Summer Course 2025
37th Residential Summer Course in Epidemiology
30 June – 4 July 2025
| Week 3, parallel morning module 1 |
| Advanced topics in statistics |
| Per Kragh Andersen, Corrado lagazio and Michaela Baccini |
The purpose of this course is to give an introduction to a number of statistical methods that we have found useful in epidemiology and that are often not part of standard courses. In each three-hour morning session (9.30-12.30), the first half will be a lecture on today’s topic to be followed by practicals using Stata. Inspiration to the coding to be used will be provided. After completion of the course, the students should be able to recognize situations where these methods could be used and to adapt the Stata code to do the analyses
|
| Week 3, parallel morning module 2 |
| Advanced topics in epidemiology: Methods to deal with unobserved information in epidemiological studies [Quantitative bias analysis, instrumental variables, self-controlled study designs, multiple imputation of missing data] |
| Irene Petersen and Henrik Stovring |
Observational studies in epidemiology are susceptible to an array of biases due to confounding, misclassification and missing data that may threaten their validity. Often such problems are qualitatively discussed in papers, but to a lesser extent quantified. In this course we will demonstrate modern analytic techniques and epidemiological study designs that will enable course participants to quantify and deal with unobserved information in observational studies. The course participants will be introduced to quantitative bias analysis, instrumental variable analysis, self-controlled study designs and multiple imputation.
|
| Week 3, parallel morning module 3 |
| Applied epidemiology: Environmental epidemiology |
| Martine Vrijheid and Cathryn Tonne |
The course on applied epidemiology is based on short lectures, group work and group discussion of case studies. We aim to review the methodological issues related to the epidemiologic study of the health consequences of exposures that are involuntary and that occur in the general environments (from cities to global, from individuals to in/outdoors and from physical to social). We cover designs, exposure measurement, co-exposures, modelisation, air pollution, built environment, climate change, exposome, child development, and impact assessment.
|
| Week 3, parallel morning module 4 |
| From epidemiology of risk to public health action: the burden of disease and health impact assessment |
| Gillian Levine and Andrea Farnham |
| Epidemiology is a core science to investigate and quantify the association between risk factors and health outcomes. However, public health professionals and policy makers need to understand the public health relevance of risks to plan and prioritize prevention and policy-making, and need to anticipate the impact of interventions on the health of populations. The epidemiology-based assessment of the burden of disease provides a bridge between public health science and policy. Health impact assessment is a tool to explore and anticipate the possible effects of interventions on health in affected populations, to mitigate risk and inform strategies to maximize health benefit. This course will link the epidemiology of risk to a population-level perspective; introducing students to concepts of the burden of disease and risk factors and health impact assessment (HIA). Students will learn about measures of disease burden and how to calculate and interpret them, the methods of the Global Burden of Disease and Risk Factors project (GBD), and how to use tools and results from GBD to inform research and policy-making. Students will learn when and how HIA should be carried out and be introduced to methods for conducting a HIA. Case-studies will be used to demonstrate key concepts. Approach: Lectures on concepts, self-study activities and exercices, group work and discussion methods will be used. The course culminates with a final group project and group presentations of work. Students are expected to bring their personal laptops to work online for certain activities and exercises. |
| Week 3, parallel afternoon module 1 |
| Applied Epidemiology: The evaluation of medical tests |
| Patrick M Bossuyt and Ewout Steyerberg |
| Modern medicine relies on lab tests, imaging, and other forms of medical tests to find out more about the likely cause of a patient’s condition, to predict the future course of disease, or to select and monitor treatment. Like other interventions in healthcare, medical tests should be thoroughly evaluated before they can be given access to the market, be reimbursed, and recommended in practice guidelines.Unfortunately, the evaluation of medical tests has received far less attention than the methods for evaluating pharmaceuticals and other interventions. It is now always clear what the best approaches are for evaluating the clinical performance of medical tests, or the best strategies for estimating their clinical effectiveness. This absence has generated interesting methodological developments, while awareness is increasing among epidemiologists.In this course, we will give an overview of current concepts and modern methods for evaluation medical tests. As guiding principle, we take the premise that decisions about tests are now based on the effect that they have on patient outcomes – clinical effectiveness – and that measures of the clinical performance of tests should be informed about the effectiveness.The course will look specifically at a few purposes for medical testing: diagnosis, prognosis, treatment selection, and screening. We will distinguish between the scientific validity of medical tests, the technical and analytical performance, the clinical performance, and the clinical effectiveness and clinical utility.We rely on a combination of online lectures and assignments, with background reading material. Sessions will be organized in Zoom, with online quizzes, and offline paper-and-pencil assignments.More information |
