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

摘要 到5岁时，低收入和中等收入国家（LMICs）的儿童所接触的辐射几乎是其他国家的五倍。 高收入国家的儿童使用抗生素的比例更高。尽管抗生素的获得是一个主要的驱动因素， 随着死亡率的下降，给儿童使用的大多数抗生素在临床上是不必要的。过度使用会 导致不良事件，药物毒性，并损害肠道微生物群和免疫系统。它还有助于 抗生素耐药性（AMR），其成本不成比例地由中低收入国家的儿童承担。虽然 在LMIC中广泛的临床上不必要的抗生素使用是有据可查的， 关于儿童过度使用的驱动因素以及这些因素如何与 抵抗力和疾病。需要这种知识来设计政策和干预措施， 平衡访问和过度使用。此K 01奖提案的重点是确定激励措施， 提供者面临的问题是如何用抗生素治疗儿童，以及这些问题与耐药性的发展有何关系。我 我的职业目标是成为独立的学者，在经济学和传染病的交叉点工作 流行病学，重点是研究，为中低收入国家的AMR政策提供信息。拟议的培训活动建立在 基于我作为经济学家的背景和进行基于人口的实验研究的经验， 进一步培训传染病流行病学，抗菌素耐药性的生物医学基础， 机器学习技术，以及传染病和社会系统的基于代理的建模。对齐 根据我的培训目标，我的研究计划旨在整合经济学和传染病的概念， 疾病流行病学，并使用最先进的机器学习方法来检查复杂的 驱动抗生素需求的因素之间的关系，疾病和耐药性的发展。做 因此，我将利用现有的微观层面的数据，这些数据来自对360个农村村庄的临床医生和家庭的调查 以及临床医生处方实践的新实验数据， 药剂师在同一地区我的具体研究目标是：1）通过实验评估处方 临床医师和药师在儿科腹泻病例中的实践; 2）评估临床医师和药师在儿科腹泻病例中的影响 关于儿童抗生素使用的建议，以及这如何随患者、临床医生和社区特征而变化; 和3）开发基于代理人的健康寻求行为、抗生素使用和细菌耐药性模型， 中国农村地区的儿童腹泻病例，并使用该模型进行反事实模拟， 为今后的研究提供参考。我的导师团队受过传染病流行病学的专门培训， 抗生素耐药性的流行病学，机器学习和基于代理的建模以及 领导跨学科团队的经验。这项研究将产生新的见解，可以为政策提供信息， 更好地平衡抗生素的获得和过度使用。K 01奖项中提出的培训和研究将 支持制定未来的R级提案，以研究中低收入国家AMR政策的设计。

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.