Patricia Cubi-Molla
OHE Lunchtime Seminar May 18, 2016London
Age, utilities, and self-reported health: issues for HTA
Introduction
• Patricia Cubi-Molla
• My presentation:
• This presentation is based on findings from a research study performed by OHE and Roche Products Ltd and funded by Roche Products Ltd. (Sept 2015- Jan 2016)
• Co-authors: Koonal Shah1, Jamie Garside2, Mike Herdman1 and Nancy Devlin1
• There was no external funding for this paper
• Views expressed in this seminar are those of the authors
1 The Office of Health Economics; 2 Roche Products Ltd
Outline
• Motivation
• What is happening, and why
• Aims
• Data
• Methodology
• Results
• Discussion
• Conclusion:There is something going on there, butmore research is needed
Motivation
• NICE TA359:
The average utility of the idelalisib plus rituximab arm (0.8127) is higher than that of the general population (0.7717), matched by age and gender
• NICE TA343:
“The Committee agreed that it was not plausible that the utility value for progression-free survival off treatment was higher than the utility value for members of the general public without the disease”
Motivation
Source: (Ara & Brazier, 2009)
Example: mean EQ-5D scores sub-grouped by history of cardiovascular disease (CVD) and number of CV conditions.
Motivation
Is it possible that a central estimate of HRQoL for a group of elderly patients could exceed that of the general population with the same age
Explanations for this?
Motivation
Explanations for this?
[1] Health of older individuals tends to be more variable than that of younger individuals
Motivation
Explanations for this?
[1] Health of older individuals tends to be more variable than that of younger individuals
[2] The same underlying state of health is interpreted differently by individuals of different ages
Example:
MotivationEQ-5D-3L
Profiles:
non-patients 11211,
patients 21111
X
X
X
X
X
X
X
X
X
X
Motivation
Imagine: non-patients 11211, patients 21111
Motivation
Explanations for this?
[1] Health of older individuals tends to be more variable than that of younger individuals
[2] The same underlying state of health is interpreted differently by individuals of different age
- Artificial effect or “Artefact”
- Real effect or “Preference heterogeneity”
Motivation
“Artefact”: how are you eliciting the values? (process x person)
E.g. Time Trade-Off (TTO)
using the MVH method
Motivation
“Artefact”: how are you eliciting the values? (process x person)
E.g. Time Trade-Off (TTO)
Older do not find the “TTO worse than death” scenario plausible (Robinson et al., 1997)
TTO values incorporate individuals’ time preferences (Essink-Bot et al., 2007)
Time horizon: 10 years vs. life expectancy (Van
Nooten & Brouwer, 2004)
Motivation
“Preference heterogeneity”: what if the elderly DO prefer differently? (person)
E.g. Time Trade-Off (TTO), Vignettes
Older seem to be less prepared to live for the next 10 years in a severe health state (Robinson et al., 1997)
Older individuals give more weight to functional limitations and to social functioning, and less weight to morbidities and pain experience, than the younger (Hofman et al., 2015)
Motivation
Explanations for this?
[1] Health of older individuals tends to be more variable than that of younger individuals
[2] The same underlying state of health is interpreted differently by individuals of different ages
- Artificial effect or “Artefact”
- Real effect or “Preference heterogeneity”
[3] Other effects related to how long a person is living in a particular health state (adaptation, response shift…) Not discussed here
Aims
We address the following questions:
[1] Does health of older individuals tend to be more variable than that of younger individuals?
• Can we find significant differences in the variation of self-reported HRQoL values around a central estimate, when comparing different age groups?
• How do these differences (if any) depend on the central measure used (mean or median)?
[2] Is the same underlying state of health interpreted differently by individuals of different age?
• How does age affect respondents’ HRQoL valuations of hypothetical EQ-5D health states?
Aims
We address the following questions:
[3] What is the rationale for age-adjusting the utilities to be used in HTA?
• Artefact vs. preference heterogeneity?
• Is there a case for using utilities specific to age (or other relevant) sub-groups?
• Policy implications
(DISCUSSION)
Data
We use the database collected in 1993 for the “Measurement and Valuation of Health” (MVH) study
Stage 1: self-reporting the description of her health state
(EQ-5D classification system)
11231
Data
We use the database collected in 1993 for the “Measurement and Valuation of Health” (MVH) study
Stage 1: self-reporting the description of her health state
(EQ-5D classification system)
11231
Stage 2: self-rate her health (described by EQ-5D) on VAS
(best-worst)
43
0 worst
100 best
Data
We use the database collected in 1993 for the “Measurement and Valuation of Health” (MVH) study
Stage 1: self-reporting the description of her health state
(EQ-5D classification system)
Stage 2: self-rate her health (described by EQ-5D) on VAS
(best-worst)
Stage 3: ranking 13 EQ-5D described states + death + unconscious,
regarding the health states as lasting for 10 years and then death.
