Source: MSF
From the newsletter
Genomic surveillance in Africa, which means using DNA to track infections, can detect the spread of drug-resistant bacteria that routine laboratory testing does not capture. A new study shows resistance genes can move between patients and hospitals without being identified as linked cases, unless through DNA tracking.
According to the study titled, Databases and Tools for Antimicrobial Resistance Detection and Surveillance, genomic surveillance analyses the DNA of bacteria to identify shared resistance genes. This allows scientists to link cases that appear unrelated in routine testing.
The likelihood of falling ill or dying from diseases that resist treatment has escalated in Africa. The continent faces the highest mortality rate from antimicrobial resistance (AMR) as compared to the death toll from HIV-AIDS, TB and Malaria combined.
More details
The review documents instances where resistance genes circulated widely without triggering outbreak alerts in hospitals relying only on lab diagnostics. A major driver of this hidden spread is plasmid-mediated resistance. Plasmids are small DNA elements that bacteria exchange easily. The study shows that plasmids carrying resistance genes can move across different bacterial species and locations which culture-based testing methods, mostly used in Africa, cannot detect.
The study shows genomics fills this gap by revealing shared genetic markers. It highlights tuberculosis as an example where genomics has already changed resistance detection. The World Health Organization’s catalogue of mutations in Mycobacterium tuberculosis was developed using more than 38,000 genomes and now supports drug resistance interpretation through targeted sequencing.
Targeted sequencing for drug-resistant tuberculosis has been implemented in some African countries, including Namibia. The study shows this approach identifies resistance mutations that may not be detected using conventional diagnostics alone, allowing more precise treatment selection based on genetic data. The study notes that similar genomic approaches can be applied to other infections common in African hospitals.
The review also identifies a limitation affecting African surveillance. Most antimicrobial resistance databases are built using data from high-income countries. African resistance variants are under-represented, which can affect how accurately genomic tools detect and interpret resistance circulating in African settings.
Our take
There is a huge data blindspot in AMR detection in hospitals which in turn leads to inadequate care.
Diagnostics that cannot track resistance flow are creating inefficiencies across hospital networks because the resistant bacteria is passed unknowingly and adds to the disease burden.