Valuing QALYs in Relation to Equity Considerations Using a Discrete Choice Experiment
Valuing QALYs in Relation to Equity Considerations Using a Discrete Choice Experiment
Liesbet van de Wetering 0 1
Job van Exel 0 1
Ana Bobinac 0 1
Werner B. F. Brouwer 0 1
0 Institute of Health Policy and Management, Erasmus University Rotterdam , 3000 DR Rotterdam , The Netherlands
1 Werner B. F. Brouwer
Background To judge whether an intervention offers value for money, the incremental costs per gained qualityadjusted life-year (QALY) need to be compared with some relevant threshold, which ideally reflects the monetary value of health gains. Literature suggests that this value may depend on the equity context in which health gains are produced, but the value of a QALY in relation to equity considerations has remained largely unexplored. Objective The objective of this study was to estimate the social marginal willingness to pay (MWTP) for QALY gains in different equity subgroups, using a discrete choice experiment (DCE). Both severity of illness (operationalized as proportional shortfall) and fair innings (operationalized as age) were considered as grounds for differentiating the value of health gains. Methods We obtained a sample of 1205 respondents, representative of the adult population of the Netherlands. The data was analysed using panel mixed multinomial logit (MMNL) and latent class models.
& Liesbet van de Wetering
Results The panel MMNL models showed
counterintuitive results, with more severe health states reducing the
probability of receiving treatment. The latent class models
revealed distinct preference patterns in the data. MWTP
per QALY was sensitive to severity of disease among a
substantial proportion of the public, but not to the age of
Conclusion These findings emphasize the importance of
accounting for preference heterogeneity among the public
on value-laden issues such as prioritizing health care, both
in research and decision making. This study emphasises the
need to further explore the monetary value of a QALY in
relation to equity considerations.
Key Points for Decision Makers
In a study estimating the social marginal willingness
to pay (MWTP) for QALY gains among the general
public, we observed distinct preference patters with
respect to the allocation of healthcare resources.
Among a considerable proportion of the public,
MWTP per QALY was sensitive to the severity of
illness. It was not at all sensitive to the age of care
These findings emphasize the importance of
accounting for heterogeneity in preferences among
the public on value-laden issues such as prioritizing
health care, both in research and decision making.
Findings about equity considerations are, however,
not consistent across studies. This underlines the
need to further explore the monetary value of a
QALY in relation to equity considerations.
Cost-utility analysis is increasingly used to inform
allocation decisions about scarce healthcare resources. To
evaluate whether an intervention yields good value for money,
the incremental costs per gained QALY (quality-adjusted
life-year) must be judged against some monetary threshold
value. The nature of this threshold is a matter of debate.
One stream of literature considers it as the opportunity
costs of spending within a fixed healthcare budget, while
the other considers it to represent the consumption value of
health gains . Here, we take the latter view and, more
precisely, consider the appropriate threshold to reflect the
social willingness to pay (WTP) for a QALY gain [2–4]. In
other words, the threshold expresses the maximum
acceptable cost to society for a QALY gained through an
intervention. Without such a threshold the results of a
costutility analysis are of limited value to healthcare decision
makers. Somehow, they must judge whether a treatment
with a cost-per-QALY ratio of, say, €50,000 offers value
for money and should be reimbursed . It need not
surprise that this threshold has generated much debate.
Societally, the idea of using a threshold expressing the value of
health in monetary terms to decide about funding
treatments has been contested . Scientifically, the debate is
especially about how to set a threshold, and whether there
should be a fixed threshold or one that could vary with
societal preferences for QALYs.
Regarding the latter issue, it is important to
acknowledge that accumulating evidence suggests that the public
prefers some QALY gains over others (e.g., those in young
children over those in elderly) [6–9]. This suggests that the
social value of a QALY does not exist  but that this
value may vary with, for example, characteristics of the
disease and the beneficiaries of treatment . The use of a
single threshold in judging the results from economic
evaluations would therefore not align with societal
preferences. The distributional preferences of society can be
incorporated in the decision framework by applying a more
flexible threshold or, under a fixed threshold, by applying
equity weights to QALYs [5, 12, 13].
