Your browser doesn't support javascript.
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.
Fisher, Louis; Hopcroft, Lisa Em; Rodgers, Sarah; Barrett, James; Oliver, Kerry; Avery, Anthony J; Evans, Dai; Curtis, Helen; Croker, Richard; Macdonald, Orla; Morley, Jessica; Mehrkar, Amir; Bacon, Sebastian; Davy, Simon; Dillingham, Iain; Evans, David; Hickman, George; Inglesby, Peter; Morton, Caroline E; Smith, Becky; Ward, Tom; Hulme, William; Green, Amelia; Massey, Jon; Walker, Alex J; Bates, Christopher; Cockburn, Jonathan; Parry, John; Hester, Frank; Harper, Sam; O'Hanlon, Shaun; Eavis, Alex; Jarvis, Richard; Avramov, Dima; Griffiths, Paul; Fowles, Aaron; Parkes, Nasreen; Goldacre, Ben; MacKenna, Brian.
  • Fisher L; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Hopcroft LE; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Rodgers S; PRIMIS, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK.
  • Barrett J; PRIMIS, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK.
  • Oliver K; PRIMIS, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK.
  • Avery AJ; Centre for Academic Primary Care, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK.
  • Evans D; PRIMIS, School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK.
  • Curtis H; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Croker R; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Macdonald O; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Morley J; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Mehrkar A; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Bacon S; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Davy S; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Dillingham I; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Evans D; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Hickman G; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Inglesby P; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Morton CE; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Smith B; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Ward T; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Hulme W; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Green A; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Massey J; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Walker AJ; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • Bates C; TPP, Leeds, UK.
  • Cockburn J; TPP, Leeds, UK.
  • Parry J; TPP, Leeds, UK.
  • Hester F; TPP, Leeds, UK.
  • Harper S; TPP, Leeds, UK.
  • O'Hanlon S; EMIS Health, Leeds, UK.
  • Eavis A; EMIS Health, Leeds, UK.
  • Jarvis R; EMIS Health, Leeds, UK.
  • Avramov D; EMIS Health, Leeds, UK.
  • Griffiths P; EMIS Health, Leeds, UK.
  • Fowles A; EMIS Health, Leeds, UK.
  • Parkes N; EMIS Health, Leeds, UK.
  • Goldacre B; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
  • MacKenna B; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, UK.
BMJ Med ; 2(1): e000392, 2023.
Artículo en Inglés | MEDLINE | ID: covidwho-20235572
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.
Palabras clave
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Tipo de estudio: Estudio de cohorte / Estudio observacional / Estudio pronóstico Idioma: Inglés Revista: BMJ Med Año: 2023 Tipo del documento: Artículo País de afiliación: Bmjmed-2022-000392

Similares

MEDLINE
LILACS
LIS
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Tipo de estudio: Estudio de cohorte / Estudio observacional / Estudio pronóstico Idioma: Inglés Revista: BMJ Med Año: 2023 Tipo del documento: Artículo País de afiliación: Bmjmed-2022-000392