Evidence map›Paper›PMID 37717030›Full record

ArticleAntimicrobial resistance and infection control2023

The impact of the COVID-19 pandemic on the treatment of common infections in primary care and the change to antibiotic prescribing in England.

Ya-Ting Yang, Xiaomin Zhong, Ali Fahmi, Simon Watts, Darren M Ashcroft, Jon Massey, Louis Fisher, Brian MacKenna, Amir Mehrkar, Sebastian C J Bacon and 4 more

Abstract read
In one paragraph

Article in Antimicrobial resistance and infection control, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Ocular Antibiotic Use in Young Danish Children From 2016 to 2024.Basic & clinical pharmacology & toxicology · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Ya-Ting YangFaculty of Biology, Medicine, and Health, Centre for Health Informatics, School of Health Sciences, The University of Manchester, Manchester, M13 9PL, UK.
Xiaomin ZhongFaculty of Biology, Medicine, and Health, Centre for Health Informatics, School of Health Sciences, The University of Manchester, Manchester, M13 9PL, UK.
Ali FahmiFaculty of Biology, Medicine, and Health, Centre for Health Informatics, School of Health Sciences, The University of Manchester, Manchester, M13 9PL, UK.
Simon WattsHealth Education England, Manchester, M1 3BN, UK.
Darren M AshcroftFaculty of Biology, Medicine and Health, Centre for Pharmacoepidemiology and Drug Safety, School of Health Sciences, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
Jon MasseyBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
Louis FisherBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
Brian MacKennaBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
Amir MehrkarBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
Sebastian C J BaconBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
Ben GoldacreBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
Kieran HandNHS England, Wellington House, Waterloo Road, London, SE1 8UG, UK.
Tjeerd van StaaFaculty of Biology, Medicine, and Health, Centre for Health Informatics, School of Health Sciences, The University of Manchester, Manchester, M13 9PL, UK.
Victoria PalinFaculty of Biology, Medicine, and Health, Centre for Health Informatics, School of Health Sciences, The University of Manchester, Manchester, M13 9PL, UK. victoria.palin@manchester.ac.uk.

Funding

Medical Research Council MR/V015737/1
6 · The paper itself

Abstract

backgroundThere is concern that the COVID-19 pandemic altered the management of common infections in primary care. This study aimed to evaluate infection-coded consultation rates and antibiotic use during the pandemic and how any change may have affected clinical outcomes.

methodsWith the approval of NHS England, a retrospective cohort study using the OpenSAFELY platform analysed routinely collected electronic health data from GP practices in England between January 2019 and December 2021. Infection coded consultations and antibiotic prescriptions were used estimate multiple measures over calendar months, including age-sex adjusted prescribing rates, prescribing by infection and antibiotic type, infection consultation rates, coding quality and rate of same-day antibiotic prescribing for COVID-19 infections. Interrupted time series (ITS) estimated the effect of COVID-19 pandemic on infection-coded consultation rates. The impact of the pandemic on non- COVID-19 infection-related hospitalisations was also estimated.

resultsRecords from 24 million patients were included. The rate of infection-related consultations fell for all infections (mean reduction of 39% in 2020 compared to 2019 mean rate), except for UTI which remained stable. Modelling infection-related consultation rates highlighted this with an incidence rate ratio of 0.44 (95% CI 0.36-0.53) for incident consultations and 0.43 (95% CI 0.33-0.54) for prevalent consultations. Lower respiratory tract infections (LRTI) saw the largest reduction of 0.11 (95% CI 0.07-0.17). Antibiotic prescribing rates fell with a mean reduction of 118.4 items per 1000 patients in 2020, returning to pre-pandemic rates by summer 2021. Prescribing for LRTI decreased 20% and URTI increased 15.9%. Over 60% of antibiotics were issued without an associated same-day infection code, which increased during the pandemic. Infection-related hospitalisations reduced (by 62%), with the largest reduction observed for pneumonia infections (72.9%). Same-day antibiotic prescribing for COVID-19 infection increased from 1 to 10.5% between the second and third national lockdowns and rose again during 2022.

conclusionsChanges to consultations and hospital admissions may be driven by reduced transmission of non-COVID-19 infections due to reduced social mixing and lockdowns. Inconsistencies in coding practice emphasises the need for improvement to inform new antibiotic stewardship policies and prevent resistance to novel infections.

Indexed as

COVID-19Respiratory Tract InfectionsAnimalsAnti-Bacterial AgentsCommunicable Disease ControlEnglandHorsesHumansPandemicsPrimary Health CareRetrospective StudiesAnti-Bacterial AgentsAntibioticsAntibiotic stewardshipCOVID-19 pandemicInfectionPrimary care

Identifiers

PMID37717030
PMCPMC10504725

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.