Cross-sectional studies as descriptive study designs instead of analytic study designs

Cross-sectional studies as descriptive study designs instead of analytic study designs Observational studies can be defined as non-interventional and non-experimental. Any experiment or intervention method is not included in this study. A cross-sectional study is considered to be descriptive study because investigated factors aren’t controlled, repetition of events isn’t generally possible and randomization facilities are limited in these studies. Their results are largely steady with real life. Cross-sectional studies (descriptive or prevalence) can be described as prevalence studies and usually examine the prevalence, epidemiology or survey of a disease or clinical outcome. They imitate the state of a disease or clinical outcome at a specific moment in a certain population. Whereas analytic studies test hypotheses about exposure-outcome relationships, measure the association between exposure and outcome and include a comparison group (Süt 2014).A disease where survival could influence the association between a possible exposure and the diseaseBecause exposure and health outcomes are usually assessed concurrently, cross‐sectional studies are often criticized for providing limited causal inference. This means that these studies may be susceptible to a reverse causation bias that is, the exposure status may be an effect of the disease rather than a cause. An example is if an employee leaving their job because they develop a respiratory disease such as asthma or if a worker change department because it is dusty. This shortcoming is not an inherent flaw of the cross‐sectional design, especially in situations where a full accounting of exposure history is determined, as was done in the study of asthma among US automotive workers exposed to metalworking fluids. Nevertheless, the cross‐sectional design may be particularly disposed to the healthy worker survivor effect in circumstances where only actively employed workers are studied. This form of bias may lead to missed or underestimated associations if the most heavily exposed, and consequently the most severely affected workers, have preferentially left employment and are hence not available for study. Attempts to identify and include former workers, although logistically challenging, can mitigate this bias (Checkoway, Pearce & Kriebel 2007).ReferenceCheckoway, H., Pearce, N. & Kriebel, D. (2007). Selecting appropriate study designs to address specific research questions in occupational epidemiology. Occupational & environmental medicine; 64(9): 633–638.Süt, N. (2014). Study Designs in Medicine. Balkan medical journal; 31(4): 273–277.