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Application of the health assessment questionnaire disability index
to various rheumatic diseases
Maaike M. van Groen •Peter M. ten Klooster •
Erik Taal •Mart A. F. J. van de Laar •
Cees A. W. Glas
Accepted: 6 June 2010 / Published online: 18 June 2010
The Author(s) 2010. This article is published with open access at Springerlink.com
Abstract
Purpose To investigate whether the Stanford Health
Assessment Questionnaire Disability Index (HAQ-DI) can
serve as a generic instrument for measuring disability
across different rheumatic diseases and to propose a scor-
ing method based on item response theory (IRT) modeling
to support this goal.
Methods The HAQ-DI was administered to a cross-
sectional sample of patients with confirmed rheumatoid
arthritis (n=619), osteoarthritis (n=125), or gout (n=
102). The results were analyzed using the generalized
partial credit model as an IRT model.
Results It was found that 4 out of 8 item categories of the
HAQ-DI displayed substantial differential item functioning
(DIF) over the three diseases. Further, it was shown that
this DIF could be modeled using an IRT model with dis-
ease-specific item parameters, which produces measures
that are comparable for the three diseases.
Conclusion Although the HAQ-DI partially functioned
differently in the three disease groups, the measurement
regarding the disability level of the patients can be made
comparable using IRT methods.
Keywords Rheumatoid arthritis Osteoarthritis
Gout Health-related quality of life Item response theory
Differential item functioning
Abbreviations
DIF Differential item functioning
HAQ-DI Health Assessment Questionnaire Disability
Index
IRT Item response theory
LM Lagrange multiplier
OA Osteoarthritis
PF Physical functioning scale
PsA Psoriatic arthritis
RA Rheumatoid arthritis
SF-36 Medical Outcomes Study 36-Item Short Form
Introduction
Besides the traditional use of physical and biochemical
measures, patient-centered outcomes have become more and
more important as outcome measures of interventions [1].
For example, patient-reported disability has become a
standard outcome in the clinical studies of rheumatic dis-
eases. One of the most widely used self-reported measures of
physical disability is the Stanford Health Assessment
Questionnaire Disability Index (HAQ-DI) [2]. Although
often referred to as a disease-specific measure, it assesses
physical disability in general and does not focus on specific
disease-associated impairments. In fact, according to its
developers, it was originally intended for use in multiple
illnesses so that the impact of different disease processes
could be compared [1,3]. As a result, the scale has been used
across a wide range of general and clinical populations.
M. M. van Groen P. M. ten Klooster (&)E. Taal
M. A. F. J. van de Laar C. A. W. Glas
Institute for Behavioral Research, Faculty of Behavioral
Sciences, University of Twente, PO Box 217, 7500 AE
Enschede, The Netherlands
e-mail: P.M.tenKlooster@utwente.nl
M. A. F. J. van de Laar
Department of Rheumatology, Medisch Spectrum Twente,
PO Box 50.000, 7500 KA Enschede, The Netherlands
123
Qual Life Res (2010) 19:1255–1263
DOI 10.1007/s11136-010-9690-9
Especially in the field of rheumatology, the HAQ-DI has
become the measure of choice for assessing physical dis-
ability in several specific rheumatic diseases. Although
physical disability is common among all musculoskeletal
conditions, rheumatic diseases can vary widely in their
underlying disease mechanisms, clinical manifestations,
progress and severity, and composition of the populations
generally affected. All of which may influence the mea-
surement characteristics and resulting disability scores
across diseases. Nonetheless, mean HAQ-DI scores are
frequently used to directly compare the severity of dis-
ability across different rheumatic diseases, whether or not
adjusted for some known covariates [4–9]. The purpose of
the current study is to investigate whether the HAQ-DI is a
generic instrument indeed, and if this proves problematic,
to model response behavior on disease-specific items of the
instrument in such a way that the measurement results are
comparable over different groups of rheumatic patients.
The construct validity of the HAQ-DI has been previ-
ously established in numerous studies [1], mostly using
classical psychometric techniques such as factor analysis.
Cole et al. (2005, 2006), for instance, show that there is
considerable support for a single-factor structure and for
comparability of scores of patients with systemic sclerosis
and patients with rheumatoid arthritis. However, some of
the results of these analyses, such as the presence of cor-
related residuals, invite further attention. In the present
article, construct validity is investigated using a unidi-
mensional item response theory (IRT) model. The relation
between IRT modeling and factor-analytic approaches will
be returned to the discussion section.
