bravo M, Pizzolato A, Khokhar H, Ryan M, Cheskes L, Carr R, Salih A, Bassiony S, Yuen R, Chrastek D, Rosen O'Sullivan H, Amajuoyi A, Wang A, Sitta O, Wye J, Qamar M, Major C, Kaushal A, Morgan C, Petrarca M, Allot R, Verma K, Dutt S, Chilima C, Peroos S, Kosasih S, Chin H, Ashken L, Pearse R, O'Loughlin R, Menon A, Singh K, Norton J, Sagar R, Jathanna N, Rothwell L, Watson N, Harding F, Dube P, Khalid H, Punjabi N, Sagmeister M, Gill P, Shahid S, Hudson-Phillips S, George D, Ashwood J, Lewis T, Dhar M, Sangal P, Rhema I, Kotecha D, Afzal Z, Syeed J, Prakash E, Jalota P, Herron J, Kimani L, Delport A, Shukla A, Agarwal V, Parthiban S, Thakur H, Cymes W, Rinkoff S, Turnbull J, Hayat M, Darr S, Khan U, Lim J, Higgins A, Lakshmipathy G, Forte B, Canning E, Jaitley A, Lamont J, Toner E, Ghaffar A, McDowell M, Salmon D, O'Carroll O, Khan A, Kelly M, Clesham K, Palmer C, Lyons R, Bell A, Chin R, Waldron R, Trimble A, Cox S, Ashfaq U, Campbell J, Holliday R, McCabe G, Morris F, Priestland R, Vernon O, Ledsam A, Vaughan R, Lim D, Bakewell Z, Hughes R, Koshy R, Jackson H, Narayan P, Cardwell A, Jubainville C, Arif T, Elliott L, Gupta V, Bhaskaran G, Odeleye A, Ahmed F, Shah R, Pickard J, Suleman Y, North A, McClymont L, Hussain N, Ibrahim I, Ng G, Wong V, Lim A, Harris L, Tharmachandirar T, Mittapalli D, Patel V, Lakhani M, Bazeer H, Narwani V, Sandhu K, Wingfield L, Gentry S, Adjei H, Bhatti M, Braganza L, Barnes J, Mistry S, Chillarge G, Stokes S, Cleere J, Wadanamby S, Bucko A, Meek J, Boxall N, Heywood E, Wiltshire J, Toh C, Ward A, Shurovi B, Horth D, Patel B, Ali B, Spencer T, Axelson T, Kretzmer L, Chhina C, Anandarajah C, Fautz T, Horst C, Thevathasan A, Ng J, Hirst F, Brewer C, Logan A, Lockey J, Forrest P, Keelty N, Wood A, Springford L, Avery P, Schulz T, Bemand T, Howells L, Collier H, Khajuria A, Tharakan R, Parsons S, Buchan A, McGalliard R, Mason J, Cundy O, Li N, Redgrave N, Watson R, Pezas T, Dennis Y, Segall E, Hameed M, Lynch A, Chamberlain M, Peck F, Neo Y, Russell G, Elseedawy M, Lee S, Foster N, Soo Y, Puan L, Dennis R, Goradia H, Qureshi A, Osman S, Reeves T, Dinsmore L, Marsden M, Lu Q, Pitts-Tucker T, Dunn C, Walford R, Heathcote E, Martin R, Pericleous A, Brzyska K, Reid K, Williams M, Wetherall N, McAleer E, Thomas D, Kiff R, Milne S, Holmes M, Bartlett J, Lucas de Carvalho J, Bloomfield T, Tongo F, Bremner R, Yong N, Atraszkiewicz B, Mehdi A, Tahir M, Sherliker G, Tear A, Pandey A, Broyd A, Omer H, Raphael M, Chaudhry W, Shahidi S, Jawad A, Gill C, Fisher IH, Adeleja I, Clark I, Aidoo-Micah G, Stather P, Salam G, Glover T, Deas G, Sim N, Obute R, Wynell-Mayow W, Sait M, Mitha N, de Bernier G, Siddiqui M, Shaunak R, Wali A, Cuthbert G, Bhudia R, Webb E, Shah S, Ansari N, Perera M, Kelly N, McAllister R, Stanley G, Keane C, Shatkar V, Maxwell-Armstrong C, Henderson L, Maple N, Manson R, Adams R, Semple E, Mills M, Daoub A, Marsh A, Ramnarine A, Hartley J, Malaj M, Jewell P, Whatling E, Hitchen N, Chen M, Goh B, Fern J, Rogers S, Derbyshire L, Robertson D, Abuhussein N, Deekonda P, Abid A, Harrison P, Aildasani L, Turley H, Sherif M, Pandey G, Filby J, Johnston A, Burke E, Mohamud M, Gohil K, Tsui A, Singh R, Lim S, O'Sullivan K, McKelvey L, O'Neill S, Roberts H, Brown F, Cao Y, Buckle R, Liew Y, Sii S, Ventre C, Graham C, Filipescu T, Yousif A, Dawar R, Wright A, Peters M, Varley R, Owczarek