Although in most countries the threshold is still rather
implicit, differentiation between QALY gains of different
types or to different beneficiaries already exists in actual
decision making. In the UK, the National Institute for
Health and Care Excellence (NICE) recently formulated a
decision rule explicitly giving higher value to costly
lifeprolonging end-of-life drugs. Under the assumption that all
QALY gains should be valued equally, these interventions
would probably have exceeded the threshold range. The
new decisions rule explicitly considers the ‘‘magnitude of
the additional weight that would need to be assigned to the
QALY benefits… for the cost-effectiveness of the
technology to fall within the current threshold range’’ . This
exception may prove to represent a first step in defining
more general rules using a flexible threshold, depending on
the context in which QALYs are gained . The
Netherlands has developed a decision-making framework
in which the relationship between equity considerations
and the value of a QALY has been made more explicit. The
value of a QALY increases with the severity of illness in
the target population, the latter being expressed using the
concept of proportional shortfall [12, 16].
A fundamental question in the development of a
decision framework using a flexible threshold is which equity
principle(s) should be the basis for differentiation. In
literature, the equity principles ‘severity of illness’ and ‘fair
innings’ have been regularly proposed as suitable
candidates. The principle of severity of illness considers severity
at the time of intervention and expected
severity—including death—in future years in case of non-intervention [17,
18]. The fair innings approach, advocated by Alan
Williams , is based on the assumption that everyone is
entitled to some ‘normal’ span of life or lifetime health
achievement. As a result, a relatively high priority would
be given to those who fall short of this norm and a
relatively low priority to those who exceed this norm.
Although obviously not without problems, age is often
taken as a proxy for lifetime health achievement. Whether
severity of illness or fair innings better reflects the
distributional preferences of society is still a matter of debate,
but both principles rely on justified normative arguments
. Proportional shortfall, the equity principle used in the
Netherlands, is based on the proportion of remaining
lifetime health lost due to some disease  and could
therefore also be seen as a measure of severity of illness
[12, 19]. Proportional shortfall measures the fraction of
QALYs lost due to illness relative to remaining life
expectancy in absence of the disease, on a scale from 0 (no
loss) to 100 (complete loss of remaining health) .
Empirical studies show mixed findings with respect to
the direction and strength of the preferences for age and
severity. These variations might be caused by the framing
of the concepts, or by context and methodological
differences between studies [19, 21]. Moreover, often only
particular aspects of potential value are investigated (e.g. only
age or only severity) rather than, arguably more relevant,
combinations. This hampers not only definite conclusions
about support for specific decision rules, but also about the
exact values (weights) attached to different QALY gains.
In that context, it also needs noting that the monetary
value of a QALY and equity weights have both received
quite some attention in the literature, but typically not
jointly in one study . Most WTP studies focus on the
individual perspective, asking respondents to value
changes in their own health, thus ignoring equity
considerations. In the context of healthcare allocation decisions it
seems to be more appropriate to consider the social value
of a QALY, defined by the amount of their own
consumption individuals are willing to forego in order to
contribute to a health gain achieved in society . Other
studies have explored public preferences for a variety of
equity principles and characteristics of the beneficiary or
the disease, but these studies have not addressed the
monetary valuation [23, 24]. To illustrate, a recent
systematic review by Whitty et al.  shows an exponential
growth in choice-based studies to elicit public preferences
with respect to healthcare priority setting. However, most
of these studies have not translated preferences into equity
weights, let alone included the monetary valuation of
QALYs for different equity considerations [6, 7, 21, 25].
The objective of the current study is to contribute to the
existing literature by estimating the social WTP for QALY
gains in different equity subgroups. More precisely, we aim
to estimate the marginal WTP (MWTP) for a QALY at
different levels of proportional shortfall, in different age
groups. The study was framed in such a way that it could be
directly helpful in further shaping the (Dutch)
decisionmaking framework and build on previous studies in this area
[12, 22, 26]. Public preferences were elicited using a
discrete choice experiment (DCE), which is currently the most
commonly applied method to elicit public preferences .
Respondents were asked to act as social decision makers.
We included both the equity principles ‘severity of illness’
(operationalized as proportional shortfall) and ‘fair innings’
(operationalized as age) in one experiment. In order to
arrive at MWTP per QALY estimates, we used the payment
vehicle of increases in insurance premiums, which is the
common financing mechanism in The Netherlands. In light
of the diversity in the literature in terms of methods and
results, we need to be modest in our aim. While we want to
inform the (Dutch) debates regarding appropriate equity
weights and thresholds, the current experiment was
especially designed to learn how respondents solve the
dilemmas they are confronted with, and to better understand
support for differentiating QALY values between groups.
