Clinical Summary | Infectious Diseases, Antimicrobials & Vaccines

Infectious Diseases:  How High Is Antimicrobial Resistance in Africa, Really? Evidence from the Largest AMR Dataset Assembled on the Continent

[July 2026]

Multimedia Summary

Study Context

Antimicrobial resistance (AMR) is a leading cause of infection-related death worldwide, and global modelling estimates suggest Africa carries one of the highest AMR burdens of any region. However, those estimates rest heavily on modelled projections rather than direct laboratory data, because bacterial culture and antimicrobial susceptibility testing (AST) are performed infrequently in many African countries and results are often not captured in reporting systems. In 2022, only 20 of the 38 African countries enrolled in the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) actually reported AMR data, and confirmed infections with AST results from the entire African continent that year were on a similar scale to what a single large tertiary hospital elsewhere in the world might generate.

To address this gap, the Mapping AMR and Antimicrobial Use Partnership (MAAP) — a consortium including the African Society for Laboratory Medicine, Africa CDC, and regional health bodies, supported by the UK Fleming Fund — collected retrospective AMR and antimicrobial consumption data from laboratories in 14 African countries: Burkina Faso, Cameroon, Gabon, Ghana, Kenya, Eswatini, Malawi, Nigeria, Senegal, Sierra Leone, Tanzania, Uganda, Zambia, and Zimbabwe. This study reports the resulting AMR prevalence estimates and associated risk factors, representing what the authors describe as the largest and most representative AMR dataset assembled from the continent to date.

Methodology

Laboratory selection: Of roughly 50,000 healthcare facilities identified across the 14 countries, only 665 (1.3%) had bacteriology testing capacity. Of the 485 facilities that responded to a capacity survey, 393 reported performing bacteriology testing, from which 205 laboratories were ultimately selected for data collection — a mix of public, private, and reference facilities chosen for geographic coverage and AMR-detection readiness.

How the data was extracted

Records were entered into WHONET, a free laboratory data-management platform, by field data collectors trained specifically for this study, then transferred to a secure cloud repository for analysis. Where unique patient identifiers were available, duplicate results across multiple visits were excluded and only the first isolate per patient per year was retained, regardless of its susceptibility profile — to avoid one frequently-tested patient skewing the resistance estimates.

Data collection & quality: All recorded inpatient and outpatient culture samples from 2016–2019 were collected retrospectively via convenience sampling and entered into WHONET. AMR prevalence was defined as the proportion of resistant isolates among tested isolates, restricted to pathogen–drug combinations with at least 30 tested isolates. A laboratory-level data quality score (0–100) was calculated from compliance with AST breakpoint reporting and expected resistant-phenotype reporting, then aggregated to country level, weighted by each laboratory's share of AST results.

Statistical analysis: AMR prevalence was estimated with cluster-robust 95% confidence intervals, nesting laboratories within countries. Trends across years were assessed using Kruskal–Wallis analysis of variance, and subgroup differences (region, department, specimen source) used Tukey–Kramer multiple-comparison correction. A composite Drug Resistance Index (DRI), combining resistance prevalence with antibiotic consumption data from co-located pharmacies, was calculated for 10 priority pathogens across 8 antimicrobial classes in countries with sufficient data. Risk factors for AMR were assessed via multivariable logistic regression with cluster-robust errors, adjusting for age, sex, country, and department; associations with prior antibiotic use were assessed separately in the 4 countries where that data was available.

Findings

Resistance by pathogen and drug class

Aggregated AMR prevalence for five priority pathogens showed no significant change between 2016 and 2018 (p > 0.337); 2019 data were insufficient for most countries and excluded from trend analysis. The table below shows the range of 2018 country-level resistance prevalence for each priority pathogen–drug combination — the year with the most complete data.

MRSA exceeded 30% in 11 of the 13 countries with sufficient data (Sierra Leone was excluded from pathogen-level analysis due to insufficient samples).

