Antimicrobial resistance in Africa: The data gap behind the crisis
Africa carries one of the world's highest burdens of drug-resistant infections, yet the laboratory data needed to track it is often missing. The bigger health-tech opportunity may be building the systems that make that data usable, not the AI layered on top.
Antimicrobial resistance (AMR) in Africa has garnered increasing attention due to the growing number of deaths and infections. A Lancet analysis revealed that western sub-Saharan Africa had the world's highest death rate attributable to bacterial AMR in 2019, at 27.3 deaths per 100,000 people. However, the data on AMR remains incomplete, as only one in five laboratory-confirmed bacterial infections in Africa are resistant to antibiotics.
This estimate fails to account for untested infections, creating a visibility gap that extends beyond the continent. Only 48% of countries reported resistance data to the WHO's Global Antimicrobial Resistance and Use Surveillance System (GLASS) in 2023, and half of those that did lacked reliable data systems.
The issue is not merely limited to antibiotics; the quality of medicines also contributes to AMR. In 2025, Ghana's Food and Drugs Authority discovered counterfeit pharmaceutical products valued at GH₵42 million ($3.6 million). Likewise, Nigeria's National Primary Healthcare Development Agency reported that about 70% of medicines distributed in the country were substandard or counterfeit.
These substandard medicines with low active ingredients can expose bacteria to insufficient concentrations, leading to resistance development. To address this problem, countries must establish robust data infrastructure that includes functioning laboratories, timely results, and systems that link results to patient records. Advanced analysis techniques, such as whole-genome sequencing, can provide more precise insights into resistance patterns and how strains are related, aiding in informed decision-making and targeted interventions.
Written by urgent.news from TechCabal's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.