ABSTRACT Cross‐sectional studies capture health states, exposures, and risk factors at a single time point, providing essential data for estimating disease prevalence and informing public health planning. These studies serve multiple epidemiological purposes: characterizing population health, monitoring temporal trends through repeated surveys, and evaluating interventions via interrupted time series designs. They also offer practical advantages for validating self‐reported measures and creating diagnostic models. Cross‐sectional designs are efficient and well‐suited to descriptive epidemiology, but they have limited utility for causal inference. The simultaneous measurement of exposures and outcomes creates temporal ambiguity that fundamentally constrains etiologic interpretation. However, causal inferences can be strengthened under specific conditions—when temporal sequence is unambiguous (e.g., genetic variants preceding outcomes) or when valid instrumental variables are available. This methodological tutorial equips readers with concepts and tools to critically appraise cross‐sectional studies across the application domains outlined and to design and analyze their own cross‐sectional studies that yield high‐quality epidemiologic descriptions.
Baumeister et al. (Mon,) studied this question.