Davis M E, Smith T J, Laden F, Hart J E, Ryan L M, Garshick E
Department of Environmental Health, Harvard School of Public Health, 401 Park Drive, Boston, Massachusetts 02215, USA.
Environ Sci Technol. 2006 Jul 1;40(13):4226-32. doi: 10.1021/es052477m.
Multi-tiered sampling approaches are common in environmental and occupational exposure assessment, where exposures for a given individual are often modeled based on simultaneous measurements taken at multiple indoor and outdoor sites. The monitoring data from such studies is hierarchical by design, imposing a complex covariance structure that must be accounted for in order to obtain unbiased estimates of exposure. Statistical methods such as structural equation modeling (SEM) represent a useful alternative to simple linear regression in these cases, providing simultaneous and unbiased predictions of each level of exposure based on a set of covariates specific to the exposure setting. We test the SEM approach using data from a large exposure assessment of diesel and combustion particles in the U.S.trucking industry. The exposure assessment includes data from 36 different trucking terminals across the United States sampled between 2001 and 2005, measuring PM2.5 and its elemental carbon (EC), organic carbon (OC) components, by personal monitoring, and sampling at two indoor work locations and an outdoor "background" location. Using the SEM method, we predict the following: (1) personal exposures as a function of work-related exposure and smoking status; (2) work-related exposure as a function of terminal characteristics, indoor ventilation, job location, and background exposure conditions; and (3) background exposure conditions as a function of weather, nearby source pollution, and other regional differences across terminal sites. The primary advantage of SEMs in this setting is the ability to simultaneously predict exposures at each of the sampling locations, while accounting for the complex covariance structure among the measurements and descriptive variables. The statistically significant results and high R2 values observed from the trucking industry application supports the broader use of this approach in exposure assessment modeling.
多层抽样方法在环境和职业暴露评估中很常见，在这种评估中，通常根据在多个室内和室外场所同时进行的测量来模拟给定个体的暴露情况。此类研究的监测数据在设计上是分层的，具有复杂的协方差结构，为了获得无偏的暴露估计值，必须考虑这一结构。在这些情况下，诸如结构方程模型（SEM）之类的统计方法是简单线性回归的一种有用替代方法，它基于一组特定于暴露环境的协变量，对每个暴露水平进行同时且无偏的预测。我们使用来自美国卡车运输行业柴油和燃烧颗粒物大型暴露评估的数据来测试SEM方法。该暴露评估包括2001年至2005年期间在美国36个不同卡车运输终端采集的数据，通过个人监测测量细颗粒物（PM2.5）及其元素碳（EC）、有机碳（OC）成分，并在两个室内工作地点和一个室外“背景”地点进行采样。使用SEM方法，我们预测如下：（1）个人暴露作为与工作相关的暴露和吸烟状况的函数；（2）与工作相关的暴露作为终端特征、室内通风、工作地点和背景暴露条件的函数；（3）背景暴露条件作为天气、附近源污染以及终端站点间其他区域差异的函数。在这种情况下，SEM的主要优势在于能够同时预测每个采样地点的暴露情况，同时考虑测量值和描述变量之间复杂的协方差结构。从卡车运输行业应用中观察到的具有统计学意义的结果和较高的R2值支持了这种方法在暴露评估建模中的更广泛应用。
