Introduction to Drug Utilization Research

‎[1] Course

Aim: ‎

This session provides an overview of drug utilization research (DUR) and presents essential methods used in ‎DUR, particularly to describe and compare DU using various data sources. It is based on the conduct of real-world studies with examples to provide participants with an ‘introductory working knowledge’ of DUR. The ‎program includes interaction with participants through short assignments, Q&A parts and discussion at the ‎end of each presentation. The session will introduce the participants to:‎
• The theoretical framework and practical applications of DUR, ‎
• Data sources, classification systems and measures used in DUR,
• Study designs and epidemiological methods in DUR.

‎[2] Requisites Statement:‎

Entry level course. This course is targeted at:‎
‎a)‎ New members of the DUR / health services research (HSR) Special Interest Group (SIG) and graduate ‎students new to DUR/HSR,
‎b)‎ Those interested in DUR who need an overview of the area and/or are interested in monitoring of ‎medication use and quality assessment,
‎c)‎ Those working in healthcare organizations involved in DU monitoring and medicines policy areas.

‎[3] Course Objectives‎:

‎a)‎ To get an overview of DUR within the context of pharmacoepidemiology and HSR,
‎b) To understand principles for classifying and quantifying DU and gain understanding of the ‎methodological challenges,
‎c)‎ To achieve basic knowledge and understanding of DU measures and studies based on aggregate or ‎individual patient data.

‎[4] Syllabus Outline:‎

Introduction to Drug Utilization Research: The Conduct of Real-World Drug Utilization Studies: Describe and ‎Compare ‎
• Introduction to DU research within context of pharmacoepidemiology, and set scene for the topic of ‎the courses (cardiovascular & diabetes) by Gillian Caughey.
• Methodological framework, study designs and challenges in DUR / HSR by Lisa Pont. ‎
• Data sources, drug classifications and aggregate measures of DU by Indre Treciokiene.
• Measures of medication use based on individual-level data by Björn Wettermark .‎