The Indonesian EQ-5D-5L Value Set
The Indonesian EQ-5D-5L Value Set
Fredrick Dermawan Purba 0 1 2 3 4 5 6
Joke A. M. Hunfeld 0 1 2 3 4 5 6
Aulia Iskandarsyah 0 1 2 3 4 5 6
Titi Sahidah Fitriana 0 1 2 3 4 5 6
Sawitri Supardi Sadarjoen 0 1 2 3 4 5 6
Juan Manuel Ramos-Gon˜ i 0 1 2 3 4 5 6
Jan Passchier 0 1 2 3 4 5 6
Jan J. V. Busschbach 0 1 2 3 4 5 6
0 Department of Clinical Psychology, Faculty of Psychology, Padjadjaran University , Jatinangor , Indonesia
1 Department of Developmental Psychology, Faculty of Psychology, Padjadjaran University , Jatinangor , Indonesia
2 Section Medical Psychology and Psychotherapy, Department of Psychiatry, Erasmus MC University Medical Center , Wytemaweg 80, Room Na-2019, 3015 CN Rotterdam , The Netherlands
3 Key Points for Decision Makers
4 Department of Clinical , Neuro and Developmental Psychology , VU University , Amsterdam , The Netherlands
5 Executive Office, EuroQol Research Foundation , Rotterdam , The Netherlands
6 Center of Applied Psychometrics, Faculty of Psychology, YARSI University , Jakarta , Indonesia
Background The EQ-5D is one of the most used generic health-related quality-of-life (HRQOL) instruments worldwide. To make the EQ-5D suitable for use in economic evaluations, a societal-based value set is needed. Indonesia does not have such a value set. Objective The aim of this study was to derive an EQ-5D5L value set from the Indonesian general population. Methods A representative sample aged 17 years and over was recruited from the Indonesian general population. A multi-stage stratified quota method with respect to residence, gender, age, level of education, religion and ethnicity was utilized. Two elicitation techniques, the composite time trade-off (C-TTO) and discrete choice experiments (DCE) were applied. Interviews were undertaken by trained interviewers using computer-assisted face-to-face interviews with the EuroQol Valuation Technology (EQ-VT) platform. To estimate the value set, a hybrid regression model combining C-TTO and DCE data was used. Results A total of 1054 respondents who completed the interview formed the sample for the analysis. Their characteristics were similar to those of the Indonesian population. Most self-reported health problems were observed in the pain/discomfort dimension (39.66%) and least in the self-care dimension (1.89%). In the value set, the maximum value was 1.000 for full health (health state '11111') followed by the health state '11112' with value 0.921. The minimum value was -0.865 for the worst state ('55555'). Preference values were most affected by mobility and least by pain/discomfort. Conclusions We now have a representative EQ-5D-5L value set for Indonesia. We expect our results will promote and facilitate health economic evaluations and HRQOL research in Indonesia.
Indonesia does not have an EQ-5D value set.
An EQ-5D-5L value set was derived from a highly representative sample of the Indonesian general population.
Data were collected using a rigorous quality control procedure which led to logical and significant models.
This Indonesian EQ-5D-5L value set is now
becoming available and will be used by all health
economic evaluations and health-related
quality-oflife studies in Indonesia that use EQ-5D.
The Indonesian government wishes to improve equal
access to healthcare by introducing universal health
insurance. To ensure health technology assessment (HTA)
can be undertaken for such an insurance scheme, Indonesia
intends to employ cost-effectiveness analysis for new and
existing medical interventions. To value the outcomes of a
medical intervention in quality-adjusted life-years
(QALYs) requires a quality-of-life instrument that can
value the health states of patients using societal
preferences, such as the EQ-5D instrument. At present, no
Indonesian EQ-5D value set is available for the calculation
of QALYs. There exists a standardized valuation protocol
for the 5-level version of EQ-5D. We employed this
protocol with over 1000 respondents representative of the
Indonesian population. Below we describe in more detail
(1) the social, economic and organizational HTA setting
that determines the demand and specifications for an
Indonesian valuation study; (2) a brief introduction to the
EQ-5D-5L, its valuation protocol and the place of the
EQ5D in HTA; and (3) why we cannot rely on values set from
European countries and/or neighbouring countries.
