Public Health Epidemiology (online)

Public health epidemiology: from basic principles to future trends

You will start by learning the basic principles of public health epidemiology, illustrated by the main successes in this field in the past. We will focus on important measures of population health, their interpretation and use in public health policy. You will learn how to calculate the effect of an exposure on life expectancy and what part of this exposure can be attributed to the development of a disease. You will also weigh and compare the effects of two prevention strategies: intervening in individuals at high risk for developing a disease, versus introducing preventive measures for the whole population. The societal, economic and health dilemmas involved in introducing population measures will be illustrated by the smoking ban. This online medical course will also cover the persistent socioeconomic and ethnic inequalities in health, including epidemiological and statistical methods, and you will learn the basic principles of how future trends in population health are modeled.

By the end of this course, you will be able to:

• describe the typical features of public health and public health epidemiology
• weigh and compare the effects of high-risk versus population strategies for prevention In particular, you will be able to:
• reproduce at least 5 different measures of population health, their interpretation and use in public health policy or practice
• calculate life expectancy from life-table data and construct survival curves
• describe dilemmas in the implementation of population measures for smoking reduction from a societal, economical and health perspective
• calculate the effect of exposure to risk factors on life expectancy and attributable fraction
• weigh and interpret the contribution of risk factors to population health
• describe types of input required to model future trends in population health
• describe the main underlying factors causing certain patterns in population health
• apply epidemiological methods to study ethnic and socioeconomic inequalities in population health
• interpret results from multilevel analysis to disentangle effects of a shared environment (contextual effects) and individual risk factors on population health

Our courses are aimed at clinical researchers, nurses, general practitioners, and other health professionals who want to improve their skills in epidemiology, statistics and (clinical) research.

3 weeks, 14 hours per week (online)

To successfully complete this course, you need to actively participate in the discussion forums and complete the learning unit assignments, including:

• Individual and group assignments
• The completion of a quiz at the end of the first four learning units
• A final assignment: this will be a presentation of a case study by the student. The submission deadline and the date for the redo session will be announced as soon as possible.

Even though you can manage your own time our courses are not intended as individual education. We offer personalized online learning with lots of interaction with peer students, the E-moderator and lecturers. Flexibility from students, a positive attitude towards teachers and peers and the willingness to learn together and help each other is invaluable to our courses. To experience maximum interaction, we advise you to log on several times per week.

Note that the starting dates of courses, interim deadlines, and dates of exams are fixed, but you can choose when and where you want to watch web lectures and work on assignments. The e-moderator of the course will inform you about the beginning of the course and about deadlines during the course.

The average required study workload for the courses of MSc Epidemiology Postgraduate Online is 14 hours per week. You will need this time to study, to keep up with the assignments and course material.

To enroll in this course, you need:

• A BSc degree
• Basic programming experience in R, e.g. the ability to read in data and run a simple linear model
• To have followed at least one course in basic statistical methods up to and including simple and multiple linear regression
• Familiarity with likelihood methods (Wald, score and likelihood ratio tests) will facilitate understanding of the theoretical background.

Please note that this course is part of an existing program within the Graduate School of Life Sciences. Tuitition fees may alter during the year.