Published in

BioMed Central, Health and Quality of Life Outcomes, 1(18), 2020

DOI: 10.1186/s12955-020-01638-z

Links

Tools

Export citation

Search in Google Scholar

Age dependency of EQ-5D-Youth health states valuations on a visual analogue scale

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Green circle
Published version: archiving allowed
Data provided by SHERPA/RoMEO

Abstract

Abstract Background Examine whether the use of different ages has an impact on the valuation of EQ-5D-Y health states for a hypothetical child or adolescent. Methods A survey was administered during regular classes among a convenience sample of university students in the Netherlands. Respondents first valued 6 EQ-5D-Y health states (2 mild, 2 moderate, 2 severe) describing a hypothetical child/adolescent of a certain age on a visual analogue scale (VAS). After 1 h respondents valued the same six health states again but this time the age of the child was different. Age differed between 4, 10 and 16 year old. Results Number of respondents was 311. No significant differences in valuation of the six health states were found between the age of 10 and 16. One moderate health state was valued significantly better for a 4-year old compared to a 10 and a 16 year old. The same applied for one severe health state that was valued higher for a 4-year old compared to a 16-year old. Conclusion Our study shows that, except for one moderate and one severe health state, other EQ-5D-Y health states were not valued significantly different when description of age differed. It is possible that problems in specific health domains are considered more severe for older children/adolescents compared to younger children who might still be dependent on their caregivers. Future research should examine whether our findings are also present in a broader set of EQ-5D-Y health states, with a choice-based method like TTO or DCE, and a more heterogeneous sample.