2.1 Discrete Choice Experiment
DCEs are based on the assumption that a good can be
described by its characteristics and that the relative
importance of these characteristics can be identified in
isolation. This makes the DCE a valuable method to
explore the preferences for healthcare allocation in relation
to equity considerations [21, 27, 28]. DCEs are modelled
according to random utility theory, which assumes that a
respondent asked to choose between multiple options
always chooses the alternative with the highest utility for
her/him. The utility of an alternative for respondent n, Un,
can be decomposed in an observable component of utility,
Vn, which reflects the utility effect of the characteristics of
the alternative, and an unobserved component, en, which
reflects the utility not captured by these characteristics,
where k is the scale parameter which presents the variance
of the unobserved component.
2.1.1 Identification and Presentation of Attributes
The main objective of this study was to estimate the
MWTP for a QALY at different levels of proportional
shortfall, in different age groups. Therefore, the following
attributes were included: quality of life if untreated, age of
death if untreated, gain in quality of life, gain in life
expectancy and cost of treatment. The quality of life
attribute was presented on a scale from 0 to 100, with 0
representing the worst imaginable health state and 100
representing perfect health. The cost attribute was
operationalized as an increase in the mandatory health insurance
premium for all Dutch adult citizens for a period of 1 year.
To be able to explore fair innings (or ageism), we designed
three versions of the questionnaire considering different
age groups: 10 year olds, 40 year olds and 70 year olds.
The levels of the attributes quality of life if untreated, gain
in quality of life and costs of treatment were identical for
all age groups. However, in order to present a
comprehensible and plausible range of proportional shortfall in
each of the three age groups to respondents, the levels of
the attributes age at death if untreated and gain in life
expectancy differed between age groups.
Next, to compensate for the smaller absolute health
gains in the older age groups, we differentiated the number
of people at risk between the age groups. The number of
affected people in the Dutch population was 2000 people in
the 10-year-old age group (age group 10), 4000 people in
the 40-year-old age group (age group 40) and 12,000 in the
70-year-old age group (age group 70). An overview of the
attributes and levels is presented in Table 1. (Note that it
has been found that people may prefer larger gains in fewer
people over smaller gains in more people, even when the
two add up to the same total ).
Following the approach adopted by Lancsar et al. ,
we used both words and diagrams to present the choice
sets, as shown in Fig. 1. Each scenario was represented by
Table 1 Overview of attributes and levels
6, 12, 18, 24
Affected people: 2000 in age group 10, 4000 in age group 40 and
12,000 in age group 70
a graph with ‘quality of life’ on the vertical axis (on a scale
from 0 to 100) and age on the horizontal axis (on a scale
from current age until 80 years old) as shown in Fig. 1.
The green area shows the health prospect without
treatment, the red area combined with the green-and-red shaded
area shows the health loss without treatment (proportional
shortfall). The green-and-red shaded area shows the
potential health gain from treatment. Below the graphs, the
percentages of remaining health without treatment,
potential health gain from treatment and the increase in monthly
premium were presented. Given the complexity of the
graphs we first showed a step-by-step introduction of the
graphs to respondents.
The attributes, levels and presentation of choice sets
were pilot-tested in a small sample of 75 respondents for
each age group version. This resulted in adjustment of the
level ranges of three attributes: age at death without
treatment, gain in life expectancy and costs of treatment. In
addition, to improve the clarity of the graphs we added the
colours green for remaining health without treatment, red
for health loss and shaded green-and-red for potential
health gain instead of the blue colours of Lancsar et al.
