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Causation is the portion of epidemiology that identifies the causes of diseases and association is the observation of the association on the effect of the disease. There are four types of a causal relationship, they are necessary and sufficient, necessary but not sufficient, sufficient but not necessary, and neither sufficient nor necessary. Necessary and sufficient is when both factors will lead to the development of the diseases, but without the necessary factor, the development of the diseases will never occur, but with the sufficient factor, the diseases will always occur. An example of necessary and sufficient is the situation where a person who had chickenpox due to the varicella-zoster virus is at risk of getting the shingles virus, but not everyone who had the chickenpox will activate the shingles virus (Celentano& Szklo, 2019).
Necessary but not sufficient is when the condition is necessary but not sufficient to cause the disease. For example, someone getting the influenza shot, but not getting enough to produce the disease because of the strong immune system, whereas, some people with the weak immune systems getting the same dosage of the flu shot, which resulted in producing the disease. Sufficient but not necessary is when each factor alone can produce the disease, along with other cofactors. For example, factors such as drinking, smoking or obesity can cause diabetes, but some people do not develop diabetes from either, but other cofactors paired with a factor can produce diabetes. Neither sufficient nor necessary is when neither factors by itself can produce the disease, it needs other factors. For example, an individual can develop cardiovascular diseases if they are exposed to high blood pressure, diabetes, obesity, drinking, and smoking. However, each risk factors are neither sufficient or necessary, but if they are paired like diabetes and drinking, or smoking and obesity, or high blood pressure and diabetes, then they can produce a disease (Celentano& Szklo, 2019).
There are nine casual guidelines that depicts whether an observed association is causal. They are temporal relationship, the strength of the association, dose-response relationship, replication of the finding, biologic plausibility, consideration of alternate explanations, cessation of exposer, consistency with other knowledge, and specificity of association. The one I think is most difficult to establish is biological plausibility. This is because it requires an in-depth observation of the body depending on the subject of the research. These observations can have a long duration meaning the body they are observing and experimenting on can die before the result is achieved. Additionally, biological plausibility depends on the interpretation of existing knowledge. These interpretations use high-tech medical technology, such as screening assays to identify toxic agents and etc. However, using past data and comparing it to existing knowledge can cause interpretation issues depending on the time frame of the past data (Celentano& Szklo, 2019; Fedak et al., 2015).

Celentano, D. D., & Szklo, M. (2019). Gordis epidemiology (6th ed.). Philadelphia, PA: Elsevier. ISBN-13: 9780323552295
Fedak, M.K., Bernal, A., Capshaw, A.Z., & Gross, S., (2015). Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology. Emerging Themes in Epidemiology, 12(14), 2-9. Doi: 10.1186/s12982-015-0037-4

Respond to the bold paragraph ABOVE by using one of the option below… in APA format with At least two references and a minimum of 200 words….. .(The List of References should not be older than 2016 and should not be included in the word count.)

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Validate an idea with your own experience.
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Be sure to support your postings and responses with specific references to the Learning Resources.
It is important that you cover all the topics identified in the assignment. Covering the topic does not mean mentioning the topic BUT presenting an explanation from the context of ethics and the readings for this class

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