A cross-sectional study (also called cross-sectional analysis, transverse study, or prevalence study) is an observational study that analyzes data from a population or a subset at a specific point in time.
It explores the relationship between diseases (or other health-related conditions) and other variables as they exist in a defined population at a single point or over a short period (e.g., a calendar year).
This type of study provides a snapshot of the frequency of a disease or other health-related characteristics (such as exposure variables) in a population.
Cross-sectional studies measure the prevalence of disease and are therefore often referred to as prevalence studies.
They are useful for assessing the burden of disease or health needs within a population and aid in health planning and resource allocation.
In cross-sectional studies, exposure and effect (disease or health outcome) are measured at the same time.
A common example is an environmental health survey, where participants are selected from both exposed and unexposed areas to gather information on health status, environmental exposures, and confounding factors.
Because data on disease and exposure are collected simultaneously, it is difficult to determine the temporal relationship (which came first) between exposure and disease.
In these studies, all factors—exposure, outcome, and potential confounders—are measured at once.
The primary outcome measure in a cross-sectional study is prevalence.
In analytical cross-sectional studies, the odds ratio can be calculated to assess the strength of association between a risk factor and a health outcome, provided the current exposure reliably reflects past exposure.
Types of Cross-sectional Study
Descriptive
Cross-sectional studies can be descriptive, used to assess the burden of a specific disease or condition within a defined population.
For example, a random sample of schools in London may be surveyed to determine the prevalence of asthma among 12–14-year-olds.
Analytical
Cross-sectional studies can also be analytical, aiming to examine the association between a potential risk factor and a health outcome.
Analytical cross-sectional surveys collect data on both exposure and outcome at the same time, which limits the ability to determine causality or the direction of the relationship.
It is often difficult to establish whether the exposure preceded or followed the disease in analytical cross-sectional studies.
In most real-world situations, cross-sectional studies may include both descriptive and analytical elements.
Applications of Cross-sectional studies
Cross-sectional studies are relatively easy and inexpensive to conduct.
They are useful for investigating exposures that are fixed characteristics of individuals, such as ethnicity or blood group.
In sudden disease outbreaks, cross-sectional studies measuring multiple exposures can serve as a convenient first step in identifying potential causes.
These studies are valuable for assessing the health care needs of populations.
Repeated cross-sectional surveys using independent random samples and standardized methods can indicate trends over time.
Many countries regularly conduct cross-sectional surveys on representative population samples, focusing on demographics, illnesses, and health-related behaviors.
The frequency of diseases and risk factors can be analyzed in relation to variables such as age, sex, and ethnicity.
Cross-sectional studies on risk factors for chronic diseases have been conducted across various countries.
Advantages of Cross-sectional studies
Relatively quick and easy to conduct, as they do not require long periods of follow-up.
Data on all variables is collected only once, reducing time and resources.
Able to measure the prevalence of all factors under investigation.
Allow the study of multiple outcomes and exposures simultaneously.
Help assess the burden of disease or other health-related characteristics in a population, which is essential for public health planning and resource allocation.
Suitable for descriptive analyses and useful for generating hypotheses for further research.
Limitations of Cross-sectional studies
Difficult to determine whether the outcome followed exposure or if the exposure resulted from the outcome.
Not suitable for studying rare diseases or diseases with a short duration.
Measure prevalent rather than incident cases, which reflects both survival and aetiology, potentially distorting causal interpretation.
Unable to measure the incidence of disease.
Associations identified may be challenging to interpret due to the simultaneous measurement of exposure and outcome.
Susceptible to bias from low response rates and misclassification due to recall bias.
Non-response can significantly affect results, especially if non-responders differ systematically from responders, leading to biased outcome measures.
References
Park K. Park’s Textbook of Preventive and Social Medicine.
Gordis L. (2014). Epidemiology (5th ed.). Philadelphia, PA: Elsevier Saunders.
Health Knowledge. Introduction to Study Design – Cross-Sectional Studies. Available at: https://www.healthknowledge.org.uk/e-learning/epidemiology/practitioners/introduction-study-design-css
The BMJ. Epidemiology for the Uninitiated: Case-Control and Cross-Sectional Studies. Available at: https://www.bmj.com/about-bmj/resources-readers/publications/epidemiology-uninitiated/8-case-control-and-cross-sectional
North Carolina Institute for Public Health. (2015). ERIC Notebook – Cross-Sectional Studies. Available at: https://sph.unc.edu/files/2015/07/nciph_ERIC8.pdf
ScienceDirect. Cross-Sectional Study – Topics in Biochemistry, Genetics and Molecular Biology. Available at: https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/cross-sectional-study