Respondents were instructed to imagine themselves being
in the position of a decision maker facing allocation
decisions in healthcare. They were then asked to imagine
that tomorrow an illness will strike two groups of people
from the Dutch population that would have otherwise lived
in perfect health until death at 80 years of age. The
demographic characteristics of the groups were the same,
but the illness and the treatment could affect the groups
differently, and the costs of treatment could also differ
between the groups. The illness would reduce the length
and quality of life of the groups of people. There was a
treatment available for each group, which would restore
some, or all, of the health loss due to the illness. However,
the treatment was not yet included in the basic benefit
package. Therefore, it would have to be financed through
an increase in the mandatory health insurance premium for
all Dutch adult citizens for the period of 1 year. The
Fig. 1 Question 1. Age group 10, version 1, choice set 1. Which of the groups below do you, as a decision maker, think should be treated?
respondents were asked which of the two groups of people
they, as decision makers in the healthcare sector, would
prefer to treat. An opt-out option was included in order to
get valid WTP values .
The program Ngene 1.1 was used to generate efficient
multinomial logit designs for the main study. An efficient
design minimizes the predicted standard errors of the
parameters in order to optimize the information obtained
from each choice set. The efficiency of the designs was
determined by the D-error, which is the most widely used
measure of efficiency . Since the levels of the attributes
were adjusted after the pilot study we could not use the
estimates of the pilot study as Bayesian priors for the main
study, but only the signs of the estimates. Bayesian priors
are more robust to misspecification because they optimize
on prior distributions instead of on fixed parameters .
Since certain combinations of levels of attributes
resulted in implausible scenarios, we imposed some
constrains in the design (e.g., the gain in life expectancy added
to the age at death if untreated could not exceed the
maximum age of 80 years). Furthermore, interaction
effects between quality of life if untreated and age at death
if untreated were included to be able to consider the
additional effect of proportional shortfall. For each age
group we used 1000 Halton sequence draws .
For each age group, designs with 24 choice sets were
generated. The choice sets were divided over three versions
using a blocking variable. This resulted in a total of nine
blocks (and versions of the questionnaire) each with eight
choice tasks. The alternatives were unlabelled, meaning
that the scenarios only varied by the included attributes,
and the choice sets were randomized within blocks to avoid
order biases in the results. Two control questions were
added to each block to detect inconsistent respondents: one
dominant choice set was presented as first choice set in all
blocks. In a dominant choice set, the attribute levels of one
scenario (the dominant scenario) are superior to the levels
of the other scenario (the dominated scenario) on each
attribute. Therefore, respondents who carefully consider
the choice set may be expected to opt for the dominant
scenario. Furthermore, the fifth choice set was repeated as
the tenth choice set, but now left and right scenarios
reversed. Respondents carefully considering the choice sets
are expected to choose the same scenario in both questions,
independent of its positioning left or right. Altogether, each
respondent received 10 choice tasks for one age group. If a
respondent chose the dominated scenario in the first choice
(i.e. the first control question) and reversed preferences in
the tenth choice (i.e. the second control question), the
respondent was removed from the data set. Furthermore,
based on the distribution of completion times in the pilot
study and a quickest possible reading and responding test
by three researchers, we determined a minimum
completion time for the ten choice sets of 150 s.
In April 2013, the questionnaire was distributed by a
professional Internet survey company to a representative
sample of the adult population of the Netherlands in terms
of gender, age and level of education. The DCE questions
were the first part of a larger questionnaire that also
contained three contingent valuation questions (as the second
part) and questions about socio-demographic
characteristics (as the third part). Each respondent was randomly
assigned to one of nine versions of the questionnaire (i.e.,
three age groups times three blocks of choice sets). For an
English copy of the questionnaire refer to the electronic
To be able to estimate the MWTP per QALY gain for
different levels of proportional shortfall the initial model
included the following parameters: total QALY gain,
proportional shortfall and the increase in health insurance
premium. These parameters were calculated from the
original attributes using the following equations:
where QG represents the gain in quality of life, AD
represents age of death without treatment, AO is age of
onset, YG is life years gained, QOL the quality of life
before treatment. Proportional shortfall was calculated
using the following formula.
where MQ represents the maximum quality of life (100)
and MY the maximum life expectancy, which was set at
80 years of age.
To determine the social MWTP, the QALY gains were
multiplied by the size of the risk group and the increase in
monthly premium was multiplied by 12 monthly
instalments and the number of health insurance payers in the
Netherlands (i.e. 13,260,000). The deterministic
components of the elemental alternatives for each age group were
VA=ks ¼ b1QALYGAIN þ b2PS þ b3COST
VB=ks ¼ b1QALYGAIN þ b2PS þ b3COST
VC=ks ¼ b0
where V is the observed component of the random utility
function for alternative A, B or C (opt-out), ks is the scale
parameter and b are the parameters to be estimated. The
constant term represents the expected utility for no
treatment over treatment. Likelihood ratio tests were used to
test different specifications of the utility functions
(categorical or numerical attribute levels and interaction effects
between QALY gain and proportional shortfall).
