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The impact of COVID-19 on antibiotic prescribing in primary care in England: Evaluation and risk prediction of appropriateness of type and repeat prescribing.
Zhong, Xiaomin; Pate, Alexander; Yang, Ya-Ting; Fahmi, Ali; Ashcroft, Darren M; Goldacre, Ben; MacKenna, Brian; Mehrkar, Amir; Bacon, Sebastian C J; Massey, Jon; Fisher, Louis; Inglesby, Peter; Hand, Kieran; van Staa, Tjeerd; Palin, Victoria.
  • Zhong X; Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, the University of Manchester, M13 9PL, UK.
  • Pate A; Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, the University of Manchester, M13 9PL, UK.
  • Yang YT; Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, the University of Manchester, M13 9PL, UK.
  • Fahmi A; Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, the University of Manchester, M13 9PL, UK.
  • Ashcroft DM; Centre for Pharmacoepidemiology and Drug Safety, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester M13 9PL, UK; NIHR Greater Manchester Patient Safety Translational Research Centre, School of Health Sciences, Faculty of Biology, Med
  • Goldacre B; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG, UK.
  • MacKenna B; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG, UK; NHS England, Wellington House, Waterloo Road, London SE1 8UG, UK.
  • Mehrkar A; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG, UK.
  • Bacon SCJ; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG, UK.
  • Massey J; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG, UK.
  • Fisher L; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG, UK.
  • Inglesby P; Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX26GG, UK.
  • Hand K; Pharmacy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK; NHS England, Wellington House, Waterloo Road, London SE1 8UG, UK.
  • van Staa T; Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, the University of Manchester, M13 9PL, UK. Electronic address: tjeerd.vanstaa@manchester.ac.uk.
  • Palin V; Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, the University of Manchester, M13 9PL, UK; Maternal and Fetal Research Centre, Division of Developmental Biology and Medicine, the University of Manchester, St Marys Hospital, Oxford Road, Manchester
J Infect ; 87(1): 1-11, 2023 Jul.
Artículo en Inglés | MEDLINE | ID: covidwho-2320905
ABSTRACT

BACKGROUND:

This study aimed to predict risks of potentially inappropriate antibiotic type and repeat prescribing and assess changes during COVID-19.

METHODS:

With the approval of NHS England, we used OpenSAFELY platform to access the TPP SystmOne electronic health record (EHR) system and selected patients prescribed antibiotics from 2019 to 2021. Multinomial logistic regression models predicted patient's probability of receiving inappropriate antibiotic type or repeat antibiotic course for each common infection.

RESULTS:

The population included 9.1 million patients with 29.2 million antibiotic prescriptions. 29.1% of prescriptions were identified as repeat prescribing. Those with same day incident infection coded in the EHR had considerably lower rates of repeat prescribing (18.0%) and 8.6% had potentially inappropriate type. No major changes in the rates of repeat antibiotic prescribing during COVID-19 were found. In the 10 risk prediction models, good levels of calibration and moderate levels of discrimination were found.

CONCLUSIONS:

Our study found no evidence of changes in level of inappropriate or repeat antibiotic prescribing after the start of COVID-19. Repeat antibiotic prescribing was frequent and varied according to regional and patient characteristics. There is a need for treatment guidelines to be developed around antibiotic failure and clinicians provided with individualised patient information.
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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Infecciones del Sistema Respiratorio / COVID-19 Tipo de estudio: Estudio experimental / Estudio observacional / Estudio pronóstico Límite: Humanos País/Región como asunto: Europa Idioma: Inglés Revista: J Infect Año: 2023 Tipo del documento: Artículo País de afiliación: J.jinf.2023.05.010

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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Infecciones del Sistema Respiratorio / COVID-19 Tipo de estudio: Estudio experimental / Estudio observacional / Estudio pronóstico Límite: Humanos País/Región como asunto: Europa Idioma: Inglés Revista: J Infect Año: 2023 Tipo del documento: Artículo País de afiliación: J.jinf.2023.05.010