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Health state utility valuesA description of their development and application for rheumatic diseases.

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Arthritis & Rheumatism (Arthritis Care & Research)
Vol. 59, No. 7, July 15, 2008, pp 1018 –1026
DOI 10.1002/art.23813
© 2008, American College of Rheumatology
Health State Utility Values: A Description of
Their Development and Application for
Rheumatic Diseases
The primary outcomes of most clinical studies in rheumatology are chosen as the most relevant and meaningful for
the clinical community. However, they have restricted
value in assisting policymakers considering resource-allocation decisions. Although clinical outcomes might constitute important results in a rheumatology trial, their use
in economic evaluation is confined to cost-effectiveness
analysis where outcomes are measured in units that are
relevant to the condition under investigation. Comparisons between cost-effectiveness studies are restricted because the outcomes are typically measured in units that
differ from study to study. Comparison across therapeutic
areas becomes almost impossible. Therefore, cost-utility
analysis (CUA; a distinct form of cost-effectiveness analysis) in which outcomes are measured in terms of a standard unit metric that combines information on the quantity and quality of life, the quality-adjusted life year
(QALY), is important (1,2). The QALY requires data that
express health-related quality of life (HRQOL) in the form
of a single value, known as a health state utility value
(HSUV), which is scored on a scale that assigns a value of
1 to a state equivalent to full health and 0 to a state
equivalent to death (3).
Although the most recent studies in rheumatology have
used some form of an HRQOL questionnaire, such as the
Nick Bansback, MSc: Centre for Health Evaluation and
Outcome Sciences, Vancouver, British Columbia, Canada,
and University of Sheffield, Sheffield, UK; 2Mark Harrison,
MSc, Linda Davies, MSc, Deborah Symmons, MD, FFPH,
FRCP: University of Manchester, Manchester, UK; 3John
Brazier, PhD: University of Sheffield, Sheffield, UK; 4Jacek
Kopec, PhD, Carlo Marra, BSc, PharmD, PhD, Aslam Anis,
PhD: University of British Columbia, Vancouver, British
Columbia, Canada.
Address correspondence to Aslam Anis, PhD, Department
of Health Care and Epidemiology, Faculty of Medicine, University of British Columbia, James Mather Building, 5804
Fairview Avenue, Vancouver, British Columbia, Canada
V6T 1Z3. E-mail:
Submitted for publication March 16, 2007; accepted in
revised form February 12, 2008.
Short Form 36, Health Assessment Questionnaire (HAQ),
or Western Ontario and McMaster Universities Osteoarthritis Index, these questionnaires typically measure and
summarize a number of aspects of quality of life as a
profile based on the responses. However, none of these
instruments alone can be used to obtain an HSUV and
therefore they are not amenable for economic evaluation.
To obtain an HSUV requires the incorporation of a preference weight (3). The resulting values can be used to compare the general population preferences for different disease states both within and across diseases. When this is
linked to the effect of an intervention, policymakers tasked
with improving the outcomes of the whole population can
allocate resources accordingly. It is from this context that
economic evaluation becomes important: by identifying
what gains in HSUVs (and life years) can be achieved by
new interventions and at what additional cost.
HSUVs have been described, analyzed, and reported in
the rheumatology literature for more than 10 years (4).
With the rising cost of health care (5), the use of economic
evaluations has escalated, hence the rising interest in the
methods and results of HSUV measures. However, although the motivation for using HSUVs is clear, the issues
surrounding their development and use, predominantly
the research of economists, is less well understood. Policies informed by these methods can impact the treatments
available to physicians, and ultimately the patients’ wellbeing. We consequently reviewed the rheumatologic literature with the objective of identifying and addressing key
issues and concepts in the valuation of HSUVs and then
reported their application in rheumatology. We make recommendations for persons wanting to obtain values in the
future and highlight issues requiring further research.
Materials and Methods
Search strategy. The review was based on a systematic
search of Medline, EconLit, and the National Health Service Economic Evaluation Database for the years 1980 –
2005. The search identified articles reporting HSUVs in
major rheumatic diseases (rheumatoid arthritis [RA], psoriatic arthritis [PsA], ankylosing spondylitis [AS], systemic
Health State Utility Values in Rheumatic Diseases
adequately describe important determinants of disease
that are known to be closely related to HRQOL.
Figure 1. Process of study selection of health state utility values.
