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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
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 p...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier Ltd on behalf of The British Infection Association.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176893/ https://www.ncbi.nlm.nih.gov/pubmed/37182748 http://dx.doi.org/10.1016/j.jinf.2023.05.010 |
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author | 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 |
author_facet | 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 |
author_sort | Zhong, Xiaomin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10176893 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Authors. Published by Elsevier Ltd on behalf of The British Infection Association. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101768932023-05-12 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 J Infect Article 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. The Authors. Published by Elsevier Ltd on behalf of The British Infection Association. 2023-07 2023-05-12 /pmc/articles/PMC10176893/ /pubmed/37182748 http://dx.doi.org/10.1016/j.jinf.2023.05.010 Text en © 2023 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article 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 The impact of COVID-19 on antibiotic prescribing in primary care in England: Evaluation and risk prediction of appropriateness of type and repeat prescribing |
title | The impact of COVID-19 on antibiotic prescribing in primary care in England: Evaluation and risk prediction of appropriateness of type and repeat prescribing |
title_full | The impact of COVID-19 on antibiotic prescribing in primary care in England: Evaluation and risk prediction of appropriateness of type and repeat prescribing |
title_fullStr | The impact of COVID-19 on antibiotic prescribing in primary care in England: Evaluation and risk prediction of appropriateness of type and repeat prescribing |
title_full_unstemmed | The impact of COVID-19 on antibiotic prescribing in primary care in England: Evaluation and risk prediction of appropriateness of type and repeat prescribing |
title_short | The impact of COVID-19 on antibiotic prescribing in primary care in England: Evaluation and risk prediction of appropriateness of type and repeat prescribing |
title_sort | impact of covid-19 on antibiotic prescribing in primary care in england: evaluation and risk prediction of appropriateness of type and repeat prescribing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176893/ https://www.ncbi.nlm.nih.gov/pubmed/37182748 http://dx.doi.org/10.1016/j.jinf.2023.05.010 |
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