Graphs, Statistical and Clinical Significance in Healthcare

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Graphs, Statistical and Clinical Significance in Healthcare

There is no doubt that the use of graphs remains a very good way to illustrate quantitative data and, therefore, demonstrate certain trends related to different spheres of life of human society. Healthypeople.gov can be called one of the most important sources of information for those whose areas of expertise include healthcare and health level in the United States. When it comes to healthcare, I suppose that there is an important topic that should be paid increased attention to food safety.

In order to extend the knowledge on this topic, I found the graph illustrating tendencies related to the prevalence of Escherichia coli O157 in the United States (Escherichia coli O15, 2017). The researchers working on the given graph were focusing on the cases when the discussed infection was transmitted due to the inappropriate quality of alimentary products consumed by the population. As for its type, the graph is an example of a line graph that demonstrates the links between two variables. In the given case, there are two variables used by the researchers: a year that acts as an independent variable and a percent of people who were infected with the disease, which is a dependent variable.

As for the effectiveness of the graph, I suppose that it can be called useful as it allows researchers and students interested in the topic to keep track of tendencies related to the incidence of the infection. Speaking about covariates, it is necessary to say that the latter is defined as the variables capable of explaining the relations between the factors studied. I suppose that there are no covariates because additional factors apart from time, and morbidity rates were not included in the research.

Speaking about graphs and other tools used in order to present the data related to healthcare, it is necessary to put an increased focus on such a notion as significance. In healthcare and medicine, there can be two different types of significance: clinical and statistical, and it is important to understand the difference between the types. As for the statistical significance, it is strictly interconnected with the credibility of the results of research. A certain conclusion can be called statistically significant in case if it is almost impossible that it has been made by mistake or due to misinterpretation of the data. Also, it is important to note that the notion of statistical significance does not align with the importance of the relevance of research. In reference to the notion of a clinical significance, it can be stated that it is closely interconnected with the importance of the idea. In other words, if a certain study possesses a clinical significance, it means that it presents all the necessary information concerning the intervention studied and the positive effects reported by patients and other healthcare specialists engaged in the research. In fact, there are studies that possess a statistical significance but lack clinical significance; for instance, the research conducted by Ogden, Carroll, Kit, and Flegal (2014) can be regarded as an example of such study. Its results are statistically significant due to the fact that the authors were using the data approved by the specialist for other agencies; therefore, the information they were focusing on is credible. Statistical significance does not warrant clinical significance. It is difficult to prove that this study possesses a clinical significance as it does not touch upon certain preventive measures. In fact, the clinical significance should be proven by the results of experiments, whereas the statistical significance can be achieved by proper interpreting of theoretical data.

References

Escherichia coli O157:H7 infections commonly transmitted through food. (2017). Web.

Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and adult obesity in the United States, 2011-2012. Jama, 311(8), 806-814.

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