RA ⫽ rheumatoid arthritis; AS ⫽ ankylosing spondylitis; PsA ⫽
psoriatic arthritis; OA ⫽ osteoarthritis; OP ⫽ osteoporosis.
lupus erythematosus, osteoarthritis [OA], and osteoporosis
[OP]). We used the search criteria for HSUVs as described
by Brazier et al (6), which identifies methods (e.g., multiattribute theory and time-tradeoff [TTO]), instruments
(e.g., EuroQol [EQ-5D] and Health Utilities Index [HUI]),
and their applications (e.g., QALYs). The search strategy is
shown in Figure 1.
We included studies that reported specific HSUV values
for rheumatic diseases. We excluded studies that reported
HSUVs solely as a description of the study population, or
reviews or CUAs that did not present primary data
sources. Studies were also identified by hand searching,
citation searching, and reference list checking, and by
those known to researchers involved in the present study.
Two databanks of HSUVs and economic evaluations were
also searched and used to complement and validate the
search strategy (7,8). For each study selected, the type and
value of each measure was extracted, along with other
details from the publication. The methodology used in
articles identified at this stage of the review was used to
identify inconsistencies that suggest the need for clarification of key issues and concepts in the initial section of the
Quality assessment. To most accurately describe the
impact of each disease across instruments and conditions,
we constructed criteria for assessing the quality of HSUVs
from each study. While no commonly accepted criteria
currently exist, we used the study conducted for the Institute of Medicine as an example (9). For studies to meet the
inclusion criteria, they had to 1) be reported in primary
articles, 2) present values where the source of preferences
was from the general population (the recommendation by
a number authoritative experts and panels [1,10 –12]), 3)
present values separately for a population with a clear
diagnosis of a rheumatologic condition, 4) be from populations of sufficient sample size (n ⱖ 60 [13]), and 5)
The broad search strategy found a total of 1,352 articles.
We initially sifted through the abstracts to identify those
potentially relevant to this review. A number of the articles were on the subject of clinical utility, and once this
was removed from the search, the number of articles was
reduced to 333. These articles were retrieved and reviewed
to identify those that contribute to the issues in the HSUV
literature. Of these, 126 articles were judged to meet the
final inclusion criteria representing useful utility values.
The number of studies has increased exponentially over
the past 5– 6 years. Among these articles, a wide variety of
instruments were used, and a number of issues and inconsistencies were found. Inconsistencies centered around
the perspective used to value health states, their description and interpretation, and the techniques used to ascertain values or preferences for health states (14). We begin
by examining the issues identified in the literature under 4
broad themes.
Issues in the development of HSUVs. What is being
valued? A health outcome is a path of health states evolving over time, often over an individual’s lifetime. Developing a numerical score for the health outcome involves
developing a score for each health state and combining
that score with duration to determine the number of
QALYs created. Because each health state comprises many
different domains, developing a score for each state requires a process that combines the effect of each domain
into a single metric (1).
Non–preference-based methods assign scores to individual components of the health state, using a Likert scale or
a binary response, and then sum the component scores to
a single score. This can be rescaled to a 0 –1 scale (e.g., the
HAQ could be divided by 3). However, this assumes that
the weight between the different domains is known (e.g.,
an improvement from pain to no pain has a weight equivalent to an improvement from immobile to mobile). It also
leaves a policymaker unaware of what aspects of health are
most important to the population. The alternative is a
preference-based methodology, where subjects are asked
to make judgments regarding the value of particular health
states. The judgments are then used to produce a score.
The incorporation of preferences is what distinguishes
most common HRQOL instruments from a health utility
measure. Judgments to inform preferences require individuals to consider a transition from their current (or a hypothetical) health state to an alternative (usually preferable)
health state that involves sacrifices of something they
value. The objective is to identify the point at which individuals are indifferent between the 2 alternative states,
which is used to calculate a valuation of the health state in
question. The greater the sacrifice or risk accepted to move
to the alternative health state, the lower the valuation of
the current health state (3).
Who should do the valuations? Having patients value
their health has the advantage of avoiding the need to
Bansback et al
Figure 2. The number of articles reporting health state utility values over time (n ⫽ 126).
describe hypothetical health states and ensures a good
understanding of the impact of each state on a person’s
life. However, it has been argued that, for the purpose of
informing resource allocation, the values of society at large
should be required. The decisions about allocation should
be made by individuals who do not stand to lose or gain by
these decisions, and because HSUVs are the basis for allocation, these should be assessed from a wider perspective
than that of the patient (1). The valuations of patient health
states also need to be compared with optimal health (equal
to 1), and while patients by definition are best able to
describe and understand their HRQOL, their perception
and expectation of optimal health may be modified by
their disease experience. Although the debate is ongoing
over whether values from patients, professionals, or members of the general population (e.g., society) are the most
legitimate, the general consensus is that societal values are
the most appropriate for policymakers making decisions
that concern a broad spectrum of the population (1,10,12).
