SFOK19005U  Precision medicine in public health – concepts, assumptions of causality and prediction, methods and societal challenges

Volume 2019/2020
Education

MSc in Public  Health - elective course

MSc in Global Health - elective course

MSc in Health Informatics - elective course

MSc in Health Science - elective course

MSc in Human Biology - elective course

Content

Much epidemiological research and recommended public health interventions propose population wide interventions such as childhood vaccinations and iodine supplements. But does “one size” fit all?

With the increase in available and diverse health data from both health registries and bio banks among others along with promising developments in machine learning algorithms, a field called “precision medicine” emerged. The idea is to incorporate individual factors such as genes, environment and life-style factors in individual prevention and treatment of disease ( link). For precision medicine in public health ( link), the vision is to target high-risk sub-populations with preventive interventions.

In this course, we will 1) discuss precision medicine in public health as a concept, 2) learn methods and assumptions when prediction is an aim, 3) appreciate the challenging step towards causal conclusions for sub-group interventions in a counterfactual framework, and 4) discuss societal challenges that a focus on precision medicine in public health can bring.
We hope to enable the course participants to critically engage in a debate of the role of precision medicine in public health – if any.

Learning Outcome

After completing the course the student is expected to:

Knowledge

  • Understand the concepts of precision medicine and precision public health
  • Know the major current initiatives in Denmark
  • Understand how precision medicine in public health can give mechanistic insight that may lead to actionable knowledge
  • Understand the underlying assumptions of prediction (e.g. risk scores) versus causality (e.g. intervention) in relation to precision public health
  • Understand the idea behind artificial neural networks for disease prediction
  • Appreciate that flexible models are only trained to the data available
  • Discuss the challenges of causality for precision public health in a counterfactual framework
  • Describe how precision public health relates to classic public health
  • Describe the development in the societal view of individualising health and the use of personalised information

 

Skills

  • Apply a neural network for disease risk prediction and evaluate its performance
  • Apply the parametric g-formula for average causal effects and argue the degree of which its assumptions are met
  • Analyse societal implications of precision medicine solutions in public health
  • Obtain new knowledge related to precision public health

 

Competencies

  • Critically evaluating precision public health initiatives by organisations and institutions.
  • Partake in designing and implementation of precision public health interventions
This course is targeted Public Health students with knowledge of epidemiology and statistics on MSc level. Students from other study programmes are welcomed to join the course, but should be aware knowledge of knowledge of epidemiology and statistics on MSc level is recommended in order to take part of this course.
Forelæsninger og øvelser

Oral feedback at dialogue-based lectures and exercises.

Credit
10 ECTS
Type of assessment
Written assignment
Oral defence under invigilation
Written assigment:
Students will in groups of max. 3 persons prepare a protocol for a research protocol (max. 3 pages of 2400 key strokes pr. page).

Oral exam:
The students will have 7 minutes to give a group presentation of the proposed research protocol. Group members will then be individually examined for 10 minutes with regards to their research protocol and the full course.

Both the research protocol, the group presentation and the individual examination are included in the final individual grading.
Aid

All aids allowed when preparing the research protocol.

No aids allowed for the oral presentation and discussion.

Marking scale
7-point grading scale
Censorship form
No external censorship
More internal examiners.
Exam period

Please see the exam schedule

Re-exam

Please see the exam schedule

Criteria for exam assesment

To receive the grade 12, the student will be able to:

Knowledge

  • Understand the concepts of precision medicine and precision public health
  • Know the major current initiatives in Denmark
  • Understand how precision medicine in public health can give mechanistic insight that may lead to actionable knowledge
  • Understand the underlying assumptions of prediction (e.g. risk scores) versus causality (e.g. intervention) in relation to precision public health
  • Understand the idea behind artificial neural networks for disease prediction
  • Appreciate that flexible models are only trained to the data available
  • Discuss the challenges of causality for precision public health in a counterfactual framework
  • Describe how precision public health relates to classic public health
  • Describe the development in the societal view of individualising health and the use of personalised information

 

Skills

  • Apply a neural network for disease risk prediction and evaluate its performance
  • Apply the parametric g-formula for average causal effects and argue the degree of which its assumptions are met
  • Analyse societal implications of precision medicine solutions in public health
  • Obtain new knowledge related to precision public health

 

Competencies

  • Critically evaluating precision public health initiatives by organisations and institutions.
  • Partake in designing and implementation of precision public health interventions
  • Category
  • Hours
  • Class Exercises
  • 25
  • Exam
  • 40
  • Lectures
  • 30
  • Preparation
  • 180
  • Total
  • 275