Resistance by region, department, and specimen source

All differences below were statistically significant (p<0.0001). The largest pairwise gap was 8.6 percentage points, between the Eastern and Southern regions.

Risk factors and the Drug Resistance Index

In multivariable analysis, higher odds of resistance among culture-positive samples were independently associated with age, sex, and inpatient status (all p<0.0001) — see the "Risk factors (aOR)" tab in the chart above for the full adjusted odds ratios. Prior antibiotic use, assessed in four countries with available data, was also associated with resistance (aOR 1.37, 95% CI 1.05–1.80, p=0.022).

The composite Drug Resistance Index (DRI) — a measure combining resistance prevalence with antibiotic consumption — was estimated for 11 countries and ranged from 40.3% in Kenya to 80.7% in Senegal. Aminopenicillins were both the most-consumed antibiotic class (median 32.0% of use) and had the highest associated resistance (median 82.8%), while carbapenems had the lowest use and resistance (medians 0.04% and 13.0%, respectively) — consistent with carbapenems being reserved as last-line agents.

Clinical Takeout

This dataset represents the largest and most representative direct AST-based picture of AMR yet assembled from Africa, and its findings are, in several respects, more concerning than prior modelled estimates. MRSA prevalence exceeded 50% in six of the study countries (Nigeria, Ghana, Gabon, Cameroon, Zambia, and Eswatini) in 2018 — substantially higher than earlier modelled estimates, which placed most of these countries below 30–50%. Estimates for third-generation cephalosporin-resistant E. coli were also generally higher here than in prior modelling, with only a few exceptions.

The authors are careful to flag two competing sources of bias that cut in opposite directions. Because routine culture and AST are not performed for most infections in these settings, patients who are tested are disproportionately those with severe illness or previous treatment failure — which likely inflates observed resistance prevalence relative to the true community-wide picture. Conversely, patients who cannot access diagnostic services at all, including those who die of untreated severe AMR infections, are entirely absent from this dataset, which could lead to underestimation. Only 12% of records carried linked clinical data, limiting the ability to identify which specific drivers (recent hospitalisation, prior antibiotic exposure, comorbidities) are most actionable at the point of care. The authors' central recommendation is investment in bacteriology laboratory capacity, electronic laboratory information systems linked to clinical data, and standardised data-quality benchmarking, so that future surveillance can better distinguish true resistance trends from testing artefacts.

What This Means for South African Practitioners

Even without South African data, this study is relevant to local practice in two respects. First, it illustrates just how large the gap can be between modelled regional AMR estimates and what direct laboratory surveillance actually finds once it is done systematically — a caution against relying solely on continental or global models (including for South Africa) when local, culture-based surveillance data exist. Second, several of the structural drivers identified here — limited bacteriology laboratory capacity relative to patient volumes, incomplete linkage between laboratory and clinical records, and testing that is skewed toward the most severely ill patients — are common challenges across sub-Saharan African health systems generally, and are worth considering when interpreting South Africa's own surveillance outputs and when advocating for continued investment in local AST capacity and data systems.

The regional pattern of highest resistance in the Eastern and Western African groupings, alongside the confirmation that inpatient status and older age are consistently associated with higher resistance, reinforces existing empiric prescribing caution for South African clinicians managing patients recently transferred from, or with travel history to, other African countries with high AMR burden — particularly for third-generation cephalosporin- and carbapenem-resistant Enterobacterales and MRSA.

Original Study

Osena, G., Kapoor, G., Kalanxhi, E., Ouassa, T., Shumba, E., Brar, S., Alimi, Y., Moreira, M., Matu, M., Sow, A., Klein, E., Ondoa, P., Laxminarayan, R., & MAAP Study Group (2025). Antimicrobial resistance in Africa: A retrospective analysis of data from 14 countries, 2016-2019. PLoS Medicine, 22(6), e1004638. https://doi.org/10.1371/journal.pmed.1004638

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