A DATA SCIENCE APPROACH TO AIR TOXICS AND CHILDREN'S ENVIRONMENTAL HEALTH

空气中毒和儿童环境健康的数据科学方法

• 批准号: 9791316
• 负责人: Jeanette A Stingone
• 金额: $ 24.67万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-25 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9791316
• 关键词: Address; Affect; Air; Air Pollutants; Algorithms; Big Data; Bioinformatics; Biological Monitoring; Biometry; Birth; Child; Child Health; Childhood; Cognition; Cognitive; Complex Mixtures; Coupled; Data; Data Linkages; Data Science; Data Set; Databases; Development Plans; Distal; Education; Environmental Exposure; Environmental Health; Environmental Monitoring; Epidemiologic Methods; Epidemiology; Exposure to; Future; Generations; Genetic; Goals; Health; Information Systems; Investigation; K-Series Research Career Programs; Knowledge; Learning; Link; Literature; Machine Learning; Measures; Mediating; Mediation; Mediator of activation protein; Mentors; Methodology; Methods; Modeling; National Institute of Environmental Health Sciences; New York City; Ontology; Outcome; Pathway interactions; Population; Positioning Attribute; Public Health; Records; Registries; Research; Research Personnel; Resources; Retrospective cohort; Rotation; Schools; Selection Bias; Standardization; Statistical Methods; Technical Expertise; Testing; Time; Training; Trees; Urban Population; base; career; career development; clinical Diagnosis; early life exposure; epidemiological model; epidemiology study; experience; experimental study; genome wide association study; health data; high dimensionality; learning strategy; multidisciplinary; neurodevelopment; pollutant; prenatal; prenatal exposure; random forest; research and development; residence; skills; third grade; urban children

PROJECT SUMMARY: This proposal aims to characterize the associations between prenatal exposure to interpretable combinations of air toxics and children’s cognitive health through the efficient use of big public health data. With guidance from multidisciplinary advisors, the candidate will develop skills in data science, machine learning and advanced biostatistics to supplement her training in epidemiologic methods. This will allow her to progress in her career and advance research on combined environmental exposures and children’s health. Previous research has found associations between prenatal exposure to single air pollutants and children’s cognitive health but has lacked the ability to investigate combined impacts of multiple pollutants, including the synergistic/antagonistic interactions between pollutants that have been observed in experimental studies. Understanding the effects of combined exposures is a strategic goal of the National Institute of Environmental Health Sciences, and the field of environmental health is transitioning from single-pollutant approaches to more holistic paradigms, such as the exposome. Identifying associations and interactions within the context of high-dimensional exposure data presents a computational challenge. Methods from domains such as data science, including machine learning methods, can be incorporated into the epidemiologic toolbox for addressing environmental mixtures and multiple exposures. The goal of this Career Development Award is to advance the candidate into an independent research career at the intersection of big data science and children’s environmental health. Through formal coursework, directed learning and field rotations, the candidate will gain skills in data science, machine learning and advanced biostatistics. Mentors, advisors and consultants have been selected for their complementary expertise, relevant research experience and mentoring abilities. The proposed research will leverage the skills gained from the training plan and apply them to characterize associations between prenatal exposure to interpretable combinations of air toxics and 3rd grade standardized test scores, a school-based measure of cognitive outcomes. Residence at birth will be used to link data on air toxics, a subset of air pollutants, to an administrative data linkage of public health registries and education data for approximately 220,000 children born in New York City from 1994-1998. The candidate will develop and validate a two-stage approach of hypotheses generation followed by targeted analyses in order to identify combinations of air toxics associated with children’s test scores within the context of high-dimensional exposure data (Aim 1). Targeted analyses using well-established epidemiologic methods for effect estimation and assessment of interaction between air toxics will be performed. (Aim2) Potential mediators of the relationship between air toxics and test scores can then be identified using statistical mediation and data science approaches. (Aim 3) Completion of these aims will uniquely position the candidate to conduct future research on combined environmental exposures and children’s health.

项目摘要：该提案旨在表征产前暴露与 通过有效利用大型公众，空气有毒物质和儿童认知健​​康的可解释组合 健康数据。在多学科顾问的指导下，候选人将发展数据科学技能， 机器学习和先进的生物统计学，以补充她在流行病学方法中的培训。这会 让她在职业生涯中进步，并提高对环境暴露的研究和 儿童健康。先前的研究发现，产前暴露于单空气污染物之间 和儿童的认知健康，但缺乏研究多种污染物的综合影响的能力， 包括在实验中观察到的污染物之间的协同/拮抗相互作用 研究。了解联合暴露的影响是国家研究所的战略目标 环境健康科学和环境健康领域正在从单污染物过渡 诸如外汇范围之类的更整体范式的方法。识别内部的关联和互动 高维曝光数据的上下文提出了计算挑战。来自域的方法 诸如数据科学（包括机器学习方法）可以纳入流行病学工具箱 用于解决环境混合和多种暴露。这个职业发展奖的目标是 将候选人推向大数据科学交集的独立研究职业 儿童的环境健康。通过正式的课程工作，定向学习和现场轮换， 候选人将获得数据科学，机器学习和高级生物统计学的技能。导师，顾问和 顾问已被选为其完整专业知识，相关研究经验和 指导能力。拟议的研究将利用培训计划中获得的技能并应用它们 为了表征产前暴露于空气毒物的可解释组合和第三次之间的关联 等级标准化考试成绩，这是一种基于学校的认知结果度量。出生时的住所将是 用于将空气毒理的数据（一部分空气污染物）链接到公共卫生的行政数据链接 1994 - 1998年在纽约市出生的大约220,000名儿童的登记和教育数据。这 候选人将开发并验证两阶段的假设生成，然后是针对性的 分析以确定与儿童测试分数相关的空气毒物组合 高维暴露数据（AIM 1）。使用公认的流行病学方法进行有针对性的分析 为了进行效果估计和空气毒物之间相互作用的评估。 （AIM2）潜力 然后可以使用统计数据来识别空气毒理学和测试分数之间关系的介体 调解和数据科学方法。 （目标3）这些目标的完成将独特地定位候选人 对环境暴露和儿童健康的联合研究进行未来的研究。