A cross-sectional study involves looking at data from a population at one specific point in time. The participants in this type of study are selected based on particular variables of interest. Cross-sectional studies are often used in several fields , but this method is also used in many other areas, including social science and education. Cross-sectional studies are observational in nature and are known as descriptive research, not causal or relational, meaning that you can’t use them to determine the cause of something, such as a disease. Researchers record the information that is present in a population, but they do not manipulate variables.
Types of cross-sectional study
A cross-sectional survey may be purely descriptive and used to assess the burden of a particular disease in a defined population. For example a random sample of schools across London may be used to assess the prevalence of asthma among 12-14 year olds.
Analytical cross-sectional surveys may also be used to investigate the association between a putative risk factor and a health outcome. However this type of study is limited in its ability to draw valid conclusions as to the association between a risk factor and health outcome. In a cross-sectional survey the risk factors and outcome are measured simultaneously, and therefore it may be difficult to determine whether the exposure proceeded or followed the disease.
In practice, cross-sectional studies will include an element of both types of design.
Issues in the design of cross-sectional surveys
Choosing a representative sample
A cross-sectional study should be representative of the population if generalizations from the findings are to have any validity. For example, a study of the prevalence of diabetes among women aged 40-60 years in Town A should comprise a random sample of all women aged 40-60 years in that town.
The sample size should be sufficiently large enough to estimate the prevalence of the conditions of interest with adequate precision. Sample size calculations can be carried out using sample size tables or statistical packages such as Epi Info.
- Potential bias in cross-sectional studies
Non-response is a particular problem affecting cross-sectional studies and can result in bias of the measures of outcome. This is a particular problem when the characteristics of non-responders differ from responders.
- Analysis of cross-sectional studies
In a cross-sectional study all factors (exposure, outcome, and confounders) are measured simultaneously. The main outcome measure obtained from a cross-sectional study is prevalence, that is:
the total number of individuals in a population who have a disease or health condition at a specific period of time, usually expressed as a percentage of the population.
Note that for continuous variables such as blood pressure or weight, prevalence may only be calculated when the variable is divided into those which fall below or above a particular pre-determined level. Alternatively, mean or median levels may be calculated.
In analytical cross-sectional studies, the odds ratio can be used to assess the strength of an association between a risk factor and health outcome of interest, provided that the current exposure accurately reflects the past exposure.
- Strengths and weaknesses of cross-sectional studies
- Relatively quick and easy to conduct (no long periods of follow-up).
- Data on all variables is only collected once.
- Able to measure prevalence for all factors under investigation.
- Multiple outcomes and exposures can be studied.
- The prevalence of disease or other health related characteristics are important in public health for assessing the burden of disease in a specified population and in planning and allocating health resources.
- Good for descriptive analyses and for generating hypotheses.
- Difficult to determine whether the outcome followed exposure in time or exposure resulted from the outcome.
- Not suitable for studying rare diseases or diseases with a short duration.
- As cross-sectional studies measure prevalent rather than incident cases, the data will always reflect determinants of survival as well as aetiology.
- Unable to measure incidence.
- Associations identified may be difficult to interpret.
- Susceptible to bias due to low response and misclassification due to recall bias.
- Hennekens CH, Buring JE. Epidemiology in Medicine, Lippincott Williams & Wilkins, 1987.