In our attempt to find appropriate explanations for the
observed patterns in the data, we estimated numerous
models. To allow for preference heterogeneity among the
population, panel mixed multinomial logit (MMNL)
models with correlated coefficients were used to analyse
the data. All parameters were included as random
parameters. MWTP per QALY values were computed as
However, including the cost parameter as a random
parameter in MMNL model may cause problems with
respect to the WTP calculations. When a normal
distribution for a price coefficient overlaps zero it will
result in undefined moments of WTP since dividing by zero
is impossible. Furthermore, divisions by numbers
arbitrarily close to zero results in very large WTP
estimates. Different solutions have been proposed in the
literature to tackle this issue, such as WTP space models,
MMNL model with a fixed parameter for the cost attribute
or constrained distributions like lognormal or triangular
distributions [33–35]. All these specifications have been
tested for the current models. WTP space models did not fit
our data. Different parameter distributions were tested
combined with large numbers of Halton draws (i.e. up to
3000), but we were not able to find a model fit. Therefore,
different specifications of the MMNL model were
estimated and compared using Log Likelihood ratio tests
and examining the Akaike and Bayesian information
criteria. The MMNL model were estimated with 1000
Halton draws, the statistical results of this process are
presented in Table 4. As this table shows, the random
parameters with restricted distributions for the costs
parameter did not result in better model fits than the
specification of a fixed coefficient for the cost attribute.
Besides, it should be noted that the specification of a
constrained distribution for the cost attribute would still
complicate the calculation of the WTP estimates and
related confidence intervals. Therefore, in our models cost
was specified as a fixed parameter [33–35].
The MMNL model based on the above-mentioned
attributes did not behave as expected. As shown later on in
the Sect. 3, counterintuitive results were found with respect
to proportional shortfall (i.e. scenarios with higher
proportional shortfall were less likely to be chosen, c.p.).
Moreover, all standard deviations of the random
parameters were significant, which implies a substantial amount of
preference heterogeneity within the sample. To further
explore these results and understand the preference
structure of respondents, we searched for decision patterns
within the data. For that reason, we relaxed our
assumptions with respect to proportional shortfall and absolute
QALY gains to explain respondents’ preferences and used
the attributes as presented to the respondents instead.
Latent class models were estimated to identify different
subgroups in the population based on unobserved
characteristics that affect their preferences. It is assumed that
preferences are homogeneous within the classes but differ
between classes . The optimal number of classes was
determined by examining the Akaike and Bayesian
information criteria of different numbers of classes and the
standard errors of the corresponding parameters. The latter
is a valid additional argument in this context, because an
increasing number of classes may lead to extremely large
standard errors of several parameters, complicating the
interpretability of the model. Latent class models with four
classes showed extremely large standard errors in age
groups 10 and 40, and insignificant coefficients—and
consequently meaningless WTP estimates—in age group
70. Thus, in all three age groups the number of classes was
limited based on the standard errors of the corresponding
parameters, despite the fact that accepting more classes
would have improved model fit [33, 37].
The results of the latent class models provided
additional insights in respondents’ preferences compared with
the MMNL model. Therefore, the latent class models were
chosen as a starting point for further analyses.
Overall MWTP values were estimated as the weighted
average of conditional class MWTPs. Confidence intervals
for MWTP estimates were estimated using the Delta
method [29, 36, 33].
Analyses were performed in Nlogit 5.0 (Econometric
The final dataset included 1205 respondents representative
of the adult population of the Netherlands with respect to
age (mean 45.0 years), gender (50.8 % female) and
education level (25.5, 42.1, and 32.4 % had lower, middle, and
higher education, respectively). Demographic statistics of
the sample are presented in Table 2. The completion time
for the ten DCE questions was, on average, 5.2 min.
