Healthy People 2020 is a project that comprises numerous recommendations related to health care and health promotion among Americans. The topic is related to access to medical insurance among the citizens of the United States.
In fact, researchers working with statistical data related to the problem were using a few criteria to distinguish between different groups of citizens and define which group has more access to health insurance in the country. The particular graph demonstrates the data on citizens who have medical insurance and helps to compare the situation for female and male patients (“Disparities overview by sex,” n.d.). The graph shows disparities by sex, and therefore, the information on two groups (men and women) is presented. The information on healthcare access disparities interrelated with gender is presented with the help of the simple tool which is called the line graph.
As for the characteristics of such type of graph, it needs to be said that it uses dependent and independent variables and two axes to demonstrate the relationships between the variables and show the changes in time. The independent variable used in the graph is time – on the x-axis, there are a few points where the particular years (from 2008 to 2015) are indicated. As for the dependent variable, it is presented by the percentage of people of different sexes who had medical insurance that year.
On the y-axis, one can notice the results (the percentage) for two groups. The graph is useful because it presents important information on healthcare access disparities in a very convenient way. The existence of covariates is another question related to the graphical representation of information. As for the given line graph, it can be supposed that the percentage of transgenders should also be regarded as a covariate because the existence of this group may interfere with the credibility of the results.
When it comes to studies in the field of medicine and nursing, it is necessary to understand that there are different types of the significance of the results. Statistical significance is the term which is used to define the importance of the results. Thus, a certain point identified during an experiment can be called statistically significant in case if it is very unlikely that the given information could be retrieved by chance or due to the influence of unwanted factors. Statistical significance differs from clinical significance because of the latter touches upon the opportunity to implement the conclusions into practice and get positive results.
Continuing on the topic of clinical significance, it needs to be noted that it defines whether the treatment, the medicinal drug, or the plan of care is effective and whether the changes caused by the proposed innovation are positive. The example of a study demonstrating a statistical but not a clinical significance is the research conducted by Grimwood, Fong, Ooi, Nathan, and Chang (2016) – the effects of amoxicillin on the physical condition of patients with pneumonia that were in the center of attention are clinically insignificant. In the end, it needs to be said that statistical significance does not warrant a clinical significance; the statistical significance of the results is a factor which excludes the significance of a null hypothesis. At the same time, the clinical significance is a more complicated notion that involves the real effects for patients. Therefore, clinical significance depends upon the factors that do not relate to statistical significance.
Disparities overview by sex. (n.d.)
Grimwood, K., Fong, S. M., Ooi, M. H., Nathan, A. M., & Chang, A. B. (2016). Antibiotics in childhood pneumonia: How long is long enough? Pneumonia, 8(1), 6.