33333
DeathUnconscious
333211321211121
11111
Data
We use the database collected in 1993 for the “Measurement and Valuation of Health” (MVH) study
Stage 1: self-reporting the description of her health state
(EQ-5D classification system)
Stage 2: self-rate her health (described by EQ-5D) on VAS
(best-worst)
Stage 3: ranking 13 EQ-5D described states + death + unconscious,
regarding the health states as lasting for 10 years and then death. Stage 4: placing the health states ranked in stage 3 over the VAS (10y)
Data
We use the database collected in 1993 for the “Measurement and Valuation of Health” (MVH) study
Stage 1: self-reporting the description of her health state
(EQ-5D classification system)
Stage 2: self-rate her health (described by EQ-5D) on VAS
(best-worst)
Stage 3: ranking 13 EQ-5D described states + death + unconscious,
regarding the health states as lasting for 10 years and then death. Stage 4: placing the health states ranked in stage 3 over the VAS (10y)
33333
Death
Unconscious
33321
13212
11121
11111
Data
We use the database collected in 1993 for the “Measurement and Valuation of Health” (MVH) study
Stage 1: self-reporting the description of her health state
(EQ-5D classification system)
Stage 2: self-rate her health (described by EQ-5D) on VAS
(best-worst)
Stage 3: ranking 13 EQ-5D described states + death + unconscious,
regarding the health states as lasting for 10 years and then death. Stage 4: placing the health states ranked in stage 3 over the VAS (10y)
Stage 5: TTO valuation of the 13 EQ-5D described states, minus 11111
plus “Unconscious”
Dolan’s N3 algorithm MVH values or tariffs
Data
We use the database collected in 1993 for the “Measurement and Valuation of Health” (MVH) study
Stage 1: self-reporting the description of her health state
(EQ-5D classification system)
Stage 2: self-rate her health (described by EQ-5D) on VAS
(best-worst)
Stage 3: ranking 13 EQ-5D described states + death + unconscious,
regarding the health states as lasting for 10 years and then death. Stage 4: placing the health states ranked in stage 3 over the VAS (10y)
Stage 5: TTO valuation of the 13 EQ-5D described states, minus 11111
plus “Unconscious”
Own VAS
Own TTO
Dolan’s N3 algorithm MVH values or tariffs Hyp VAS
Hyp TTO
Data
We use the database collected in 1993 for the “Measurement and Valuation of Health” (MVH) study
• Final sample size of 2,997 (identical to the one originally used to calculate TTO values in Dolan, 1997)
• HRQoL measures:
What is described
HRQoL measure Label
Own health state (EQ-5D profile)
EQ VAS score (adjusted) ownVAS
MVH utility assigned to the EQ-5D profile (based on the algorithm in Dolan, 1997)
ownTTO
Hypothetical health state (42 EQ-5D profiles)
VAS values (adjusted) hypVAS
TTO values hypTTO
Methodology [1] Health of older more variable than
that of younger?
What is described
HRQoL measure Label
Own health state (EQ-5D profile)
EQ VAS score (adjusted) ownVAS
MVH utility assigned to the EQ-5D profile (based on the algorithm in Dolan, 1997)
ownTTO
Hypothetical health state (42 EQ-5D profiles)
VAS values (adjusted) hypVAS
TTO values hypTTO
Population health norms are constructed (plot with IQR, 95% central area, mean, median)
Testing differences in the variance across age (ANOVA)
Coefficients of Variation (CV, % ratio of the standard deviation to the mean)
Methodology [2] Different health preferences
by age?
What is described
HRQoL measure Label
Own health state (EQ-5D profile)
EQ VAS score (adjusted) ownVAS
MVH utility assigned to the EQ-5D profile (based on the algorithm in Dolan, 1997)
ownTTO
Hypothetical health state (42 EQ-5D profiles)
VAS values (adjusted) hypVAS
TTO values hypTTO
Average health values assigned to hypothetical health states, by age group (plot, OLS fitting)
Testing differences in valuations by age (with multiple comparisons corrections as Bonferroni adjusted significance of the difference between means)
Methodology [3] Rationale for age-adjusting the
utilities in HTA?
