A Data Science Approach to Air Toxics and Children's Environmental Health

项目摘要：这项提议旨在描述产前暴露于 通过有效地使用大公众，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方面的技能， 机器学习和高级生物统计学，以补充她在流行病学方法方面的培训。这将是 允许她在事业上取得进步，并推进对环境暴露和联合环境的研究 儿童健康。先前的研究发现，出生前接触单一空气污染物与 和儿童的认知健康，但缺乏调查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 学习。了解联合暴露的影响是美国国立卫生研究院的战略目标。 环境健康科学，环境健康领域正在从单一污染物过渡 通向更全面的范式的方法，例如曝光组。确定内部的关联和交互 高维暴露数据的背景是一个计算挑战。来自领域的方法 例如数据科学，包括机器学习方法，可以被纳入流行病学工具箱 用于解决环境混合和多重曝光问题。这一职业发展奖的目标是 让候选人在大数据科学和科学的交汇处进入独立的研究生涯 儿童的环境健康。通过正式的课程作业、指导学习和实地轮换， 应聘者将获得数据科学、机器学习和高级生物统计学方面的技能。导师、顾问和 顾问因其互补的专业知识、相关的研究经验和 指导能力。拟议的研究将利用从培训计划中获得的技能并将其应用于 研究产前暴露于可解释的空气有毒物质组合与第三次接触之间的关系。 年级标准化考试分数，这是一种基于学校的认知结果衡量标准。出生时的居住地将是 用于将空气有毒物质(空气污染物的子集)的数据链接到公共卫生的管理数据链接 1994-1998年期间在纽约市出生的大约220 000名儿童的登记和教育数据。这个 候选人将开发和验证假设生成的两个阶段方法，然后是有针对性的 分析以确定环境中与儿童考试成绩相关的空气污染物组合 高维曝光数据(目标1)。使用成熟的流行病学方法进行有针对性的分析 为了估计和评估空气有毒物质之间的相互作用，将进行效果评估。(AIM2)潜力 然后，可以使用统计方法来确定空气污染物和考试成绩之间的关系的中介因素 调解和数据科学方法。(目标3)完成这些目标将使应聘者处于独特的位置 开展未来关于环境暴露与儿童健康的联合研究。

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.