Indonesia is located in South East Asia, with 255.5
million inhabitants in 2015 [
]. Commencing in January
2014, Indonesia has implemented universal healthcare
coverage organized by the ‘Badan Penyelenggara Jaminan
Sosial Kesehatan’ or BPJS Kesehatan: the Healthcare and
Social Security Agency. The aim of the BPJS Kesehatan is
to include all Indonesian citizens in the National Health
Insurance system to enable them to obtain access to
healthcare benefits and to provide protection with respect
to basic health needs [
]. The decision-making process
related to the implementation of this national health
coverage and the adoption of new technologies can benefit
from an evidence-based strategy and the application of
HTA, a decision-making process involving economic
evaluation and other considerations such as those of an
ethical and organizational nature, to ensure the optimal use
of health technologies for the population. In 2015, the
Ministry of Health of Indonesia formed a national HTA
committee (Komite Penilaian Teknologi Kesehatan). The
committee’s expected output is a policy recommendation
to the Minister on the feasibility of the health service(s) to
be included in the National Health Insurance benefit
Economic evaluation uses clinical evidence to provide
systematic consideration of the effects of all available
alternatives regarding health, healthcare costs, and other effects
regarded as valuable [
]. Cost-utility analysis (CUA) is used to
evaluate health-related quality-of-life (HRQOL) outcomes
and to compare costs and outcomes between different
healthcare programmes in terms of cost per QALY [
A QALY is obtained by integrating a health state utilities
function, measured by multi-attribute utility instruments
(MAUIs), differentiated over a lifetime. The three most
widely used MAUIs are the EQ-5D, the Health Utility Index
(HUI), and the Short Form 6D (SF-6D) [
]. Several national
HTA organizations, for example in the UK and Thailand, have
recommended EQ-5D as the preferred method for deriving
]. Developed by the EuroQol Group, EQ-5D is a
standardized generic instrument that collects descriptive
HRQOL data on five dimensions: mobility, self-care, usual
activities, pain/discomfort, anxiety/depression); followed by a
self-rating of overall health status on a visual analogue scale
(EQ VAS) ranging from 0 (‘worst imaginable health state’) to
100 (‘best imaginable health state’) [
]. In 2011, the
EuroQol Group expanded the levels of severity of the classic
version of EQ-5D, renamed EQ-5D-3L, from three to five
levels. This new instrument is designated ‘EQ-5D-5L’ [
Recent studies have shown that EQ-5D-5L produces a richer
description of health states, a higher discriminatory power,
and a lower ceiling effect compared with EQ-5D-3L [
The EuroQol Group has also developed a valuation protocol
for EQ-5D-5L , and the EuroQol Group Valuation
Technology (EQ-VT) template computerized the interview
method to standardize EQ-5D-5L valuation studies in
different countries. This protocol provides a value set for the
calculation of QALYs using a societal perspective, the preferred
perspective in health economics [
Indonesia does not have an EQ-5D value set, either for
the 3-level or for the new 5-level version. Previous EQ-5D
studies conducted in Indonesia measured health
preferences using the Malaysian value set or values derived from
citizens of the UK [
]. However, for a value set to be
valid for Indonesia it should represent the culture and
living standards of Indonesia . Moreover, the values
should match the particular wording of the Indonesian
instrument: for instance, if ‘cukup’ (i.e. ‘moderate’) is less
worse in Bahasa Indonesia than in the Malaysian language
(‘sederhana’) or in English, then the values should match
that difference. For these reasons the aim of our study was
to obtain preferences from the general population in order
to derive a national EQ-5D-5L value set for the calculation
of QALYs from a societal, Indonesian perspective.
A representative sample was recruited from the Indonesian
general population, with a minimum of 1000 respondents
aged 17 and over, based on the work of Ramos-Gon˜i et al.,
to obtain a 0.01 standard error (SE) of the observed mean
composite time trade-off (C-TTO), 9735 C-TTO responses
were needed. Therefore, the 1000 respondents interviewed
provided 10,000 C-TTO and 7000 discrete choice
responses to estimate the models [
]. The adult population was
defined as aged 17 and over, because in Indonesia, the legal
age to obtain an ID card, a driving license, and access to
voting is 17. To ensure the representativeness of the final
sample for the Indonesian general population, we used a
multi-stage stratified quota method with respect to
residence (urban/rural, as registered by the official national
register); gender (male/female); age (17–30/31–50/
[50 years); and level of education: basic (primary school
and below), middle (primary school plus at least 1 year of
high school) and high (all others). This resulted in the first
stage of 36 quota groups. Two other categories, religion
(Islam/Christian/Others) and ethnicity (own-declared
Jawa/Sunda/Sumatera/Sulawesi/Madura-Bali/Others), were considered important as well. However,
including them in the same way as residence, gender, age,
and education would result in 36 9 3 9 6 = 3888 quota
groups. We therefore used religion and ethnicity quotas
independently from the other factors. So religion and
ethnicity are representative over the whole sample, but within
the individual 36 quota groups this might not be the case.
To take account of this second layer of sampling, we called
this a ‘multi-stage stratified quota’. The predefined quotas
were based on updated data from the Indonesian Bureau of
We designed and used an online tool to ensure that the
recruitment of respondents was in accordance with
predefined quotas while the sampling was employed in different
parts of the country. Interviews were conducted in the
following six cities and their surroundings, located in
different parts of Indonesia: Jakarta, Bandung, Jogjakarta,
Surabaya, Medan, and Makassar. Respondents were
recruited through a mixed strategy, i.e. through personal
contact, local leader assistance, and from public places
such as mosques and shopping streets. We also asked
respondents to introduce us to other potential respondents.
Interviews were conducted at the respondents’ or
interviewers’ homes. For their participation, all respondents
received a mug or a t-shirt specifically designed for the
valuation study. Informed consent was obtained from all
respondents included in the study. The study was approved
by the Health Research Ethics Committee, Faculty of
Medicine, Padjadjaran University, Indonesia.