S, Hartley S, Khattak M, Iqbal A, Ali M, Durrani B, Narang Y, Bethell G, Horne L, Pinto R, Nicholls K, Kisyov I, Torrance H, English W, Lakhani S, Ashraf S, Venn M, Elangovan V, Kazmi Z, Brecher J, Sukumar S, Mastan A, Mortimer A, Parker J, Boyle J, Elkawafi M, Beckett J, Mohite A, Narain A, Mazumdar E, Sreh A, Hague A, Weinberg D, Fletcher L, Steel M, Shufflebotham H, Masood M, Sinha Y, Jenvey C, Kitt H, Slade R, Craig A, Deall C, Reakes T, Chervenkoff J, Strange E, O'Bryan M, Murkin C, Joshi D, Bergara T, Naqib S, Wylam D, Scotcher S, Hewitt C, Stoddart M, Kerai A, Trist A, Cole S, Knight C, Stevens S, Cooper G, Ingham R, Dobson J, O'Kane A, Moradzadeh J, Duffy A, Henderson C, Ashraf S, McLaughin C, Hoskins T, Reehal R, Bookless L, McLean R, Stone E, Wright E, Abdikadir H, Roberts C, Spence O, Srikantharajah M, Ruiz E, Matthews J, Gardner E, Hester E, Naran P, Simpson R, Minhas M, Cornish E, Semnani S, Rojoa D, Radotra A, Eraifej J, Eparh K, Smith D, Mistry B, Hickling S, Din W, Liu C, Mithrakumar P, Mirdavoudi V, Rashid M, Mcgenity C, Hussain O, Kadicheeni M, Gardner H, Anim-Addo N, Pearce J, Aslanyan A, Ntala C, Sorah T, Parkin J, Alizadeh M, White A, Edozie F, Johnston J, Kahar A, Navayogaarajah V, Patel B, Carter D, Khonsari P, Burgess A, Kong C, Ponweera A, Cody A, Tan Y, Ng A, Croall A, Allan C, Ng S, Raghuvir V, Telfer R, Greenhalgh A, McKerr C, Edison M, Patel B, Dear K, Hardy M, Williams P, Hassan S, Sajjad U, O'Neill E, Lopes S, Healy L, Jamal N, Tan S, Lazenby D, Husnoo S, Beecroft S, Sarvanandan T, Weston C, Bassam N, Rabinthiran S, Hayat U, Ng L, Varma D, Sukkari M, Mian A, Omar A, Kim J, Sellathurai J, Mahmood J, O'Connell C, Bose R, Heneghan H, Lalor P, Matheson J, Doherty C, Cullen C, Cooper D, Angelov S, Drislane C, Smith A, Kreibich A, Palkhi E, Durr A, Lotfallah A, Gold D, Mckean E, Dhanji A, Anilkumar A, Thacoor A, Siddiqui Z, Lim S, Piquet A, Anderson S, McCormack D, Gulati J, Ibrahim A, Murray S, Walsh S, McGrath A, Ziprin P, Chua E, Lou C, Bloomer J, Paine H, Osei-Kuffour D, White C, Szczap A, Gokani S, Patel K, Malys M, Reed A, Torlot G, Cumber E, Charania A, Ahmad S, Varma N, Cheema H, Austreng L, Petra H, Chaudhary M, Zegeye M, Cheung F, Coffey D, Heer R, Singh S, Seager E, Cumming S, Suresh R, Verma S, Ptacek I, Gwozdz A, Yang T, Khetarpal A, Shumon S, Fung T, Leung W, Kwang P, Chew L, Loke W, Curran A, Chan C, McGarrigle C, Mohan K, Cullen S, Wong E, Toale C, Collins D, Keane N, Traynor B, Shanahan D, Yan A, Jafree D, Topham C, Mitrasinovic S, Omara S, Bingham G, Lykoudis P, Miranda B, Whitehurst K, Kumaran G, Devabalan Y, Aziz H, Shoa M, Dindyal S, Yates J, Bernstein I, Rattan G, Coulson R, Stezaker S, Isaac A, Salem M, McBride A, McFarlane H, Yow L, MacDonald J, Bartlett R, Turaga S, White U, Liew W, Yim N, Ang A, Simpson A, McAuley D, Craig E, Murphy L, Shepherd P, Kee J, Abdulmajid A, Chung A, Warwick H, Livesey A, Holton P, Theodoreson M, Jenkin S, Turner J, Entwisle J, Marchal S, O'Connor S, Blege H, Aithie J, Sabine L, Stewart G, Jackson S, Kishore A, Lankage C, Acquaah F, Joyce H, McKevitt K, Coffey C, Fawaz A, Dolbec K, O'Sullivan D, Geraghty J, Lim E, Bolton L, FitzPatrick D, Robinson C, Ramtoola T, Collinson S, Grundy L, McEnhill P, Harbhajan Singh G, Loughran D, Golding D, Keeling R, Williams R, Whitham R, Yoganathan S, Nachiappan R, Egan R, Owasil R, Kwan M, He