Department of Statistics, University of Florida, 102 Griffin-Floyd Hall, Gainesville, FL, USA.
Department of Statistics, Colorado State University, 851 Oval Drive, Fort Collins, CO 80523, USA.
Biostatistics. 2023 Dec 15;25(1):1-19. doi: 10.1093/biostatistics/kxac038.
Distributed lag models are useful in environmental epidemiology as they allow the user to investigate critical windows of exposure, defined as the time periods during which exposure to a pollutant adversely affects health outcomes. Recent studies have focused on estimating the health effects of a large number of environmental exposures, or an environmental mixture, on health outcomes. In such settings, it is important to understand which environmental exposures affect a particular outcome, while acknowledging the possibility that different exposures have different critical windows. Further, in studies of environmental mixtures, it is important to identify interactions among exposures and to account for the fact that this interaction may occur between two exposures having different critical windows. Exposure to one exposure early in time could cause an individual to be more or less susceptible to another exposure later in time. We propose a Bayesian model to estimate the temporal effects of a large number of exposures on an outcome. We use spike-and-slab priors and semiparametric distributed lag curves to identify important exposures and exposure interactions and discuss extensions with improved power to detect harmful exposures. We then apply these methods to estimate the effects of exposure to multiple air pollutants during pregnancy on birthweight from vital records in Colorado.
分布式滞后模型在环境流行病学中很有用，因为它们允许用户研究暴露的关键窗口期，即暴露于污染物对健康结果产生不利影响的时间段。最近的研究集中在估计大量环境暴露或环境混合物对健康结果的健康影响。在这种情况下，了解哪些环境暴露会影响特定的结果很重要，同时要承认不同的暴露可能有不同的关键窗口期。此外，在环境混合物的研究中，识别暴露之间的相互作用并考虑到这种相互作用可能发生在两个具有不同关键窗口期的暴露之间是很重要的。一个人在早期接触一种暴露可能会使其在稍后的时间里更容易或更不容易受到另一种暴露的影响。我们提出了一种贝叶斯模型来估计大量暴露对结果的时间效应。我们使用尖峰和哑板先验和半参数分布滞后曲线来识别重要的暴露和暴露相互作用，并讨论了具有提高检测有害暴露能力的扩展。然后，我们将这些方法应用于从科罗拉多州的生命记录中估计怀孕期间接触多种空气污染物对出生体重的影响。
