HSA535 Applications Of Epidemiology 3
Question:
Discuss the many types of epidemiologic study designs and touch on causal inference.
Causal inference can be defined as the art and science of making a causal claim about the relationship between two factors – which is in many ways the heart of epidemiologic research. Under most circumstances if we see an association between an exposure and a health outcome of interest, we would like to answer the question: is one causing the other?
We care about causal inference because, ultimately, we want to intervene to improve public health, and interventions can be targeted on removing known causes of adverse health outcomes (or adding known causes of beneficial health outcomes).
That said, think of two examples of exposure/outcome relationships that you believe are causal, and describe why you believe that the relationship is a causal one. What points of evidence are or would be necessary in your evaluation of a causal relationship? Feel free to be brief (2-3 sentences for each example).
Answer:
Nursing Epidemiology:
Causal inference is the core of nursing epidemiologic research, and it involves making claims about the relationship of two factors that meet three conditions: co-variation, temporal order, and alternative explanations (Gordis, 2014; Hernan & Robins, 2010). The relationship is about what happens and what causes it. The example of correlation between ice cream sales as well as the temperature and smoking and how it affects health are casual relationships.
Information on the correlation between ice cream sales and temperature can help companies to manage their businesses. During warm weather, the companies make more sales than when it is cold. However, ice cream sales cannot cause hot weather, and any suggestions that it can do so would be causation (Gordis, 2014). In this case, the warm weather comes first and then sales increase and not the other way round. Besides, any other possible cause of the change in sales is ignored to meet the conditions of causal relations.
There is also a causation relationship between smoking and lung cancer. Smoking causes the disease. The statement is not referring to any particular smoker but the relationship between the two subjects (Hernan & Robins, 2010). In this case, also, people must smoke first before they can get this type of cancer, and the concept of causal relations ignores the possible contribution of other factors in the emergence of the phenomenon.
The two examples satisfy the three conditions for causal relationships. First, in each of the cases, the effect only occurs when the cause is present, which is the co-variant factor (Hernan & Robins, 2010). Second, the cause precedes the effect, and that creates a temporal order. Third, in each of the examples, other factors that might have caused the change are ruled out.
References
Gordis, Leon. (2014) Epidemiology, 5th Edition, Elsevier Saunders
Hernan, M. A., & Robins, J. M. (2010). Causal inference. Boca Raton, FL: CRC.
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