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BRINGING MODERN DATA SCIENCE TOOLS TO BEAR ON ENVIRONMENTAL MIXTURES

利用现代数据科学工具来研究环境混合物

基本信息

批准号：
10304211
负责人：
Marie Lynn Miranda
金额：
$ 48.73万
依托单位：
UNIVERSITY OF NOTRE DAME
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-30 至 2023-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10304211
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 urban setting web site

项目摘要

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.

项目概述：将现代数据科学工具应用于环境混合

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A spatiotemporal case-crossover model of asthma exacerbation in the City of Houston.

DOI：
10.1002/sta4.357
发表时间：
2021-12
期刊：
STAT
影响因子：
1.7
作者：
Schedler, Julia C.;Ensor, Katherine B.
通讯作者：
Ensor, Katherine B.

Bayesian variable selection for understanding mixtures in environmental exposures.

DOI：
10.1002/sim.9099
发表时间：
2021-09-30
期刊：
Statistics in medicine
影响因子：
2
作者：
Kowal DR;Bravo M;Leong H;Bui A;Griffin RJ;Ensor KB;Miranda ML
通讯作者：
Miranda ML

Disparities in air quality downscaler model uncertainty across socioeconomic and demographic indicators in North Carolina.

DOI：
10.1016/j.envres.2022.113418
发表时间：
2022-05
期刊：
Environmental research
影响因子：
8.3
作者：
Sha Zhou;R. Griffin;A. Bui;Aaron Lilienfeld;M. Bravo;Claire Osgood;M. Miranda
通讯作者：
Sha Zhou;R. Griffin;A. Bui;Aaron Lilienfeld;M. Bravo;Claire Osgood;M. Miranda

Weekly prenatal PM2.5 and NO2 exposures in preterm, early term, and full term infants: Decrements in birth weight and critical windows of susceptibility.

早产儿、早产儿和足月儿每周产前 PM2.5 和 NO2 暴露：出生体重和关键易感性窗口的减少。