Particulate Matter (PM) Metals and Racial Disparity in Cardiovascular (CVD) Risk Factors

总结/摘要 考虑到环境中颗粒物PM等污染物的复杂组成， 我相信，我们接触的化学品并不局限于一次一种，而是几种可能的混合物， 改变组成和混合比。这些混合物的形成可能是因为它们是在 同时间、同来源、同起源的潜在因素。因此，人类更容易受到 暴露于这些混合物，其特征在于这些潜在因素。因素分析是一种工具， 文献来定义混合物。不幸的是，识别混合物的统计方法是有限的。 此外，已知怀孕期间接触PM化学品对母亲和母亲都有害。 孩子为了解决这个问题，提出了一种基于扩展的混合物暴露识别方法， 将采用传统的因素分析结合源解析方法。这项建议会 检验以下假设：假设1：怀孕期间接触PM混合金属的情况增加 心血管疾病（CVD）的风险因素在黑人和西班牙裔少数民族和不同的 三个月假设2：CVD危险因素是PM金属暴露和儿童之间的中介 发展，通过以下目标。1)量化混合物并估计母亲在 怀孕的各个阶段。我们将使用新发展的贝叶斯扩展因子分析 (FA)，基于贝叶斯统计的灵活性和误差协方差矩阵的分解 结合SAM模型来估计金属混合物。我们将进行时空分析， 混合模型采用广义加性混合模型（GAMM）。我们将使用距离的倒数 加权法估计母亲在妊娠各期的吸入量。（二） 探讨黑、白色人群心血管疾病危险因素的差异及空气污染与心血管疾病的交互作用 种族/民族三个月。我们将使用逻辑回归分析和广义空间线性模型 为了这个目的。3)混合金属暴露与婴幼儿心血管病危险因素关系的研究 mortality.我们将使用结构方程模型（SEM）来研究哪些危险因素在 暴露与儿童死亡率之间的关系。拟议研究的结果可能具有 识别混合物暴露的意义、其化学毒性和对CVD的相对影响 孕期

Summary/Abstract Given the complex composition of pollutants such as particulate matter PM in the environment, it is believed that our exposure is not limited to one chemical at a time but to several possible mixtures with varying composition and mix ratios. These mixtures could be formed because they are emitted at the same time from the same source origin as latent factors. As a result, humans are more susceptible to exposure to these mixtures as characterized by these latent factors. Factor analysis is one tool used in the literature to define mixtures. Unfortunately statistical methods of identifying mixtures are limited. Additionally, exposure to PM chemicals during pregnancy is known to be harmful to both mother and child. To address the problem a new method of identifying mixtures exposure based on extension of the traditional factor analysis combined with source apportionment methods will be used. This proposal will test the following hypothesis: Hypothesis 1: Exposure to PM mixture metals during pregnancy increased the risk of cardiovascular disease (CVD) risk factors among black and Hispanic minorities and vary by trimester. Hypothesis 2: CVD risk factors are mediators between PM metals exposure and child development, through the following Aims. 1) Quantify mixtures and estimate mother's exposure during various trimesters of pregnancy. We will use the newly developed Bayesian extended factor analysis (FA), based on the flexibility of Bayesian statistics and the decomposition of the error covariance matrix combined with SAM models to estimate metal mixtures. We will perform a spatio-temporal analysis of the mixtures using Generalized Additive Mixed Models(GAMM). We will use the inverse distance weighting method to estimate mother's inhaled quantities during each trimester of pregnancy. 2) Investigate CVD risk factors difference among black and white and interaction between air pollution and race/ethnicity by trimester. We will use logistic regression analysis and generalized spatial linear model for this aim. 3) Investigate CVD risk factors moderating between mixture metals exposure and infant mortality. We will use structural equation modeling (SEM) to investigate wich risk factors is in the pathway between exposure and child mortality. Results from the proposed studies could have implications in identifying mixtures exposures, their chemical toxicity and relative implication in CVD during pregnancy.