Epidemiological research is ubiquitous. Even if you don’t realise it, you come across epidemiological studies and the impact of their findings every single day. You have probably heard that obesity is increasing in high income countries or that malaria is killing millions of people in low income countries. It is common knowledge that smoking causes cancer and that physical activity is protective against heart disease. These facts may seem obvious today, but it took decades of epidemiological research to produce the necessary evidence. In this course, you will learn the fundamental tools of epidemiology which are essential to conduct such studies, starting with the measures used to describe the frequency of a disease or health-related condition. You will also learn how to quantify the strength of an association and discuss the distinction between association and causation. In the second half of the course, you will use this knowledge to describe different strategies for prevention, identify strengths and weaknesses of diagnostic tests and consider when a screening programme is appropriate.
Overview
Syllabus
- Measures of disease frequency
- One of the main purposes of epidemiology is to describe the frequency of diseases or other conditions that are important for the health of populations. Depending on the circumstances, there are different types of measures that you can use to do this. In this module, you will learn to calculate measures such as the prevalence, odds, cumulative incidence and incidence rate. We will highlight what they have in common as well as their differences. By the end of the module, you will be able to select and calculate the appropriate measure of frequency in a variety of contexts.
- Measures of association
- This module starts by introducing the distinction between association and causation, which is critical not only for epidemiology, but for research in general. Subsequently, you will learn all the main measures epidemiologists use to quantify association; mainly risk and rate differences and risk, rate and odds ratios. Over the course of this module, you will develop the skills to calculate and interpret measures of frequency. This is not enough by itself though, so you will also learn to select the most appropriate measure depending on the research question and the availability of data.
- Attributable risk and strategies for prevention
- Published studies often report the magnitude of the association they investigate, which is clearly important when trying to identify causal links. Sometimes though, what we are interested in is the impact of a factor or of a disease on the population as a whole. This is when the concepts of attributable risk and of population attributable risk come in handy. These measures quantify the population impact of a health-related factor and therefore are particularly useful for health policy. Equipped with this knowledge, you will then explore the two main approaches to disease prevention: the high-risk and the population approach.
- Disease detection and screening
- Diagnostic tests are used all the time to determine whether an individual is sick or not. However, these tests are far from perfect. Quantifying their imperfection allows us to understand their limitations and interpret their results. In this module, you will learn to calculate and interpret the metrics used to do this, including sensitivity, specificity, positive and negative predictive values. Using these metrics, you will subsequently learn to evaluate whether a screening programme can be effective or not considering its methodological and practical aspects.
Taught by
Filippos Filippidis
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Reviews
5.0 rating, based on 1 Class Central review
4.7 rating at Coursera based on 749 ratings
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This is a great introduction to both the Coursera Specialization where it belongs. The lectures, quizzes and other contents are engaging and well-made.
