The Planetary Child Health Observatory: an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs

行星儿童健康观察站：一项跨学科研究计划和基于网络的仪表板，用于绘制中低收入国家肠道传染病及其危险因素和干预措施

• 批准号: 10591991
• 负责人: Josh M Colston
• 金额: $ 13.04万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-20 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10591991
• 关键词: Adult; Age; Area; Bacteria; Big Data; Biological Process; Biometry; COVID-19; Campylobacter; Cessation of life; Child; Child Health; Childhood; Cholera; Climate; Communicable Diseases; Communities; Country; Cryptosporidium; Data; Data Set; Databases; Development; Development Plans; Diagnosis; Diagnostic; Differential Diagnosis; Disease; Disease Outcome; Enteral; Epidemiologist; Etiology; Exclusion; Funding; Geographic Distribution; Geography; Goals; HIV; Health; Health Information System; Household; Interdisciplinary Study; International; Intervention; Lower Respiratory Tract Infection; Malaria; Maps; Marketing; Mentors; Mentorship; Methodology; Methods; Modeling; Modernization; Molecular; Monitor; National Institute of Allergy and Infectious Disease; Norovirus; Online Systems; Outcome; Perception; Personal Satisfaction; Planet Earth; Population; Population Intervention; Populations at Risk; Prevalence; Process; Public Health; Publications; Reporting; Research; Research Activity; Research Personnel; Resolution; Resources; Risk; Risk Factors; Sampling Studies; Secure; Shigella; Source; Standardization; Surveys; Target Populations; Testing; Time; Training; United States National Aeronautics and Space Administration; Universities; Update; Vaccines; Variant; Viral; age group; burden of illness; career development; central database; climate change; comparative; dashboard; diagnostic technologies; disability-adjusted life years; economic outcome; enteric pathogen; evidence base; improved; inter-institutional; large datasets; low and middle-income countries; molecular diagnostics; novel; novel vaccines; pathogen; peer; peer learning; priority pathogen; public repository; remote sensing; skills; sociodemographic factors; sociodemographics; spatiotemporal; stool sample; success; surveillance study; tool; transmission process; trend; vaccine candidate; web page; web portal; years of life lost

PROJECT SUMMARY/ABSTRACT Mapping spatiotemporal variation in the burden of infectious diseases is critical for targeting interventions like vaccines and prioritizing at-risk populations, particularly in Low- and Middle-Income Countries (LMICs), where such diseases are most prevalent. While many priority pathogens have been mapped over entire endemic regions, enteric infectious diseases (EIDs) have not, due to a perception that the necessary spatially referenced data on their prevalence and environmental determinants are not available. However, improvements in differential diagnosis of EIDs, geostatistical methods, and accessibility and accuracy of environmental data mean that it is now possible to carry out such a mapping. The long-term goal of this proposal is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The overall objective is to apply a big data approach to the modeling of EIDs in combination with advanced geostatistical analyses and global earth observation (EO)- derived datasets, resulting in generalizable estimates of the geographical distribution of these outcomes and of their associations with environmental drivers disseminated via an interactive web-based dashboard. The central hypothesis is that the prevalence of many enteropathogens varies spatiotemporally as a function of climatic, environmental, and socio-demographic factors in a way that can be modelled using global EO datasets and similar products. The rationale underlying the proposed research is that it will enable the identification of target populations for interventions. Specifically, building on existing partnerships between epidemiologists, climatologists, and hydrologists as well as investigators in numerous LMICs, Dr. Colston will: 1) compile and maintain a large database of georeferenced results from studies that diagnosed EIDs in children in LMICs; 2) Apply geostatistical models to EID outcome data (aim 1) and spatiotemporally matched, high resolution environmental covariates to a). draw inferences about underlying biological processes and b). generate prediction maps to identify geographical foci of transmission risk. 3) Establish a Planetary Child Health Observatory (PCHO), an interinstitutional initiative consisting of a). an interactive web-based dashboard and b). an international consortium of investigators. In addition to these research activities, Dr. Colston proposes a career development plan that includes mentorship, experiential and peer-to-peer learning, coursework, publications, and presentations with the objectives of: 1) gaining skills and formal training in geostatistical inference, biostatistics, and large datasets; 2) expanding expertise in applications of environmental and remote sensing-derived datasets in health research; 3) transitioning to research independence by securing follow-on R01 funding. His proposal will be supervised by an outstanding, interdisciplinary mentoring team with complementary methodological and substantive skills. The project will have a positive impact on public health by providing data inputs urgently needed for targeting EID interventions to priority populations in LMICs.

项目摘要/摘要 绘制传染病负担的时空变化对于针对诸如诸如 疫苗和优先级别的高风险人口，特别是在低收入和中等收入国家（LMIC） 这种疾病最普遍。尽管许多优先病原体已在整个流行病上绘制 由于认为必要的空间引用的地区，肠道传染病（EID）尚未 有关其患病率和环境决定因素的数据。但是，改进 EID，地统计方法的鉴别诊断以及环境数据的可及性和准确性 现在可以进行这样的映射。该建议的长期目标是提供研究 以及利益相关者社区，具有证据基础，可用于肠病特异性的地理靶向 儿童健康干预措施，例如新型疫苗。总体目标是将大数据方法应用于 EID与先进的地统计分析和全球地球观测（EO）结合建模 - 派生的数据集，导致对这些结果的地理分布的普遍估计和 他们与环境驱动程序的关联通过基于互动的网络仪表板传播。中央 假设是，许多肠病原体的患病率随着气候的函数而变化， 环境和社会人口统计学因素，可以使用全局EO数据集建模 类似的产品。拟议研究的基本原理是，它将能够识别目标 干预措施的种群。具体而言，基于流行病学家之间的现有伙伴关系， 气候学家，水文学家以及许多LMIC的研究人员Colston博士将：1）编译和编译 维持大量的Georecreted结果数据库，该研究是诊断出LMIC儿童的研究的研究； 2） 将地理模型应用于EID结果数据（AIM 1）和时空匹配的高分辨率 环境协变量为a）。提出有关潜在生物过程和b）的推论。产生 预测图以识别传输风险的地理焦点。 3）建立行星儿童健康 天文台（PCHO），一项由a）组成的机构间倡议。一个基于网络的交互式仪表板和b）。 一个国际调查人员联盟。除了这些研究活动外，科尔斯顿博士还提出了 职业发展计划，包括指导，体验和对等学习，课程，课程， 出版物，以及以下目的的介绍：1）在地统计学上获得技巧和正式培训 推论，生物统计学和大数据集； 2）扩大在环境和远程应用中的专业知识 在健康研究中传感衍生的数据集； 3）通过确保跟随的方式过渡到研究独立性 R01资金。他的建议将由一个杰出的跨学科指导团队监督 互补的方法论和实质技能。该项目将对公共卫生产生积极的影响 迫切需要提供数据输入，以针对LMIC的优先人群将开斋节干预措施定位。