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

了解中国儿童腹泻治疗中抗生素使用的驱动因素及其与抗生素耐药性的关系

• 批准号: 10370831
• 负责人: Sean Y Sylvia
• 金额: $ 13.19万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-22 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10370831
• 关键词: Address; Adverse event; Affect; Age; Antibiotic Resistance; Antibiotics; Antimicrobial Resistance; Area; Automobile Driving; Bacterial Drug Resistance; Behavioral; Caregivers; Characteristics; Child; Childhood; China; Clinic; Clinical; Communities; Complex; Country; Data; Data Analyses; Development; Diagnostic; Diarrhea; Disease; Disease Resistance; Drug toxicity; Economics; Effectiveness; Epidemiology; Equilibrium; Exposure to; Face; Feces; Future; Goals; Health system; Household; Immune system; Improve Access; Incentives; Income; Individual; Infectious Disease Epidemiology; Intervention; Knowledge; Lead; Learning; Link; Machine Learning; Mentored Research Scientist Development Award; Mentors; Modeling; Mortality Decline; Nature; Oral; Outcomes Research; Parents; Patients; Pharmacists; Pharmacy facility; Policies; Population; Positioning Attribute; Prevalence; Provider; Psychological reinforcement; Quality of Care; Rapid diagnostics; Rehydrations; Research; Research Activity; Resistance; Resistance development; Rural; Series; Side; Social Sciences; Source; Standardization; Statistical Data Interpretation; Superbug; Surveys; System; Techniques; Testing; Time; Training; Training Activity; United States National Institutes of Health; Ursidae Family; Work; analytical tool; bacterial resistance; base; behavioral response; career; community setting; community-level factor; cost; design; development policy; diarrheal disease; effective intervention; experience; experimental study; global health; gut microbiota; health care quality; health seeking behavior; infectious disease model; insight; low and middle-income countries; models and simulation; population based; prevent; programs; simulation; social

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.

抽象的 到5岁时，低收入和中等收入国家（LMIC）的儿童接触了近五倍 抗生素比高收入乡村儿童。尽管改善了抗生素的机会是主要驱动力 死亡率下降，对儿童的大多数抗生素在临床上是不必要的。过度使用可以 导致不良事件，药物毒性并损害肠道菌群和免疫系统。它也有助于 抗菌素耐药性（AMR），其成本是由LMIC的儿童承担的。虽然 在LMIC中，广泛的临床不必要使用抗生素是有据可查的，很大的知识差距 关于儿童过度使用的驱动因素以及它们如何与动态有关 抵抗和疾病。需要这些知识来设计适当的政策和干预措施 平衡访问权限和过度使用。该K01奖励提案的重点是确定照顾者和 提供者面对治疗抗生素的儿童以及与抗药性的发展如何相关的儿童。我的 职业目标是成为经济学和传染病交集的独立学者 流行病学的重点是为LMIC的AMR政策提供信息。拟议的培训活动建设 在我作为经济学家的背景下，并经验进行基于人群的实验研究 进一步的传染病流行病学培训，抗菌素耐药性的生物医学基础， 机器学习技术以及基于代理的传染病和社会系统的建模。对齐 有了我的培训目标，我的研究计划旨在整合经济学和传染性的概念 疾病流行病学并使用最先进的机器学习方法检查复合物 推动抗生素，疾病和抗药性发展的因素之间的关系。做 因此，我将从360个农村村庄的临床医生和家庭调查中获取现有的微观数据 在中国西南部以及有关临床医生处方惯例的新实验数据和 同一地区的药剂师。我的具体研究目的是1）实验评估处方 小儿腹泻病例的临床医生和药剂师的实践； 2）估计临床医生的影响 有关儿童抗生素使用的建议，以及这与患者，临床医生和社区特征有何不同； 3）开发一种基于代理的寻求健康行为，抗生素使用和细菌抗性的模型 中国农村地区的小儿腹泻病例，并使用此模型进行反事实模拟以优先考虑 未来研究的干预措施。我的指导团队接受了传染病流行病学专业培训， 抗菌素耐药性，机器学习和基于代理的建模的流行病学以及 经验领先的跨学科团队。这项研究将产生新的见解，可以告知政策 更好地平衡获得抗生素的机会和过度使用。该K01奖提出的培训和研究将 支持未来R级建议的发展，以研究LMIC中AMR政策的设计。