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|Title:||Valuing Child Health Utility 9D health states with young adults: insights from a time trade off study|
|Citation:||Applied Health Economics and Health Policy, 2015; 13(5):485-492|
|Publisher:||Springer International Publishing|
|Julie Ratcliffe, Gang Chen, Katherine Stevens, Sandra Bradley, Leah Couzner, John Brazier, Michael Sawyer, Rachel Roberts, Elisabeth Huynh, Terry Flynn|
|Abstract:||In contrast to the proliferation of studies incorporating health state values from adults of all ages, relatively few studies have reported upon the application of the time trade off (TTO) approach to generate health state values from populations of younger adults. This study sought to employ a conventional TTO approach to obtain values for a selection of Child Health Utility 9D (CHU9D) health states from a sample of young adults aged 18-29 years and to compare with the values generated from application of the original UK adult standard gamble scoring algorithm and the Australian adolescent scoring algorithm.A convenience sample of Flinders University undergraduate students aged 18-29 years were invited to participate in an interviewer administered conventional TTO task to value a series of five CHU9D health impairment states using the widely used variant developed by the York EQ-5D team.A total of 152 students within the target age range were approached to participate in the study of whom n = 38 consented to participate, giving an overall participation rate of 25%. With the exception of one health state, the mean TTO values were consistently lower than those generated from application of the original scoring algorithm for the CHU9D elicited with adults of all ages. A significant proportion of participants (n = 17, 45%) considered the most severe CHU9D (PITS) state to be worse than death.This study adds to a growing body of evidence indicating that the values attached to identical health states are typically lower for younger people in comparison with adults of all ages and dependent upon the elicitation method utilised. The values obtained are applicable for re-scaling raw CHU9D health state values obtained from younger adolescent samples using profile case best-worst scaling.|
|Keywords:||Humans; Age Factors; Health Status; Algorithms; Quality of Life; Adolescent; Adult; Female; Male; Young Adult; Patient Preference; Surveys and Questionnaires|
|Rights:||© Springer International Publishing Switzerland 2015|
|Appears in Collections:||Medicine publications|
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