Abstract
The transmission of antibiotic-resistant bacteria across multi-species networks is a contributor to the global challenge of antimicrobial resistance (AMR). AMR transmission inferencing, a retrospective process, is critical for refining the evidence underpinning current control strategies. In Africa, where AMR is associated with an estimated 1.05 million deaths annually, it is crucial to evaluate how AMR transmission inferences are made and to consider their implications for achieving national action plan goals. Key questions that need to be addressed in these settings include: (a) How is transmission defined? (b) How are transmission studies designed? (c) Which pathogens or commensal bacteria are used to infer AMR transmission? (d) How granular and reliable is the data used to make transmission inferences? (e) Can the frequency of transmission events be quantified? and (f) Can the directionality of transmission between hosts be established? In this systematic review, we examine the evidence informing current control strategies by analysing 34 studies from Africa, involving 18,604 human and livestock samples and 16 sentinel bacteria. Transmission inferences largely rely on cross-sectional studies with limited sample representativeness. Gram-negative bacteria, mainly Escherichia coli (64.71%), form the basis of most inferences. Most inferences remain qualitative, and analyses often overlook uncertainty quantification. In addition, studies are potentially underpowered as only 25% of collected samples are used for transmission inferencing. Based on this analysis, we propose a framework that leverages the growing use of genomic epidemiology to infer AMR transmission with an aim of supporting the design and evaluation of targeted interventions.