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
Paul Griffiths,
Ben Goldacre,
David Evans,
Sam Harper,
Orla Macdonald,
Alex J Walker,
Richard Croker,
Anthony J Avery,
Caroline E Morton,
Jessica Morley,
Brian MacKenna,
William Hulme,
Sarah Rodgers,
Amir Mehrkar,
Peter Inglesby,
Jonathan Cockburn,
John Parry,
Frank Hester,
James Barrett,
Amelia Green,
Helen Curtis,
Sebastian Bacon,
Simon Davy,
George Hickman,
Tom Ward,
Louis Fisher,
Jon Massey,
Iain Dillingham,
Christopher Bates,
Lisa EM Hopcroft,
Kerry Oliver,
Dai Evans,
Becky Smith,
Shaun O’Hanlon,
Alex Eavis,
Richard Jarvis,
Dima Avramov,
Aaron Fowles,
Nasreen Parkes
Affiliations
Paul Griffiths
EMIS Health, Leeds, UK
Ben Goldacre
11 Bennett Institute for Applied Data Science, 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
Sam Harper
TPP, Leeds, UK
Orla Macdonald
Oxford Health NHS Foundation Trust, Oxford, UK
Alex J Walker
The DataLab, 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
Anthony J Avery
professor of primary healthcare
Caroline E Morton
Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
Jessica Morley
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
Brian MacKenna
10 NHS England, Redditch, UK
William Hulme
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
Amir Mehrkar
11 Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
Peter Inglesby
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
Jonathan Cockburn
TPP, Leeds, UK
John Parry
TPP, Leeds, UK
Frank Hester
TPP, Leeds, UK
James Barrett
PRIMIS, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
Amelia Green
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
Helen Curtis
Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
Sebastian Bacon
11 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, Oxford University, Oxford, UK
George Hickman
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
Louis Fisher
Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
Jon Massey
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
Iain Dillingham
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
Christopher Bates
TPP, Leeds, UK
Lisa EM Hopcroft
Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
Kerry Oliver
PRIMIS, 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
Becky Smith
Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK
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.