A, Goh R, Bhome R, Wilson H, Teoh P, Raji K, Jayakody N, Matthams J, Chong J, Luk C, Greig R, Trail M, Charalambous G, Rocke A, Gardiner N, Bulley F, Warren N, Brennan E, Fergurson P, Wilson R, Whittingham H, Brown E, Khanijau R, Gandhi K, Morris S, Boulton A, Chandan N, Barthorpe A, Maamari R, Sandhu S, McCann M, Higgs L, Balian V, Reeder C, Diaper C, Sale T, Ali H, Archer C, Clarke A, Heskin J, Hurst P, Farmer J, O'Flynn L, Doan L, Shuker B, Stott G, Vithanage N, Hoban K, Nesargikar P, Kennedy H, Grossart C, Tan E, Roy C, Sim P, Leslie K, Sim D, Abul M, Cody N, Tay A, Woon E, Sng S, Mah J, Robson J, Shakweh E, Wing V, Mills H, Li M, Barrow T, Balaji S, Jordan H, Phillips C, Naveed H, Hirani S, Tai A, Ratnakumaran R, Sahathevan A, Shafi A, Seedat M, Weaver R, Batho A, Punj R, Selvachandran H, Bhatt N, Botchey S, Khonat Z, Brennan K, Morrison C, Devlin E, Linton A, Galloway E, McGarvie S, Ramsay N, McRobbie H, Whewell H, Dean W, Nelaj S, Eragat M, Mishra A, Kane T, Zuhair M, Wells M, Wilkinson D, Woodcock N, Sun E, Aziz N, Ghaffar MKA. Critical care usage after major gastrointestinal and liver surgery: a prospective, multicentre observational study. Br J Anaesth 2019; 122:42-50. [PMID: 30579405 DOI: 10.1016/j.bja.2018.07.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 07/19/2018] [Accepted: 07/23/2018] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Patient selection for critical care admission must balance patient safety with optimal resource allocation. This study aimed to determine the relationship between critical care admission, and postoperative mortality after abdominal surgery. METHODS This prespecified secondary analysis of a multicentre, prospective, observational study included consecutive patients enrolled in the DISCOVER study from UK and Republic of Ireland undergoing major gastrointestinal and liver surgery between October and December 2014. The primary outcome was 30-day mortality. Multivariate logistic regression was used to explore associations between critical care admission (planned and unplanned) and mortality, and inter-centre variation in critical care admission after emergency laparotomy. RESULTS Of 4529 patients included, 37.8% (n=1713) underwent planned critical care admissions from theatre. Some 3.1% (n=86/2816) admitted to ward-level care subsequently underwent unplanned critical care admission. Overall 30-day mortality was 2.9% (n=133/4519), and the risk-adjusted association between 30-day mortality and critical care admission was higher in unplanned [odds ratio (OR): 8.65, 95% confidence interval (CI): 3.51-19.97) than planned admissions (OR: 2.32, 95% CI: 1.43-3.85). Some 26.7% of patients (n=1210/4529) underwent emergency laparotomies. After adjustment, 49.3% (95% CI: 46.8-51.9%, P<0.001) were predicted to have planned critical care admissions, with 7% (n=10/145) of centres outside the 95% CI. CONCLUSIONS After risk adjustment, no 30-day survival benefit was identified for either planned or unplanned postoperative admissions to critical care within this cohort. This likely represents appropriate admission of the highest-risk patients. Planned admissions in selected, intermediate-risk patients may present a strategy to mitigate the risk of unplanned admission. Substantial inter-centre variation exists in planned critical care admissions after emergency laparotomies.