The debate is important as our review highlighted a
number of studies that found large differences in the outcomes dependent on who valued the disease (15,16).
When valuations of purely hypothetical (no group actually
experiencing the condition) health states are compared
between patients and the general population (17,18) and
between the general population and health professionals
(15), clear differences in valuation between groups are
evident but the direction is inconsistent. However, higher
valuations of poor health states by patients actually experiencing a condition than members of the general population trying to imagine the same states have been reported
The difference in values between patients experiencing
the condition and the general population’s imagination of
a certain health state is not unexpected. The unknown is
often more frightening than reality (1). Patients experiencing a health state may develop coping mechanisms and
modify their behavior, which may minimize the impact of
a health state on their quality of life. Also, a change in
expectation can mean that the reference point patients
compare their condition against is somewhat lower than
they may have considered previously, thus the valuation
of their current health state is inflated (19).
How are health states described? When asking members
of society to value hypothetical health states, it is necessary that the description ensures they have an understanding of the impact of the state. Participants may be asked to
value specially constructed vignettes that describe each
health state, or to use generic health state descriptions that
are not specific to any condition. Consequently, a questionnaire with a number of questions and levels can be
administered to the patient population of interest, for
which each combination of health states has an a priori
HSUV value from the survey of the general population.
The review found that 6 such preference-based instruments have been used in rheumatology patients, with the
EQ-5D (20) being the most popular, followed by the HUI2/3 (21), the Short Form 6D (SF-6D) (22), the Quality of
Well-Being scale (QWB) (23), the 15D (24), and the Rosser
index (25) (Figure 2). Each instrument covers different
domains, with a different number of levels (Table 1).
In practice, general population valuation surveys have
mostly been used to populate generic health status measures by providing a set of utility weights. However, similar surveys can be conducted for disease-specific instruments or outcomes. Examples include the valuation of
different types of treatment response, based on the American College of Rheumatology criteria in patients with RA
(26); a reduced version of the Cedars-Sinai HRQOL instrument in patients with RA (27); and 3 health states related
to OP (16).
How should health states be valued? Common techniques for measuring preference directly include the standard gamble (SG), the TTO, and rating scales (RS)/visual
analog scales (VAS) (Figure 3). The SG involves trading
alternative health states against each other with a risk of
immediate death, whereas the TTO trades duration of life
against quality of life. In the SG, the patient is offered 2
alternatives. The first has the certain outcome of disease
state A for life (no gamble). The second is an intervention
with 2 possible outcomes (gamble): either the patient returns to full health for life, or the patient dies immediately.
Probability is varied until the respondent is indifferent
between the 2 alternatives. The preference value for state
A is then equal to the probability. In the TTO, the patient
is offered 2 alternatives. The first has the certain outcome
Health State Utility Values in Rheumatic Diseases
Table 1. Selected characteristics of generic preference measures*
HUI (19,64)
EQ-5D (18)
QWB (21)
Rosser index
15D (22)
SF-6D (20)
Method of elicitation
of health states
Descriptive domains
Health states
Vision, hearing, speech, emotion,
cognition, self-care, pain,
fertility, mobility (v2),
ambulation (v3), dexterity (v3)
Mobility, pain/discomfort,
anxiety/depression, self-care,
usual activities
Mobility, physical activity, social
activity, 27 symptoms or
Disability, distress
24,000 (v2),
972,000 (v3)
⫺0.03 to 1 (v2),
⫺0.36 to 1 (v3)
⫺0.59 to 1
0 to 1
⫺1.49 to 1
Mobility, vision, hearing,
breathing, sleep, eating,
speech, bladder/bowel
function, usual activities,
mental function, discomfort/
pain, depression, distress,
vitality, sexual activity,
cognitive function
Physical functioning, role
limitation, social functioning,
bodily pain, mental health,
31 billion
0.11 to 1
0.3 to 1
* HUI ⫽ Health Utilities Index; v2 ⫽ HUI2; v3 ⫽ HUI3; SG ⫽ standard gamble; VAS ⫽ visual analog scale; EQ-5D ⫽ EuroQol; TTO ⫽ time-tradeoff;
QWB ⫽ Quality of Well-Being scale; RS ⫽ rating scale; SF-6D ⫽ Short Form 6D.
