When should you use repeated cross-sectional studies?

• A. When you want to understand associations at different time intervals and are unlikely to have the same individuals in the samples.
• B. When you want to follow the same individuals over time.
• C. When you want to measure incidence.
• D. When exposure is randomized.

Answer: (A)

Repeated cross-sectional studies are designed to track changes in a population over time by taking distinct samples at each time point. They’re best when you want to understand associations or trends across different time intervals and you’re unlikely to have the same individuals in every survey. This approach lets you compare exposure–outcome associations at multiple time points and monitor secular trends without the logistical challenges of recontacting the same people. However, because different people are sampled each time, you can’t infer temporality for a given individual (you don’t know the sequence of exposure and outcome within the same person). If your goal were to follow the same people over time, you’d use a cohort design; to measure incidence with person-time, you’d prefer longitudinal follow-up; and to test causality with random assignment, you’d use a randomized trial.