Selecting Spatial Scale of Covariates in Regression Models of Environmental Exposures
Selecting Spatial Scale of Covariates in Regression Models of Environmental Exposures
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DOI：
10.4137/cin.s17302
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发表时间：
2015-01-01
影响因子：
2
通讯作者：
Wheeler, David C.
中科院分区：
文献类型：
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作者：
Grant, Lauren P.;Gennings, Chris;Wheeler, David C.
在回归模型中用来解释环境化学品暴露或健康结果的环境因素或社会经济地位变量在实践中往往是在相同的缓冲距离或空间尺度上建模的。在本文中，我们提出了四个模型选择算法，选择最佳的空间尺度为每个缓冲区为基础的或区域级的协变量。硝酸盐对饮用水的污染在美国农业地区是一个日益严重的问题，因为摄入的硝酸盐会导致内源性N-亚硝基化合物的形成，这是一种潜在的致癌物质。我们应用我们的方法来模拟爱荷华州私人威尔斯的硝酸盐水平。我们发现，在不同的空间尺度上选择环境变量，并且允许空间尺度在协变量之间变化的模型提供了最佳拟合优度。我们的方法可用于调查多个空间尺度或缓冲距离下可用的环境风险因素与疾病（包括癌症）指标之间的关联。
Environmental factors or socioeconomic status variables used in regression models to explain environmental chemical exposures or health outcomes are often in practice modeled at the same buffer distance or spatial scale. In this paper, we present four model selection algorithms that select the best spatial scale for each buffer-based or area-level covariate. Contamination of drinking water by nitrate is a growing problem in agricultural areas of the United States, as ingested nitrate can lead to the endogenous formation of N-nitroso compounds, which are potent carcinogens. We applied our methods to model nitrate levels in private wells in Iowa. We found that environmental variables were selected at different spatial scales and that a model allowing spatial scale to vary across covariates provided the best goodness of fit. Our methods can be applied to investigate the association between environmental risk factors available at multiple spatial scales or buffer distances and measures of disease, including cancers.