of disease state A for a specified time (t; can be the rest of
the respondent’s life). In the second, the patient returns to
full health for a time ⱕt. The time in full health is reduced
until the respondent is indifferent between the 2 alternatives. The preference value for state A is then the time
chosen in full health divided by the time in state A. For the
VAS, the patient is asked to rate his or her health by
drawing a line on the scale above where 100 is the best
state they can imagine and 0 is the worst. The utility value
is simply the numerical point on the scale divided by 100.
Although experience and clear presentation have allowed for high response rates and internal validity in both
the SG and TTO, they can be burdensome and difficult for
some individuals to understand (28). This is not the case
with the VAS, where the respondent has to simply place a
mark on a 0 –100 scale indicating the rating for the health
state (1). Likely for this reason alone, our review found that
of studies that measured HSUVs directly from patients,
most used some form of RS.
However, the validity of RS as a measure of the strength
of preference has been questioned (29). Although the SG
and TTO are not without criticism, they are generally
preferred. Theoretical matters would not be quite as critical as long as the underpinning theory methods are valid
and the measures produce comparable results. Articles
included in the review found this not to be the case.
HSUVs derived from RS were consistently lower than TTO
or SG ratings (15,18,30 –35). Values derived from the SG
were higher than those from the TTO, possibly due to the
incorporation of risk in the rating (15,36).
The methods that have been used to attach HSUVs to
generic health status questionnaire profiles vary greatly
(37). The EQ-5D and Rosser index used predominantly the
TTO to value health states. The HUI and SF-6D used the
Figure 3. An example of the standard gamble, time-tradeoff, and visual analog scale.
Bansback et al
Figure 4. Characteristics of identified studies. Open bars represent the number of studies
with primary data identified for each condition, and shaded bars represent the number that
met the quality assessment criteria. The pie charts show the different instruments used in
each study for each condition, where those in gray represent a preference-based instrument,
and those in white represent direct measures. E ⫽ EuroQol; S ⫽ Short Form 6D; H ⫽ Health
Utilities Index; Q ⫽ Quality of Well-being scale; 15 ⫽ 15D; V ⫽ visual analog scale or rating
scale; SG ⫽ standard gamble; T ⫽ time-tradeoff; Ro ⫽ Rosser index; O ⫽ expert opinion.
SG (the HUI used power transformations to map some RS
valuations to SG), whereas the 15D and QWB relied on an
RS. It is impractical to value all potential health state
combinations (the number varies from 243 in the EQ-5D to
⬎20,000 for the HUI and 15D); some form of regression is
used to estimate a scoring algorithm that can assign a value
to all possible health states.
Utility values in the literature. Of the 126 articles initially reviewed, only 27 studies met the quality assessment
criteria comprising patients with RA, AS, PsA, OP, and
OA (Figure 4). For ease of comparison, we present the
results in comparison with age- and sex-adjusted population norms (Figure 5) (38,39).
Rheumatoid arthritis. We found 13 studies that reported values meeting the quality assessment criteria in
RA (17,23,40 –50). Many studies were excluded because
they only measured HSUV directly. Mean values tended to
be in the range of 0.5– 0.75 for patients recruited from
routine clinical practice (23,40,41,44,48). It is clear that
HSUVs differ depending on a number of characteristics
of the patients because mean values for the same instrument give widely different results (the EQ-5D means
varied from ⫺0.1 to ⫺0.3 units less than the population
norm). Important determinants of different utility values
within patients with RA appeared to be (in order of magnitude) functional class (46), self-report severity (41), disability (43), income (45), treatment (49,50), and education
(45). Patients with a functional class of IV differed from
patients with a functional class of I by 0.9 units when
assessed with the EQ-5D (46). While we report the univariate association, it is likely that multivariate (e.g., other
than functional class) determinants exist within this patient group.
The agreement between HSUVs from different instruments varied according to the severity of the health state in
question. The HUI measure had the largest range of values
between the least and the most severe self-reported RA
severity (0.52), approximately twice that of the SF-6D
(0.33) (41).