We used the official EQ-5D-5L Bahasa Indonesia version
provided by the EuroQol Group. This translation of
EQ5D-5L was produced using a standardized translation
protocol that followed international recommendations [
As briefly mentioned in the introduction, EQ-5D-5L
consists of five dimensions: mobility (MO), self-care (SC),
usual activities (UA), pain/discomfort (PD), and anxiety/
depression (AD). Each dimension has five levels: no
problems, slight problems, moderate problems, severe
problems, and unable/ extreme problems [
EQ-5D5L instrument describes 3125 (55) unique health states. A
1-digit number expresses the level selected for that specific
dimension. Hence, combining a 5-digit number for five
dimensions will describe a specific health state. For
example, state ‘11111’ indicates ‘no problems on any of
the five dimensions’, while state ‘54321’ indicates ‘unable
to walk about, severe problems washing or dressing,
moderate problems doing usual activities, slight pain or
discomfort, and no anxiety or depression’ [
]. Each health
state has a so-called ‘sum score of the level digits’, which
means the sum of the levels across domains; for example,
‘11111’ sum score of the level digits is 5 and ‘54321’ is 15.
This EQ-5D descriptive system is followed by self-rating
of overall health status on a visual analogue scale (EQ
VAS) ranging from 0 (‘worst health you can imagine’) to
100 (‘best health you can imagine’).
2.2.2 Valuation Protocol
The EQ-5D-5L valuation protocol consists of five sections
1. A general welcome, where the interviewer explains the
objectives of the research, followed by filling in the
informed consent when the individuals agree to
2. Introduction to and completion of the descriptive
system, VAS and background questions (age, sex,
experience of illness, religion, ethnicity and
3. C-TTO (see Sect. 2.2.3 below) tasks followed by a
‘Feedback Module’ task. Each respondent has to
complete one example (health state: being in a
wheelchair), three practice health states (mild:
‘21121’; severe: ‘35554’; and moderate but difficult
to imagine: ‘15411’) and ten ‘real’ C-TTO tasks
valuing hypothetical EQ-5D-5L health states. In the
Feedback Module task, the respondents check whether
they agree with the order of the health states they
valued before. The EQ-VT screen shows health states
for 10 C-TTO tasks arranged based on their value
given by the respondents: from the lowest value at the
bottom to the highest value at the top. Respondents are
allowed to ‘flag’ the health state(s) for which they do
not agree with the previously given relative position to
other health states, but they are not allowed to alter
their initial values. Three debriefing questions
regarding the difficulties of the C-TTO tasks are added at the
end of this section.
4. A discrete choice experiment (DCE, see Sect. 2.2.3
below) followed by three debriefing questions
regarding the DCE. Each respondent has to complete seven
5. A round-up, where respondents can comment on the
6. Country-specific questionnaire(s) (if any).
All sections were administered utilizing
computer-assisted face-to-face interviews employing the EQ-VT
platform version 2.0.
2.2.3 Preference Elicitation Methods
Time trade-off (TTO) has been widely used as a standard
method to elicit preferences [
]. C-TTO uses
conventional TTO to elicit better-than-dead (BTD) values, and
lead-time TTO to elicit worse-than-dead (WTD) values.
Details regarding C-TTO can be found in the study by
Oppe et al. . In summary, respondents were first faced
with ‘conventional’ TTO where they had to choose
between 10 years in an impaired health state (Life B) and
10 years of full health (Life A). After a series of
choicebased iterations, respondents achieved a point of
equivalence between the length of time in full health (Life A): ‘x’
and a period of time (10 years) in the impaired health state
(Life B). The impaired health state value is defined as x/10.
For example, if a respondent could not differentiate
between 3 years of full health in Life A and 10 years living
in Life B, then that health state value would be 0.3 (3/10).
For a really poor health state, respondents might prefer to
die immediately; that is, the value for that specific health
state is \0 (death value = 0). In this case, the lead-time
TTO approach was introduced to allow respondents to
express a value below the value of death; that is, below 0.
The two lives in the lead-time TTO are 10 years of full
health (Life A) and 10 years of full health followed by
10 years in the impaired health state (Life B). When
respondents reach an indifference point between the
amount of time ‘x’ in Life A and Life B, the health state
value is defined as (x - 10)/10. Hence, -1 is the lowest
possible value of a given health state, generated from
trading the full 10 years of Life A in a lead-time TTO.
The EQ-5D-5L valuation protocol included 86
EQ-5D5L health states to be valued using C-TTO. The 86 health
states were distributed into ten blocks with a similar level
of severity. Eighty unique heath states were selected using
Monte Carlo simulation (eight unique heath states included
in each block), five very mild states (only one dimension at
level 2 and all others at level 1, e.g. ‘11112’) (each
included in two blocks) and the most severe/‘pits’ state
(‘55555’) (included in all blocks) [
]. Respondents were
randomly assigned to one of the ten C-TTO blocks. Each
state of the block was presented in random order to
respondents using the EQ-VT platform.
