Published in PLOS One on 2026-09-24, this study is presented as an attributed evidence briefing. The excerpts below are the original researchers’ words, reproduced under their Creative Commons Attribution licence. Health Signal has selected the abstract and, where needed, opening background paragraphs; the complete methods, results, limitations and references remain in the linked paper.
Original authors: Wendy Akushika Dogbegah, Stephen Obol Opiyo, Peace Proscovia Aber, Christian Kaembou Tiambo
Source: PLOS One • DOI 10.1371/journal.pone.0356734
Read the complete primary study ↗ · Creative Commons Attribution licence
From the researchers
Abstract
Antimicrobial resistance (AMR) continues to pose a major public health threat across Africa, yet available surveillance data remain highly fragmented across private, public, and academic sources. This study analysed continent-wide AMR surveillance patterns by integrating datasets from multiple independent repositories and visualising them through interactive Geographic Information System (GIS) dashboards. The objective was to generate an integrated evidence base that highlights resistance patterns, surveillance disparities, reporting gaps, and opportunities for improved AMR monitoring across Africa. Data were compiled from major private AMR surveillance programmes including Pfizer’s ATLAS, GSK’s SOAR, Johnson & Johnson’s DREAM, Venatorx’s GEARS, and Shionogi’s SIDERO-WT covering the period 2004–2022. Public datasets from the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) and the Fleming Fund’s Mapping Antimicrobial Resistance and Antimicrobial Use Partnership (MAAP) were incorporated for 2016–2020, together with published AMR studies conducted between 2010 and 2024. Datasets were harmonised to align key variables including bacterial species, isolate identifiers, antibiotics tested, surveillance source, geographical location, and categorical AMR outcomes while preserving the original structure of the contributing datasets. Interactive dashboards were developed using R Shiny to support spatial visualisation and dynamic analytical exploration of resistance patterns, temporal trends, species distribution, and country-level surveillance coverage. Descriptive analyses including means, standard deviations, medians, interquartile ranges (IQR), frequency distributions, Gini coefficients, Shannon entropy, Herfindahl–Hirschman Index (HHI), and Lorenz curves were used to assess inequality and concentration in country-level AMR reporting across surveillance systems. The integrated analyses revealed substantial heterogeneity and concentration in AMR surveillance reporting across Africa, reflecting major differences in surveillance intensity, laboratory infrastructure, reporting systems, and diagnostic capacity across countries. Private datasets demonstrated broader antibiotic panels and longer temporal coverage, whereas public datasets exhibited substantial gaps in country participation and pathogen-antibiotic representation. Published AMR studies additionally highlighted important surveillance information absent from formal surveillance databases. By integrating multiple streams of AMR evidence, this study demonstrates the value of interactive GIS dashboards as exploratory and updateable surveillance-support tools for improving visibility of fragmented AMR datasets, identifying surveillance disparities, supporting geographically informed interpretation of resistance trends, and strengthening future AMR surveillance harmonisation efforts across Africa.
Introduction
Antimicrobial resistance (AMR) has rapidly emerged as one of the most critical challenges facing global public health in the 21st century [1]. The growing prevalence of drug resistant pathogens threatens to undermine decades of medical progress, as treatments that were once highly effective now struggle to combat infections [2]. This rise in resistance is not limited to a specific region or demographic but spans across continents, affecting both developed and developing countries alike. The consequences are stark: infections that were once easily treatable now result in prolonged illnesses, higher mortality rates, and an increasing burden on healthcare systems [3]. The escalating presence of AMR exacerbates medical complexities and necessitates longer hospital stays, placing significant pressure on already strained healthcare resources worldwide [4].
Reading this evidence
Use the full paper to check the study design, participant selection, comparator and outcome definitions before drawing a practical conclusion. An association does not by itself show cause and effect. A reported performance measure needs its denominator and uncertainty. A finding from a particular facility, sample or time period should retain that scope when it is discussed elsewhere.
This briefing makes the source accessible within the wider technology & industry desk. It does not assess an individual patient or certify a product. When considering relevance to another health system, examine the original authors’ account of context and limitations, then seek local implementation evidence. The related reporting below provides other studies to read alongside this one; it is a topic connection, rather than a claim that their results are directly comparable.
© 2026 Wendy Akushika Dogbegah, Stephen Obol Opiyo, Peace Proscovia Aber, Christian Kaembou Tiambo. Excerpts reproduced under CC BY. Selection, formatting and contextual notes by Health Signal. The researchers are the authors of the source study, not members of Health Signal’s staff.
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