Evidence map›Paper›PMID 42488787›Full record

ArticleJAC-antimicrobial resistance2026

Global surveillance quality gaps in antimicrobial resistance monitoring: a composite-index and inequality analysis of 85 countries.

Oluwaseun E Agboola, Samuel S Agboola, Margaret A Adekanle, Ajibola O Akinloye, Zainab A Ayinla, Bolade A Fatukasi, Temitope C Aribigbola, Aniefon S Umoekam, Oluwatoyin A Oyeniran, Victoria O Adenigba and 1 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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

11 authors.

Oluwaseun E AgboolaInstitute for Drug Research and Development, S.E. Bogoro Centre, Afe Babalola University, Ado-Ekiti 360001, Nigeria.ORCID https://orcid.org/0000-0002-6794-4749
Samuel S AgboolaDepartment of Pharmacology and Toxicology, College of Pharmacy, Afe Babalola University, Ado-Ekiti 360001, Nigeria.
Margaret A AdekanleDepartment of Medical Microbiology and Parasitology, College of Health Sciences, Osun State University, Osogbo, Nigeria.
Ajibola O AkinloyeFaculty of Medical Laboratory Science, College of Allied Health Sciences, Mercy Medical University, Iwara-Iwo, Nigeria.
Zainab A AyinlaDepartment of Biology, University of Waterloo, Ontario, Canada.
Bolade A FatukasiDepartment of Science Laboratory Technology, University of Ilesa, Ilesa, Osun State, Nigeria.
Temitope C AribigbolaDepartment of Medical Microbiology and Immunology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2E1, Canada.
Aniefon S UmoekamDepartment of Clinical Pharmacy and Public Health, College of Pharmacy, Afe Babalola University, Ado-Ekiti, Nigeria.
Oluwatoyin A OyeniranDepartment of Medical Microbiology and Parasitology, University of Ilesa, P.M.B. 5089, Ilesa, Nigeria.
Victoria O AdenigbaDepartment of Science Laboratory Technology, Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria.
Anuoluwapo B ShaleyeDepartment of Social and Administrative Pharmacy, College of Pharmacy, Afe Babalola University, Ado-Ekiti 360001, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Globally, antimicrobial resistance surveillance generates extensive data, yet the quality of the infrastructure producing it is rarely assessed systematically, even though this is essential for interpreting resistance trends and targeting improvements. Methods: We evaluated surveillance across 85 countries using WHO Global Antimicrobial Resistance and Use Surveillance System bloodstream infection data from 2018 to 2023. A composite Surveillance Quality Index combined six dimensions weighted by principal component analysis. We quantified testing-coverage inequality with the Gini coefficient, used a detection-probability model to assess relative ascertainment of resistance, applied a random forest to identify which dimensions drive the index and used multi-criteria optimization to rank countries for intervention. Results: The index averaged 33.3 (range 9-67). Testing coverage was highly unequal: the top 20% of countries performed about 85% of global testing (Gini 0.783, 95% CI 0.690-0.832). Surveillance quality and reported resistance were not significantly associated (r = 0.13, 95% CI -0.09-0.34, Conclusions: Global surveillance capacity is highly unequal, with the largest quality gaps in high-burden settings; targeted strengthening would address most of the disparity.

Identifiers

PMID42488787
PMCPMC13390922

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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.