Understanding the Drivers of Antibiotic use in the Treatment of Childhood Diarrhea and Relationship to Antibiotic Resistance in China

ABSTRACT By the age of 5, children in low and middle-income countries (LMICs) are exposed to nearly five times more antibiotics than high-income country children. Although improved access to antibiotics has been a major driver of mortality declines, most antibiotics administered to children are clinically unnecessary. Excessive use can

lead to adverse events, drug toxicity, and harm the gut microbiota and immune system. It also contributes to antimicrobial resistance (AMR), the costs of which are disproportionately borne by children in LMICs. Although widespread clinically unnecessary use of antibiotics in LMICs is well-documented, substantial knowledge gaps

remain regarding the drivers of overuse among children and how these are linked to the dynamics of resistance and disease. This knowledge is required to design policies and interventions that appropriately balance access and overuse. This K01 Award proposal focuses on identifying incentives that caregivers and

providers face to treat children with antibiotics and how these are related to the development of resistance. My career goal is to become independent scholar working at the intersection of economics and infectious disease epidemiology with a focus on research to inform AMR policies in LMICs. The proposed training activities build

on my background as an economist and experience conducting population-based experimental research with further training in infectious disease epidemiology, the biomedical underpinnings of antimicrobial resistance, machine learning techniques, and agent-based modeling of infectious disease and social systems. Aligned

with my training goals, my research program aims to integrate concepts from economics and infectious disease epidemiology and to use state-of-the-art machine learning approaches to examine the complex relationship between factors driving demand for antibiotics, disease, and the development of resistance. To do

so, I will draw on existing micro-level data from a survey of clinicians and households across 360 rural villages in southwest China as well as new experimental data on the prescription practices of clinicians and pharmacists in the same area. My specific research aims are 1) to experimentally evaluate the prescribing

practices of clinicians and pharmacists for pediatric diarrhea cases; 2) to estimate the influence of clinician advice on antibiotic use in children, and how this varies with patient, clinician, and community characteristics; and 3) to develop an agent-based model of health-seeking behavior, antibiotic use, and bacterial resistance for

pediatric diarrhea cases in rural China and use this model to conduct counterfactual simulations to prioritize interventions for future study. My mentoring team has specialized training in infectious disease epidemiology, the epidemiology of antimicrobial resistance, machine learning, and agent-based modeling as well as

experience leading interdisciplinary teams. This research will generate new insights that can inform policies to better balance access to antibiotics and overuse. The training and research proposed in this K01 award will support the development of future R-level proposals to study the design of AMR policies in LMICs.