International comparability of reference unit costs of education services: when harmonizing methodology is not enough (PECUNIA project). (2nd January 2023)

• Record Type: Journal Article
• Authors: Pokhilenko, I.; Kast, T.; Janssen, L.M.M.; Evers, S.M.A.A; Paulus, A.T.G; Simon, J.; Mayer, S.; Berger, M.; Konnopka, A.; Muntendorf, L.; Brodszky, V.; García-Pérez, L.; Park, A.; Salvador-Carulla, L.; Drost, R.M.W.A.
• Is Part Of: Expert review of pharmacoeconomics & outcomes research. Volume 23:Number 1(2023)
• Page Start: 135
• Page End: 141
• Publication Date: 2023-01-02
• Subjects: Economic evaluation -- intersectoral costs and benefits -- PECUNIA -- reference unit costs -- valuation; Pharmacology -- Economic aspects -- Periodicals; 338.47615105
• DOI: 10.1080/14737167.2023.2152331
• Languages: English
• ISSNs: 1473-7167

Abstract

ABSTRACT: Background: Health problems can lead to costs in the education sector. However, these costs are rarely incorporated in health economic evaluations due to the lack of reference unit costs (RUCs), cost per unit of service, of education services and of validated methods to obtain them. In this study, a standardized unit cost calculation tool developed in the PECUNIA project, the PECUNIA RUC Template for services, was applied to calculate the RUCs of selected education services in five European countries. Methods: The RUCs of special education services and of educational therapy were calculated using the information collected via an exploratory gray literature search and contact with service providers. Results: The RUCs of special education services ranged from €55 to €189 per school day. The RUCs of educational therapy ranged from €6 to €25 per contact and from €5 to €35 per day. Variation was observed in the type of input data and measurement unit, among other. Discussion: The tool helped reduce variability in the RUCs related to costing methodology and gain insights into other aspects that contribute to the variability (e.g. data availability). Further research and efforts to generate high quality input data are required to reduce the variability of the RUCs.