Age, utilities, and self-reported health: issues for HTA

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


[1] Health of older individuals tends to be more variable than that of younger individuals

Motivation



[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



[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



[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




11231

Stage 2: self-rate her health (described by EQ-5D) on VAS

(best-worst)

43

0 worst

100 best

Data





(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





(best-worst)


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





(best-worst)



33333

Death

Unconscious

33321

13212

11121

11111

Data





(best-worst)



Stage 5: TTO valuation of the 13 EQ-5D described states, minus 11111

plus “Unconscious”

Dolan’s N3 algorithm MVH values or tariffs

Data





(best-worst)



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


• 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




ownTTO



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




ownTTO



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




ownTTO



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


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


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


(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


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


Trend for some mild, moderate and severe profilesAverage VAS score (hypVAS) by age group


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


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


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


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


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?


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.


• 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



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”



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.


• 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?


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.

Presentations & Public Speaking

Age, utilities, and self-reported health: issues for HTA