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Zaslavsky O, Thompson H, Landis C, McCurry S, Ward T, Heitkemper M, Demiris G. FEASIBILITY AND ACCEPTABILITY OF MHEALTH TECHNOLOGIES FOR BEHAVIORAL TRACKING AMONG OLDER ADULTS WITH ARTHRITIS. Innov Aging 2018. [DOI: 10.1093/geroni/igy023.2503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Ward T, Skelley A, Ghandi K, Campos-González R. Efficient production of T-central memory cells from apheresis product using microfluidic chips. Cytotherapy 2018. [DOI: 10.1016/j.jcyt.2018.02.288] [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: 11/28/2022]
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Dudzevicius V, Tariq A, Owens J, Oxley C, Sathyamurthy R, Mustafa R, Spence D, Peedell C, Aynsley E, Hartley R, Taylor C, Wood A, Dunning J, Earl U, Ferguson J, Devaraj M, Ward T, Mansy T, Li L. Emergency presentation of lung cancer patients via A&E increases the chances of having advanced disease stage and worse performance status. Lung Cancer 2017. [DOI: 10.1183/1393003.congress-2017.pa4244] [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: 11/05/2022]
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De Ruysscher D, Defraene G, Ramaekers B, Lambin P, Briers E, Stobart H, Ward T, Bentzen S, Van Staa T, Kerns S, West C. EP-1419: Optimal design and patient selection for interventional trials using radiogenomic biomarkers. Radiother Oncol 2017. [DOI: 10.1016/s0167-8140(17)31854-6] [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: 11/24/2022]
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Garrison MM, Ward T. 0986 PARENT QUALITY OF LIFE: IMPACT OF A CHILD SLEEP INTERVENTION. Sleep 2017. [DOI: 10.1093/sleepj/zsx050.985] [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: 11/13/2022] Open
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Liu B, Limback J, Kendall M, Valente M, Armaly J, Grekoski V, Pinizzotto A, Pepe J, Burt J, Ward T. Safety of computed tomographic–guided bone marrow biopsy in thrombocytopenic patients: a retrospective review of 1020 bone marrow biopsies. J Vasc Interv Radiol 2017. [DOI: 10.1016/j.jvir.2016.12.764] [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: 10/20/2022] Open
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Liu B, Kendall M, O’Dell M, Flores M, Limback J, Pepe J, Burt J, Contreras F, Lewis A, Ward T. Cortical tangential vs. a non-tangential approach in computed tomography (CT)-guided native medical renal biopsy: a comparison of efficacy and safety. J Vasc Interv Radiol 2017. [DOI: 10.1016/j.jvir.2016.12.947] [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: 10/20/2022] Open
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Ward T, Gordon J, Bennett H, Webster S, Sugrue D, Jones B, Brenner M, McEwan P. Tackling the burden of the hepatitis C virus in the UK: characterizing and assessing the clinical and economic consequences. Public Health 2016; 141:42-51. [PMID: 27932014 DOI: 10.1016/j.puhe.2016.08.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Revised: 08/03/2016] [Accepted: 08/05/2016] [Indexed: 01/18/2023]
Abstract
OBJECTIVES The hepatitis C virus (HCV) remains a significant public health issue. This study aimed to quantify the clinical and economic burden of chronic hepatitis C in the UK, stratified by disease severity, age and awareness of infection, with concurrent assessment of the impact of implementing a treatment prioritization approach. STUDY DESIGN AND METHODS A previously published back projection, natural history and cost-effectiveness HCV model was adapted to a UK setting to estimate the disease burden of chronic hepatitis C and end-stage liver disease (ESLD) between 1980 and 2035. A published meta-regression analysis informed disease progression, and UK-specific data informed other model inputs. RESULTS At 2015, prevalence of chronic hepatitis C is estimated to be 241,487 with 22.20%, 