However, it was realized that TTO has its limitations.
EuroQol Group considered different valuation techniques
to be used in conjunction with TTO to make the valuation
studies more robust and valid. Previous experiments with
DCE, like the study by Stolk et al. using EQ-5D-3L [
Ramos-Gon˜i et al. using EQ-5D-5L [
], showed that the
DCE is a valid valuation technique to get health
preferences from respondents. Since both TTO and DCE try to
measure the same concept, it was anticipated that DCE
could be used in combination with TTO [
]. In the light of
this reasoning, DCE was included in the EuroQol VT
Each DCE task was conducted by presenting two health
states and asking the respondent to select the preferred state
for him/her. The DCE design consisted of 196 pairs of
EQ5D-5L health states distributed over 28 blocks, each
consisting of seven pairs with a similar severity [
seven paired comparisons were presented in random order
by the EQ-VT; in addition, the right–left order of the two
health states presented was also randomized.
2.3 Data Collection
At the outset, 13 interviewers were recruited and trained
intensively in a 1-day workshop at two locations: (1)
Jakarta for interviewers who worked in Jakarta, Bandung
and Makassar; and (2) Jogjakarta for interviewers who
worked in Jogjakarta, Surabaya and Medan. Each
interviewer performed at least five pilot interviews in the week
after training. Their experiences were discussed and
feedback was given by the daily supervisor. Only after this
were they permitted to conduct real data interviews. Three
additional interviewers were hired during the data
collection and they received similar training and met similar
requirements to the first 13. Interviews were performed
between March 9, 2015 and January 24, 2016. After 102
interviews we evaluated the quality of the interviews (see
Sect. 2.5 below) and we concluded that their quality was
not yet sufficient. Hence we retrained the interviewers and
treated the 102 interviews collected thus far as pilot
interviews, excluding the 102 interviews in the data
analysis. A detailed description of this decision-making process
and the retraining of the interviewers is provided elsewhere
2.4 Exclusion Criteria
There were two main criteria for data exclusion: lack of
completion of an interview and characteristics of
respondents’ answers that related to poor understanding of the
task or to errors [
]. Note that the first criterion concerns
excluding respondents and the second excludes respondent
With respect to the first criterion, interviews were
excluded when respondents did not finish the interview for
the following reasons: (1) the respondent indicated that he/
she did not want to continue the interview process, (2)
interviewers concluded that the respondent was unable to
differentiate between the different dimensions and levels of
EQ-5D-5L, (3) interviewers concluded that the respondent
was not able to comprehend the C-TTO task during the
practice session. When an interview had to be stopped
during the C-TTO task it was excluded from the study.
With respect to the second criterion, completed
interview responses were excluded on account of any of the
following characteristics: (1) a respondent had a positive
slope on the regression between his/her values on C-TTO
and the ‘sum score of the level digits’, as this would
indicate that the respondent provided higher utility values
for poorer health states on average—the slope of the
regression between C-TTO and the ‘sum score of the level
digits’ was generated as part of the standard quality control
report; (2) when a response in the C-TTO tasks was judged
to be irrational: for instance, preferring life B (10 years in
the corresponding health state) to life A (10 years in full
health) and not shifting after his/her initial response was
reconfirmed by the interviewer; (3) responses that were
marked by the respondents in the Feedback Module task,
which was a sign that the respondents disagreed with the
valuation of those responses.
2.5 Quality Control
To ensure data quality, we followed the quality control
(QC) process described by Ramos-Gon˜i et al. [
consisted of minimum quality criteria and cyclical
feedback to improve interviewers’ skills. The EuroQol Group
facilitates use of the EQ-VT QC tool, which is a software
programme that automates the production of QC reports
based on data from EQ-VT studies. Bi-weekly meetings
(teleconference-based) were organized to discuss the QC
reports with the EQ-VT support team. The aim of these
meetings was to evaluate and improve the interviewers’
performance and to check for possible non-compliance to
the interview protocol.
2.5.1 Minimum Quality Criteria
The QC reports provided a number of statistics related to
the quality of the data collected thus far, differentiated by
1. Wheelchair time: when the duration of time an
interviewer used to explain the ‘wheelchair example’
preceding the actual C-TTO tasks was \3 min.
2. Wheelchair lead-time: when the interviewer did not
explain the WTD element of the wheelchair example.
3. C-TTO duration: if completing the ten C-TTO tasks
took \5 min.
4. Inconsistency: the value for state ‘55555’ was not the
lowest and it was at least 0.5 higher than that of the
state with the lowest value
If any of the four above-mentioned signs are observed,
the interview is ‘flagged’ as being of suspicious quality. If
four or more of the interviews are flagged as being of poor
quality, all ten interviews thus far conducted by that
specific interviewer are removed and retraining of that
interviewer is conducted. After a further ten interviews, the
performance and compliance are re-evaluated. If again four
or more interviews are flagged, the next set of ten
interviews will also be removed and the interviewer is removed
from the data collection process. Quality control focused
on the interviewer; responses in flagged interviews were
not removed from the data that was analysed.
