Valuing a paediatric preference-based measure: the CHU-9D-NL

Donna Rowen

School of Health and Related Research (ScHARR)

University of Sheffield

Project team

• Brendan Mulhern (University of

Technology Sydney, Australia)

• Katherine Stevens (University of Sheffield,

UK)

• Erik Vermaire (TNO, Netherlands)

• Acknowledgements: Richard Norman

(Curtin University)

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

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

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

CHU-9D ClassificationDimension 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

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)

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?

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

CHU-9D-NL valuation

• Whose values?

• Perspective?

• Elicitation technique and mode of

administration?

• Influenced by choice of population and

perspective

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

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

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

Example questionHealth 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 yourwork/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 yourdaily routine

You can join in with any activities You can join in with any activities

Modelling DCE with duration dataModel specification (Bansback et al, 2012):

𝜇𝑖𝑗 = 𝛼𝑖 + 𝛽1𝑡𝑖𝑗 + 𝛽′2𝐱𝑖𝑗𝑡𝑖𝑗 + 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

The sampleSample

n=1,276

%

Netherlands

n=16,979,120

%

Male 49.8% 49.8%

Age under 3016.5

18.7

30-3915.4

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)

Utility decrements

Utility decrements for UK and The Netherlands

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

ExamplesYou feel a little bit worriedYou feel a little bit sadYou have a little bit of painYou feel a little bit tiredYou feel a little bit annoyedYou have a few problems with your work/houseworkYou have a few problems sleepingYou have a few problems with your daily routineYou can join in with most activities

You feel very worriedYou feel very sadYou have a lot of painYou feel very tiredYou feel very annoyedYou can’t do work/houseworkYou can’t sleep at allYou can’t do your daily routineYou can join in with no activities

State 111111111

NL = 0.788

UK = 0.679

State 444444444

NL = -0.568

UK = 0.326

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

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?

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

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health state utility values. J Health Econ. 2012;31(1):306-18.

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specific health state values for economic evaluation: an application of profile case best worst

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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

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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

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preference-based CHU-9D using a discrete choice experiment with duration. HEDS

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