Bringing Modern Data Science Tools to Bear on Environmental Mixtures

将现代数据科学工具应用于环境混合物

• 批准号: 9882999
• 负责人: Marie Lynn Miranda
• 金额: --
• 依托单位: RICE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9882999
• 关键词: Address; Air; Air Pollutants; Air Pollution; Architecture; Bayesian Method; Biological Availability; Biological Factors; Birth; Birth Records; Carbon Monoxide; Censuses; Chemicals; Child; Child Health; Childhood; Communities; Complex; Complex Mixtures; Computer software; Coupled; Cox Models; Crime; Data; Data Science; Data Set; Data Sources; Development; Environmental Exposure; Exposure to; Gaussian model; Genetic; Geography; Housing; Individual; Industrialization; Lead; Link; Machine Learning; Methodology; Methods; Modeling; Modernization; Mothers; Movement; Nature; Neighborhoods; Nitrogen Dioxide; North Carolina; Nursery Schools; Outcome; Outcome Measure; Output; Ozone; Particle Size; Particulate Matter; Personal Satisfaction; Pollution; Poverty; Process; Relative Risks; Research; Research Personnel; Risk; Schools; Shapes; Siblings; Social Environment; Source; Sulfur Dioxide; System; Taxes; Time; Unemployment; Ursidae Family; Validation; Work; cohort; data resource; deprivation; early childhood; elementary school; experience; flexibility; health care availability; indexing; lead exposure; longitudinal dataset; machine learning method; pollutant; programs; rural environment; simulation; social; social stress; social stressor; statistical and machine learning; tool; web site

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.

项目摘要：带来现代数据科学工具来承担环境混合物 环境暴露通常在特定地理位置和复杂混合物的性质上累积 这些暴露的特征仍然被理解。此外，不利的环境暴露 发生在面临多个社会压力源的社区中 医疗保健，贫困学校，高失业，犯罪和贫困 - 所有这些都可能使 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 评估在存在的环境混合中暴露于环境混合如何形成教育结果或 缺乏社会压力。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）实施拟议的工作，该州以潜水员环境为特征 特征，工业活动和大型污染排放来源和导致污染物的示例 混合物。 为了实现这一中心目标，我们将首先开发，记录和传播建筑物的方法 时空环境和社交数据体系结构。我们将为所有NC实施此功能，编码数据 关于1990 - 2015年以上的空气污染，铅暴露风险和社会暴露（数据集1）。其次，我们将完善 链接无关数据集以构建时空儿童运动和结果数据架构的方法 （数据集2）。第三，我们将通过共享地理连接曝光（数据集1）和结果（数据集2）数据 并暂时地归于一个全面的地理数据库。第四，我们将实施越来越复杂的 在存在或不存在社会压力源对及早的情况下评估环境混合的影响的方法 儿童教育成果。我们将记录并传播所有基础方法论工作 通过公共网站。 拟议的工作利用了调查人员已经可以使用的大量数据资源（有些 大量的后处理），并允许在时间和时间上跟踪儿童。我们的团队带来了工具 现代数据科学（具有可变选择，空间点过程模型的层次结构贝叶斯方法， 机器学习）要解决环境混合如何直接塑造儿童结果的关键问题 在存在社会压力的情况下，以不同的方式。