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: 10688168
• 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/10688168
• 关键词: Address; Adverse event; Affect; Age; Antibiotic Resistance; Antibiotics; Antimicrobial Resistance; Area; Automobile Driving; Bacterial Drug Resistance; Behavioral; Calibration; Caregivers; Characteristics; Child; Childhood; China; Clinic; Clinical; Communities; Complex; Country; Data; Data Analyses; Development; Diagnostic; Diarrhea; Disease; Drug toxicity; Economics; Effectiveness; Epidemiology; Equilibrium; Exposure to; Face; Feces; Future; Goals; Health system; Household; Hydration status; Immune system; Improve Access; Incentives; Income; Individual; Infectious Disease Epidemiology; Intervention; Knowledge; Learning; Link; Machine Learning; Marketing; Mentored Research Scientist Development Award; Mentors; Modeling; Mortality Decline; Nature; Oral; Outcomes Research; Parents; Patients; Pharmacists; Pharmacy facility; Policies; Population; Positioning Attribute; Prevalence; Psychological reinforcement; Quality of Care; Quasi-experiment; Rapid diagnostics; Recommendation; 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; Work; analytical tool; bacterial resistance; behavioral response; care providers; 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; structural determinants

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.

摘要 到5岁时，低收入和中等收入国家(LMIC)的儿童接触到的风险几乎是 比起高收入的农村儿童，抗生素的比例更高。尽管抗生素获得的改善一直是一个主要的驱动力 在死亡率下降的情况下，大多数给儿童使用的抗生素在临床上是不必要的。过度使用可能会 导致不良事件、药物毒性，并损害肠道微生物区系和免疫系统。它还有助于 抗菌素耐药性(AMR)，其费用不成比例地由LMIC中的儿童承担。虽然 在LMICs中广泛使用临床上不必要的抗生素是有充分证据的、实质性的知识差距 仍然关注儿童过度使用的驱动因素，以及这些驱动因素如何与 抵抗力和疾病。这种知识是设计适当的政策和干预措施所必需的 平衡访问和过度使用。本K01奖提案侧重于确定照顾者和 供应商面临着用抗生素治疗儿童的问题，以及这些与耐药性发展的关系。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上工作 流行病学，专注于为低收入国家的AMR政策提供信息的研究。拟议的培训活动建立 关于我作为一名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，我的研究项目旨在将经济学和传染性的概念结合起来 疾病流行病学和使用最先进的机器学习方法来检查复杂的 推动抗生素需求、疾病和耐药性发展的因素之间的关系。去做 因此，我将利用360个农村的临床医生和家庭调查中现有的微观数据 在西南中国以及新的实验数据的处方实践中的临床医生和 同一地区的药剂师。我的具体研究目标是：1)对处方进行实验评估 临床医生和药师在儿科腹泻病例中的实践；2)评估临床医生的影响 关于儿童使用抗生素的建议，以及这如何随患者、临床医生和社区特征的不同而变化； 以及3)开发基于代理的健康行为、抗生素使用和细菌耐药性的模型 农村中国儿童腹泻病例调查及利用该模型进行反事实模拟 未来研究的干预措施。我的指导团队接受过传染病流行病学方面的专门培训， 抗菌素耐药性的流行病学、机器学习、基于代理的建模以及 有领导跨学科团队的经验。这项研究将产生新的见解，为政策提供信息 更好地平衡抗生素的使用和过度使用。本K01奖项中建议的培训和研究将 支持制定未来R级提案，以研究低收入中等收入国家AMR政策的设计。