To Comment:

Clinical epidemiology, the study of disease prevalence and incidence in a given population, generates evidence based medicine to support clinical decision making. It is what clinicians use to inform patients of the risk and benefits in order to make health care decision together related to disease prevention, causes, frequency, prognosis and treatment (Fletcher, Fletcher, & Fletcher, 2014).

This is significant because clinicians can use epidemiology to foresee and positively influence the negative outcomes that patients are concerned with which include death, disease, discomfort, disability, dissatisfaction (Fletcher, et al., 2014). Epidemiology studies aid clinicians in a meaningfully discussion with their patient about their plan of care.

There are several reasons to explain variations in data that must be taken into consideration when interpreting results of research. Variation in the measurement of the data collected depends on the reliability or repeatability of the tool used to measure and the consistency of the observers, validity or accuracy of the research design method used and the uncontrolled, natural difference in different people.

In a study by Klompas (2010) high interrater reliability was specifically pointed. I see this as a way to establish that there was minimal variation in measuring the data to support validity or accuracy of the results.

I was drawn to the "healthy people" promotion from The National Center for Health Statistics (2017) website. Every ten years the National Center for Health Statistics seeks to improve the health of the general population by putting forth objectives to improve peoples' health.

They are already working on "healthy people 2030" to identify opportunities to help people maintain health. One of the objectives for 2020 is to increase access to health care. You can view the site to see statistics related to the number of people who have insurance. I see this as a good tool to use to help evaluate the health of the nation.

Reference

Fletcher, R. H., Fletcher, S. W., & Fletcher, G. S. (2014). Clinical epidemiology: the essentials (5th ed.). Baltimore, MD: Lippincott Williams & Wilkins.

Klompas, M. (2010). Interobserver variability in ventilator-associated pneumonia surveillance. American journal of infection control, 38(3), 237-239.

National Center for Health Statistics (2017).