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.

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.

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.