Evidence map›Paper›PMID 39353733›Full record

ArticleBJGP open2025

Antibiotic prescribing intensity and community UTI resistance: a cross-sectional ecological study.

Lucy McDonnell, Mark Ashworth, Peter Schofield, Stevo Durbaba, Patrick Redmond

Abstract read
In one paragraph

Article in BJGP open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
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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

5 authors.

Lucy McDonnellDepartment of Population Health, King's College London, London, UK lmcdonnell@doctors.net.uk.ORCID https://orcid.org/0000-0002-8299-0612
Mark AshworthDepartment of Population Health, King's College London, London, UK.ORCID https://orcid.org/0000-0001-6514-9904
Peter SchofieldDepartment of Population Health, King's College London, London, UK.ORCID https://orcid.org/0000-0003-1201-527X
Stevo DurbabaDepartment of Population Health, King's College London, London, UK.ORCID https://orcid.org/0000-0002-4098-5399
Patrick RedmondDepartment of Population Health, King's College London, London, UK.ORCID https://orcid.org/0000-0002-3929-2018

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAntibiotic overuse is associated with antimicrobial resistance (AMR). It is unclear whether community AMR is driven by overall antibiotic use or by high levels of repeated (intense) use by individual patients.

aimTo determine the association between high antibiotic prescribing intensity (all antibiotic prescriptions; any indication), and rates of urinary tract infection (UTI) resistance among patients within small communities. DESIGN &

settingRepeated cross-sectional ecological analysis of geographical areas (population averaging 1500) from 2012-2015 using anonymised primary care data and urine cultures.

methodFor each area, we compared the percentage of patients prescribed antibiotics who received≥5 prescriptions over 3 years or≥4 prescriptions over 1 year, with subsequent or same year UTI resistance rates. We also compared Average Daily Quantities (ADQ) of all antibiotics prescribed, with UTI resistance rates, per year, per area. Results were adjusted for covariates and analysed at area level using mixed effects logistic regression.

resultsOf 196 513 patients prescribed antibiotics in 69 areas, 16% were prescribed intensively (≥5 prescriptions in 3 years), receiving almost 30% of prescriptions. Of 12 308 confirmed UTI specimens (80%

conclusionWe found no relationship between concurrent high intensity 'any' antibiotic prescribing, and UTI resistance rates in small urban communities, pre-COVID-19. Individual patient use of multiple antibiotics, even at high intensity, may not be an independent risk factor for community UTI resistance.

Indexed as

anti-bacterial agentsantibioticprescribingprimary health careresistanceurinary tract infections

Identifiers

PMID39353733
PMCPMC12138012

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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.