What is a cross-sectional study?

A cross-sectional study, sometimes called a prevalence study, is an observational study that collects data on the exposure and the outcome at a specific point in time. In other words, a cross-sectional study, allows the researcher to take a “snapshot” of the study population.

When is it useful to conduct a cross-sectional study?

Cross-sectional studies allow us to assess the prevalence of diseases in a given population. These studies are not ideal for diseases or illnesses that have a short duration. Imagine attempting to determine the prevalence of the flu, but conducting your study after flu season. On the other hand, cross-sectional studies skew us towards observing diseases or illness that have a long duration.

Example of a cross-sectional study

The National Health and Nutrition Examination Survey (NHANES) is an example of a cross-sectional study. Even though NHANES is not collecting information from the same people each time, the data still provides a snapshot of population-level trends.

Final thoughts

Cross-sectional studies are easy, time efficient, and usually not very expensive to do. Cross-sectional studies allow us to calculate the odds that a disease will occur or not occur when a particular exposure is present. However, there are significant limitations to consider.


I love cross-sectional studies because they are easy to do, but you can not assess temporality or causality.

With cross-sectional studies, you are always trying to determine which came first, the chicken or the egg? The outcome or the exposure? How will you know which came first if all you have is a snapshot of the population?

Similar to ecological studies, cross-sectional studies are useful for generating hypotheses and can be conducted before more rigorous cohort studies.

I hope this post is helpful to readers. I started MyStudentHq as a way to learn and write about what I learn. My foray into epidemiologic study designs on this site is related to my commitment to learning and writing boldly.

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