Epidemiological Study Designs: A Guide

Generally, there are two main types of the epidemiological study designs; the analytic epidemiological study design and the descriptive epidemiological study design. In the case where a researcher examines the association between vitamin D and migraines, and the researcher recruits 70 participants with frequent migraines, and then matches each case to a control based on age and gender, then the best study design applicable in such a case is the analytic epidemiological study design. This is because descriptive epidemiological study design classifies a disease or other health outcome according to the categories of person, place, and time ( Friis, 2017 p. 147). On the other hand, analytic epidemiologic studies are concerned with the etiology (causes) of diseases and other health outcomes ( Friis, 2017 p. 147). This research is concerned about the relationship between vitamin D and migraines. The researcher is interested to know whether deficiency of vitamin D causes migraines. The other factors of age and gender are held constant; therefore, this kind of research falls best under analytic epidemiological study design. 

Analytic epidemiological study design may sometimes be encompassed by some degree of biasness, which may lead to deviation of the results from the accurate results. In this case, wrong results may arise as a result of Hawthorne effect, recall bias, and confounding ( Friis, 2017 p. 161). To avoid Hawthorne effect biasness, the researcher should set a questionnaire that gives the respondents freedom of answering the questions at will. This will ensure that, if a respondent chooses not to respond to the questionnaire, he or she is not counted among the selected 70 participants. Recall biasness can be minimized through selecting participants who occupy the same profession. This will ensure that the participants have the same exposure to sunlight so that the problem of not being able to remember how long they are exposed to sunlight is eliminated, since sunlight is a factor contributing to the synthesis of vitamin D by the skin. To mitigate the deviation of results due to confounding biasness, the researcher should avoid incorporating participants who are prone to disease attack as a result of other predisposing factors such as pregnancy, malnutrition, and poor working conditions. 

Reference 

Friis, R. H. (2017).  Epidemiology 101 . Jones & Bartlett Learning.