Measuring spillover effects of reactive, focal malaria elimination interventions

衡量反应性局部疟疾消除干预措施的溢出效应

• 批准号: 10203751
• 负责人: Jade Benjamin-Chung
• 金额: $ 14.68万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-10 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10203751
• 关键词: Affect; Anti-malarial drug resistance; Antimalarials; Area; Award; Biological; Biology; Biomass; Biometry; Biostatistical Methods; California; Clinic; Cluster randomized trial; Communicable Diseases; Communities; Complex; Country; Culicidae; Data; Data Set; Detection; Educational process of instructing; Epidemiologist; Epidemiology; Faculty; Falciparum Malaria; Filarial Elephantiases; Foundations; Future; Goals; Heterogeneity; Hot Spot; Human; Incidence; Individual; Infection; Insecticides; Interruption; Intervention; Intervention Trial; Knowledge; Lead; Learning; Machine Learning; Malaria; Maps; Masks; Measures; Mentored Research Scientist Development Award; Mentorship; Meta-Analysis; Methods; Modeling; Modification; Namibia; Onchocerciasis; Outcome; Parasites; Participant; Pharmaceutical Preparations; Plasmodium falciparum; Poliomyelitis; Prevalence; Public Health; Randomized Controlled Trials; Reaction Time; Research; Residual state; Resources; Risk; San Francisco; Site; Smallpox; Social Network; Statistical Methods; Swaziland; Testing; Time; Trachoma; Training; Tuberculosis; Universities; Vaccination; World Health Organization; Zambia; base; career; career development; chemotherapy; disorder control; experience; infectious disease model; low income country; malaria transmission; mathematical model; migration; novel; scale up; skills; software development; transmission process; treatment arm; trial comparing

PROJECT SUMMARY / ABSTRACT This proposed K01 award will support the career development of Dr. Jade Benjamin-Chung, an Epidemiologist in the Division of Epidemiology & Biostatistics at the University of California (UC), Berkeley. Dr. Benjamin- Chung’s career goal is to become a leader in the application of rigorous biostatistical methods to infectious disease control and elimination. To support her career development, this application proposes a study she will lead to fill an important gap in research on malaria elimination interventions. As malaria transmission declines it becomes more heterogeneous and is characterized by focal hot spots of transmission. Blanket coverage of interventions becomes impractical and is not cost-effective. Reactive, focal interventions target hot spots by delivering antimalarials to people residing near to a symptomatic malaria case that presents to a surveillance site. Focally delivered interventions aim to reduce transmission to those outside focal treatment zones, including to asymptomatic malaria cases, who are thought to be responsible for the majority of transmission in elimination settings. Thus, information about whether interventions reduce illness among intervention recipients (“direct effects”) vs. non-intervention recipients in intervention clusters (i.e., “spillover effects”) is critical to understanding whether these interventions can eliminate malaria, yet current studies have not estimated such spillover effects. This study will estimate site-specific and pooled direct effects and spillover effects in three cluster-randomized trials of reactive, focal malaria elimination interventions in low malaria transmission settings in Namibia, Swaziland, and Zambia. The specific aims are to (1) estimate direct effects and spillover effects of reactive, focal malaria elimination interventions on Plasmodium falciparum malaria incidence and prevalence and (2) assess whether direct effects and spillover effects of reactive, focal malaria elimination interventions vary by distance to intervention, intervention coverage, and time from incident case detection. Evidence of spillover effects would suggest that reactive, focal interventions hold promise for malaria elimination when scaled up. The absence of spillover effects would suggest that interventions did not interrupt transmission; if so, information about the spatial configuration of infections would inform who and how many people to treat using redesigned interventions. This study will apply novel machine learning-based methods for estimation of causal effects appropriate for infectious disease data. This application proposes a 4-year training plan including mentorship from two leading biostatisticians at UC Berkeley and two malaria epidemiologists at UC San Francisco. Dr. Benjamin-Chung’s training goals are to (1) develop skills in machine learning and causal inference methods for dependent data, (2) learn about malaria biology and epidemiology, and (3) enhance her software development and spatial analysis skills. UC Berkeley is the optimal place for Dr. Benjamin-Chung to advance her career goals because of its leading faculty in biostatistics, strong track record of research and teaching in infectious diseases and biostatistics, and close proximity to UCSF’s Malaria Elimination Initiative.

项目摘要 /摘要 该提出的K01奖将支持流行病学家Jade Benjamin-Chung博士的职业发展 伯克利分校（UC）的流行病学与生物统计学系。本杰明博士 Chung的职业目标是成为应用严格的生物统计学方法的领导者 疾病控制和消除。为了支持她的职业发展，这项申请提出了一项她的研究 导致弥补消除疟疾干预措施的重要差距。随着疟疾传播的衰落 变得更加异质，并以焦点的传播斑点为特征。毯子的覆盖范围 干预变得不切实际，并且没有成本效益。反应性，局灶性干预措施针对热点 向居住在有症状的疟疾病例附近的人提供抗疟疾药物，以进行监视 地点。焦点交付的干预措施旨在减少向外部焦点治疗区的传播， 包括无症状的疟疾病例，被认为是导致大多数传播的原因 消除设置。这是有关干预措施是否减少干预接受者疾病的信息 （“直接效应”）在干预簇中的非干预接受者（即“溢出效应”）对 了解这些干预措施是否可以消除疟疾，但是目前的研究尚未估计 Spilover效应。这项研究将估算特定地点和汇总的直接效应和Spilover的效果 在低疟疾传播设置中，反应性，局灶性疟疾消除干预措施的聚类随机试验 在纳米比亚，斯威士兰和赞比亚。具体目的是（1）估计直接效应和间谍作用 恶性疟疾疟疾疟疾的反应性，局灶性疟疾干预措施 （2）评估反应性，局灶性疟疾消除干预措施的直接效应和间谍作用是否存在 随着干预措施，干预覆盖范围以及事件案例检测的时间而有所不同。证据 Spilover效应表明，反应性，局灶性干预措施在消除疟疾时会有望 缩放。缺乏Spilover效应会表明干预措施不会中断传播。如果 因此，有关感染空间配置的信息将告知谁和多少人可以治疗 使用重新设计的干预措施。这项研究将采用新颖的基于机器学习的方法来估计 适用于传染病数据的因果影响。该申请提议为期4年的培训计划 包括加州大学伯克利分校两位领先的生物统计学家的精神制，以及UC的两名疟疾流行病学家 旧金山。本杰明·奇（Benjamin-Chung）博士的培训目标是（1）发展机器学习和因果关系的技能 依赖数据的推理方法，（2）了解疟疾生物学和流行病学，（3）增强她 软件开发和空间分析技能。加州大学伯克利分校是本杰明·奇博士的最佳场所 由于其生物统计学领域的领先，研究的良好记录和 传染病和生物统计学教学，并与UCSF的疟疾消除倡议紧密相邻。