| Week 3, parallel afternoon module 2 |
| Advanced topics in epidemiology: Within sibling designs, negative controls, Mendelian Randomization and other instrumental variable approaches, target trial emulation, and triangulation |
| Deborah Lawlor and Carolina Borges |
| Summary of course:
In this course, we will discuss how to make better causal inference using different approaches and triangulating evidence from different approaches. To facilitate learning, we will focus on real applied examples from different medical areas, such as pregnancy/perinatal, cardiovascular and mental health. Level: Intermediate to Advanced To get the most out of this course students should have:
What will be covered: We will introduce each of the following methods:
For each method, we will describe their aims, assumptions and how they can be implemented, with examples of their use. In practicals, you will use these methods with code provided for use in both Stata and R. We will also demonstrate triangulation of evidence, i.e. where we integrate results from different methods, such as conventional multivariable regression and the above studies in order to improve causal understanding. Directed Acyclic Graphs (DAGs) are introduced in the first two weeks of the course and will be used in this module; we will revise how they are constructed and used. Students do not need advanced statistical knowledge; this is an introductory course aimed at providing students with overarching principles that will be valuable in future research and / or in understanding papers that use these methods. |
| Week 3, parallel afternoon module 3 |
| Epidemiology and public health: Principles of prevention in the precision medicine, Big Data and Covid-19 time |
| Rodolfo Saracci and Maja Popovic |
| This module presents to researchers, health professionals and clinicians particularly interested in prevention a perspective critically examining how the individualized and the population approaches, as a classically outlined by Geoffrey Rose in the 1980s, may represent useful concepts and operational principles in a time when on one side the availability of massive health data on each person promotes a ‘precision prevention’ approach and on the other the Covid-19 pandemic makes population level interventions mandatory. Each of the four days will be focused on a main theme : 1.Concepts. 2. Prediction 3. Choices 4. Questions. Relevant methodological aspects will be reviewed, including an introductory presentation of causal versus predictive models and of machine learning instruments. Specific ethical issues that prevention research and measures raise will be sketched for discussion. Approach: lectures, reading of papers with critical discussion and ‘pros and cons’ arguments. |
| Week 3, parallel afternoon module 4 |
| Applied Epidemiology: Infectious disease epidemiology |
| Tyra Grove Krause and Steen Ethelberg |
| Synopsis Infectious disease is closely integrated with human existence. Progress in the understanding of infectious disease epidemiology over the past few centuries, have fundamentally transformed human societies. Vaccines, antibiotics and hygiene measures have played an important role in this fight against infectious diseases. Today, however, infectious diseases still remain important aspects of everyday life both in high- and low-income countries. Worldwide inequalities in accessing health care including treatments and vaccines, re-emergence of vaccine preventable diseases, and the threat of antimicrobial resistance all underline the fact that infectious diseases remain a global public health challenge. The Covid-19 pandemic has put the ever-present risk of new emerging pathogens high on the agenda and shown how a new infectious disease may pose severe clinical and public health problems and also have vast societal, economic and political consequences.Purpose & content The purpose of this course is to introduce the field of infectious disease epidemiology. The course will introduce the epidemiological fields of transmissibility, vaccinology, disease surveillance and outbreak investigations. We will use the recent Covid-19 pandemic in addition to other disease epidemics and recent outbreaks as examples in the course. We will try to describe what sets this field apart from classical epidemiology and stress the applied aspects of the discipline (the ’field epidemiology’ aspects). We are both working at a national public health institute (the SSI, in Copenhagen, Denmark) and in our daily work use epidemiology for disease surveillance, risk assessment and outbreak control – and we’ll discuss infectious disease epidemiology from this perspective. The course does not cover mathematical models for epidemics – except for a brief introduction to this area.Objective By the end of this module, the student should be able to understand/discuss: 1. The terminology and definitions used in infectious disease epidemiology 2. Principles of infectious disease surveillance and interpretation of surveillance data 3. Principles of disease transmission 4. Basic concepts of vaccinology 5. The 10 steps of an outbreak investigation – with a focus on water/foodborne outbreaks 6. The use and interaction of microbiological and epidemiological methods in outbreak detection and control. 7. The use of epidemiological study designs in infectious disease epidemiology. Requirements Practicalities |