Modern Health Epidemiology – Epidemiology Example

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"Modern Health Epidemiology" is a perfect example of a paper on epidemiology. In epidemiology, the odds ratio is used to mean the probability with which an event is likely to occur to that of its non-occurrence. Therefore, the odds ratio is used to create an association between a risk factor and the disease. The odds ratio is used to compare the likelihood of disease under two extremely different circumstances. Although it is related to the concept of relative risk, it is possible to calculate the odds ratio from a case-control study and does not need the knowledge of incidence rates. On the other hand, the risk applies to the ratio of chance of death or disease among those who are exposed to the risk factor against the risk among those who are not exposed to the risk factor.

Its calculation requires a cohort study from where the frequency can be calculated. Incidence denotes the number of new cases experienced within a specific period. It thus refers to the number of people who are at risk. It is conducted for a period of one year hence leads to the annual incidence where every individual studied as to be followed.

It thus possesses a number of challenges that include death and loss of follow-up. The main difference between specificity and sensitivity in epidemiology is that sensitivity measures the proportion of the actual positives while specificity measures that of real negatives that are correctly identified. Therefore, sensitivity helps to rule out disease while specificity helps to confirm the existence of the disease (Rothman, Greenland & Lash, 2008). There is a significant difference between statistical and practical significance. It is based on the idea that statistical significance reflects whether an observed effect larger than what is expected by chance.

It explains if the null hypothesis that is thereby chance can be rejected. Practical significance shows whether researchers should care if the effect is useful in a particular context. Therefore, statistical significance helps ion hypothesis testing while clinical significance explains the rate of effectiveness. As such, a study showing no statistical significance difference can still have some clinical relevance. In some cases, there is still a need for a treatment effect despite the fact that it does not have a palpable effect.

It is because the null hypothesis will never be the same in two completely different setups. For instance, the expectations in personality psychology are likely to be different from those of clinical research in terms of what is expected. It is also evident in the case of the study of a new drug that does not have a significant impact on the patients despite its high rate of effectiveness than the others in the market (Polit & Beck, 2012). Healthy people, 2020 has a significant impact on clinical practice in the sense that it focuses on life and well-being in relation to quality.   They entail all domains of life thus the ability to develop wholesome assistance for individuals by increasing the physical, emotional, social, and mental functions.

It will thus impact the practical setting by bringing about a lot of improvements in the lives of individuals. It is likely to promote the quality of life, develop health and its behaviors across all the stages of life.   This will be important in my practice as it will enhance the nature of services offered to patients (Healthy people, 2014).

References

Healthy people. (2014). Health-Related Quality of Life & Well-Being. Retrieved 27 October 2014 from https://www.healthypeople.gov/2020/topics-objectives/topic/health-related-quality-of-life-well-being

Polit, D. F., & Beck, C. T. (2012). Nursing Research: Generating Evidence for Nursing Practice. Philadelphia: Wolters Klower/Lippincott Williams & Wilkins.

Rothman, J.K., Greenland, S., & Lash, L. T. (2008). Modern epidemiology. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins.

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