In epidemiological studies, relationships between variables are often nonlinear. For example, the effect of air pollution on respiratory health may vary at different levels of exposure. Spline regression can capture these variations more effectively than traditional linear models. Other applications include assessing dose-response relationships, studying seasonal trends, and adjusting for confounders in a flexible manner.