What is described
HRQoL measure Label
Own health state (EQ-5D profile)
EQ VAS score (adjusted) ownVAS
MVH utility assigned to the EQ-5D profile (based on the algorithm in Dolan, 1997)
ownTTO
Hypothetical health state (42 EQ-5D profiles)
VAS values (adjusted) hypVAS
TTO values hypTTO
“Put-together” discussion
Results [1] Health of older more variable than
that of younger?
TTO (MVH) tariffs from self-reported EQ-5D profiles
Population health norms (age and health)
0.2
.4.6
.81
EQ
-VA
S
20 25 30 35 40 45 50 55 60 65 70 75 80 85Age
median
mean
IQR
95%
Age interval: 5 years
(ownVAS)
Relationship Age-Health
EQ-VAS scores
Results [1] Health of older more variable than
that of younger?
Mean values dragged down by the instances of very poor health states experienced by older age groups?
Average level (1 = no problems, 2 = moderate problems, 3 = severe problems) in every dimension, by age group
1
1.2
51.5
1.7
5
2
2.2
52.5
Ave
rage
leve
l
18 23 28 33 38 43 48 53 58 63 68 73 78 83 88Age
Mobility
Self-care
Usual act.
Pain/disc
Anx/depr
Mean level at every dimension - by age
Results [1] Health of older more variable than
that of younger?
How “big” are the differences in variance across age?
Variability of TTO tariffs and EQ VAS scores (in relation to the mean) by age group
010
20
30
40
50
60
Co
effic
ient o
f vari
atio
n
20 25 30 35 40 45 50 55 60 65 70 75 80 85Age
ownTTO
ownVAS
Age interval: 5 years
(ownTTO and ownVAS)
Relationship Age-Variability of health
Initial 12% of variability
for those 18-22
53% variability for those
78-90
Results [2] Different health preferences
by age?
TTO values
Average values by age group, for a selection of (hypothetical) EQ profiles
VAS values
-1-.
8-.
6-.
4-.
20
.2.4
.6.8
1
TT
O v
alu
e
18 23 28 33 38 43 48 53 58 63 68 73 78 83Age
Mild:
21111
12111
11211
11121
11112 Moderate:22222
21133
21232
12223 Severe:33323
33333
Profiles
Age interval: 10 years
Trend for some mild, moderate and severe profilesAverage TTO value (hypTTO) by age group
-.4
-.2
0.2
.4.6
.81
VA
S s
co
re
18 23 28 33 38 43 48 53 58 63 68 73 78 83Age
Mild:
21111
12111
11211
11121
11112 Moderate:22222
21133
21232
12223 Severe:33323
33333
Profiles
Age interval: 10 years
Trend for some mild, moderate and severe profilesAverage VAS score (hypVAS) by age group
Results [2] Different health preferences
by age? TTO
From OLS regressions:
28 out of 42 profiles fit better into the quadraticmodel: TTO values = a + b*age + c*age2 + d*sex, with b>0, c<0
Age
TTO values
4835
All
Max
Results [2] Different health preferences
by age? TTO
From OLS regressions:
28 out of 42 profiles fit better into the quadratic model: TTO values = a + b*age + c*age2 + d*sex, with b>0, c<0
Age
TTO values
48
80%
Max42
Results [2] Different health preferences
by age? TTO
From Bonferroni:
Significant differences between means (1%) for 22
(out of 42) hypothetical
health profiles
Associated to health profiles with level 2 or
3 in “Mobility” or “Self-care”
18-27 28-37 38-47 48-57 58-67 68-90
18-27
28-37
38-47
48-57
58-67 33212 32211
68-90
12223
33212
33232
33323
33333
12111
12223
13332
22122
22222
22233
22323
23313
32211
32223
32313
32331
33212
33323
33333
12111
12211
12222
22122
22222
22233
22323
23232
32211
32223
32232
32331
33212
33323
33333
12111
12211
13332
21111
22122
22222
23321
32211
32223
32232
32331
33212
12111
12211
Results [2] Different health preferences
by age? TTO
“Smile plots”:(Newson et al. 2003)
[28-37],11112
[38-47],11112[48-57],11112[58-67],11112
