CPE Track Health Systems and Prevention

In this course, you learn to tell a compelling story with data. You will refresh and deepen your knowledge of statistics. You learn to apply sophisticated statistical techniques such as multiple linear least square regression or survival analysis. You will be introduced to problem-solving practices in biostatistics and epidemiology in a data-driven manner.

During this course, you learn about the development, progression and burden of common chronic diseases such as diabetes, cancer, cardiovascular diseases, chronic obstructive pulmonary disease, dementia or mental health disorders. You acquire (or strengthen) medical knowledge, including etiology, pathophysiology, prevention, diagnosis and treatment of chronic diseases, as well as their epidemiology, covering incidence and prevalence, risk factors, and trends. The potential influence of large range of biological, psychological, social, and environmental factors will be addressed.

In this course, the development, adaptation, feasibility, evaluation and implementation of complex interventions will be introduced. Complex interventions are delivered and evaluated on different levels (e.g. patients, healthcare professionals, organisation). Understanding how interventions work, where it works and for whom are key elements of this course. You learn about co-creation methods and how diverse stakeholder perspectives can be included in intervention research. You learn about the methods to adapt interventions to to a new context, and the evaluation of effectiveness, cost-effectiveness and implementation processes.

In this course, you will delve into the fundamentals of creating and assessing measurement instruments. The focus will encompass key themes, including understanding various types of measurements and the process of operationalizing them. Additionally, attention will be given to crucial aspects such as validity and factor analyses, reliability, responsiveness, and interpretability. Various challenges will be addressed such as measurement of mental health disorders and cross-cultural adaptation of questionnaires.

Using topics in health psychology and psychiatric epidemiology, this course will delve into the dynamics of behavior change, considering diverse perspectives such as those of patients and healthcare professionals. It will incorporate concepts and models from psychology to comprehensively address the complexities of behavior change within prevention and healthcare contexts.

In this course, students learn about conducting prevention and public health research at population and health system level using practical approaches. They learn about using open data sources and register data in population research and applying various quantitative methods in policy evaluations. Next to this attention is paid to translating research results into practical information for policy makers. In the final assignment, a written policy brief, all aspects learned in this course come together.

This course gives an overview of major theories and concepts of health and prevention on a systems level. You will learn to apply interdisciplinary and intersectoral research to practical policy questions, aiming to solve complex public health problems related to societal issues like pandemics, ageing, increasing levels of chronic diseases, widening health inequalities, migration and urbanization. Key to tackling these issues is the application of a systems approach to health.

In this course, you explore health challenges in society through the lens of different disciplines such as medicine, public health, psychology, sociology, demography and health economics. The aim is to enhance your interdisciplinary thinking by closely examining and analysing a specific health issue through the perspectives of various relevant disciplines. The course covers key concepts, theories, and research methods from different fields, shedding light on the strengths and challenges unique to each discipline. Examples of novel interdisciplinary research will be discussed.

Leadership, communication, collaboration and creative problem solving skills may influence the quality and impact of research. During this course you will strengthen and acquire valuable professional skills. A variation of strategies are used to strengthen skills, such as lectures, self-assessment tools, workshops, assignments and pizza-meetings. The skills training is complemented by mentor meetings in small groups (6-8 students) and individual coaching.

In the project-based learning you use research methods, knowledge, models and frameworks from separate disciplines to address a complex health challenge. In each project, you start with a challenging problem or question connected to a real-world challenge. You will take part in 4 projects during the first year. Policy makers, healthcare professionals, patients and other experts play an important role in this project-based learning. In these projects, you will try to solve challenging research problems by learning about and applying the key concepts introduced in the courses. The project-based learning provides you with relevant practical experiences as a researcher.

In this course the principles and practice of epidemiological research are taught. Different study designs will be introduced and different sources of bias in epidemiological studies will be assessed. By the end of this course, you will have gained the essential scientific and methodological knowledge to effectively engage in epidemiological studies.

The aim of the elective courses is to broaden and deepen your knowledge and skills in the field of clinical and psychosocial epidemiology. Students can choose courses that match their own learning goals and contribute to their development as a researcher in this field. Ideally, the elective courses support the master thesis project, but this is not a strict requirement.

First you will learn the basics of R through short lectures and many computer exercises. The following topics will be treated: the R language, R variables (objects), R data structures, reading and writing data files, manipulating datasets, making graphs, performing basic statistics (only one day!), simulations, and creating functions.

In week 3, students work on a case study, in pairs. This case study is an integrated set of exercises in R that build on the knowledge that they gained during the first eight days of the course. The goal of the exercises is to determine whether students are able to use R without hands on instructions in order to answer a scientific research question and visualize results.

The final result of the case study will be the R-script that performs all assignments and answers all questions.

The aim of the Master Thesis project is to conduct a research project and to analyze, describe and interpret the results in the master thesis. During the conduction and writing of the results, students will gain experience in the full research cycle by (a) carrying out the master thesis project, (b) describing the results in a scientific way, and (c) presenting and defending the results during a scientific symposium.

The academic knowledge and research skills acquired during the CPE programme culminate in the final phase of the programme, during which students design and write their own PhD research proposal. Most often, the PhD research proposal builds on the Master's thesis project.

The primary aim of the PhD research proposal is to set up a PhD research project that can be carried out in a period of three years. Specific aims are (a) to set up and describe the PhD research project, taking into account current developments in the area of research and at least one other different discipline and taking into account relevant methodologies, (b) to write this as a scientific research proposal, and (c) to present, discuss and defend the proposal during a scientific symposium.