Evidence map›Paper›PMID 39739781›Full record

ArticlePloS one2024

Evaluation of the impact of COVID-19 pandemic on hospital admission related to common infections: Risk prediction models to tackle antimicrobial resistance in primary care.

Ali Fahmi, Victoria Palin, Xiaomin Zhong, Ya-Ting Yang, Simon Watts, Darren M Ashcroft, Ben Goldacre, Brian MacKenna, Louis Fisher, Jon Massey and 5 more

Erratum issuedAbstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Ali FahmiCentre for Health Informatics, Faculty of Biology, Medicine and Health, School of Health Sciences, the University of Manchester, Manchester, United Kingdom.ORCID 0000-0002-1661-4475
Victoria PalinCentre for Health Informatics, Faculty of Biology, Medicine and Health, School of Health Sciences, the University of Manchester, Manchester, United Kingdom.
Xiaomin ZhongCentre for Health Informatics, Faculty of Biology, Medicine and Health, School of Health Sciences, the University of Manchester, Manchester, United Kingdom.ORCID 0000-0002-3448-6702
Ya-Ting YangCentre for Health Informatics, Faculty of Biology, Medicine and Health, School of Health Sciences, the University of Manchester, Manchester, United Kingdom.
Simon WattsNorth West Ambulance Service, Manchester NHS Foundation Trust, Manchester, United Kingdom.
Darren M AshcroftCentre for Pharmacoepidemiology and Drug Safety, Faculty of Biology, Medicine and Health, School of Health Sciences, University of Manchester, Manchester, United Kingdom.ORCID 0000-0002-2958-915X
Ben GoldacreNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, United Kingdom.
Brian MacKennaNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, United Kingdom.
Louis FisherNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, United Kingdom.
Jon MasseyNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-2497-4040
Amir MehrkarNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, United Kingdom.
Seb BaconNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, United Kingdom.
OpenSAFELY Collaborative
Kieran HandNHS England, London, United Kingdom.ORCID 0000-0002-3834-2415
Tjeerd Pieter van StaaCentre for Health Informatics, Faculty of Biology, Medicine and Health, School of Health Sciences, the University of Manchester, Manchester, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAntimicrobial resistance (AMR) is a multifaceted global challenge, partly driven by inappropriate antibiotic prescribing. The objectives of this study were to evaluate the impact of the COVID-19 pandemic on treatment of common infections, develop risk prediction models and examine the effects of antibiotics on infection-related hospital admissions.

methodsWith the approval of NHS England, we accessed electronic health records from The Phoenix Partnership (TPP) through OpenSAFELY platform. We included adult patients with primary care diagnosis of common infections, including lower respiratory tract infection (LRTI), upper respiratory tract infections (URTI), and lower urinary tract infection (UTI), from 1 January 2019 to 31 August 2022. We excluded patients with a COVID-19 record in the 90 days before to 30 days after the infection diagnosis. Risk prediction models using Cox proportional-hazard regression were developed for infection-related hospital admission in the 30 days after the common infection diagnosis.

resultsWe found 12,745,165 infection diagnoses from 1 January 2019 to 31 August 2022. Of them, 80,395 (2.05%) cases were admitted to the hospital during follow-up. Counts of hospital admission for infections dropped during COVID-19, for example LRTI from 3,950 in December 2019 to 520 in April 2020. Comparing those prescribed an antibiotic to those without, reduction in risk of hospital admission were largest with LRTI (adjusted hazard ratio (aHR) of 0.35; 95% confidence interval (CI), 0.35-0.36) and UTI (aHR 0.45; 95% CI, 0.44-0.46), compared to URTI (aHR 1.04; 95% CI, 1.03-1.06).

conclusionsA substantial variation in hospital admission risks between infections and patient groups was found. Antibiotics appeared more effective in preventing infection-related complications with LRTI and UTI, but not URTI. While this study has several limitations, the results indicate that a focus on risk-based antibiotic prescribing could help tackle AMR in primary care.

Indexed as

Anti-Bacterial AgentsCOVID-19HospitalizationPrimary Health CareAdultAgedAged, 80 and overEnglandFemaleHumansMaleMiddle AgedPandemicsProportional Hazards ModelsRespiratory Tract InfectionsSARS-CoV-2Anti-Bacterial Agents

Identifiers

PMID39739781
PMCPMC11687718

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.