Modelling Interventions to Combat Antibacterial Resistance in East Africa Using Causal Bayesian Networks

Antibacterial resistance (ABR) poses significant challenges to combating infections worldwide. ABR drivers are interconnected, complicating identification of intervention points. Researchers need a systems-based perspective that considers interrelated drivers collectively. We focus on urinary tract infections (UTIs), which are increasingly impacted by emergence of multi-drug resistant (MDR) bacteria. We analysed 2,007 adult outpatients with UTIs in Kenya, Tanzania, and Uganda in 2019–2020. We applied structure learning in Bayesian networks, a graphical probabilistic model, alongside expert knowledge to construct a causal diagram of drivers of prevalence of MDR UTI. MDR prevalence was influenced more by demographic, socioeconomic and environmental conditions than recent antibiotic use. We conducted hypothetical interventions to estimate drivers’ causal effects, revealing that improving education access, providing protected drinking water and flush toilets, and reducing overcrowding would decrease MDR prevalence. A systems-based approach identified underlying causal patterns contributing to prevalence of MDR, and could guide the development of complexity-aware targeted interventions.