Cross-sectional study design is a type of observational study design. In a cross-sectional study, the investigator measures the outcome and the exposures in the study participants at the same time. Unlike in case–control studies (participants selected based on the outcome status) or cohort studies (participants selected based on the exposure status), the participants in a cross-sectional study are just selected based on the inclusion and exclusion criteria set for the study. Once the participants have been selected for the study, the investigator follows the study to assess the exposure and the outcomes. Cross-sectional designs are used for population-based surveys and to assess the prevalence of diseases in clinic-based samples. These studies can usually be conducted relatively faster and are inexpensive. They may be conducted either before planning a cohort study or a baseline in a cohort study. These types of designs will give us information about the prevalence of outcomes or exposures; this information will be useful for designing the cohort study. However, since this is a 1-time measurement of exposure and outcome, it is difficult to derive causal relationships from cross-sectional analysis. We can estimate the prevalence of disease in cross-sectional studies. Furthermore, we will also be able to estimate the odds ratios to study the association between exposure and the outcomes in this design.
For example, sometimes the National AIDS Programme conducted cross-sectional sentinel surveys among high-risk groups and ante-natal mothers every year to monitor the prevalence of HIV in these groups.
The investigator can study the association between these variables. It is also possible that the investigator will recruit the study participants and examine the outcomes in this population . The investigator may also estimate the prevalence of the outcome in those surveyed. Open in a separate window. Figure 1.
Since this is a 1-time measurement of exposure and outcome, it is difficult to derive causal relationships from cross-sectional analysis
The National AIDS Control Organisation's Sentinel Surveillance of HIV is an example of “serial cross-sectional study” or “serial survey.” This may be less expensive compared with a cohort study
Retrospective cohort studies are also 'longitudinal,' because they examine health outcomes over a span of time. The distinction is that in retrospective cohort studies some or all of the cases of disease have already occurred before the investigators initiate the study.
Retrospective cohort studies are less expensive and more efficient than prospective cohort studies, because subjects don't need to be followed for years. However, the disadvantage is that the quality of the data is generally inferior to that of a prospective study.
In cohort studies investigators enroll individuals who do not yet have the health outcomes of interest at the beginning of the observation period, and they assess exposure status for a variety of potentially relevant exposures. The enrollees are then followed forward in time (i.e., these are longitudinal studies rather than cross-sectional) and health outcomes are recorded. With this data investigators can sort the subjects according to their exposure status for one of the exposures of interest and compare the incidence of disease among the exposure categories.
From the discussion above, it should be obvious that one of the basic requirements of a cohort type study is that none of the subjects have the outcome of interest at the beginning of the follow-up period, and time must pass in order to determine the frequency of developing the outcome.
We previously discussed descriptive epidemiology studies, noting that they are important for alerting us to emerging health problems, keeping track of trends in the population, and generating hypotheses about the causes of disease. Analytic studies provide a basic methodology for testing specific hypotheses. The essence of an analytic study is that groups of subjects are compared in order to estimate the magnitude of association between exposures and outcomes. This module will build on descriptive epidemiology and on measuring disease frequency and association by discussing cohort studies and intervention studies (clinical trials). Our discussion of analytic study designs will continue in module 5 which addresses case-control studies. Pay particular attention to the strengths and weaknesses of each design. This is important for being able to select the most appropriate design to answer a given research question. In addition, a firm understanding of the strengths and weaknesses of each design will facilitate building your skills in critical reading of studies by alerting you to possible pitfalls and weaknesses that can undermine the validity of a study.
In contrast to prospective studies , retrospective studies are conceived after some people have already developed the outcomes of interest. The investigators jump back in time to identify a cohort of individuals at a point in time before they had developed the outcomes of interest, and they try to establish their exposure status at that point in time. They then determine whether the subjects subsequently developed the outcome of interest.
In a prospective study like the Nurses Health Study baseline information is collected from all subjects in the same way using exactly the same questions and data collection methods for all subjects. The investigators design the questions and data collection procedures carefully in order to obtain accurate information about exposures before disease develops in any of the subjects.