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Survey presentation
1. Economic Evaluation of Health and Social
Care Interventions Policy Research Unit
Elicitation of societal preferences for Burden
of Illness, Therapeutic Improvement and End
of Life from a UK online panel
Donna Rowen
John Brazier, Clara Mukuria, Sophie Whyte, Anju Keetharuth, Aki Tsuchiya,
Phil Shackley, Arne Risa Hole
3. Value-based pricing: wider
considerations
• There is a ‘basic’ NHS cost per QALY threshold
• Costs and QALYs (through weighting) to take into account:
– conditions with greater ‘burden of illness’ as reflected in
QALY loss from a condition
– greater therapeutic innovation and improvement (size of
QALY gain)
– wider societal benefits (e.g. productivity and carer time)
(DH, 2010)
• Basic threshold adjusted to reflect the opportunity cost of
displaced activities weighted using same methods
• Price negotiated on the basis of the cost per weighted QALY
compared to the new threshold
4. Elicitation of societal preferences
Discrete choice experiment (DCE) survey using online
UK panel to elicit societal preferences for:
• Burden of illness (QALY loss from condition) (BOI)
• Therapeutic improvement (size of QALY gain from
treatment) (TI)
• End of life (e.g. NICE weights QALY gain more where
expected survival is less than 24 months and survival
gain is 3 months or more) (EOL)
8. Preparatory work
• Review on the social value of a QALY
• Large preparatory online survey to pilot DCE and
person trade-off questions
• Qualitative survey to further explain the findings
of the large preparatory online survey
• Six-arm online and face-to-face survey examining
different framings of questions and mode of
administration
• For further details see Brazier et al (2013)
9. Attributes to capture BoI, TI and EoL
Attribute
Levels
No. of
levels
Life expectancy without condition
5, 20, 40, 80
4
(years)
Life expectancy without treatment
3 months, 1 year, 2 years, 5 years, 7
(years)
10 years, 30 years, 60 years
Survival gain from treatment
0, 3 months, 6 months, 1 year, 3
7
years, 10 years, 60 years
Health before treatment (%)
10, 20, 40, 60, 80
5
Health gain from treatment (%)
0, 2, 5, 10, 30, 60
6
Note: Extra scenarios with small life expectancy without treatment and survival gains estimated for 5
years normal life expectancy
10. Attributes to capture BoI, TI and EoL
•Same across pairs
Attribute
Levels
Life expectancy without condition
•Respondents see only 1
No. of
•Scenarios selected using
separate design for each levels
level
5, 20, 40, 80
4
(years)
Life expectancy without treatment
3 months, 1 year, 2 years, 5 years, 7
(years)
10 years, 30 years, 60 years
Survival gain from treatment
0, 3 months, 6 months, 1 year, 3
7
years, 10 years, 60 years
Health before treatment (%)
10, 20, 40, 60, 80
5
Health gain from treatment (%)
0, 2, 5, 10, 30, 60
6
Note: Extra scenarios with small life expectancy without treatment and survival gains estimated for 5
years normal life expectancy
11. Main survey design
• Internet panel sample – allows for large numbers, collection fast
Survey content
•
•
•
•
Introduction video played
2 practice and 10 real DCE questions
9 questions asking general attitudes assessed in survey
17 questions on ‘you and your health’ and understanding
Design
• 4 life expectancies without the condition (5, 20, 40, 80 years)
• Both small and large starting point and gains in health and survival
• 580 pairs selected using D-efficient design. Impossible scenarios
not included
• 58 ‘card blocs’ in total across 4 life expectancies without the
condition
14. Modelling
• Estimation using conditional logit regression model with
clustering of the standard errors at the respondent level
• Dependent variable = Choice of patient group A or
patient group B
• Estimated for pooled data and each of the 4 separate life
expectancies without the condition
Basic model:
V1 = β1 QALY + β2 BOI (or EOL)
V2 = β1 QALY + β2 BOI (or EOL) + β3 QALY2
Where a positive β3 would suggest TI
15. Marginal rate of substitution
The marginal rate of substitution between BOI and QALY
(or EOL and QALY) provides a measure of the weight of
