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: 9761612
• 负责人: Jeanette A Stingone
• 金额: $ 24.75万
• 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/9761612
• 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; forest; genome wide association study; health data; high dimensionality; learning strategy; multidisciplinary; neurodevelopment; pollutant; prenatal; prenatal exposure; 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：本提案旨在描述产前暴露于 通过有效利用大型公共场所，可解释空气毒物与儿童认知健​​康的组合 在多学科顾问的指导下，候选人将培养数据科学方面的技能， 机器学习和先进的生物统计学将补充她在流行病学方法方面的培训。 让她能够在职业生涯中取得进步并推进对综合环境暴露和 先前的研究发现，产前接触单一空气污染物之间存在关联。 和儿童的认知健康，但缺乏调查多种污染物综合影响的能力， 包括在实验中观察到的污染物之间的协同/拮抗相互作用 了解暴露的综合影响是美国国立卫生研究院的一个战略目标。 环境健康科学，环境健康领域正在从单一污染物转向 更全面的范式的方法，例如识别暴露组中的关联和相互作用。 高维暴露数据的背景提出了来自领域的计算挑战。 例如数据科学，包括机器学习方法，可以纳入流行病学工具箱 该职业发展奖的目标是解决环境混合物和多重暴露问题。 推动候选人进入大数据科学和大数据科学交叉领域的独立研究生涯 通过正式课程、定向学习和实地轮换， 候选人将获得数据科学、机器学习和高级生物统计学方面的技能。 顾问的选择是基于他们互补的专业知识、相关的研究经验和 拟议的研究将利用从培训计划中获得的技能并应用它们。 描述产前暴露于可解释的空气毒物组合与第三种物质之间的关联 年级标准化考试成绩是一种以学校为基础的认知结果衡量标准。 用于将空气毒物（空气污染物的一个子集）数据与公共卫生管理数据链接起来 1994 年至 1998 年间纽约市出生的约 220,000 名儿童的登记和教育数据。 候选人将开发和验证假设生成的两阶段方法，然后是有针对性的 进行分析，以确定与儿童测试分数相关的空气毒物组合 使用成熟的流行病学方法进行有针对性的分析。 将进行空气毒物之间相互作用的影响估计和评估（目标2）潜力。 然后可以使用统计数据来确定空气毒物与测试分数之间关系的中介因素 （目标 3）完成这些目标将使候选人处于独特的地位。 未来对综合环境暴露和儿童健康进行研究。