Bringing Modern Data Science Tools to Bear on Environmental Mixtures

项目摘要：将现代数据科学工具应用于环境混合物 环境暴露通常在特定的地理区域累积，复杂混合物的性质 这些暴露的特征仍然没有得到充分研究。此外，不利的环境暴露往往 发生在面临多种社会压力的社区，如住房恶化， 医疗保健，贫困的学校，高失业率，犯罪和贫困-所有这些都可能加剧的影响， 环境暴露。 我们的核心目标是开发新的数据架构、统计和机器学习方法， 评估暴露于环境混合物如何塑造教育成果， 没有社会压力。我们专注于空气污染混合物，儿童铅暴露和社会压力。 我们将在北卡罗来纳州（NC）实施我们提出的工作，该州的特点是环境多样性 特征、工业活动和以不同污染排放源和产生的污染物为代表的空气区 混合物。 为了实现这一中心目标，我们将首先开发、记录和传播用于构建 时空环境和社会数据架构。我们将在所有NC中实现此功能， 1990-2015年+的空气污染、铅暴露风险和社会暴露（数据集1）。第二，我们将细化 链接不相关数据集以建立时空子运动和结果数据体系结构的方法 （数据集2）。第三，我们将通过共享地理位置连接暴露（数据集1）和结果（数据集2）数据 和时间性整合到一个综合地理数据库中。第四，我们将实施日益复杂的 评估在存在或不存在社会应激源的情况下环境混合物对早期 儿童教育成果。我们将记录和传播所有基本的方法学工作 通过公共网站。 拟议的工作利用了调查人员已经可用的丰富的数据资源（其中一些 后处理），并允许跨空间和时间跟踪儿童。我们的团队带来了来自 现代数据科学（具有变量选择的分层贝叶斯方法，空间点过程模型， 机器学习）来解决环境混合物如何直接塑造儿童结果的关键问题 在社会压力的存在下，

Project Summary: Bringing Modern Data Science Tools to Bear on Environmental Mixtures Environmental exposures often cumulate in particular geographies, and the nature of the complex mixtures that characterize these exposures remains understudied. In addition, adverse environmental exposures often occur in communities facing multiple social stressors such as deteriorating housing, inadequate access to health care, poor schools, high unemployment, crime, and poverty – all of which may compound the effects of environmental exposures. Our central objective is to develop new data architecture, statistical, and machine learning methods to assess how exposure to environmental mixtures shapes educational outcomes in the presence or absence of social stress. We focus on air pollution mixtures, childhood lead exposure, and social stressors. We will implement our proposed work in North Carolina (NC), a state characterized by diverse environmental features, industrial activities, and airsheds typified by varying pollution emission sources and resulting pollutant mixtures. To accomplish this central objective, we will first develop, document, and disseminate methods for building space-time environmental and social data architectures. We will implement this for all of NC, incorporating data on air pollution, lead exposure risk, and social exposures from 1990-2015+ (dataset 1). Second, we will refine methods for linking unrelated datasets to build a space-time child movement and outcome data architecture (dataset 2). Third, we will connect exposures (dataset 1) and outcomes (dataset 2) data via shared geography and temporality into a single, comprehensive geodatabase. Fourth, we will implement increasingly complex methods to assess the effect of environmental mixtures in the presence or absence of social stressors on early childhood educational outcomes. We will document and disseminate all of the underlying methodological work via public website. The proposed work leverages a rich array of data resources already available to the investigators (with some significantly post-processed) and allows tracking of children across space and time. Our team brings tools from modern data science (hierarchical Bayesian methods with variable selection, spatial point process models, machine learning) to bear on the critical question of how environmental mixtures shape child outcomes directly and differentially in the presence of social stress.