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.

Stata .do, R script, and example dataset files to reproduce step by step the examples in the tutorial paper. Supplementary_Stata_code.do Stata .do file to reproduce step by step the examples in the tutorial paper using Stata. Supplementary_R_code.R R script file to reproduce step by step the examples in the tutorial paper using R. valencia.csv - example dataset for the city of Valencia 2002-2006. london.csv - example dataset for the city of London 2002-2006.

Peer reviewed