Evidence map›Paper›PMID 35241573›Full record

ArticleBMJ quality & safety2022

Comparing antibiotic prescribing between clinicians in UK primary care: an analysis in a cohort study of eight different measures of antibiotic prescribing.

Tjeerd Van Staa, Yan Li, Natalie Gold, Tim Chadborn, William Welfare, Victoria Palin, Darren M Ashcroft, Joanna Bircher

Open access · hybridAbstract read
In one paragraph

Article in BMJ quality & safety, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.5field-weighted citation impact, top 12% of its field
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

9 citing papers in PubMed, 16 citations in OpenAlex.

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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

8 authors at 3 institutions in 2 countries.

Tjeerd Van StaaDivision of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK tjeerd.vanstaa@manchester.ac.uk.ORCID 0000-0001-9363-742X
Yan LiDivision of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK.
Natalie GoldBehavioural Insights and Evaluation, Public Health England, London, UK.
Tim ChadbornBehavioural Insights and Evaluation, Public Health England, London, UK.
William WelfareNorth West Health Protection Team, Public Health England North West, Manchester, UK.
Victoria PalinDivision of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK.
Darren M AshcroftCentre for Pharmacoepidemiology and Drug Safety and NIHR Greater Manchester Patient Safety Translational Research Centre, University of Manchester, Manchester, UK.
Joanna BircherLockside Medical Centre, Stalybridge, Manchester, UK.
University of Manchester · GBPublic Health England · GBWest Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is a need to reduce antimicrobial uses in humans. Previous studies have found variations in antibiotic (AB) prescribing between practices in primary care. This study assessed variability of AB prescribing between clinicians.

methodsClinical Practice Research Datalink, which collects electronic health records in primary care, was used to select anonymised clinicians providing 500+ consultations during 2012-2017. Eight measures of AB prescribing were assessed, such as overall and incidental AB prescribing, repeat AB courses and extent of risk-based prescribing. Poisson regression models with random effect for clinicians were fitted.

results6111 clinicians from 466 general practices were included. Considerable variability between individual clinicians was found for most AB measures. For example, the rate of AB prescribing varied between 77.4 and 350.3 per 1000 consultations; percentage of repeat AB courses within 30 days ranged from 13.1% to 34.3%; predicted patient risk of hospital admission for infection-related complications in those prescribed AB ranged from 0.03% to 0.32% (5th and 95th percentiles). The adjusted relative rate between clinicians in rates of AB prescribing was 5.23. Weak correlation coefficients (<0.5) were found between most AB measures. There was considerable variability in case mix seen by clinicians. The largest potential impact to reduce AB prescribing could be around encouraging risk-based prescribing and addressing repeat issues of ABs. Reduction of repeat AB courses to prescribing habit of median clinician would save 21 813 AB prescriptions per 1000 clinicians per year.

conclusionsThe wide variation seen in all measures of AB prescribing and weak correlation between them suggests that a single AB measure, such as prescribing rate, is not sufficient to underpin the optimisation of AB prescribing.

Indexed as

Anti-Bacterial AgentsElectronic Health RecordsCohort StudiesHumansPrimary Health CareUnited KingdomAnti-Bacterial Agentsantibiotic managementgeneral practicehealthcare quality improvement

Identifiers

PMID35241573
PMCPMC9606525
OpenAlexW4214824591

What OpenQuestion holds

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LicenceCC BY-NC
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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.