This tutorial introduces the time-stratified case-crossover design for analyzing aggregated environmental exposures and health outcomes in environmental epidemiology. The case-crossover design compares exposure levels on the day of a health event (case day) with exposure levels on nearby days (control days) to identify potential associations. The time-stratified approach improves accuracy by accounting for long-term trends and seasonality through conditioning on time-stratified strata. It allows for the adjustment of time-varying confounders using conditional Poisson regression, which is more efficient than conditional logistic regression for aggregated data. The design can be extended to include subpopulation characteristics (e.g., age groups) by reshaping data into a long format and conditioning on these variables. Effect modification can be investigated by including interaction terms in the regression model. The approach also allows for spatial analysis by combining time and spatial dimensions, enabling the assessment of environmental effects across different geographic areas. The tutorial provides examples using real data from Valencia and London, showing how PM10 levels are associated with daily mortality. The results indicate that increases in PM10 are linked to increased mortality risk, with varying magnitudes across different age groups and cities. The time-stratified case-crossover design is flexible and can be applied to multilevel data, offering a robust alternative to traditional time-series regression methods in environmental epidemiology. It is particularly useful for analyzing short-term effects of environmental exposures on acute health outcomes. The design is also applicable to multi-location studies, where spatial patterns can be incorporated into the analysis. The tutorial emphasizes the importance of considering confounding factors and provides practical guidance for implementing the time-stratified case-crossover design in environmental epidemiology research.This tutorial introduces the time-stratified case-crossover design for analyzing aggregated environmental exposures and health outcomes in environmental epidemiology. The case-crossover design compares exposure levels on the day of a health event (case day) with exposure levels on nearby days (control days) to identify potential associations. The time-stratified approach improves accuracy by accounting for long-term trends and seasonality through conditioning on time-stratified strata. It allows for the adjustment of time-varying confounders using conditional Poisson regression, which is more efficient than conditional logistic regression for aggregated data. The design can be extended to include subpopulation characteristics (e.g., age groups) by reshaping data into a long format and conditioning on these variables. Effect modification can be investigated by including interaction terms in the regression model. The approach also allows for spatial analysis by combining time and spatial dimensions, enabling the assessment of environmental effects across different geographic areas. The tutorial provides examples using real data from Valencia and London, showing how PM10 levels are associated with daily mortality. The results indicate that increases in PM10 are linked to increased mortality risk, with varying magnitudes across different age groups and cities. The time-stratified case-crossover design is flexible and can be applied to multilevel data, offering a robust alternative to traditional time-series regression methods in environmental epidemiology. It is particularly useful for analyzing short-term effects of environmental exposures on acute health outcomes. The design is also applicable to multi-location studies, where spatial patterns can be incorporated into the analysis. The tutorial emphasizes the importance of considering confounding factors and provides practical guidance for implementing the time-stratified case-crossover design in environmental epidemiology research.