Ankylosing spondylitis. Four studies met the quality
criteria in studies of patients with AS (51–54). All studies
solely used the EQ-5D. Again, mean values varied by an
amount similar to patients with RA (⫺0.1 to ⫺0.3 units
below the population norm) (51–53). The Bath Ankylosing
Spondylitis Functional Index (BASFI) and the Bath Ankylosing Spondylitis Disease Activity Index both appeared to
be important determinants of utility in patients with AS
(54). Although the univariate association appeared larger
for the BASFI, an interaction between both instruments
would be important (54).
Psoriatic arthritis. Only 1 study of PsA was included
(48). The mean EQ-5D value of 0.59 was again in the same
bounds as the RA and AS studies (0.22 points worse than
general population values). Although not conclusively
shown, it appears that the determinants of HSUV in patients with PsA would be important in the differential
impact of psoriasis and joint disease (48).
Osteoarthritis. Five studies reported values in patient
groups with OA (55–59). The largest deficit in utility val-
Health State Utility Values in Rheumatic Diseases
Figure 5. Characteristics of studies meeting the quality assessment criteria. Dotted lines represent changes between a single outcome. Each
mark corresponds to a level described in the brackets, where the first is the smallest change, and the last is the largest deficit. EQ5D ⫽
EuroQol; HUI ⫽ Health Utilities Index; SF6D ⫽ Short Form 6D; QWB ⫽ Quality of Well-being scale; HAQ ⫽ Health Assessment
Questionnaire; BAC ⫽ bachelor’s degree; HS ⫽ high school; T ⫽ trade; TNF ⫽ tumor necrosis factor; BASFI ⫽ Bath Ankylosing Spondylitis
Functional Index; BASDAI ⫽ Bath Ankylosing Spondylitis Disease Activity Index.
ues reported was for those awaiting a hip arthroplasty.
Here, utility values increased by between 0.4 (EQ-5D) and
0.1 (SF-6D) 6 months after the operation. For the EQ-5D,
postsurgery values were equivalent to those in the general
population (an improvement of 0.4 units). However, this
change was smaller for both the HUI and SF-6D. The
choice of instrument would therefore have an important
influence on the result of any CUA of hip arthroplasty
Osteoporosis. Studies of OP generally reported the
change in HSUV utility postfracture, and therefore were
not included in Figure 5. Five studies met the quality
assessment criteria (60 – 64). The most comprehensive
study provided values for fractures (in descending order of
impact) of the hip, vertebrae, rib, pelvis, and wrist, varying
from ⫺0.09 to 0 using the HUI (61). Another study found
that increasing the number of vertebral fractures from 1 to
ⱖ4 led to a reduction in EQ-5D of 0.09 (63).
A clear rationale exists for the use of HSUVs to aid important policy decisions regarding the reimbursement of
health technologies. This comprehensive search and review of HSUV articles in rheumatology documents the
dramatic increase in the use of HSUVs in rheumatic diseases over recent years. Our review reveals some complex
issues regarding the development of instruments to measure HSUVs, and demonstrates that different instruments
consistently produce dissimilar results in multiple rheumatologic diseases.
It is predominantly the need for societal values of health
utility that has led to the development of preference-based
instruments. Because patients with rheumatic diseases
adapt to their health conditions, patient valuations of
HSUVs would be higher than societal values and would
subsequently result in small QALY gains for even the most
efficacious treatments. Our review demonstrates that
while societal HSUVs are increasingly measured in the
rheumatology literature, only 5 articles meeting our criteria reported the change in HSUVs due to interventions
(49,50,55–57). Without such evidence, it is difficult to
demonstrate the cost utility of the intervention. Another
important finding was that for all diseases, HSUVs differed
depending on which preference-based instrument was used.
The cost of using societal values is that a number of new
problems are introduced. Persons in the valuation survey
have to imagine they have hypothetical health states,
which limits the number and scale of domains that can be
included, and requires them to accurately understand the
implications of such a health state (37). The value of the
health state must then be elicited, for which there are
alternative methods that give varying results (14). Then,
the health states not directly valued must be modeled,
for which alternative statistical methods again exist (65).
Finally, the type of persons used in the valuation survey
may give different results (66). The consequence of these
issues is that the instruments are sometimes insensitive
(for example, the HUI was found to be insensitive in conditions that affect the lower limbs or hands and fingers
[67]), are subject to floor and ceiling effects (68), and, as
seen in our review of values, result in different values for
similar health states. Different values mean that the costacceptability conclusions can be hugely affected by the
type of instrument used (15,69).