The results of the panel MMNL model for the three age
groups are presented in Table 3. As already briefly
discussed in the Sect. 2, we strongly questioned whether this
model accurately represents respondents’ preferences. The
results with respect to proportional shortfall were
counterintuitive and the standard deviations of the random
parameters were all statistically significant with relatively
Table 2 Demographic statistics (N = 1205)
Number of observations per version of the questionnaire: 411 for age
group 10; 410 for age group 40; 384 for age group 70; General
population statistics: 45 years of age (18?), 50.9 % female (18?) and
33.0 % elementary school, 40.2 % high school, 26.8 % university
SD standard deviation, VAS visual analogue scale
large coefficients, which suggest a substantial
heterogeneity in preferences in the sample.
Table 4 presents the results of the MMNL model and
latent class models using the attributes as presented to the
respondents, that is, health gain as a percentage, remaining
health without treatment (%) and the increase in health
insurance premium. The MMNL model were comparable
to the MMNL model of Table 3 with respect to preference
Table 3 Results from MNL and MMNL models with QALY gain and proportional shortfall
heterogeneity and counterintuitive results for health state
before treatment (i.e. an average preference was observed
to treat people who already were relatively healthy).
Although the MMNL model had a slightly better model fit
than the latent class models, we preferred to use the latent
class models since they seem to provide additional insight
in the heterogeneous preference structures of the
respondents. The results for the selection of number of classes are
presented in Sect. 5. For all three age groups, the most
appropriate model consisted of three classes (as explained
in the Sect. 2).
Respondents belonging to the first latent class of age
group 10 had a relatively strong preference not to choose
between one of the groups of patients as indicated by the
positive significant constant term. In case respondents were
willing to treat one of the groups of patients, more
remaining health without treatment increased the
probability to receive treatment. Remarkably, the coefficients of
health gain from treatment and remaining health without
treatment were comparable in magnitude and sign. This
indicates that these respondents did not really differentiate
between these two attributes. The increase in monthly
health insurance premium was the least important attribute
in this class. The significant negative constant term in class
two of age group 10 indicates a general preference toward
treating one of the groups of patients. Respondents
belonging to this class were more likely to treat patients
with larger health gains and a more severe health state
before treatment. Larger increases in monthly health
insurance premium decreased the probability to be chosen.
Respondents belonging to the third class preferred not to
choose between the groups of patients. The increase in
health insurance premium had the largest marginal effect
on respondents’ choice. Probabilities of class membership
were 47.6, 40.7 and 11.7 %, respectively.
MNL multinomial logit model, MMNL mixed multinomial logit model, SD standard deviation
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A similar preference structure was found for age group
40, although the highest probability was to be assigned to
class 2 (49.6 %), implying a preference to treat patients
with more severe health states before treatment.
Somewhat distinct preferences were observed for age
group 70. The insignificant constant terms in the first and
third classes indicate that respondents did not have a
general preference for either choosing between groups of
patients, or not. Respondents had a 57 % probability to be
in first class in which health gain was the most important
attribute, followed by the increase in health insurance
premium. Respondents belonging to this class preferred to
treat patients with a relatively good health state before
treatment which is different from what we expected but in
line with the other age groups. Respondents had a 30 %
probability to be in class 2. These respondents were willing
to choose between groups of patients and preferred to treat
patients with a more severe health state before treatment.
Respondents in class 3 seemed to be mainly driven by the
increase in health insurance premium in their decision.
Remaining health without treatment did not significantly
influence respondents’ preferences.
The probability weighted MWTP values ranged from
€206,408 in age group 10 to €296,756 in age group 40, but
were not significantly different between the age groups.
This indicates that we did not find a significant age effect in
our data. Interaction effects between health state before
treatment and health gain were not significant and therefore
not included in the final models. This indicates that,
statistically, the value of a health gain was not different for
different levels of severity. However, the main effect of
severity was significant, which indicates that severity did
influence preferences between groups.
It is increasingly recognized that a monetary threshold
value against which health gains from an intervention can
be evaluated should vary with distributional preferences in
society. However, most WTP per QALY studies so far
have focused on the individual perspective and have not
incorporated such equity considerations. Studies exploring
public preferences for QALYs, on the other hand, rarely
translate these preferences into equity weights or
subgroupspecific QALY values. Therefore, the aim of this study was
to contribute to the existing literature by estimating the
social MWTP for QALY gains in different equity
subgroups, considering the equity principles severity of illness
(operationalized as proportional shortfall) and fair innings
(operationalized as age). Our results show substantial
preference heterogeneity among members of the public. As
discussed further below, this finding may be helpful in
explaining the mixed findings in literature with respect to
the value of a QALY in relation to severity of illness and
age of care recipients.