The DCE part of the valuation study was also monitored
to detect suspicious response patterns. Assuming that A is
the health state at the left of the screen and B is the health
state at the right of the screen, then a consistent preference
for the left (A) would be suspicious (AAAAAAA). The
same would apply for the response pattern BBBBBBB,
ABABABA, BABABAB. This was also reported in the QC
2.5.2 Cyclical Feedback
The retraining programme conducted by the daily
supervisor was held in 2 locations: (1) Jakarta for interviewers
who worked in Jakarta, Bandung and Makassar; and (2)
Jogjakarta for interviewers who worked in Jogjakarta,
Surabaya and Medan. The QC reports for their interviews
were presented, discussions were held to address
noncompliance problems, and suitable solutions were agreed
upon among the interviewers. After the retraining
programme, the daily supervisor continuously created QC
reports, made notes at the group and individual levels, and
sent feedback to the interviewers, so that they were able to
learn from their own and other interviewers’ performance.
2.6 Data Analysis
We describe the sample characteristics including
self-reported health on the EQ-5D-5L descriptive system and the
EQ-VAS using percentages for discrete variables and
means and standard deviations for continuous variables in
comparison with the Indonesian population. A general
z test was used to investigate whether the proportions in the
sample were similar to, or different from, the general
In this investigation we used TTO (specifically C-TTO)
and DCE. TTO has limitations such as loss of aversion
], but also has advantages as the TTO-based value sets
are anchored on a scale of (0) death to (1) full health. DCE
is not exempt from limitations, as lexicographic behaviour
from respondents has been widely reported in the literature
]. It is also noticeable that DCE, in its present form,
where time is not incorporated in health state presentations,
does not anchor value sets on a (0) death to (1) full health
scale. Therefore, DCE produces value sets on an arbitrary
scale based on the relative distances between health states.
However, both techniques attempt to measure health
states preference, but using different underlying
assumptions, and seem to not share the same limitations.
Therefore, the data obtained from these two elicitation methods
could be seen as complementary, not necessarily
competing with each other. Hence, we chose the solution
presented by Oppe and van Hout [
], who combined DCE
with C-TTO in a ‘hybrid model’, imposing the (0) death to
(1) full health scale as determined by C-TTO.
To illustrate how the hybrid model combined C-TTO
and DCE responses in this study, we also present the results
from the models estimated from each C-TTO and DCE
separately, with the same assumptions as those used for the
hybrid model. We used the 20-parameter main effects
model, which estimates four parameters for the five levels
of each of the five dimensions: mobility, self-care, usual
activities, pain/discomfort and anxiety/depression. Each
coefficient represents the additional utility decrement of
moving from one level to another. Hence, the overall
decrement of moving from ‘no problems’ to
‘unable/extreme problems’ is calculated as the sum of the coefficients
of ‘no problems to slight problems’, ‘slight problems to
moderate problems’, ‘moderate problems to severe
problems’, and ‘severe problems to unable/extreme problems’.
Presenting the TTO, the DCE and the hybrid model also
allows us to compare the value distribution in the form of
the correlations between the predicted values of the
models, and we can compare the weights of the individual
dimensions. This gives information about construct validity
in the form of ‘convergent validity’, or ‘concordance’.
Modelling was undertaken using the STATA statistical
package. C-TTO data were modelled using the response
values as dependent variables and the health states as
explanatory variables. This was achieved by the
implementation of a Tobit model (hyreg with ll() option), which
assumes a latent variable Y*it underlying the observed Yit
of C-TTO values when there is either left- or
right-censoring in the dependent variable. The C-TTO data, in
particular the lead-time C-TTO for WTD health states, is
by nature censored at -1 [ll(-1) option on hyreg
command]. This means that observed preference values were
valued by the C-TTO method at -1, despite the latent
preferences of respondents possibly including values lower
than -1 [
]. The Tobit model accounts for this censoring
by estimating the latent variable Y*it, which can take on
predicted preference values extrapolated beyond the range
of the observed values. Variance of C-TTO data is not
homogeneous among health states; this led us to model
C-TTO data as heteroskedastic data. We used the hetcont()
option of the hyreg command as suggested by Ramos-Gon˜i
et al. [
]. The dummy variables included in the hetcont()
option were the same as those included in the main model,
that is, the 20 dummies that specified the main effects
DCE (forced pair comparisons in our case) responses
were modelled as a conditional logistic regression model
including the same 20 dummy parameters as those used for
the C-TTO data. Nevertheless, we did not use the
coefficients estimated from a conditional logit model because
they were expressed on a latent arbitrary utility scale. We
rescaled the DCE coefficients using the same parameter h
that was estimated in the hybrid model. This rescaling
assumes that the C-TTO model coefficients are
proportional to the DCE model coefficients. For more details on
the modelling see Ramos-Gon˜i et al. [
Pearson product-moment correlation analysis was
applied to measure the strength and direction of association
that exists between C-TTO, DCE rescaled and hybrid
predicted values for 3125 health states.
