Validation of the PECUNIA reference unit costs templates in Spain: a useful tool for multi-national economic evaluations of health technologies.

Abstract

Background: The PECUNIA Project was funded by the H2020 programme in which 10 partners from six countries participated. The aim was to develop standardized, harmonized and validated methods and tools to calculate costs in different sectors (such as health and social care, education among others), with the purpose of facilitating comparability of economic evaluations of health technologies across European countries. In this paper we report the first validation of the developed reference unit cost templates in Spain.

Methods: The evaluation of the PECUNIA Reference Unit Cost (RUC) Templates involved usability, transferability and feasibility assessment. Applicability tests were performed to estimate the cost of a selection of 15 resource items by means of the RUC templates in Spain and in four Spanish regions. External validation involved comparison with existing unit costs.

Results: It was possible to estimate the cost of five services (dental care and general practitioner in the Canary Islands, general practitioner in Spain [tariffs], health-related day care centre and education services provided in a special education school in the Basque Country), car vandalism as an example of potential health-related consequences, and informal care in Spain. The templates were feasible although data completeness depended on the type of data needed to estimate the costs. The templates are transferable across countries although comparability depends on the services available in each jurisdiction.

Conclusions: The PECUNIA RUC Templates are free and feasible tools to estimate comparable reference unit costs across countries. Although more validation exercises are needed, they seem useful tools to perform robust multi-national economic evaluations and increase the transferability of cost-effectiveness studies of health technologies in Europe. However, they cannot compensate for the lack of data across jurisdictions.