Before the results are discussed in more detail, our
approach to the data analysis warrants further discussion. A
variety of model specifications were tested to analyse the
data. Given the aim of this study, levels of proportional
shortfall and QALY gains were calculated from the
original attributes and included in MMNL model. The results
(Table 3) showed substantial preference heterogeneity and
counterintuitive results: we found that respondents were
less likely to choose patients with higher levels of
proportional shortfall. It should be noted that, although
counterintuitive, this finding is consistent with Lancsar
et al. , Dolan and Tsuchiya  and Skedgel et al. .
In order to better understand how respondents made
their decisions, latent class models were estimated with the
attributes as presented to respondents. These latent class
models demonstrated distinct preference structures in the
data, which seem plausible and were helpful in clarifying
some of the counterintuitive results we found in the mixed
models. It is often suggested that different views exist in
society regarding the distribution of health and health care
. Exploring mean preferences may therefore not be
most insightful in the context of such value-laden issues.
We suggest that future studies in this area should account
for these heterogeneous preferences in society by
considering multiple models to explore possible decision patterns
underlying the data.
The results of the latent class models (Table 4) showed
some interesting decision patterns with respect to equity
considerations in healthcare allocation decisions, which
were more or less consistent across the different age groups.
The first class of each age group showed aforementioned
counterintuitive preferences for treating persons who were
already in a relatively good health state before treatment
(i.e. less severe diseases). In addition, in the first class of
age group 40, respondents reported fairly equal preferences
for health state without treatment and health gain (and also
in age groups 10 and 70 the differences were relatively
small). This might indicate that respondents in this class
were driven by the best health state after treatment,
irrespective of whether this was a consequence of the health
state before treatment or the health gain from treatment.
Other studies also have found that respondents consider
health state after treatment more important than health state
before treatment . However, it is also possible that this
finding was (partly) induced by the presentation of the
scenarios in our study. A closer look at the graphs of the
scenarios (Fig. 1) shows that the best end state after
treatment automatically coincides with the smallest health
loss, indicated by the red area in the graph. It is
conceivable that some respondents just opted for the smallest
health loss (i.e. the smallest red area). Using graphs to
clarify the scenarios might thus be helpful in presenting
complex choice problems to respondents, but at the same
time unintentionally influence their choices. As the use of
such graphs is relatively new in this field, this deserves
further study, and future studies should be aware of this
issue when they consider using graphs to present their
attributes to respondents.
The second latent class of all age groups aligned with
the principle of proportional shortfall, thus expressing
concerns for severity of illness. These respondents were the
only ones willing to choose between the groups of patients
and, ceteris paribus, preferred to treat patients with a
relatively more severe health state without treatment. The
probabilities of membership of this class were
considerable, which highlights considerable support for considering
severity in healthcare priority setting in the general public.
Respondents assigned to class 3, the smallest class of
each age group, seemed to consist of individuals with a
general aversion to prioritising patients based on the health
characteristics included in the study. The remaining health
state without treatment attribute was not significant in age
group 70, and only marginally significant in age group 40,
suggesting that differences in health state without treatment
were not a relevant argument for them to prioritise between
different groups of patients. Moreover, the constant term
indicated that these respondents generally preferred not to
choose between patients, and when they did choose, their
decision was mainly driven by the change in monthly
health insurance premium.
In other words, in each age group we found two latent
classes with a general preference not to choose between
patients, and one class that was willing to choose and
displayed preferences that aligned with what was expected
from the theory of proportional shortfall. The first two
classes represented the majority of respondents in all three
age groups, but a substantial minority thus supports
accounting for severity in priority setting.
Interaction effects between remaining health without
treatment and health gain were found not to be significant.
This indicates that, statistically, severity did not influence
the value of a QALY itself in our sample. Nevertheless, the
significant coefficients of the main effects suggest that
health state before treatment does influence respondents’
choices. However, theoretically, these two cannot be
valued separately since a certain health gain is always
accompanied by a certain health state before treatment (or
proportional shortfall). This suggests that at least indirectly
the MWTP for a QALY depends on the health state without
treatment. Overall, it seems worthwhile to investigate these
preferences with respect to severity in more detail, in
particular taking the preference heterogeneity within the
general public into consideration.