3.1 Respondent Characteristics
In total, 1056 of 1117 respondents who were approached
after the retraining of the interviewers completed the
interview. Reasons for interview failure were refusal to
participate (36, 3.2%), conflicting schedules (14, 1.25%),
discontinuation of the interview at the respondent’s request
(10, 0.89%), and discontinuation of the interview by the
interviewer’s decision because of the respondent’s lack of
understanding (1, 0.09%). From the remaining 1056
respondents, we excluded two respondents who had a
positive slope on the regression between their values on
* Significant difference at a = 0.05 from z test
a Data from Indonesian Bureau of Statistics (BPS)
C-TTO and the sum score of the level digits of the health
states, indicating that the respondent provided higher utility
values for poorer health states on average, leaving 1054
respondents in the final sample. No interviewers were
removed because of persistent low-quality data.
Characteristics of the respondents in the final sample
were similar to those of the Indonesian population in terms
of residence, gender, and religion. There were some
statistically significant differences in some of the age groups,
education levels, and ethnicities, but the absolute
differences are small as these are \4% (Table 1).
3.2 Self-Reported Health Problems
Table 2 shows that the highest proportion of health problems
was reported in the pain/discomfort dimension (39.66%
reported ‘any problems’) and the lowest in the self-care
dimension (1.9%). From the final sample, 464 (44.02%)
reported no health problems on any dimension (‘11111’).
3.3 Data Characteristics
The 1054 respondents provided 10540 C-TTO observations
(respondents valued 10 health states each). We excluded 45
observations because they were ‘irrational answers’:
preferring life B (10 years in the corresponding health state,
which is worse than full health) to life A (10 years in full
health) and not shifting after his/her initial response was
reconfirmed by the interviewer. Furthermore, 1033
observations that were pointed out by the respondents in the
Feedback Module task were removed. Accordingly, the
C-TTO dataset contained 9462 observations. Of these, 187
(1.97%) observations relayed the value 0, and another 3349
(35.39%) were negative values (see Fig. 1 for the
histogram of the observed C-TTO values). The 86 observed
mean C-TTO values ranged from -0.719 for state ‘55555’
to 0.909 for state ‘12111’. The mean observed values were
negative for 29 health states out of 86 used in the C-TTO
design (see Online resource 1 in the electronic
The DCE dataset comprised 7378 observations (all
respondents completed seven paired comparisons). Twenty
respondents (1.89%) answered with suspicious patterns:
AAAAAAA (always chose the health state at the left of the
screen), BBBBBBB (always chose the health state at the
right of the screen), ABABABA or BABABAB; however,
their responses were not excluded from the final dataset.
3.4 Modelling Results
There were 657 (6.92%) left-censored C-TTO
observations: when respondent gave the lowest possible value (-1)
for a health state in the C-TTO task. The Tobit C-TTO
model results were logically consistent. Conditional
logistic regression was used to model the DCE responses
that were also logically consistent (we used the rescaled
DCE coefficients). C-TTO and rescaled DCE predicted
values for 3125 health states were correlated, as Fig. 2a
shows (r = 0.9881, p value \0.0001). Table 3 shows that
both sets of coefficients were in relative agreement; that is,
the most important dimension was mobility and the least
important was pain/discomfort. The hybrid model, which
utilized both C-TTO and DCE data, was also in relative
agreement with both C-TTO and DCE models. Figure 2b, c
EQ-5D-5L descriptive system with scores in %
Usual activities Pain/discomfort Anxiety/depression
show a high correlation of hybrid predicted utility with
models predicted from C-TTO (r = 0.995, p \ 0.0001)
and rescaled DCE (r = 0.997, p \ 0.0001).
The hybrid model with main effects was logically
consistent (Table 3). Using this as the final model to obtain
3125 EQ-5D-5L health states, the maximum value was
1.000 for full health (health state ‘11111’) followed by the
health state ‘11112’ with value 0.921. The minimum value
was -0.865 for the ‘55555’ state. Of the 3125 health states,
1108 (35.46%) had negative values using the hybrid model.
The coefficients from the hybrid model were also in
agreement with the previous two models regarding
mobility appearing as the most important dimension and
pain/discomfort as the least important.
To obtain utility for an EQ-5D-5L health state, for
instance ‘12345’, the following calculation based on the
hybrid model (final value set) is needed:
Utility weight (‘12345’) = 1 - no problems in MO
(0) - no problems to slight problems in SC (0.101) - no
problems to slight problems in UA (0.090) - slight
problems to moderate problems in UA (0.066) - no problems
to slight problems in PD (0.086) - slight problems to
moderate problems in PD (0.009) - moderate problems to
severe problems in PD (0.103) - no problems to slight
problems in AD (0.079) - slight problems to moderate
problems in AD (0.055) - moderate problems to severe
problems in AD (0.093) - severe problems to extreme
problems in AD (0.078) = 0.240.
Note that each coefficient represents the additional
utility decrement of moving from one level to another.
The aim of this study was to obtain social preferences and
thus derive an EQ-5D-5L value set from the Indonesian
general population. To obtain values for 3125 EQ-5D-5L
health states, 1054 respondents were interviewed using the
computer-assisted EuroQol Group valuation protocol.
