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Changes in English medication safety indicators throughout the COVID-19 pandemic: a federated analysis of 57 million patients' primary care records in situ using OpenSAFELY
Louis Fisher; Lisa E M Hopcroft; Sarah Rodgers; James Barrett; Kerry Oliver; Anthony J Avery; Dai Evans; Helen Curtis; Richard Croker; Orla Macdonald; Jessica Morley; Amir Mehrkar; Seb Bacon; Simon Davy; Iain Dillingham; David Evans; George Hickman; Peter Inglesby; Caroline E Morton; Becky Smith; Tom Ward; William Hulme; Amelia Green; Jon Massey; Alex J Walker; Chris Bates; Jonathan Cockburn; John Parry; Frank Hester; Sam Harper; Shaun O'Hanlon; Alex Eavis; Richard Jarvis; Dima Avramov; Paul Griffiths; Aaron Fowles; Nasreen Parkes; Ben Goldacre; Brian MacKenna.
Affiliation
  • Louis Fisher; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Lisa E M Hopcroft; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • 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 of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Richard Croker; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Orla Macdonald; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Jessica Morley; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Amir Mehrkar; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Seb Bacon; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Simon Davy; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Iain Dillingham; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • David Evans; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • George Hickman; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Peter Inglesby; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Caroline E Morton; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Becky Smith; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Tom Ward; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • William Hulme; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Amelia Green; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Jon Massey; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Alex J Walker; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Chris Bates; TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX
  • Jonathan Cockburn; 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
  • Shaun O'Hanlon; EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds, LS19 6BA
  • Alex Eavis; EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds, LS19 6BA
  • Richard Jarvis; EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds, LS19 6BA
  • Dima Avramov; EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds, LS19 6BA
  • Paul Griffiths; EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds, LS19 6BA
  • Aaron Fowles; EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds, LS19 6BA
  • Nasreen Parkes; EMIS Health, Fulford Grange, Micklefield Lane, Rawdon, Leeds, LS19 6BA
  • Ben Goldacre; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
  • Brian MacKenna; Bennett Institute of Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG
Preprint in English | medRxiv | ID: ppmedrxiv-22273234
ABSTRACT
ObjectiveTo describe the impact of the COVID-19 pandemic on safe prescribing, using the PINCER prescribing indicators; to implement complex prescribing indicators at national scale using GP data. DesignPopulation based cohort study, with the approval of NHS England using the OpenSAFELY platform. SettingElectronic health record data from 56.8 million NHS patients general practice records. ParticipantsAll NHS patients registered at a GP practice using TPP or EMIS computer systems and recorded as at risk of at least one potentially hazardous PINCER indicator between September 2019 and September 2021. Main outcome measureMonthly trends and between-practice variation for compliance with 13 PINCER measures between September 2019 and September 2021. ResultsThe indicators were successfully implemented across GP data in OpenSAFELY. Hazardous prescribing remained largely unchanged during the COVID-19 pandemic, with only small reductions in achievement of the PINCER indicators. There were transient delays in blood test monitoring for some medications, particularly ACE inhibitors. All indicators exhibited substantial recovery by September 2021. We identified 1,813,058 patients at risk of at least one hazardous prescribing event. ConclusionGood performance was maintained during the COVID-19 pandemic across a diverse range of widely evaluated measures of safe prescribing. Summary box O_TEXTBOXWHAT IS ALREADY KNOWN ON THIS TOPICO_LIPrimary care services were substantially disrupted by the COVID-19 pandemic. C_LIO_LIDisruption to safe prescribing during the pandemic has not previously been evaluated. C_LIO_LIPINCER is a nationally adopted programme of activities that aims to identify and correct hazardous prescribing in GP practices, by conducting manual audit on subgroups of practices. C_LI WHAT THIS STUDY ADDSO_LIFor the first time, we were able to successfully generate data on PINCER indicators for almost the whole population of England, in a single analysis. C_LIO_LIOur study is the most comprehensive assessment of medication safety during the COVID-19 pandemic in England, covering 95% of the population using well-validated measures. C_LIO_LIGood performance was maintained across many PINCER indicators throughout the pandemic. C_LIO_LIDelays in delivering some medication-related blood test monitoring were evident though considerable recovery was made by the end of the study period. C_LI C_TEXTBOX
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Cohort_studies / Experimental_studies / Observational study / Prognostic study Language: English Year: 2022 Document type: Preprint
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Cohort_studies / Experimental_studies / Observational study / Prognostic study Language: English Year: 2022 Document type: Preprint