Towards the automated economic assessment of newborn screening for rare diseases.

Objective: Economic assessments of newborn screening programs for rare diseases involve the use of models and require huge efforts to synthesize information from different sources. Sharing and automatically or semi-automatically reusing this information for new assessments would be desirable, but it is not possible nowadays due to the lack of suitable tools.

Material And Methods: We designed and implemented the Rare Diseases Ontology for Simulation (RaDiOS) after performing two reviews, and critically appraising the existing data repositories on rare diseases. The first review involved previous published economic assessments, and served to identify the main parameters required to model newborn screening. The second review aimed at locating existing data repositories potentially available to inform these parameters.

Results: We found key model parameters on epidemiology, screening methods, diagnose methods, pathogenesis, treatment and follow-up tests. We also identified seven data repositories directly related to rare diseases. None of such repositories was well-suited for the automated generation of simulation models. We incorporated the identified parameters as structured classes and properties of the new ontology (RaDiOS). We carefully set the relationships among the parameters so to allow automated inference from the ontology.

Conclusions: RaDiOS is an ontology that serves as a data repository to automatically build simulation models for the economic assessment of newborn screening for rare diseases.