Cross-Sectional Study

Lead Author(s): Jeff Martin, MD

Definition of a Cross-Sectional Study

A cross-sectional study can be described as a sample of a population at one point in time, a cross-section of that population.

Cross-Sectional Design Measures

Measures prevalence of disease at one point in time. Two types:

(1) POINT PREVALENCE: Do you currently have a backache? (e.g., study takes 4 months)

Generally when we speak of prevalence, we are speaking of point prevalence. If we ask for the prevalence of asthma, we usually mean how many persons in the United States have asthma right at a given point in time?

(2) PERIOD PREVALENCE: Have you had a backache in the past 6 months? (e.g., study takes 4 months)

You will also encounter the expression period prevalence in which a wider time period is specified.

Weakness of Cross-Sectional Design

(1) MAIN WEAKNESS: Cannot determine whether putative cause preceded the disease outcome.

(2) OTHER WEAKNESSES: Cannnot distinguish factors associated with disease from factors associated with survival with disease.

In a cross-sectional study taking aspirin or non-steroidal anti-inflammatories would likely be associated with having a backache, but you would not conclude that those medications cause backache because you know they came after the backache as a treatment not before as a causative agent.

Diagram - Cross-Sectional Study Design - Basic Study Design in Analytical Epidemiology

It is easy to describe a cross-sectional study as a sample of a population at one point in time, a cross-section of that population. What is perhaps not so well appreciated is the point illustrated in the diagram below showing the design in the setting of a hypothetical cohort. It demonstrates the prevalent nature of the sample. In other words, only those individuals who were present at the time of the cross-sectional sample have a chance to be included.


So, for example, in the illustration there are two members of the cohort who were diagnosed with the disease outcome who did not survive to the time of the sample. Likewise, those without disease are also prevalent, meaning that persons with certain characteristics may be more or less likely to be represented in the cross-sectional sample. This would be the case if the individuals who left the population, represented by the arrows in the schematic, differed on characteristics of interest from those who remained to the time of study.

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Simplest case is to have a dichotomous outcome and dichotomous exposure variable

Everyone in the sample is classified as diseased or not and having the exposure or not, making a 2 x 2 table.

The proportions with disease are compared among those with and without the exposure.

NB: Exposure=risk factor=predictor

This schematic shows all the possible ratio and difference measures that can be calculated from cross-sectional data.

Study Type Ratio Difference
Cross Sectional Study prevalence ratio prevalence difference
Cross Sectional Study odds ratio odds difference

With cross-sectional data