Evidence map›Paper›PMID 41939268›Full record

ArticleJAC-antimicrobial resistance2026

Identifying hospital-level predictors for antibiotic use: a Global Point Prevalence Survey study among Belgian, Philippine and South African hospitals.

Annelies Boven, Anna Ivanova, Ann Versporten, Ines Pauwels, Rhenalyn Bo, Jemelyn Garcia, Mari Rose De Los Reyes, Reshma Misra, Heather Finlayson, Shaheen Mehtar and 3 more

Abstract read
In one paragraph

Article 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.

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0citing papers in PubMed
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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

13 authors.

Annelies BovenGlobal Health Institute, University of Antwerp, Antwerp, Belgium.ORCID https://orcid.org/0000-0003-0713-7491
Anna IvanovaInteruniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Hasselt, Belgium.
Ann VersportenGlobal Health Institute, University of Antwerp, Antwerp, Belgium.ORCID https://orcid.org/0000-0002-3551-6643
Ines PauwelsGlobal Health Institute, University of Antwerp, Antwerp, Belgium.ORCID https://orcid.org/0000-0003-0106-2118
Rhenalyn BoResearch Institute for Tropical Medicine, Filinvest Corporate City, Alabang, Muntinlupa, Philippines.
Jemelyn GarciaResearch Institute for Tropical Medicine, Filinvest Corporate City, Alabang, Muntinlupa, Philippines.
Mari Rose De Los ReyesResearch Institute for Tropical Medicine, Filinvest Corporate City, Alabang, Muntinlupa, Philippines.
Reshma MisraKwaZulu-Natal Department of Health, Durban, South Africa.
Heather FinlaysonDepartment of Paediatrics and Child Health, Stellenbosch University, Stellenbosch, South Africa.
Shaheen MehtarInfection Control Africa Network, Cape Town, South Africa.
Nele BrusselaersGlobal Health Institute, University of Antwerp, Antwerp, Belgium.ORCID https://orcid.org/0000-0003-0137-447X
Erika VliegheGlobal Health Institute, University of Antwerp, Antwerp, Belgium.
Global-PPS participants

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Understanding determinants that influence antibiotic use is crucial for developing effective stewardship interventions that optimize the use of antibiotics and ultimately mitigate the burden of antibiotic resistance. This study aims to find hospital-level predictors of antibiotic use among hospitals worldwide. Methods: Antibiotic use data were collected from hospitals that participated in 2022-23 in the Global Point Prevalence Survey (Global-PPS) basic inpatient and Healthcare-Associated Infection (HAI) modules from three countries with high participation degrees: Belgium, the Philippines and South Africa. A linear mixed model was applied to predict hospital-level prevalence, considering all variables from the Global-PPS as potential predictors. Stepwise forward and backward selection modelling were conducted to identify the best-fitting models per country separately. Results: In total, data from 138 hospitals were retrieved, including 19 Belgian, 55 Philippine and 64 South African hospitals. No predictors were shared across the three countries, such as hospital type, proportion of occupied medical beds and proportion of available guidelines. However, the proportions of admitted patients with certain invasive devices were significant predictors for increased odds of hospital-wide antibiotic prescribing, despite the type of invasive device varying between countries. Conclusions: Factors related to the patient mix, including the proportion of patients with an invasive device, are associated with increased odds of antibiotic prescribing at hospital level. Considering substantial differences in predictors associated with country-specific patient populations and hospital characteristics, more research is needed to explore additional determinants, such as healthcare system and broader contextual factors that may influence hospital-level antibiotic prevalence.

Identifiers

PMID41939268
PMCPMC13048902

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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.