Future relevance of genetic testing: a systematic horizon scanning analysis.
OBJECTIVE
The aim of this study was to identify research and development on genetic testing to find out if research addresses important disease areas, how far it is from potential clinical use, and what consequences might arise for the prioritization of health technology assessment (HTA) activities. Also a horizon scanning methodology developed in Germany is demonstrated.


METHODS
A systematic search on genetic testing was conducted in an innovation database (ZIM database). Based on a daily monitoring of literature and Internet sources, reports from 2003 up to 2005 were classified related to the type of innovation, the addressed disease categories, and the developmental phase of the technology. More detailed analyses for the most frequently addressed groups of diseases were conducted.


RESULTS
From 239 relevant reports, 41 percent referred to neoplasms; 10 percent to diseases of the cardiovascular system; 9 percent to diseases of the nervous system; 7 percent to mental and behavioral disorders; and 5 percent to endocrine, nutritional, and metabolic diseases. A total of 69 percent of research is situated in basic preclinical research, 22 percent in clinical/experimental research, and 6 percent are genetic tests being used. Diagnostic applications were most frequently reported (28 percent), followed by therapeutic prediction (22 percent), preventive prediction (18 percent), pharmacogenetics (16 percent), and screening (16 percent).


CONCLUSIONS
Widespread diseases are frequently addressed in research. HTA on genetic testing might focus on innovations addressing neoplastic diseases (in particular breast, colon, and prostate cancers) and pharmacogenetic applications for therapeutic prediction. The horizon scanning approach seems useful in the early steps of HTA processes to identify emerging new technologies that might have significant impact on future health care.
Storz
Philipp
P
Institute for Health and Social Research, Wichmannstr. 5, Berlin, Germany. sz@iges.de
Kolpatzik
Kai
K
Perleth
Matthias
M
Klein
Silvia
S
Häussler
Bertram
B
D016206:Databases, Bibliographic; D030342:Genetic Diseases, Inborn / Q000175:diagnosis*; D005820:Genetic Testing / Q000706:statistics & numerical data*; D005858:Germany; D006801:Humans; D013673:Technology Assessment, Biomedical