R09.5 Antimicrobial resistance in food animals: priority drugs maps to guide global surveillance

Abstract

Antimicrobials are life-saving drugs used to treat infections. However, the majority (73%) of antimicrobials sold globally are used in animals raised for food (Van Boeckel et al., 2017) . Antimicrobial resistance (AMR) in food animals is a growing threat to animal health and potentially to human health. In resource-limited settings, allocating resources to address AMR, as for any disease of global importance, can be guided with maps. However, in low- and middle-income countries (LMICs), attempts at documenting trends in AMR so far relied on summary metrics of resistance across antimicrobial classes. This is a major limitation for potentially taking targeted actions on individual antimicrobial classes.

Here, we mapped AMR prevalence of 7 antimicrobials in E. coli and Salmonella across LMICs, using a geospatial model with data from 1,169 point prevalence surveys. We also developed a novel approach, named co-resistance contextual models, to predict the antimicrobial with the highest probability of its resistance exceeding 50% in the future (2.8 to 6.9 years) at each location on the map. This approach exploited correlations of resistance between antimicrobials to predict the resistance profiles that will most likely occur in the future, producing a map of priority antimicrobials for AMR surveillance.

Hotspots of AMR were predicted in southern and eastern China, Iran, Turkey, northeastern India, southern Brazil, and southern Chile. The highest resistance prevalence was for tetracycline (55%, average across LMICs) and the lowest was for ciprofloxacin (25%). In Africa and South America, 87% locations were associated with penicillins or tetracyclines crossing 50% resistance in the future. In contrast, in Asia, 71% locations were associated with penicillins or sulphonamides, because resistance to tetracyclines has already exceeded 50% in 84% of locations across Asia. Our maps could help to adapt policies to local epidemiological context across LMICs, and provide policy makers with targets –geographic locations and priority drug classes- where scaling up surveillance could carry the greatest benefits.