Evidence map›Paper›PMID 38328263›Full record

ArticleJAC-antimicrobial resistance2024

Antimicrobial consumption in an acute NHS Trust during the COVID-19 pandemic: intervention time series analysis.

Sidra Khan, Stuart E Bond, Jade Lee-Milner, Barbara R Conway, William J Lattyak, Mamoon A Aldeyab

Abstract read
In one paragraph

Article in JAC-antimicrobial resistance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

6 authors.

Sidra KhanDepartment of Pharmacy, School of Applied Sciences, University of Huddersfield, Huddersfield, HD1 3DH, UK.
Stuart E BondDepartment of Pharmacy, School of Applied Sciences, University of Huddersfield, Huddersfield, HD1 3DH, UK.ORCID https://orcid.org/0000-0001-9640-2474
Jade Lee-MilnerPharmacy Department, Mid Yorkshire Teaching NHS Trust, Wakefield, WF1 4DG, UK.
Barbara R ConwayDepartment of Pharmacy, School of Applied Sciences, University of Huddersfield, Huddersfield, HD1 3DH, UK.
William J LattyakStatistical Consulting Department, Scientific Computing Associates Corp., River Forest, IL 60305, USA.
Mamoon A AldeyabDepartment of Pharmacy, School of Applied Sciences, University of Huddersfield, Huddersfield, HD1 3DH, UK.ORCID https://orcid.org/0000-0001-8148-7612

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To determine the impact of the COVID-19 pandemic on antimicrobial consumption and trends of therapeutic drugs for COVID-19 treatments, including corticosteroids, remdesivir and monoclonal antibodies (tocilizumab) from April 2017 to September 2022 in a secondary care NHS Trust in England. Methods: A retrospective intervention time series analysis was conducted for April 2017 to September 2022 at the Mid Yorkshire Teaching NHS Trust. Data were retrieved from the pharmacy dispensing system as defined daily doses (DDDs) monthly and reported per 1000 occupied bed days (OBDs). Antimicrobial consumption and COVID-19 treatment options were measured. DDDs were calculated according to the classification of antimicrobials for systemic use (J01) and for other drugs classification. Trends for antimicrobial consumption and other therapeutic drugs for treating COVID-19 were also determined in each wave in England. Results: During the pandemic: total antibiotic consumption decreased from 826.4 to 728.2 DDDs per 1000 OBDs ( Discussion: This study determined the consumption of antimicrobials trends before and during the pandemic. The individual wave antimicrobial consumption indicates maximum consumption in the first wave, advocating for antimicrobial stewardship and preparedness for future pandemics.

Identifiers

PMID38328263
PMCPMC10848649

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Registered trials

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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.