33.72%, 17.22%, 16.67% and 10.19% of patients in METAVIR stages F0, F1, F2, F3 and F4, respectively, but is estimated to fall to 193,999 by 2035. ESLD incidence is predicted to peak in 2031. Assuming all patients are diagnosed and treatment is prioritized in F3 and F4 using highly efficacious direct-acting antiviral (DAA) regimens, a 69.85% reduction in ESLD incidence is predicted between 2015 and 2035, and the cumulative discounted medical expenditure associated with the lifetime management of incident ESLD events is estimated to be £1,202,827,444. CONCLUSIONS The prevalence of chronic hepatitis C is expected to fall in coming decades; however, the ongoing financial burden is expected to be high due to an increase in ESLD incidence. This study highlights the significant costs of managing ESLD that are likely to be incurred without the employment of effective treatment approaches.
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Affiliation(s)
- T Ward
- Health Economics and Outcomes Research Ltd, Cardiff, UK.
| | - J Gordon
- Health Economics and Outcomes Research Ltd, Cardiff, UK; Department of Public Health, University of Adelaide, Australia; School of Medicine, University of Nottingham, UK
| | - H Bennett
- Health Economics and Outcomes Research Ltd, Cardiff, UK
| | - S Webster
- Health Economics and Outcomes Research Ltd, Cardiff, UK
| | - D Sugrue
- Health Economics and Outcomes Research Ltd, Cardiff, UK
| | - B Jones
- Health Economics and Outcomes Research Ltd, Cardiff, UK
| | - M Brenner
- UK HEOR, Bristol-Myers Squibb Pharmaceuticals Ltd, Uxbridge, UK
| | - P McEwan
- Health Economics and Outcomes Research Ltd, Cardiff, UK; School of Human & Health Sciences, Swansea University, Swansea, UK
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Ward T, Shah R, Louie J, Sze D. Yttrium-90 radioembolization using resin microspheres without prophylactic embolization of the gastroduodenal artery. J Vasc Interv Radiol 2016. [DOI: 10.1016/j.jvir.2015.12.400] [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] Open
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Bec S, Ward T, Farman M, O'Donnell K, Hershman D, Van Sanford D, Vaillancourt LJ. Characterization of Fusarium Strains Recovered From Wheat With Symptoms of Head Blight in Kentucky. Plant Dis 2015; 99:1622-1632. [PMID: 30695966 DOI: 10.1094/pdis-06-14-0610-re] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Fusarium graminearum species complex (FGSC) members cause Fusarium head blight (FHB) of wheat (Triticum aestivum L.) and small grains in the United States. The U.S. population is diverse and includes several genetically distinct local emergent subpopulations, some more aggressive and toxigenic than the majority population. Kentucky is a transition zone between the Mid-Atlantic and Midwestern wheat production areas. Sixty-eight Fusarium strains were isolated from symptomatic wheat heads from central and western Kentucky and southern Indiana in 2007. A multilocus genotyping assay and a variety of additional molecular markers, including some novel markers developed using the F. graminearum genome sequence, were used to characterize the pathogen population. Five of the isolates were identified as members of two non-FGSC species, F. acuminatum and F. cf. reticulatum, but they did not cause symptoms in greenhouse tests. All the FGSC isolates belonged to the 15-ADON chemotype of F. graminearum. Comparative genetic analysis using variable nuclear tandem repeat (VNTR) markers indicated that the population in Kentucky and Indiana belonged to the dominant North American population, with some diversification likely due to local evolution. Telomere and RFLP fingerprinting markers based on repetitive sequences revealed a high degree of genetic diversity within the population, with unique genotypes found at each location, and multiple genotypes isolated from the same head.