The limitation in using HSUVs for reimbursement decisions does not end with how the values are obtained. The
QALY methodology and use of CUA in reimbursement
decisions are the subject of continuing debate and controversy (70,71). Critics of the QALY methodology have criticized it based on assumptions regarding linearity of the
value of health per unit time (72). Assuming linearity of
the value of health per unit time may lead to situations of
preference reversal where the preference of an individual
between alternatives may be overruled (72). QALYs have
also been criticized for assuming, for purposes of aggregation, that a year in full health is of equal value to everybody (70,72). However, societal valuations of medical interventions vary according to the severity of a condition
and sequence and duration of health states (70). The process of aggregating individual QALYs to provide an overall
QALY has been criticized as an oversimplification that
may lead to overlooking important information about the
nature of an intervention’s effectiveness (73). Criticisms of
using CUA have focused on the simplification of assumptions such as perfect divisibility, constant returns to scale,
and comparison against arbitrary thresholds that ignore
opportunity cost (71).
Given the described limitations in obtaining both HSUVs
and QALYs, alternatives to CUA have been sought. For example, healthy life years equivalent and cost-effectiveness
analysis using clinical outcomes have been proposed as
alternatives to QALYs and CUA, respectively. However,
so far no alternative has been found to be as practically
informative as QALYs and CUA, and therefore the majority of policymakers who consider economic value in
their allocation decisions have accepted the limitations
in HSUVs from a purely pragmatic decision-making perspective. Consequently, reimbursement of technologies
for rheumatologic conditions will depend on the measurement of HSUV in clinical studies and further research
to overcome some of the limitations that are pertinent
to health states experienced by patients with rheumatic
Although there is no conclusive superior instrument,
some general rules can help make the decision of which to
use. First, the use of a preference-based instrument would
be recommended to obtain societal values of utility benefit
and to limit patient burden. Second, the instrument with
the type of domains that are most relevant to the patients
in whom the instrument will be used should be chosen.
Last, the country in which the research will be primarily
disseminated should be considered because some countries prefer certain instruments (e.g., the EQ-5D in the UK
Bansback et al
and the HUI in Canada because the surveys for each instrument were conducted in the respective countries).
With the current uncertainties, using multiple instruments
might also be prudent, especially the inclusion of the
EQ-5D because it is quick to administer, is popular, and
the results can be compared with other studies.
We propose both the further analysis of current instruments and the development of new instruments to improve future policy decisions regarding rheumatologic
technologies. With the current data available, a thorough
examination of the current preference-based instruments
should be conducted. Essentially, this should aim to examine the comparable reliability, content and construct
validity, and responsiveness of each instrument to assess
whether one has more scientific evidence to support its
use. The development of a new instrument should only be
considered if it will notably reduce the issues with preference-based instruments, otherwise it will only add to the
confusion of which instrument and value to use. Proponents of condition-specific measures argue that using domains that focus on the aspects pertinent to the disease
will increase sensitivity and construct validity (14). For
example, none of the current instruments include domains
specifically for fatigue, which has been found to be important in certain rheumatic conditions (74). Basing the
new health state classification on an existing, well-used,
disease-specific questionnaire would reduce the need to
burden the patient with an additional questionnaire and
would increase the number of clinical studies that could
derive HSUVs. However, concerns of comparability of
HSUVs from condition-specific measures across diseases
will have to be overcome.
Rheumatologic conditions must compete alongside other
areas of medicine for health care resources. CUAs, which
use HSUVs, are increasingly applied by policymakers
across the world to allocate health care resources. Although the use of HSUVs in rheumatology is rapidly increasing, numerous gaps in data remain. Alarmingly, relatively few trials of interventions for rheumatic diseases
were included in our review, indicating that HSUVs are
not being collected or reported in these studies. It is in
these interventions that CUA will be required. Consequently, only by further understanding the use, limitations, and importance of HSUVs from a resource allocation
perspective will the rheumatology community effectively
provide evidence for policymakers.
Dr. Anis had full access to all of the data in the study and takes
responsibility for the integrity of the data and the accuracy of the
data analysis.
Study design. Bansback, Brazier, Marra, Symmons, Anis.
Acquisition of data. Bansback, Harrison.
Analysis and interpretation of data. Bansback, Harrison, Brazier,
Marra, Anis.
Manuscript preparation. Bansback, Harrison, Brazier, Davies,
Kopec, Marra, Symmons, Anis.
Statistical analysis. Bansback.
Health State Utility Values in Rheumatic Diseases
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development, health, description, application, rheumatic, state, disease, values, utility
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