PROJECT SUMMARY/ABSTRACT Shigella is a leading cause of diarrhea and associated mortality among children under 5 years of age in low- and middle-income countries. Targeted antibiotic treatment of Shigella diarrhea may effectively improve outcomes across diverse settings, but current syndromic treatment guidelines and an inability to diagnose Shigella at the point-of-care result in underuse of active antibiotics for most cases. At the same time, overtreatment of viral and parasitic diarrhea episodes contributes to antimicrobial resistance. The goal of this project is to increase appropriate treatment of Shigella while limiting antimicrobial resistance by characterizing the contributions of inappropriately treated Shigella to poor diarrhea outcomes in a high mortality setting, evaluating novel biomarker-based strategies to identify Shigella, and designing population-level interventions to target antibiotic treatment to Shigella and decrease total antibiotic use. The hypothesis of this study is that appropriate identification and treatment of Shigella could prevent poor diarrhea outcomes and reduce antibiotic use for diarrhea among children in low-resource settings. We will enroll a prospective cohort of children hospitalized with diarrhea in Haydom, Tanzania and estimate the impact of Shigella on death and rehospitalization, diarrhea duration, and child growth. We will further evaluate whether those impacts are mitigated by treatment with antibiotics expected to be active for Shigella. Next, we will evaluate the performance of fecal inflammatory biomarkers to identify shigellosis cases in the cohort, incorporating the biomarkers into clinical prediction algorithms for Shigella diarrhea. We will further characterize biomarker dynamics and assess their generalizability in an existing cohort of children enriched for shigellosis in Dhaka, Bangladesh. Finally, we will model the impact of population-level interventions to target antibiotic treatment to Shigella on diarrhea outcomes and antibiotic use to determine which strategies would be most effective to prevent the poor outcomes of Shigella and reduce antibiotic use for diarrhea overall. The hypothetical interventions will target Shigella based on etiology, clinical syndrome, fecal inflammatory biomarkers, and/or season, and will be pre-specified or optimized using machine learning. The research team includes leading epidemiologists in enteric infectious diseases with expertise in molecular diagnostics, large field studies in Haydom and Dhaka, and epidemiologic methods in causal inference and machine learning. This study will address key knowledge gaps around antibiotic treatment decisions for diarrhea in low-resource settings, specifically by characterizing outcomes that could be prevented by the targeted treatment of Shigella and by evaluating resource-efficient strategies to identify shigellosis at the point-of-care. This work will build an evidence-base for precision public health interventions th...