C-TTO and DCE were part of the protocol employed in six
cities and their surrounding areas. We used an iterative
quality control approach in order to obtain high-quality
data. The socio-demographic characteristics of the
respondents were similar to those of the Indonesian
population with respect to residence, gender, age, level of
education, ethnicity, and religion. This makes EQ-5D-5L
suitable for health economic evaluations that will benefit
the national health insurance scheme. Furthermore,
nonHTA studies in Indonesia such as those using
patient-reported outcome measures (PROMs), clinical trials or
improvements in hospital care could use EQ-5D-5L as an
instrument to measure HRQOL, with the notion that the
values attached to the health states are societal values.
Several limitations of this study should be considered. It
could be argued that there are still statistically significant
differences in the distribution of background variables in
the sample compared with the data provided by the
National Bureau of Statistics. There are statistically
significant differences, but these are small, and limited to
some age groups, some education levels, and some
ethnicity groups. As a check to see if such small differences
were of importance, we compared observed C-TTO values
for each health state between respondents with different
levels of age, education, and ethnicity. There was no clear
pattern of differences in the health state values. Moreover,
as can be seen in Table 1, the percentage deviations were
small and statistical significance should be seen in the light
of the statistical power of more than 1000 respondents.
Given these observations, and given that weighting for
background variables would add additional complexity, we
chose not to introduce weighting for these small deviations
from full representativeness.
The strategy of finding respondents using personal
networks of the interviewers and the respondents could raise
questions about the objectivity/representativeness of the
study sample. Yet we preferred this way of recruitment in
order to find respondents who fit into the pre-determined
quota groups because we judged it to be a lesser problem
than insufficiently filled categories in the quota sampling.
The quota groups were determined on the variables that
were considered to be important in defining
representativeness. In that respect, we have constructed a
representative sample based on pre-determined variables:
rural/urban, gender, age, level of education, religion and
ethnicity. A further investigation could be conducted to
find out whether recruiting respondents via personal
networks of interviewers and/or respondents is not preferable
Indonesia has five major islands that are inhabited by
93.5% of the population [
]. However, 92.9% of
respondents interviewed in this study were living on Java Island.
This might raise questions about the representativeness of
the study sample. However, we focused the data collection
on Java island because it is the most populous island (57%
of the population) and the main target of migration from all
over Indonesia. The diversity of its residents in terms of
ethnicity helps to fulfil all the categories in our quota
sampling in a cost-effective way. We do not know whether
the values obtained in Java from these migrants would have
differed from the values should the interviews have been
conducted on their original islands. One way to investigate
whether location is indeed an issue in valuing health in
AIC Akaike information criteria, BIC Bayesian information criteria, C-TTO composite time trade-off, DCE discrete choice experiments, SE
Indonesia would be to sample values for health states at
different places/islands in the republic. For instance, the
same health states could be valued in Aceh (west), Java
(middle) and Papua (east). Such a study could then be used
to provide the motivation for additional studies that sample
the values for people living in other parts of the
archipelago. For the time being, we conclude that the
present value set is the best representative values set for the
EQ-5D-5L now available for Indonesia.
Several study findings are worth highlighting. First, this
is the first study in Asia to have used the hybrid model to
maximize information obtained from C-TTO and DCE.
The models demonstrated logical consistency and
significant regression coefficients. Two possible reasons that the
data led to logical and significant models could be that the
data were of high quality, which was assured by (1) the
extensive use of the QC report provided by the EuroQol
Group, and (2) the retraining programme conducted after
dropping the first 102 interviews owing to their poor
]. The QC report identified the first 102
interviews as problematic; indeed, further analysis using the
hybrid model demonstrated that the results of these
interviews showed logical inconsistencies in self-care and pain/
discomfort dimensions, together with a regression
coefficient that was not significant for pain/discomfort level 4
(p = 0.179). The lesson learned here is that even
sophisticated models profit from high-quality data.
Second, the Indonesian results present more negative
values than any other EQ-5D-5L valuation study
undertaken so far (i.e. in the UK, the Netherlands, Canada,
Uruguay, Japan and Korea [
]). It could be argued that
the high number of negative values is the result of
interaction between a process-related factor—quality control
process and a cultural-related factor—interdependence
among the members of a society (collectivism vs
individualism). This study implemented the quality control
process rigorously. It is possible that this quality control
process provides the interviewer with better feedback and
therefore better skills to administer the complex WTD
trade-offs. Therefore, the more valid administration of the
C-TTO means that more interviewers follow the protocol,
which could have led to a higher proportion of negative
values. The cultural factor, namely collectivism, might
play a role. People from collectivistic cultures, such as
Indonesia, are more concerned with how their illness might
affect their closest circles such as family and friends .
