The case-crossover design is widely used in environmental epidemiology as an effective alternative to the conventional time-series regression design to estimate short-term associations of environmental exposures with a range of acute events. This tutorial illustrates the implementation of the time-stratified case-crossover design to study aggregated health outcomes and environmental exposures, such as particulate matter air pollution, focusing on adjusting covariates and investigating effect modification using conditional Poisson regression. Time-varying confounders can be adjusted directly in the conditional regression model accounting for the adequate lagged exposure–response function. Time-invariant covariates at the subpopulation level require reshaping the typical time-series data set into a long format and conditioning out the covariate in the expanded stratum set. When environmental exposure data are available at geographical units, the stratum set should combine time and spatial dimensions. Moreover, it is possible to examine effect modification using interaction models. The time-stratified case-crossover design offers a flexible framework to properly account for a wide range of covariates in environmental epidemiology studies.

中文翻译：

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环境流行病学中汇总数据的时间分层病例交叉研究：教程

病例交叉设计广泛应用于环境流行病学中，作为传统时间序列回归设计的有效替代方案，用于估计环境暴露与一系列急性事件的短期关联。本教程说明了如何实施时间分层病例交叉设计来研究总体健康结果和环境暴露（例如颗粒物空气污染），重点是调整协变量并使用条件泊松回归研究效果修正。时变混杂因素可以在条件回归模型中直接调整，以考虑足够的滞后暴露-反应函数。子群体级别的时不变协变量需要将典型的时间序列数据集重塑为长格式，并在扩展的层集中调整协变量。当地理单位的环境暴露数据可用时，分层集应结合时间和空间维度。此外，可以使用交互模型来检查效果修改。时间分层病例交叉设计提供了一个灵活的框架，可以正确解释环境流行病学研究中的各种协变量。