No clear support was found for the fair innings argument
in this study, since the MWTP per QALY estimates did not
significantly differ between age groups—although the
value in age group 40 appears considerably higher
(Table 4). The confidence interval of the MWTP estimate
of age group 40 was large, which may be due to the low
significance of the health insurance premium attribute in
the first class. The relatively small coefficient for health
insurance premium in this class resulted in a fairly high
MWTP for a QALY estimate (€533.015), which in turn
(given the substantial probability to be part of group 1) led
to a relatively high MWTP estimate for age group 40.
Apart from the common limitations that come with
DCEs and online surveys, the following limitations of this
study need to be mentioned. First of all, as discussed here,
a possible explanation for part of the preference
heterogeneity observed in this study might relate to the graphical
presentation of the scenarios. Such graphs, also used before
by Lancsar et al. , Shah et al.  and Brazier et al.
, may unintentionally give room to different
interpretations of the scenarios by respondents, and therefore may
not be the best way to present the attributes to respondents.
How respondents perceive the information contained in
such graphs deserves further study, for instance using a
Second, finding that fair innings is of no relevance for
the value of a QALY may be a result of framing, since age
was part of the scenario description and not an attribute in
the choice set. This implies that respondents did not trade
age against other characteristics of the recipients, which
may have given a different meaning to age in the choices
made. In the literature there has been a growing interest in
the context and framing of studies in order to improve the
consistency and comparability between studies. Our results
are in line with those reported by Lancsar et al.  and
Diederich et al. . It would be interesting for future
research to investigate whether a DCE with a fixed level of
severity in each scenario and age included as an attribute
would result in opposite findings.
Concluding, this study aimed to contribute to the
existing literature by bridging the gap between WTP per
QALY studies from an individual perspective and the
growing literature exploring societal preferences for health
and health care. A recent review of Whitty et al. 
underlined the importance of multi-criteria studies and the
translation of public preferences into equity weights that
can be used for policy making. In this study, we estimated
MWTP per QALY for different age groups and found no
support for the fair innings argument, or for prioritizing
based on health characteristics more generally. We did find
support for considering severity of illness among a
substantial minority of the public, but since interaction terms
between health state without treatment and QALY gains
were not significant, we cannot say that the MWTP per
QALY estimates differed statistically significantly for
different levels of severity of illness.
While some of our results may be related to the design
of our study, including the graphical presentation of the
scenarios, they are insightful and, most of all, highlight the
importance of accounting for heterogeneity in preferences
among the public on value-laden issues such as prioritizing
health care, both in research and in decision making.
Acknowledgments We would like to thank Steef Baeten for
preparing the graphical presentation of the scenarios.
Author contributions Liesbet van de Wetering designed the study,
carried out the analyses and drafted the manuscript. Ana Bobinac
helped design the study and provided comments to the analyses. Job
van Exel and Werner Brouwer provided comments to the design,
analyses and draft versions of the manuscript. Werner Brouwer
supervised the project. All authors read and approved the final
Compliance with Ethical Standards
This study was financially supported by Zorgonderzoek Nederland
(ZonMW), Netherlands Organization for Health Research and
Development (project number 152002038). The usual disclaimer
Liesbet van de Wetering, Job van Exel, Ana Bobinac and Werner
Brouwer declare that they have no competing interests.
This study has not been approved by an ethics committee because this
is not required in the Netherlands for population-based studies.
The data sampling of this study was conducted by a professional
panel company (Survey Sampling International, http://www.
surveysampling.com). People voluntarily subscribe to these panels
and regularly participate in a variety of online surveys. All panel
members agreed with the privacy statements of Survey Sampling
International. After completion of the survey, a small donation is
made to the participant’s charity of choice.
Open Access This article is distributed under the terms of the
Creative Commons Attribution-NonCommercial 4.0 International
License (http://creativecommons.org/licenses/by-nc/4.0/), which
permits any noncommercial use, distribution, and reproduction in any
medium, provided you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons
license, and indicate if changes were made.
Appendix: Selection number of classes in the latent
Age group 10
Age group 40
Age group 70
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