Using sewage for surveillance of antimicrobial resistance

Abstract

Antimicrobial resistance (AMR), a cross-cutting and increasing threat to global health (1–3), is a complex problem with multiple and interconnected drivers. Reliable surveillance data that accurately describe and characterize the global occurrence and distribution of AMR are essential for tracking changes in resistance over time, setting national and global priorities, assessing the impacts of interventions, identifying new kinds of resistance, and supporting investigation of (international) outbreaks of resistant pathogens. AMR surveillance data can also inform development of treatment guidelines. Yet it has proven difficult to achieve these objectives on a global scale, and especially in low- and middle-income countries (LMICs), largely because current surveillance systems deliver data that are extremely variable in quality and quantity and highly heterogeneous in terms of which population is sampled (usually a category of hospital patients) and what drug-bug combinations are included (1). Here, we outline a plan for a global AMR surveillance system based on applying next-generation sequencing (NGS) to human sewage that will be especially helpful for community AMR surveillance, which is difficult to achieve in other ways, and will provide an affordable surveillance option in resource-poor settings. NGS is a powerful technology that has transformed the health data landscape. Among many other benefits, it has drastically improved our ability to determine the presence of AMR genes (bacterial genes known to confer resistance to an antimicrobial drug) in single isolates and to quantify them in complex microbiomes (4, 5). Millions of random DNA fragments sequenced by NGS can be mapped to reference sequence databases, and the number of reads coming from any of several thousand known AMR genes can be counted to provide easily shared information on their occurrence and abundance. Increasing numbers of people globally are connected to sewage treatment systems (6) and, as recently highlighted by the World Bank (3), metagenomics-based, near realtime quantification of AMR genes in sewage is a potentially useful surveillance tool even in remote locations without microbiology laboratories (7). Such an approach could quickly plug current gaps in the geographic, population, and agent coverage of AMR surveillance, especially by providing data on AMR outside hospitals (90% of antibiotic usage in humans occurs outside hospitals) (see the figure). It could also provide information on environmental transmission in populations exposed to raw sewage.