Active one health surveillance in LMICs to monitor and predict Antimicrobial Resistance Using Metagenomics (ALARUM)

Lead Research Organisation: University of Oxford
Department Name: Experimental Medicine


Infections caused by bacteria that are resistant to antibiotics are estimated to be associated with 5 million human deaths each year, with the highest death rates attributable to antibiotic resistance in sub-Saharan Africa.

Conventionally, microbiology laboratories are used to identify bacteria causing human infections, monitor the spread of antibiotic resistance in these bacteria, and collect data to inform decisions about how to best use antibiotics. However, such laboratories are in short supply in much of sub-Saharan Africa and are expensive to build and run. Alternative lower-cost approaches to classical microbiological surveillance are needed.

Many of the bacteria causing life-threatening infections are carried by patients in the gut prior to causing infections. In previous work, we have shown that for certain species of bacteria, by looking at the DNA from bacteria in pooled stool samples from hospital patients, we can accurately predict drug-resistance in serious infections. Working in Kenya and Burkina Faso, we plan to extend this work to other bacterial species, and also to determine whether samples from human stool in the community and household environment can help predict drug-resistance in serious infections. This work will also enable us to find out more about how the bacterial genes that cause drug-resistance are spreading between humans, animals and the environment in the community. Our proposed work includes an economic evaluation to determine under what circumstances the proposed surveillance system represents an efficient use of resources.

Technical Summary

We will develop and validate approaches for sentinel One Health AMR surveillance in rural sub-Saharan Africa. Building on previous novel work showing that metagenomic profiling of pooled faecal material can accurately predict AMR prevalence in clinical isolates of Enterobacterales, we aim to extend this work to other bacterial species and One Health compartments.

We will conduct metagenomic profiling on pooled DNA extracts from human stool samples (hospital and community-level) and from household environments in Burkina Faso and Kenya. We will assemble existing local data from hospital-based microbiology diagnostic laboratories. We will perform community-level surveys and use randomly selected households within village clusters. We will sample suspected environmental AMR exposure sites in and around households and collect data on community-level human and animal antibiotic use, hygiene practices, contact with domestic animals, and sanitary facilities. Data analysis will seek to quantify community-level exposure risks and evaluate the accuracy of predicting resistance in clinical isolates using metagenomic data. A cost-utility analysis will determine under what circumstances the use of pooled metagenomic data to inform empirical antibiotic policies would represent an efficient use of resources.

The proposed work will establish how this approach could provide a viable, low-cost and convenient approach for AMR sentinel surveillance in settings which - like many in rural sub-Saharan Africa - lack systematic microbiological diagnostics and where sewage systems are non-existing.


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