Cross-Sectional Study

The cross-sectional study is an observational study design that examines a representative group of individuals at a specific point or short segment in time. These studies are often performed to gather “snapshot” information regarding prevalence of disease, condition, or exposure. Cross-sectional studies are used primarily for descriptive or surveillance purposes that do not require them to be hypothesis driven. Cross-sectional studies are occasionally used to investigate exposure–outcome relationships, but this kind of interpretation of cross-sectional data should only be done cautiously.

Cross-sectional studies have the advantage of being relatively inexpensive, and the sampling frame can sometimes be selected to be specific to study a particular question. These studies do not suffer from loss to follow-up as longer, time-dependent studies do.

Cross-sectional studies are frequently used to examine prevalence of disease or condition. The duration or severity of disease, however, might result in a number of issues: For example, diseases or conditions of long duration might be overrepresented, while diseases of very short duration might be missed because of the short window during which the study is conducted. Cross-sectional studies should be used only cautiously to support exposure–outcome relationships. Drawing conclusions regarding etiology based on cross-sectional information is difficult for a number of reasons, most due primarily to the inability of [Page 460]such studies to establish temporality. Causality might be inferred in specific circumstances: when the exposure is a permanent characteristic that certainly preceded the outcome of interest, or when the exposure occurs immediately before the disease or condition in question. Even so, without other biological or epidemiological evidence of an exposure's relationship to the disease or condition, it becomes difficult to determine whether the exposure has a causal, mediating, moderating, or even mitigating effect on the outcome. Because longitudinal cohort studies have an element of temporality, they are considered better suited for the purposes of establishing causation. Issues of confounding (or the differential distribution of nonexposure factors associated with the outcome) must also be taken into consideration in evaluation association.

A last disadvantage of these studies is their vulnerability to selection bias. Segments of the population might be more or less difficult to survey cross-sec-tionally. The researcher should consider this issue in his or her sampling framework.