De elektronische studiegids voor het academiejaar 2026 - 2027 is onder voorbehoud.





Environmental Epidemiology DL (3783)

Coordinating lecturer:Prof. dr. Tim NAWROT 
Member of the teaching team:dr. Esmee BIJNENS 
 dr. Janneke HOGERVORST 


Credits: 3,0
Study load hours: 81
Period: semester 1 (3sp)

Language of instruction: English
Exam contract: not possible

2nd Chance Exam1: Yes
Final grade2: Numerical
Tolerance3: See included in these programmes

Sequentiality
Mandatory sequentiality bound on the level of programme components
 
 
  Following programme components must have been included in your study programme in a previous education period
    Concepts of Epidemiology DL (3585) 4.0 stptn  
    Generalized Linear Models DL (5465) 6.0 stptn  
 


Content

Content:

Air pollution, pesticides, metals are just some examples of environmental factors that have been linked to adverse health effects such as cardiovascular and respiratory disease. With increasing Risks associated with environmental exposures are generally small in the exposed population, because the population exposed is large the population attributable risks might be considerable. This course focus on basic principles and in more detail special environmental epidemiological designs. With increasing attention on genetic susceptibility as effect-modificator of environmental related health effects we will also focus on how gene-environment interactions can be studied.

Learning outcomes:

With increasing attention on environmental health issues form the public and government this is a growing area of those working in the field of public health. The course provides a public health interpretation of epidemiological concepts in the field of environmental epidemiology.



Recommended course material
 

The use of R software is recommended in this course. 



Organisational and teaching methods
Organisational methods  
Lecture  
Small group session  


Evaluation

Semester 1 (3,00sp)

Evaluation method
Oral exam100 %

Second examination period

Evaluation second examination opportunity different from first examination opprt
No


Learning outcomes
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Master of Statistics and Data Science
  •  EC 
  • The student is capable of acquiring new knowledge.

  •  EC 
  • The student knows the international nature of the field of statistical science and data science.

  •  EC 
  • The student knows the societal relevance of statistics and data science.

 

Included in these programmesTolerance3
second year Data Science - distance learning Y
second year Master Bioinformatics - distance learning Y
second year Master Biostatistics - distance learning Y
second year Quantitative Epidemiology - distance learning Y



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.