Evidence map›Paper›PMID 42488790›Full record

ReviewJAC-antimicrobial resistance2026

Interpreting point prevalence survey data on antibiotic use: guidance for facility antimicrobial stewardship teams and national policymakers.

Vivian Twemanye, Jonathan Mayito, Suzan Nakasendwa, John Paul Waswa, Silvia Figueiredo Costa, Evangelos I Kritsotakis

Abstract readReview
In one paragraph

Review in JAC-antimicrobial resistance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Vivian TwemanyeIndependent Researcher, Kampala, Uganda.ORCID https://orcid.org/0000-0002-0291-1604
Jonathan MayitoInternational Centre for Antimicrobial Resistance Solutions (ICARS), Copenhagen, Denmark.
Suzan NakasendwaMakerere University, School of Public Health, Kampala, Uganda.
John Paul WaswaInfectious Diseases Institute, Makerere University, Kampala, Uganda.
Silvia Figueiredo CostaDepartment of Infectious and Parasitic Diseases, University of Sao Paulo, São Paulo, Brazil.
Evangelos I KritsotakisLaboratory of Biostatistics, School of Medicine, University of Crete, Heraklion, Greece.ORCID https://orcid.org/0000-0002-9526-3852

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Point Prevalence Surveys (PPS) are widely used to describe antibiotic use in hospitals to inform antimicrobial stewardship programmes and national policies on Antimicrobial Resistance (AMR). While PPS offer valuable insight into patterns of antibiotic exposure and prescribing practices, their metrics are often interpreted beyond their technical design. This guidance provides a practical framework for interpreting PPS data appropriately and translating them into facility-level actions and policy learning. PPS provides a cross-sectional snapshot of antibiotic exposure and prescribing practice indicators (e.g. indication, route, documentation, guideline compliance and AWaRe classification). While these parameters support quality improvement, prevalence of antibiotic use is not a proxy for antibiotic consumption or stewardship impact because it reflects both initiation and duration of therapy and is influenced by factors like case-mix. Misinterpretation can lead to policy misalignment, inappropriate benchmarking and counterproductive performance targets. We propose a structured interpretation framework to enable stewardship learning leading to corrective actions rather than performance scoring.

Identifiers

PMID42488790
PMCPMC13390919

What OpenQuestion holds

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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.