The unequal burden of antimicrobial resistance in Sub-Saharan Africa from 2015 to 2025 with implications for epidemiology, economic impact, and policy

Antimicrobial resistance (AMR) is a growing global threat, disproportionately affecting Sub-Saharan Africa (SSA). Health-system limitations, high infectious disease prevalence, and regulatory gaps amplify transmission and clinical impact.

To map and synthesize evidence from 2015 to 2025 on AMR epidemiology, resistance patterns, economic burden, and policy responses in SSA, using a One Health perspective.

A scoping review following Arksey and O’Malley and PRISMA-ScR guidelines included peer-reviewed studies, surveillance reports, and policy documents from human, animal, and environmental settings. Data were synthesized narratively, distinguishing empirical estimates (e.g., 2019 deaths) from modeled projections (e.g., 2050 economic losses). Resistance ≥ 40% highlights substantial limitations in empirical hospital therapy.

SSA exhibits high resistance among WHO priority pathogens. Carbapenem resistance in Klebsiella pneumoniae and Acinetobacter baumannii exceeds 30–40% in hospitals. MRSA prevalence ranges 25–50%, though most studies did not differentiate hospital vs. community-associated strains. ESBL production in Escherichia coli commonly exceeds 40%. Empirical data indicate ~ 1.05 million AMR-associated deaths in 2019; modeled projections estimate US$0.3–1.2 trillion in economic losses by 2050. Key drivers include inappropriate antibiotic use, weak infection prevention and control, informal drug markets, high infectious disease burden, and environmental transmission. Stewardship and regulatory interventions across human and animal sectors demonstrate measurable reductions in antibiotic consumption.

AMR in SSA reflects intertwined epidemiological, structural, and economic vulnerabilities. Strengthening surveillance, laboratory capacity, stewardship, and regulation within a One Health framework is critical. Findings should be interpreted cautiously due to heterogeneous data, modeling uncertainty, and limited pathogen subtype granularity.