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Affiliation(s)
- S Bec
- Department of Plant Pathology, University of Kentucky, Lexington, KY 40546-0312
| | - T Ward
- Bacterial Foodborne Pathogens and Mycology Research Unit, USDA-ARS, Peoria, IL 61604-3999
| | - M Farman
- Department of Plant Pathology, University of Kentucky, Lexington, KY 40546-0312
| | - K O'Donnell
- Bacterial Foodborne Pathogens and Mycology Research Unit, USDA-ARS, Peoria, IL 61604-3999
| | - D Hershman
- Department of Plant Pathology, University of Kentucky, Lexington, KY 40546-0312
| | - D Van Sanford
- Department of Plant and Soil Sciences, University of Kentucky, Lexington, KY 40546-0312
| | - L J Vaillancourt
- Department of Plant Pathology, University of Kentucky, Lexington, KY 40546-0312
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Ward T, Voss J, Yuwen W, Foll D, Gohar F, Ringold S. AB1017 Sleep Fragmentation and Biomarkers in Juvenile Idiopathic Arthritis. Ann Rheum Dis 2015. [DOI: 10.1136/annrheumdis-2015-eular.1748] [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: 11/04/2022]
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McEwan P, Bennett H, Ward T, Bergenheim K. Refitting of the UKPDS 68 risk equations to contemporary routine clinical practice data in the UK. Pharmacoeconomics 2015; 33:149-161. [PMID: 25344660 DOI: 10.1007/s40273-014-0225-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
OBJECTIVE Economic evaluations of new diabetes therapies rely heavily upon the UK Prospective Diabetes Study (UKPDS) equations for prediction of cardiovascular events; however, concerns persist regarding their relevance to current clinical practice and appropriate use in populations other than newly diagnosed patients. This study refits the UKPDS 68 event equations, using contemporary data describing low- and intermediate-risk patients. RESEARCH DESIGN AND METHODS Anonymized patient data describing demographics, risk factors and incidence of cardiovascular and microvascular events were extracted from The Health Improvement Network (THIN) database over the 10-year period from 1 January 2000 to 31 December 2009. Following multiple imputation of missing values, accelerated failure-time Weibull regression equations were refitted to produce new coefficients for each risk group. Discriminatory performance was assessed and compared with both UKPDS 68 and UKPDS 82 risk equations, and the implication of coefficient choice within an economic evaluation was assessed using the Cardiff type 2 diabetes model. RESULTS When applied to patient-level data, the three sets of coefficients (UKPDS, THIN low-risk and intermediate-risk) lead to fairly consistent predictions of the 5-year risk of events. Exceptions include lower predicted rates of myocardial infarction and higher rates of ischaemic heart disease, congestive heart failure and end-stage renal disease with both sets of revised THIN coefficients compared with UKPDS. Over a modelled lifetime, the coefficients derived from the low-risk data predict fewer total cardiovascular events compared with UKPDS, while those from the intermediate-risk data predict a greater number. The areas under the receiver-operating characteristic curves demonstrated a marginal improvement in the discriminatory performance of the refitted equations. The incremental cost-effectiveness ratio associated with dapagliflozin versus sulphonylurea in addition to metformin changed from £7,708 to £7,519 and £6,906 per QALY gained, using the THIN intermediate- and low-risk coefficients, respectively. CONCLUSION The results suggest that while the UKPDS equations perform best in newly diagnosed patients, they may overpredict the lifetime risk in this group and underpredict it in patients with more advanced diabetes. Implementation of the revised coefficients will result in different absolute numbers of predicted diabetes-related events; however, they are not expected to significantly affect the conclusions of economic modelling.
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Affiliation(s)
- P McEwan
- Swansea Centre for Health Economics, Swansea University, Wales, UK
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