The budgetary impact of genetic testing for hereditary breast cancer for the statutory health insurance. (2nd December 2019)

• Record Type: Journal Article
• Authors: Neusser, Silke; Lux, Beate; Barth, Cordula; Pahmeier, Kathrin; Rhiem, Kerstin; Schmutzler, Rita; Engel, Christoph; Wasem, Jürgen; Huster, Stefan; Dabrock, Peter; Neumann, Anja
• Is Part Of: Current medical research and opinion. Volume 35:Number 12(2019)
• Page Start: 2103
• Page End: 2110
• Publication Date: 2019-12-02
• Subjects: Predictive genetic testing -- breast cancer -- ovarian cancer -- economic impact -- cost effectiveness; Clinical medicine -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1080/03007995.2019.1654689
• Languages: English
• ISSNs: 0300-7995

Abstract

Abstract: Objectives: Potential opportunities and challenges of predictive genetic risk classification of healthy persons are currently discussed. However, the budgetary impact of rising demand is uncertain. This project aims to evaluate budgetary consequences of predictive genetic risk classification for statutory health insurance in Germany. Methods: A Markov model was developed in the form of a cohort simulation. It analyzes a population of female relatives of hereditary breast cancer patients. Mutation carriers are offered intensified screening, women with a BRCA1 or BRCA2 mutation can decide on prophylactic mastectomy and/or ovarectomy. The model considers the following scenarios: (a) steady demand for predictive genetic testing, and (b) rising demand. Most input parameters are based on data of the German Consortium for Hereditary Breast and Ovarian Cancer. The model contains 49 health states, starts in 2015, and runs for 10 years. Prices were evaluated from the perspective of statutory health insurance. Results: Steady demand leads to an expenditure of €49.8 million during the 10-year period. Rising demands lead to additional expenses of €125.5 million. The model reveals the genetic analysis to be the main cost driver while cost savings in treatment costs of breast and ovarian cancer are indicated. Conclusions: The results contribute to close the knowledge gap concerning the budgetary consequences due to genetic risk classification. A rising demand leads to additional costs especially due to costs for genetic analysis. The model indicates budget shifts with cost savings due to breast and ovarian cancer treatment in the scenario of rising demands.