P Dolan1. 1. Sheffield Health Economics Group, University of Sheffield, UK.
Abstract
OBJECTIVES: Valuations for health states defined by the EQ-5D measure of health-related quality of life, which were elicited using the time trade-off method, from a large sample of the UK general population have previously been shown to be related to the age of the respondent. This paper presents population valuation tariffs for all EQ-5D health states for those aged 18-59 and those aged 60 and over. METHODS: Health state valuations were elicited from a representative sample of over 3000 members of the UK general population using one-to-one interviews. The same regression technique which had previously been used to estimate the EQ-5D tariff for the whole population is used in the current analysis. Additional variables were created which were the product of the original variables and the dummy attached to the age group of the respondent. An earlier qualitative study had suggested that some of the observed differences in valuations might have resulted from more older respondents doubting the plausibility of the 'worse than dead' scenario. Therefore, the model was estimated both when the scores of those aged 60 and over were unmodified and when they were modified to account for this possibility. RESULTS: The tariff based on values from those aged 60 and over, was considerably lower than that based on values from those aged 18-59. When changes between health states were calculated, the difference could be as much as 0.15 on a scale between -1 and 1. However, when the scores of older respondents were adjusted, on the assumption that their lower values for states rated as worse than dead were due to an experimental artefact, the differences between the valuation tariffs of the two age groups greatly diminished. CONCLUSION: These results suggest that whose values are used may have an important effect on health state valuations, and in turn, therefore, on resource allocation decisions, but more research is needed to understand the source of the differences between the values given by different population subgroups.
OBJECTIVES: Valuations for health states defined by the EQ-5D measure of health-related quality of life, which were elicited using the time trade-off method, from a large sample of the UK general population have previously been shown to be related to the age of the respondent. This paper presents population valuation tariffs for all EQ-5D health states for those aged 18-59 and those aged 60 and over. METHODS: Health state valuations were elicited from a representative sample of over 3000 members of the UK general population using one-to-one interviews. The same regression technique which had previously been used to estimate the EQ-5D tariff for the whole population is used in the current analysis. Additional variables were created which were the product of the original variables and the dummy attached to the age group of the respondent. An earlier qualitative study had suggested that some of the observed differences in valuations might have resulted from more older respondents doubting the plausibility of the 'worse than dead' scenario. Therefore, the model was estimated both when the scores of those aged 60 and over were unmodified and when they were modified to account for this possibility. RESULTS: The tariff based on values from those aged 60 and over, was considerably lower than that based on values from those aged 18-59. When changes between health states were calculated, the difference could be as much as 0.15 on a scale between -1 and 1. However, when the scores of older respondents were adjusted, on the assumption that their lower values for states rated as worse than dead were due to an experimental artefact, the differences between the valuation tariffs of the two age groups greatly diminished. CONCLUSION: These results suggest that whose values are used may have an important effect on health state valuations, and in turn, therefore, on resource allocation decisions, but more research is needed to understand the source of the differences between the values given by different population subgroups.
Authors: Jorge Cortés; Juan F Blanco; David Pescador; Nuria Asensio; Claudio Castro; Juan Moncada Herrera Journal: Qual Life Res Date: 2010-08-09 Impact factor: 4.147
Authors: Eve Wittenberg; Elkan Halpern; Nomia Divi; Lisa A Prosser; Sally S Araki; Jane C Weeks Journal: Qual Life Res Date: 2006-05 Impact factor: 4.147
Authors: A Simon Pickard; Yash J Jalundhwala; Helen Bewsher; Lisa K Sharp; Surrey M Walton; Glen T Schumock; Rachel N Caskey Journal: Qual Life Res Date: 2018-01-04 Impact factor: 4.147
Authors: Ken Stein; Matthew Dyer; Tania Crabb; Ruairidh Milne; Alison Round; Julie Ratcliffe; John Brazier Journal: Health Qual Life Outcomes Date: 2006-11-27 Impact factor: 3.186