PDF(Pubmed)
Abstract：
Environmental epidemiologic studies routinely utilize aggregate health outcomes to estimate effects of short-term (eg, daily) exposures that are available at increasingly fine spatial resolutions. However, areal averages are typically used to derive population-level exposure, which cannot capture the spatial variation and individual heterogeneity in exposures that may occur within the spatial and temporal unit of interest (eg, within a day or ZIP code). We propose a general modeling approach to incorporate within-unit exposure heterogeneity in health analyses via exposure quantile functions. Furthermore, by viewing the exposure quantile function as a functional covariate, our approach provides additional flexibility in characterizing associations at different quantile levels. We apply the proposed approach to an analysis of air pollution and emergency department (ED) visits in Atlanta over 4 years. The analysis utilizes daily ZIP code-level distributions of personal exposures to 4 traffic-related ambient air pollutants simulated from the Stochastic Human Exposure and Dose Simulator. Our analyses find that effects of carbon monoxide on respiratory and cardiovascular disease ED visits are more pronounced with changes in lower quantiles of the population\'s exposure. Software for implement is provided in the R package nbRegQF.
摘要：
环境流行病学研究通常利用总体健康结果来估计短期影响(例如，daily)exposuresthatareavailableatincreasinglyfinespaceresolutions.然而,面积平均值通常用于得出人口水平的暴露，无法捕获可能在感兴趣的空间和时间单位内发生的曝光的空间变化和个体异质性(例如，在一天或邮政编码内)。我们提出了一种通用的建模方法，通过暴露分位数函数将单元内暴露异质性纳入健康分析。此外，通过将曝光分位数函数视为功能协变量，我们的方法在表征不同分位数水平的关联方面提供了额外的灵活性.我们将拟议的方法应用于分析亚特兰大四年来的空气污染和急诊室（ED）访问。该分析利用了从随机人类暴露和剂量模拟器模拟的4种与交通相关的环境空气污染物的每日ZIP代码级分布。我们的分析发现，一氧化碳对呼吸和心血管疾病ED就诊的影响随着人群暴露量的低分位数的变化而更加明显。用于实现的软件在R包nbRegQF中提供。