Moreover, they are more reluctant to explicitly ask for help
]. Some comments from our respondents support this:
having severe or extreme/unable problems in EQ-5D
dimensions was very bad for them individually, but will
also be a burden for their closest circles (family and
friends). For other respondents, they preferred to die than
to bother their families and friends when they have a severe
illness. The EQ-5D-3L value set of Singapore, a
neighbouring country of Indonesia and a collectivistic country as
well, showed the all-worst state ‘33333’ was -0.769 [
When more national valuation studies are published, it will
be possible for a further investigation to disentangle the
effect of these factors on proportion of WTD values in an
EQ-5D-5L valuation study.
Third, we had a low level of non-response: only 61 of
the 1117 respondents. Our recruitment strategy, which
involved local leaders and asking respondents to
recommend our study to other people, contributed to this low
Fourth, this study was performed according to the
EuroQol Group’s EQ-5D-5L valuation protocol. Hence, the
results are comparable to findings obtained in other
countries. The final Indonesian value set shows that the mobility
dimension influenced utility estimates the most, similar to
EQ-5D-5L valuation study results from Uruguay and South
]. The pain/discomfort dimension had the
least influence on utility estimates, quite the opposite of the
EQ-5D-5L value sets of England and the Netherlands
where this dimension was in the top two most influential,
after anxiety/depression [
]. Perhaps this was because,
in countries such as the Netherlands and the UK, problems
with mobility had less influence due to better infrastructure
provision and less emphasis on manual labour. It could also
be argued that Indonesian people have adapted to mild
levels of pain or discomfort, or perhaps they considered a
mild level of pain or discomfort something they have to
live with. The same line of reasoning applies to anxiety/
depression. Indonesian people report more problems with
anxiety/depression and have adapted to these mild levels of
anxiety/ depression, or they consider this as part of normal
life. It could also be a result of small differences in
translation. If the translated Indonesian words for
depression and anxiety refer to a lighter problem, then it makes
sense that the prevalence was higher and the disutility
lower. Indeed, there are some indications that this was the
case. In the Indonesian EQ-5D translation, the word
‘sedih’, which might also be translated as ‘sadness’, is
added to the description of the anxiety/depression
dimension. These kinds of interactions between the description of
the dimensions and the values attached justify attempts to
utilize local and linguistically matched value sets for utility
questionnaires such as EQ-5D. If not, value sets based on
other languages might apply the wrong (higher) utilities to
the descriptors. For instance, it is now clear that one cannot
use the UK value for anxiety/depression for the Indonesian
descriptor with an additional word ‘sadness’.
Several policy implications of the present study can be
considered. The finding that the mobility dimension most
affects utility could be implemented in Indonesian
government policies, such as allocating more funds to the
prevention of diabetic foot ulcers or other interventions that
improve mobility like better wheelchairs. Moreover, the
anxiety and depression problems reported should be
addressed. If so, the discussion concerning the translation
of the anxiety and depression dimension mentioned in the
paragraph above should be taken into account. If indeed
anxiety and depression are such common afflictions in
Indonesia, mental health treatment by professionals such as
psychologists and psychiatrists within the national health
insurance scheme should be considered.
Indonesia is endeavouring to implement HTA
comprehensively. The present research shows that in measuring
and valuing quality of life, Indonesia bears comparison
with the leading countries employing HTA. Evidently
Indonesia still has ground to conquer when dealing with
models, cost data and decision making. Nevertheless, this
research shows that it is possible to arrive at an established
level of HTA methodology in a short time-span when
cooperating at an international level.
This paper contains the EQ-5D-5L value set for Indonesia
based on Indonesian citizens’ health preferences. We
expect our results to promote and facilitate health
economic evaluations in Indonesia which can help to inform
decision makers concerning resource allocation decisions.
Data Availability Statement The datasets generated
during and/or analyzed during the current study are
available from the corresponding author on reasonable request.
Acknowledgements The Dutch and Indonesian non-profit
organization ‘Van Deventer-Maas Stichting (VDMS)’ and the ‘Indonesia
Bhadra Utama (IBU) Foundation’ respectively, helped us with the
data collection. The paper benefited from the efforts, insights and
annotations of Elly Stolk and Arnd Jan Prause. Any errors or
omissions are the responsibility of the authors only.
Author contribution JB, JP and JH designed the study and acquired
the funding. SS and AI provided detailed information regarding data
collection processes in Indonesia during the study design phase. FDP
coordinated the data acquisition, assisted by TSF and AI. FDP and
JMR conceived the analysis. FDP prepared the first draft of the
manuscript. All authors played a role in the review of the analysis,
interpretation of the results, and reviewed and recommended revisions
to the final submitted manuscript to ensure accuracy and fair balance.
Compliance with Ethical Standards
All procedures performed in studies involving human participants
were in accordance with the ethical standards of the institutional
research committee [Padjadjaran University Health Research Ethics
Committee (no: 065/UN6.C1.3.2/KEPK/PN/2015)].
Funding The research was financed by the EuroQol Group and
Directorate General of Higher Education of Indonesia (http://www.
dikti.go.id). Both grants were unrestricted.
Conflicts of interest The following authors are members of the
EuroQol Research Foundation (the copyright holders of EQ-5D-5L):
JB and JMR. There are no other conflicts of interest.
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.
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