BOI in terms of the QALY gain equivalent
e.g. MRS1 = -β2 /β1
MRS2 = -β2 /(β1+ 2*β3QALY)
So MRS2 varies by size of QALY
16. Main results
Sample
• 3669 respondents (55% response rate of those accessing the
survey)
• Representative for age and gender but more unemployed
respondents and less healthy than UK norm
Practice questions
• PQ1 – Majority chose larger QALY gain (90.7-92.5%)
• PQ2 - No evidence of preference for higher BOI (46.8% - 54.3%)
Regression results
• QALYs matter but at a decreasing rate – no support for TI
• BOI matters – but not always significant
• EOL is significant
• Coefficients change for different variants of life expectancy without
the condition
19. Weights for BOI
• MRS(1) of 1 more unit of
BOI is -0.040 QALYs
95% CI (-0.068, -0.013)
• Generated using all data
QALY
gain
MRS(2)
0.05
- 0.063
0.1
- 0.063
0.5
- 0.063
1
- 0.064
2
- 0.066
5
- 0.073
10
- 0.087
20
- 0.141
20. Weights for EOL
• MRS(1) of moving from
not being EOL to being
EOL is -3.331 QALYs
95% CI (-3.711, -2.950)
• Generated using all data
QALY
gain
MRS(2)
0.05
-2.170
0.1
-2.173
0.5
-2.197
1
-2.229
2
-2.294
5
-2.516
10
-3.000
20
-4.875
21. Attitudinal questions
• Modal response across questions was that the
same priority should be given to all patients
• Overall responses indicated most respondents
believed the NHS should give preference to the
group with the largest treatment gain over BOI or
EOL
• Some support for BOI - approx 43%
• Some support for EOL – approx 45-57%
– only 6% if it is at the natural end of their life
– only 4% if patients will live in very poor health
• Little support for TI
22. Discussion
• Social value of a QALY is not equal for all recipients
• Results provide some support for BOI and support for
EOL
– Weights for BOI and EOL should not both be used
– Finding for EOL contrary to Shah et al (2012) and Linley
and Hughes (2013)
• No support for TI – arguably consistent with literature
• In attitudinal questions the modal response was that
the same priority should be given to all patients
– Questions do not involve trade-offs
• Weights estimated using MRS using pooled data
– choice of variant and specification affects results
23. Limitations
•
•
•
•
Limited range of characteristics (e.g. no age)
Online data collection
Additive design
Robustness - many respondents may have continued
to make the mistake of assuming the profiles were
for them even after feedback
– Identified respondents who chose a profile with smaller
QALY gain and lower BOI but larger number of lifetime
QALYs
– Once these were excluded (remaining n=2247) then BOI
coefficients were all positive, significant and larger than for
the whole sample
24. Summary
•
•
•
•
Some support for BOI
QALY gain matters – but no support for TI
Support for EOL
Social value of a QALY is not equal for all
recipients
25. References
• Brazier J, Rowen D, Mukuria C, Whyte S, Keetharuth A, Risa Hole A,
Tsuchiya A, Shackley P. Eliciting societal preferences for burden of illness,
therapeutic improvement and end of life for value based pricing. EEPRU
Research Report 01/13, Universities of Sheffield and York; 2013
http://www.eepru.org.uk/VBP%20survey%20research%20report.pdf
• Department of Health. Value based pricing: impact assessment. London.
Department of Health; 2010
http://webarchive.nationalarchives.gov.uk/20130107105354/http://www.
dh.gov.uk/en/Consultations/Liveconsultations/DH_122760.
• Linley WG and Hughes DA. Societal views on NICE, cancer drugs fund and
value-based pricing criteria for prioritising medicines: A cross-sectional
survey of 4118 adults in Great Britain. Health Economics 2013;22: 948-964
• Shah KK, Tsuchiya A, Hole AR and Wailoo A Valuing health at the end of
life: A stated preference discrete choice experiment. Report by NICE DSU;
2012
26. Economic Evaluation of Health and Social
Care Interventions Policy Research Unit
Elicitation of societal preferences for Burden
of Illness, Therapeutic Improvement and End
of Life from a UK online panel
Donna Rowen
John Brazier, Clara Mukuria, Sophie Whyte, Anju Keetharuth, Aki Tsuchiya,
Phil Shackley, Arne Risa Hole
Acknowledgements: Angela Robinson (University of East Anglia) and Gavin Roberts
(DH)
