This document outlines a study valuing the CHU-9D-NL, a pediatric preference-based quality of life measure, using an online discrete choice experiment with Dutch adults. It discusses challenges in valuing pediatric quality of life, including whose preferences to use and perspective to adopt. The study used adult general population preferences from their own perspective to allow comparability. Results generated sensible utility decrements, though with some inconsistencies and contrast to prior UK valuations. The study provides insights into valuing pediatric quality of life but also raises issues around appropriate perspective and population.

1. Valuing a paediatric
preference-based measure:
the CHU-9D-NL
Donna Rowen
School of Health and Related Research (ScHARR)
University of Sheffield

2. Project team
• Brendan Mulhern (University of
Technology Sydney, Australia)
• Katherine Stevens (University of Sheffield,
UK)
• Erik Vermaire (TNO, Netherlands)
• Acknowledgements: Richard Norman
(Curtin University)

3. Introduction
• Child valuation
• Valuing the CHU-9D-NL in the
Netherlands
• Outline the CHU-9D
• Normative decisions
• Methodology
• Results
• Comparison to CHU-9D-UK value set

4. Child valuation
• Whose values?
• Adult valuation arguments often focus around whether
values should be elicited from general population or
patients
• For children the argument is less around experience of
the health state and more around the population –
General population? Adolescents?
• Which perspective?
• Elicitation technique and mode of administration?
• Comparability with adult values for consistency
and combined use in HTA e.g. vaccinations

5. CHU-9D
• CHU-9D is a paediatric preference-based
measure of quality of life (Stevens, 2009;2010)
• Developed using qualitative interviews with
over 70 school children aged 7-11 in the UK
• Dimensions and wording selected using the
transcripts and Framework analysis
• 9 dimensional self-completed measure
• Translated into 6 languages including Dutch
• Suitable for self-report in ages 7-17 years
• Used in over 180 studies to date

6. CHU-9D Classification
Dimension Wording Severity levels
Worried I don’t feel worried today A little bit / a bit / quite / very
Sad I don’t feel sad today A little bit / a bit / quite / very
Pain I don’t have any pain today A little bit / a bit / quite a lot / a lot
Tired I don’t feel tired today A little bit / a bit / quite / very
Annoyed I don’t feel annoyed today A little bit / a bit / quite / very
School work
/homework
I have no problems with my
schoolwork / homework
today
A few problems / some problems
/ many problems / can’t do
Sleep Last night I had no problems
sleeping
A few problems / some problems
/ many problems / can’t sleep
Daily routine I have no problems with my
daily routine today
A few problems / some problems
/ many problems / can’t do
Able to join in
activities
I can join in with any
activities today
Most / some / a few / no

7. Existing value sets
• UK – adults using standard gamble (SG)
(Stevens, 2012)
• Australia – adolescents using best-worst scaling
– adults using best-worst scaling
(BWS)
(both anchored using time trade-off)
(Ratcliffe et al, 2012;2015;2016)

8. Whose values?
Adult preferences
• Tax payers
• Understanding of tasks
• Able to answer questions involving ‘dead’
• Do not necessarily reflect child or young adolescent preferences
Child and adolescent preferences
• Children and adolescents experience the health states
• Adolescents have understanding of some tasks e.g. BWS, DCE
• Children 7-11 unlikely to fully understand tasks
• Are adolescent preference weights more appropriate for 7-11 year
olds than adult preferences?
• Unable to answer questions involving ‘dead’ so require adult (or
young adult) data e.g. standard gamble or time trade-off to anchor
the states on 1-0 full health-dead scale
• Is this preferable to using only adult values?

9. Perspective?
If asking adults, they could be asked to imagine:
• The health state in the context of a 10 year old child
• Which child matters
• Will incorporate respondents views about children and child
health (may think it is much worse for a child to be sick, may
not want to sacrifice years of life for a child)
• The health states for themselves as a child
• Recall bias, also some of the concerns raised above
• The health state for themselves
• ‘Veil of ignorance’, value is not influenced by respondents
views about children and child health (comparability)
• If society values child health more, QALY weighting or
deliberation could be used at decision level for HTA e.g. NICE

10. CHU-9D-NL valuation
• Whose values?
• Perspective?
• Elicitation technique and mode of
administration?
• Influenced by choice of population and
perspective

11. CHU-9D-NL valuation
• Whose values?
Adult general population sample
• Perspective?
Themselves
(reworded school work/homework dimension
to work/house work)
• Elicitation technique and mode of
administration?
Online DCE with duration

12. The survey and sample
• Participants recruited via existing online panel,
paid via points from market research agency
• Information sheet, informed consent
• Sociodemographic questions
• CHU-9D and EQ-5D-5L
• 1 practice DCE plus 12 DCE questions

13. Selecting profiles
• Profiles selected using Ngene software taking
into account regression model specifications
• 3 dimensions fixed across both profiles in a pair,
built into design
• Duration of 1, 4, 7, 10 years – successfully used
previously for other surveys
• Selected 204 choice sets and allocated each to
one of 17 blocks of 12 for each survey version
using a D-Optimal design
• Choice sets randomly ordered within a block for
each participant but dimension order fixed

