Project 3: Cumulative Risk and Geospatial Health Disparities Related to Chemical and Non-Chemical Stressor Exposures

项目摘要 环境健康差异的基础是对多种化学品和非化学品的不同接触 弱势群体的压力因素，许多与个人社会人口因素有关，其他与 他们居住的房屋和社区。确定环境健康的性质和程度 在人口水平的差异，并确定有意义的干预战略，我们需要的信息， 多压力暴露模式和分析技术，以确定风险特征和评估 整个地理区域。数据局限性包括缺乏空间分辨率， 社会人口和住房数据，或缺乏空间分辨数据的多变量属性。此外，本发明还提供了一种方法， 尽管许多研究者直接或间接地讨论了环境健康差异， 采用适当的分析技术，从数量上确定是否存在差异（以及对谁而言）。 在项目3中，我们建议建立累积风险模型，并描述 与整个生命周期的健康结果有关的环境健康差异，包括婴儿 人体测量指标（如出生体重）、儿童生长轨迹和成人心血管死亡率。 在目标1中，我们将使用第一个广泛的 与环境健康差异有关的地理数据库，整合了众多公共数据库， 应用新的工具，如网络抓取，以获得新的数据流。在目标2中，我们将开发新的曝光 从我们的地理空间差异数据库构建与住房，邻里，物质困难， 社会人口压力，评估他们使用项目1和2的信息。暴露构造 将用于描述环境健康差异模式，也将与项目1共享， 纳入流行病学分析。为了描述健康结果的差异，在目标3中，我们将使用 新的技术来构建模拟人口与个人层面的压力暴露和属性， 切尔西和多切斯特，马，应用方法，可以推断到任何美国社区。我们将 结合化学和非化学应激源暴露模型进行累积风险评估 模拟合成人群和项目1中开发的多应激源流行病学模型。 最后，我们将开发和应用环境健康差异的量化措施，并确定 假设的干预措施在多大程度上改变了这些措施。总之，项目3引入了一个独特的 结合尖端的地理空间数据和分析方法， 高地理分辨率的社会人口统计学和暴露，以及解释和 解释环境健康差异。这些技术将提供有关环境的新见解 千年评估中的健康差异，使用可以外推到任何地理区域的方法。

PROJECT SUMMARY Environmental health disparities are based on differential exposures to multiple chemical and non-chemical stressors among vulnerable populations, many related to individual sociodemographic factors, others related to the housing and neighborhoods in which they live. To determine the nature and degree of environmental health disparities at a population level, and to identify meaningful intervention strategies, we need information about multi-stressor exposure patterns and analytical techniques to characterize risk and evaluate disparities across entire geographical regions. Data limitations include lack of spatial resolution in available individual sociodemographic and housing data, or lack of multivariable attributes in spatially resolved data. In addition, although many investigators have discussed environmental health disparities directly or indirectly, few have applied appropriate analytical techniques to determine quantitatively whether disparities exist (and for whom). In Project 3 we propose to build cumulative risk models and characterize the nature and extent of environmental health disparities related to health outcomes across the life span, including infant anthropometric measures (e.g. birth weight), childhood growth trajectories, and adult cardiovascular mortality. In Aim 1 we will evaluate exposure disparities across Massachusetts (MA) using the first extensive geographical database relevant to environmental health disparities, integrating numerous public databases and applying novel tools like web scraping to obtain new data streams. In Aim 2 we will develop novel exposure constructs from our geospatial disparities database related to housing, neighborhood, material hardship, and sociodemographic stressors, evaluating them using information from Projects 1 and 2. Exposure constructs will be used to describe environmental health disparity patterns and will also be shared with Project 1 to incorporate into epidemiological analyses. To characterize disparities in health outcomes, in Aim 3 we will use novel techniques to construct simulated populations with individual-level stressor exposures and attributes in Chelsea and Dorchester, MA, applying methods that can be extrapolated to any US community. We will conduct a cumulative risk assessment by combining chemical and non-chemical stressor exposure models with the simulated synthetic population and the multi-stressor epidemiological models developed in Project 1. Finally we will develop and apply quantitative measures of environmental health disparities and determine the extent to which hypothetical interventions change these measures. In summary, Project 3 introduces a unique combination of cutting-edge geospatial data and analytical methods, approaches to simulate individual-level sociodemographics and exposures at high geographic resolution, and quantitative methods to explain and interpret environmental health disparities. These techniques will provide novel insight about environmental health disparities in MA, with methods that can be extrapolated to any geographical region.