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Statistical methods for assessing source-specific health effects of air pollution.

机译:评估空气污染源特定健康影响的统计方法。

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摘要

A primary objective of current air pollution research is the assessment of health effects related to specific sources of air particles, or particulate matter (PM). Quantifying source-specific risk is a challenge, because most PM health studies do not directly observe the contributions of the pollution sources themselves. Instead, given knowledge of the chemical characteristics of known sources, investigators infer pollution source contributions based on a collection of observed chemical species. Methods such as source apportionment and multivariate receptor modeling use standard factor analytic techniques to estimate the source-specific contributions from complex mixtures of exposure. In the interest of a more flexible source apportionment, we propose a multiplicative factor analysis with a mixed model on the latent source contributions. Modeling exposure in this way maintains the non-negativity of the measured chemical concentrations and adjusts for systematic effects on source activity as well as residual correlation in the source-specific exposures. We propose a structural equation approach to estimate the health effects associated with source-specific PM. By modeling the observed exposures and measured health outcomes jointly as a function of the unobserved source contributions, inference on the health effects account for the fact that uncertainty is associated with the source-specific exposures. Since the structural equation model typically involves a large number of parameters, for small sample settings we propose a fully Bayesian estimation approach that leverages historical exposure data from previous related exposure studies. We conduct simulation studies to evaluate the statistical properties of the health effect estimates obtained using the proposed methods. Finally, we apply our methods to data collected from animal toxicology studies investigating the mechanisms of morbidity and mortality associated with inhalation of ambient air PM.
机译:当前空气污染研究的主要目标是评估与特定空气颗粒或颗粒物(PM)源有关的健康影响。量化特定来源的风险是一个挑战,因为大多数PM健康研究不会直接观察污染源本身的贡献。取而代之的是,只要了解已知来源的化学特征,研究人员便可以根据观察到的化学物种的集合推断出污染源的贡献。源分配和多元受体建模等方法使用标准因子分析技术来估算来自复杂暴露混合物的源特定贡献。为了更灵活地分配源,我们提出了对潜在源贡献进行混合模型的乘法因子分析。以这种方式对暴露进行建模可保持所测化学浓度的非负性,并针对对源活动的系统影响以及特定源暴露中的残留相关性进行调整。我们提出了一种结构方程方法来估计与特定于源的PM相关的健康影响。通过将观察到的暴露量和测量到的健康结局建模为未观察到的源贡献的函数,对健康影响的推断说明了不确定性与特定源暴露相关的事实。由于结构方程模型通常涉及大量参数,因此对于小样本设置,我们提出了一种完全贝叶斯估计方法,该方法利用了先前相关暴露研究的历史暴露数据。我们进行模拟研究,以评估使用建议方法获得的健康影响估计值的统计属性。最后,我们将我们的方法应用于从动物毒理学研究中收集的数据,这些数据研究了与吸入环境空气PM相关的发病率和死亡率的机制。

著录项

  • 作者

    Nikolov, Margaret Claire.;

  • 作者单位

    Harvard University.;

  • 授予单位 Harvard University.;
  • 学科 Statistics.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 116 p.
  • 总页数 116
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 统计学;
  • 关键词

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