SITB23001U Epidemiology

Volume 2024/2025
Education

Bachelor's degree in Health and Informatics - mandatory course

Content

The course is a general introduction to epidemiology including the basic concepts and study designs that are used in the field. After this course, students should be able to reproduce, explain, judge, and criticize scientific papers within the field of health, including calculating and interpreting basic frequency and association measures.

Learning Outcome

Knowledge

  • List different data types and sources within epidemiology.
  • Describe characteristics of epidemiological study designs, including case-control, cohort, intervention, and cross-sectional studies.
  • Describe epidemiological non-systematic and systematic sources of error, selection bias, and information bias.
  • Describe the epidemiological phenomena of effect modification, mediation, and confounding.
  • Describe frequently used statistical models in epidemiology.

 

Skills

  • Explain the concept of causality and the epidemiological causal model.
  • Calculate and interpret epidemiological frequency and association measures.
  • Understand and assess how, when, and in which direction systematic errors (bias) can distort the results of epidemiological studies. Furthermore, assess whether effect modification, mediation, and confounding occur in epidemiological studies.
  • Rank epidemiological studies according to the hierarchy of evidence.

 

Competences

  • Analyze, interpret, and discuss frequency and association measures.
  • Critically assess the internal validity of epidemiological studies.
  • Assess whether epidemiological evidence can be generalized (external validity).
  • Be critical of the importance of data collection for valid conclusions.
Literature

A textbook, a collection of scientific articles as learning material, and instructive videos will be made available on the course Absalon page.

Lectures and exercises
  • Category
  • Hours
  • Lectures
  • 13
  • Class Instruction
  • 20
  • Preparation
  • 169
  • Exam
  • 4
  • Total
  • 206
Collective
Peer feedback (Students give each other feedback)
Credit
7,5 ECTS
Type of assessment
On-site written exam, 4 hours under invigilation
Type of assessment details
The exam consists of a scientific article in the field of medical research and a series of questions. The article is handed out 24 hours before the exam itself. The questions that are to be answered are handed out at the start of the exam.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Re-exam

In case of 10 or fewer students registered for re-exam

Type of assessment: Oral examination

Assessment details: 15 minutes examination

Preparation: 30 minutes 

Aids: All aids allowed

Criteria for exam assesment

To achieve grade 12, the student must be able to:

Knowledge

  • List different data types and sources within epidemiology.
  • Describe characteristics of epidemiological study designs, including case-control, cohort, intervention, and cross-sectional studies.
  • Describe epidemiological non-systematic and systematic sources of error, selection bias, and information bias.
  • Describe the epidemiological phenomena of effect modification, mediation, and confounding.
  • Describe frequently used statistical models in epidemiology.

 

Skills

  • Explain the concept of causality and the epidemiological causal model.
  • Calculate and interpret epidemiological frequency and association measures.
  • Understand and assess how, when, and in which direction systematic errors (bias) can distort the results of epidemiological studies. Furthermore, assess whether effect modification, mediation, and confounding occur in epidemiological studies.
  • Rank epidemiological studies according to the hierarchy of evidence.

 

Competences

  • Analyze, interpret, and discuss frequency and association measures.
  • Critically assess the internal validity of epidemiological studies.
  • Assess whether epidemiological evidence can be generalized (external validity).
  • Be critical of the importance of data collection for valid conclusions.