[68-90],11112
[28-37],11113
[38-47],11113
[48-57],11113[58-67],11113
[68-90],11113[28-37],11121[38-47],11121
[48-57],11121
[58-67],11121[68-90],11121[28-37],11122
[38-47],11122[48-57],11122
[58-67],11122[68-90],11122
[28-37],11131[38-47],11131
[48-57],11131[58-67],11131
[68-90],11131[28-37],11133
[38-47],11133[48-57],11133
[58-67],11133
[68-90],11133
[28-37],11211
[38-47],11211[48-57],11211
[58-67],11211[68-90],11211[28-37],11312
[38-47],11312[48-57],11312
[58-67],11312
[68-90],11312[28-37],12111
[38-47],12111
[48-57],12111
[58-67],12111
[68-90],12111
[28-37],12121
[38-47],12121
[48-57],12121
[58-67],12121[68-90],12121
[28-37],12211[38-47],12211
[48-57],12211
[58-67],12211
[68-90],12211
[28-37],12222
[38-47],12222
[48-57],12222
[58-67],12222
[68-90],12222[28-37],12223
[38-47],12223
[48-57],12223
[58-67],12223
[68-90],12223
[28-37],13212
[38-47],13212[48-57],13212
[58-67],13212
[68-90],13212
[28-37],13311[38-47],13311[48-57],13311
[58-67],13311
[68-90],13311
[28-37],13332[38-47],13332
[48-57],13332
[58-67],13332
[68-90],13332
[28-37],21111
[38-47],21111
[48-57],21111
[58-67],21111
[68-90],21111
[28-37],21133[38-47],21133
[48-57],21133
[58-67],21133
[68-90],21133
[28-37],21222
[38-47],21222
[48-57],21222
[58-67],21222
[68-90],21222
[28-37],21232
[38-47],21232
[48-57],21232
[58-67],21232
[68-90],21232
[28-37],21312[38-47],21312
[48-57],21312
[58-67],21312
[68-90],21312
[28-37],21323[38-47],21323[48-57],21323[58-67],21323
[68-90],21323
[28-37],22112[38-47],22112
[48-57],22112
[58-67],22112
[68-90],22112
[28-37],22121
[38-47],22121
[48-57],22121
[58-67],22121
[68-90],22121
[28-37],22122
[38-47],22122[48-57],22122
[58-67],22122
[68-90],22122
[28-37],22222[38-47],22222
[48-57],22222
[58-67],22222
[68-90],22222
[28-37],22233
[38-47],22233
[48-57],22233
[58-67],22233
[68-90],22233
[28-37],22323
[38-47],22323
[48-57],22323[58-67],22323
[68-90],22323
[28-37],22331[38-47],22331
[48-57],22331
[58-67],22331
[68-90],22331
[28-37],23232[38-47],23232[48-57],23232
[58-67],23232
[68-90],23232
[28-37],23313
[38-47],23313[48-57],23313[58-67],23313
[68-90],23313
[28-37],23321[38-47],23321
[48-57],23321
[58-67],23321
[68-90],23321
[28-37],32211
[38-47],32211[48-57],32211
[58-67],32211
[68-90],32211
[28-37],32223
[38-47],32223
[48-57],32223
[58-67],32223
[68-90],32223
[28-37],32232[38-47],32232
[48-57],32232
[58-67],32232
[68-90],32232
[28-37],32313[38-47],32313[48-57],32313
[58-67],32313
[68-90],32313
[28-37],32331[38-47],32331
[48-57],32331
[58-67],32331
[68-90],32331
[28-37],33212[38-47],33212
[48-57],33212
[58-67],33212
[68-90],33212
[28-37],33232[38-47],33232
[48-57],33232[58-67],33232
[68-90],33232
[28-37],33321[38-47],33321
[48-57],33321
[58-67],33321[68-90],33321
[28-37],33323
[38-47],33323
[48-57],33323
[58-67],33323
[68-90],33323
[28-37],33333[38-47],33333
[48-57],33333
[58-67],33333
[68-90],33333
.05
.0002381
1
.1
.01
.001
.0001
.00001
P-v
alu
e
-.3 -.2 -.1 0 .1 .2Parameter estimate
TTO values = a + b*age28_37 ++ c*age38_47 + …
… + f*age68_90
(age18_27as reference)
With a probability of 0.9998, the
mean TTO value for the profile 33232 reported by those 68-90 years old, is 0.23 points lower than that of 18-27
years old
Results [2] Different health preferences
by age? VAS
Only 3 out of 42 of the profiles fit into the cubic model, and no profile fits into the quadratic one.
No pattern
No significant differences between means in most of the profiles
Discussion [3] Rationale for age-adjusting TTO?
