A cross sectional study is observational in nature. It involves collection of information about a population at a particular point in time.
- A descriptive cross sectional study would assess distribution and frequency eg. measuring the prevalence of cancer amongst a defined population
- An analytical cross sectional study would examine the association between variables to identify determining factors related to health eg. examining the association between living a sedentary lifestyle and having hypertension
Hierarchy of evidence
Advantages
- affordable – a cross sectional study requires no follow-ups since only one set of data is analysed, making this a low-cost research method
- efficient – a cross sectional study is ideal for studying exposures or conditions that are reasonably common, and which require only one-time assessment
- no risks – this type of study requires no long-term considerations.
- potential completeness – due to easily accessed key data points
Disadvantages
- collecting data at one point in time leads to limited causation testing especially where exposure and/or outcome are expected to change over time
- needs to be an adequate representation of the population being studied
- requires a larger sample size for accuracy basis
- bias may affect results if for example incomplete responders are related to a specific group
- may result in an association, however such association may not be the reason for the association
- unable to measure incidence
Critically Appraising a Cross Sectional Study
When critically appraising a cross sectional study you need to focus on the following:
- Sampling
- Non-response
- Methods used for measuring variables of interest
- Controlling for confounders in the analysis
Sampling
- note sampling bias – population needs to be clearly identified since final results will be inferred onto the target population
- consider choice of sampling frame – how was the sample selected from the actual population? Remember that when it comes to measuring prevalence, the actual population is of utmost importance. Thus, consider sampling procedure used eg. random vs convenience sampling, using inclusion or exclusion criteria etc
- consider the procedure used for the selection of participants – was inclusion/exclusion criteria used? And was the sample taken at random or was it convenience sampling?
- consider sampling size – ideally, previous studies performed within the same area should be sought so that the occurrence frequency within the sample reflects the occurrence within the target population
- consider expected precision of results – rare occurrence and precise results require a bigger sample
Non-Response
- respondents may differ from non-respondents – respondents are more likely to be interested in the subject being studied, which may lead to more adherence to suggestions/requirements. Thus, replacing non-respondents to increase the sample size may still not bypass the sampling bias resulting from no response
- researchers are required to report the response rate as well as to compare the characteristics of both the respondents and non-respondents
Controlling Confounders
A confounding factor is a third variable in a study which examines a possible cause-and-effect connection. It is related to both the supposed cause and supposed effect of the study. At times it is difficult to separate the true effect of the independent variable from the effect of the confounding variable.
Whilst performing a research study, it is important that potential confounding variables are identified and a plan is drawn so that their impact is reduced.
Example of a Cross Sectional Study
Breast feeding and obesity: cross sectional study (full reference in the references section): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC28161/
Appraisal Tool for Cross Sectional Studies (AXIS)
Cross Sectional Study Appraisal Checklist
NOTE: To view blogpost featuring Cochrane videos on all types of studies please click here.
References
BMJ Open (2016). Appraisal Tool for Cross-Sectional Studies (AXIS). Retrieved from https://bmjopen.bmj.com/content/bmjopen/6/12/e011458/DC2/embed/inline-supplementary-material-2.pdf?download=true on 17th December 2022
von Kries, R., Koletzko, B., Sauerwald, T., von Mutius, E., Barnert, D., Grunert, V., & von Voss, H. (1999). Breast feeding and obesity: cross sectional study. BMJ (Clinical research ed.), 319(7203), 147–150. https://doi.org/10.1136/bmj.319.7203.147
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Claire is keen about public education on health-related subjects as well as holistic patient-centered care. She is also passionate about spreading awareness on the negative effects that domestic abuse leaves on its victims’ mental, emotional, social and physical wellbeing.
Claire aspires to continue studying following completion of her Nursing Degree, because she truly believes in lifelong education.
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