dc.contributor.author | Dahlstrand, Jens | |
dc.contributor.author | Sandberg, Fredrik | |
dc.date.accessioned | 2016-10-03T12:49:47Z | |
dc.date.available | 2016-10-03T12:49:47Z | |
dc.date.issued | 2016-10-03 | |
dc.identifier.uri | http://hdl.handle.net/2077/47913 | |
dc.description | MSc in Economics | sv |
dc.description.abstract | The Dental and Pharmaceutical Benefits Agency (TLV) is the Swedish government agency
who decides if a new medicine should be included in the benefit scheme or not. This study
investigates which implicit factors influence the agency’s reimbursement decisions and how the
TLV values different properties of a medicine. The dataset used for this study consists of 116
observations and was extracted by analyzing all decision documents published on the TLV’s
website between the years 2008-2015. We model the TLV’s reimbursement decisions as binary
choices and investigate eight potentially important factors influencing the decisions. Six factors
are identified as being of importance in the decision-making process: cost-effectiveness, the
severity of the disease, the existence of an alternative treatment, the size of the applying firm
and if the medicine is a preventive treatment or an orphan drug. We also estimate the TLV’s
valuation of four different characteristics often associated with a medicine. The results indicate
that the TLV has the highest WTP for medicines categorized as palliative treatments, followed
by medicines intended to treat severe diseases, orphan drugs and preventive treatments. | sv |
dc.language.iso | eng | sv |
dc.relation.ispartofseries | Master Degree Project | sv |
dc.relation.ispartofseries | 2016:162 | sv |
dc.title | An Analysis of the Decision-making Process of the TLV and the Willingness to Pay for Healthcare in Sweden | sv |
dc.type | Text | |
dc.setspec.uppsok | SocialBehaviourLaw | |
dc.type.uppsok | H2 | |
dc.contributor.department | University of Gothenburg/Graduate School | eng |
dc.contributor.department | Göteborgs universitet/Graduate School | swe |
dc.type.degree | Master 2-years | |