• [1] Health of older more variable than that of younger? YES: thus it is plausible for a patient group utility to exceed that of the general population, due to increasing variance with age and that mean scores are skewed toward the lower IQR.
• [2] Different health preferences by age? It seems so (TTO).Artefact vs. preference heterogeneity? Can’t tell: results suggest artefact (VAS as a comparator which is not be affected by the time preference;
Essink-Bot et al., 2007); but can we trust this VAS?
Discussion [3] Rationale for age-adjusting TTO?
Who will mostly be affected by a potential age-adjustment?
o Patient groups with high baseline utility (usually moderate to mild health states).
Note that for patient groups with low baseline utility (severe health states), utility is more likely to be below that of the general population)
o The elderly population
Note that if patient utility for a health state is 0.8 then age-adjustment will not affect those patients whose age-matched general population scores are above 0.8 (usually those aged 60 years or less).
• Policy implications:
Establishing valid utilities and preferences is both an empirical issue (do the health state values of older people differ from those of younger people?) and a normative issue (whose health state values should be used in HTA decision making?).
Unclear how best to deal with extreme values associated with a particular sub-group of individuals –e.g. age.
If using mean values, then this could lead to a situation in which the extreme values have the ability to trump the majority values.
On the other hand, excluding the ‘vote’ of an age-related subgroup (however extreme or unusual their views may be) might contravene anti-discrimination legislation that health care decision makers are required to respect.
Discussion [3] Rationale for age-adjusting TTO?
• Arguments for/against:
“resources should be allocated so as to maximise the number of
QALYs gained” (Dolan, 2001)
“There may exist a sub-group of raters whose preferences are
sufficiently different to the whole-group average so as to produce
qualitatively different incremental cost-effectiveness ratios”
(Sculpher & Gafni, 2001)
This opens up the possibility that a technology is cost-ineffective on
average but cost-effective for a sub-group. The use of preference
sub-groups can therefore increase overall health and improve
efficiency by making the technology available only for the relevant
sub-group
Discussion [3] Rationale for age-adjusting TTO?
• Arguments for/against:
Sculpher & Gafni’s proposal has been criticised: e,g. “using the
average values of sub-groups defines these as sub-communities,
which […] is only consistent with a separate health service for each
of them” (Robinson & Parkin, 2002)
Sub-group values can reasonably be used to inform decisions being
made within a particular clinical context, but not when making “global
resource allocation decisions involving community preferences”
UK health care decision makers are required to respect anti-
discrimination legislation that states that patients must not be denied
(or have restricted) access to NHS care because of their race,
disability, age, gender, sexual orientation, religion, beliefs or
socioeconomic status. This suggests a “No”
Discussion [3] Rationale for age-adjusting TTO?
• Arguments for/against:
On the other hand, NICE’s Social Value Judgements guide notes
that its guidance might be able to refer to age if, amongst other
things, “there is good evidence, or good grounds, for believing
that because of their age patients will respond differently to the
treatment in question” (NICE, 2008) (p.24). In other words, age-
based sub-groups are acceptable if they are clinically relevant.
Discussion [3] Rationale for age-adjusting TTO?
• Need to better identify the reasons why older and younger people value the same health state differently
Differences in imagining or understanding the health states described
Differences in how these groups respond to the techniques used to elicit their preferences (artefact)
Preference heterogeneity need for a preference sub-
group analysis?
Discussion [3] Rationale for age-adjusting TTO?
Thanks!References:
Ara, R. & Brazier, J., 2009. Health related quality of life by age, gender and history of
cardiovascular disease: results from the Health Survey for England. HEDS
Discussion Paper 09/12.
Robinson, A., Dolan, P. & Williams, A., 1997. Valuing health status using VAS and TTO:
what lies behind the numbers? Social science & medicine, pp. 1289-1297.
Essink-Bot, Marie-Louise, et al. "Individual differences in the use of the response
scale determine valuations of hypothetical health states: an empirical study." BMC
health services research 7.1 (2007): 1.
Van Nooten, F. & Brouwer, W., 2004. The influence of subjective expectations about
length and quality of life on time trade-off answers. Health Economics, 13(8), p.819–823
Hofman, C. et al., 2015. The influence of age on health valuations: the older olds
prefer functional independence while the younger olds prefer less morbidity.
Clinical Interventions in Aging, Volume 10, p. 1131–1139.
Newson, Roger, and ALSPAC Study Team. "Multiple-test procedures and smile plots."
Stata J 3 (2003): 109-132.