14. Example question
Health description A Health description B
You live for 10 years with the following
then you die:
You live for 1 year with the following
then you die:
You feel a little bit worried You feel a little worried
You feel a bit sad You feel very sad
You have a bit of pain You don’t have any pain
You feel quite tired You feel quite tired
You feel quite annoyed You don’t feel annoyed
You can’t do work/housework You have many problems with your
work/housework
You have a few problems sleeping You can’t sleep at all
You can’t do your daily routine You have a few problems with your
daily routine
You can join in with any activities You can join in with any activities

15. Modelling DCE with duration
data
Model specification (Bansback et al, 2012):
𝜇𝑖𝑗 = 𝛼𝑖 + 𝛽1 𝑡𝑖𝑗 + 𝛽′2 𝐱 𝑖𝑗 𝑡𝑖𝑗 + 𝜀𝑖𝑗
𝜇𝑖𝑗 represents the utility of individual 𝑖 for profile j
𝑡𝑖𝑗 represents time
𝛽1 is the coefficient for duration in life years t
𝛽′2 represents the coefficients on the 36 interaction terms of duration
and attribute levels
• Anchored using the Marginal Rate of Substitution
• Divide through by the duration coefficient:
𝛽2𝑖𝑗
𝛽1
• Conditional logit model with robust standard errors

16. The sample
Sample
n=1,276
%
Netherlands
n=16,979,120
%
Male 49.8% 49.8%
Age under 30
16.5
18.7
30-39
15.4
15.3
40-49 18.8 18.9
50-59 17.0 17.7
60+ 32.2 29.4
Employed 56.0 53.6
Married 63.8 62.3
EQ-5D-5L NL
Mean (s.d.)
0.795 (0.230) 0.869 (0.170)

17. Utility decrements

18. Utility decrements for UK
and The Netherlands

19. Regression models
First model NL DCE UK SG (OLS)
Statistically
significant
31 (out of 37) 30 (out of 36)
Incorrect sign 2 0
Inconsistencies 4 14

20. Examples
You feel a little bit worried
You feel a little bit sad
You have a little bit of pain
You feel a little bit tired
You feel a little bit annoyed
You have a few problems with your work/housework
You have a few problems sleeping
You have a few problems with your daily routine
You can join in with most activities
You feel very worried
You feel very sad
You have a lot of pain
You feel very tired
You feel very annoyed
You can’t do work/housework
You can’t sleep at all
You can’t do your daily routine
You can join in with no activities
State 111111111
NL = 0.788
UK = 0.679
State 444444444
NL = -0.568
UK = 0.326

21. Robustness
• Models re-estimated excluding:
• All responses less than 5 seconds
• All responses over 10 minutes
• Excludes 9.3% of responses
• Slightly larger coefficients
• Same problems with inconsistencies and
incorrect signs
• One exception that work levels 3 and 4 are consistent

22. Discussion
• Valuation of CHU-9D-NL using online DCE with duration
with adult general population sample feasible and
generated sensible results
• Large contrast in size of utility decrements to UK SG with adult
general population – with more consistent coefficients
• Problems of dimension framing and interpretation of
“work/housework” rather than “school work/homework”
• In the Netherlands income loss from being off work due to illness
is minimal
• Should child or adult preferences be used to value child
health states?
• What is the appropriate perspective?
• Does the use of ‘informed’ adult values offer a solution?

23. Discussion
• Which values are most appropriate for informing
resource allocation decisions?
• Complication of generating QALYs from birth or toddlers
through to adulthood and beyond
• For comparability reasons could argue for use of adult
general population values elicited from own perspective
• Utility values are not affected by additional factors
such as views around child or child health
• Arguably is the health state that is important not who
experiences it or the cause
• Potentially raises issue of QALY weights or different
threshold

24. References
• Bansback N, Brazier J, Tsuchiya A, Anis A. Using a discrete choice experiment to estimate
health state utility values. J Health Econ. 2012;31(1):306-18.
• Ratcliffe J, Flynn T, Terlich F, Brazier J, Stevens K, Sawyer M. Developing adolescent
specific health state values for economic evaluation: an application of profile case best worst
scaling to the Child Health Utility-9D. Pharmacoeconomics 2012; 30:713-27.
• Ratcliffe J, Chen G, Stevens K, Bradley S, Couzner L, Brazier J, Sawyer M, Roberts R,
Huynh E, Flynn T. Valuing Child Health Utility 9D Health States with Young Adults: Insights
from A Time Trade Off Study. Applied Health Economics and Health Policy, 2015; 13:485-492
• Ratcliffe J, Huynh E, Stevens K, Brazier J, Sawyer M, Flynn, T. Nothing about us without us?
A comparison of adolescent and adult health-state values for the child health utility-9D using
profile case best-worst scaling. Health Economics, 2016; 25: 486-496
• Rowen D, Mulhern B, Stevens K, Vermaire E. Estimating a Dutch value set for the paediatric
preference-based CHU-9D using a discrete choice experiment with duration. HEDS
Discussion Paper 2017, University of Sheffield, available online.
• Stevens, K J. Working With Children to Develop Dimensions for a Preference-Based,
Generic, Paediatric Health-Related Quality-of-Life Measure. Qualitative Health Research.
2010; vol. 20: 340 - 351
• Stevens, K J. Developing a descriptive system for a new preference-based measure of
health-related quality of life for children. Quality of Life Research. 2009; 18 (8): 1105-1113
• Stevens K. Valuation of the Child Health Utility 9D Index. Pharmacoeconomics 2012; 30:8:
729-747.