Cross-sectional studies correlate exposures and risk factors with the presence of disease without the benefit of knowing the timing or sequence of exposure and disease development. An example of a cross-sectional study is one in which a researcher collects a blood sample from patients and records their lens status (phakic, pseudophakic, or aphakic) at the same time. The study could evaluate the association between a history of cataract surgery (case status) and cholesterol level and gender (potential risk factors). However, if the mean cholesterol level is higher in cases than in those without a history of cataract surgery, the researcher would not know whether the elevated cholesterol level occurred before cataract surgery. With this study design, it is also important to consider whether a confounding factor may be affecting the association. For this study, age could be a confounding factor, because cholesterol levels increase with age, as does the likelihood of cataract surgery. The researcher could use data analysis tools such as stratification and/or regression analysis to adjust for age and then determine whether the cholesterol–cataract surgery association is still present in each of the age strata.
Excerpted from BCSC 2020-2021 series: Section 1 - Update on General Medicine. For more information and to purchase the entire series, please visit https://www.aao.org/bcsc.