MPH516 2021 August Module 1 Discussion Latest

Question # 00809849 Posted By: Ferreor Updated on: 08/24/2021 07:05 AM Due on: 08/24/2021
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MPH516 Epidemiology and Biostatistics for Public Health Application 2

Module 1 Discussion

Measures of Effect

Purpose

Discussions are designed to promote dialogue between faculty and students, and students and their peers. In the discussions, students should

demonstrate an understanding of the concepts for the module;

integrate professional resources;

engage in meaningful and respectful dialogue with classmates; and

express thoughts clearly and logically.

Course Outcomes

This discussion provides documentation of student ability to meet the following course outcomes:

CO1: Develop correct use of advanced biostatistical methods and discipline specific epidemiological terminology as related to public health programs.

CO3: Explain the impact of proposed, ongoing and/or completed epidemiological studies on current public health programs and initiatives.

CO6: Evaluate how ethics, economics, and science impact epidemiology’s role in public health policy development.

CO7: Assess factors associated with environmental, biological, social, and behavioral aspects of infectious and chronic diseases in relation to health outcomes.

Effect measures are used in epidemiological studies to assess the strength of an association between a risk factor or exposure and a disease outcome. It requires the incidence of disease in a community and the amount of exposure. This relationship may be described as association or causality. There are several different ways to express measures of effect and one thing to consider is that exposures do not always carry the same risk to the individual and the community.

For the discussion this week, pick a topic of interest containing an exposure or risk factor and a disease outcome. Find reports of the measures of effect associated with it both for the community and the individual, explaining any differences. Also, tell if the relationship is an association or causal, providing information about any model of causality that you might find in your research.

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