In IRT models, observed responses are related to a
unidimensional latent trait, that is, to some underlying
scale. The unidimensional latent scale of the HAQ-DI
pertains to the disability level of the patients. The observed
responses are explained by the persons’ disability param-
eters and by item parameters related to the probability that
a person with a certain disability parameter endorses an
item. One of the common assumptions of IRT is mea-
surement invariance, that is, the latent scale applies to all
respondents from some population and items have the
same measurement characteristics, that is, the same item
parameters, for these respondents. A violation of these two
assumptions is known as differential item functioning
(DIF). An item shows DIF if the probability of responding
in the different categories of the item varies across groups
of patients with the same disability level [10,11]. In other
words, an item is biased if the observed item score, con-
ditional on the latent disability level of the patients, differs
between subgroups [12]. In the current study, the construct
validity of the HAQ-DI is investigated by assessing DIF for
patients with three different types of arthritis.
DIF is often investigated using the generalized partial
credit model as an IRT model [11]. The generalized partial
credit model [13] applies to polytomously scored items,
such as the items of the HAQ-DI. The probability of a score
in category xof item iis given by the item response curve
PðXni ¼xjhÞ¼
exp P
x
j¼1
aihndij
"#
1þP
mi
r¼1
exp P
r
j¼1
aihndij
"#
;
where h
n
is the latent disability level of patient n. In the
model, m
i
denotes the number of item categories. Further,
d
ij
and a
i
are item parameters. d
ij
is a category intersection
parameter, that is, it is the point in which the probability of
responding in category j-1is equal to the probability of
responding category j. Finally, a
i
is a discrimination
parameter that indicates the extent to which the item
response is related to the latent scale. This discrimination
parameter is comparable to a factor loading in a factor
analysis model.
If DIF is not present, this is unambiguous support for the
construct validity of the instrument. If DIF is present,
however, the type of DIF becomes important. As previ-
ously noted, measurement invariance pertains to the pres-
ence of the same latent variable in all subgroups and
constancy of item parameters over subgroups. If only the
latter assumption is violated by a limited number of items,
comparability can often still be realized and construct
validity may still be defendable. For example, a question
regarding the number of cars in the household may be a
good item for measuring the latent variable Wealth, though
the metric in downtown New York and in Texas may be
quite different. In IRT, such differences can be modeled by
group-specific item parameters. This approach is, of
course, only defendable if it can be explicitly shown that
the responses to the items given in the two groups pertain
to the same latent variable, that is, that it can be shown that
the same IRT model holds for the entire set of response
data. This approach to modeling DIF, which has a con-
siderable tradition in educational measurement [14–17] and
in consumer research [18], will also be applied in the
present study.
Patients and methods
Respondents for this study were recruited during several
waves of data collection in the period between 2005 and
2008 at the outpatient rheumatology clinic of the Medisch
Spectrum Twente hospital in Enschede, the Netherlands.
During data collection days, consecutive patients visiting
1256 Qual Life Res (2010) 19:1255–1263
123
the outpatient clinic were asked to participate. As the study
did not interfere with usual treatment, ethical approval was
not required according to national legislation and local
institutional policy.
In total, 846 patients with physician-confirmed rheu-
matoid arthritis (RA), osteoarthritis (OA), or gout agreed to
participate. Of the included patients, 619 patients were
treated for RA, 125 for OA, and 102 for gout. Table 1gives
a number of characteristics of the sample. The majority of
the patients were women, but as would be expected, the
gout sample consisted of only 18% women. Mean age was
62 with a standard deviation of 13.6 years. A validated
Dutch version of the HAQ was used [19]. The average
scores on the Medical Outcomes Study 36-Item Short Form
(SF-36) health survey [20] were reasonably comparable
across the three conditions. HAQ-DI scores were similar
for patients with RA and OA, whereas patients with gout
reported substantially less disability.
Scoring the HAQ-DI
The Health Assessment Questionnaire Disability Index
(HAQ-DI) consists of 20 questions regarding the limita-
tions patients experience in performing daily physical
activities [2]. Patients are asked how difficult it is to per-
form an activity on a scale of 0 (without any difficulty) to 3
(unable to do). Patients are also asked whether they need
assistance or aids for the activity.
The questions of the HAQ-DI are ordered into eight
categories of daily living, covering Rising, Walking,
Dressing and grooming, Reach, Eating, Grip, Activities,
and Hygiene. The highest item score within a category is
used as the score for this category, essentially reducing the
HAQ-DI to an 8-item scale. If a respondent indicates the
use of assistance or aids for a category and his highest item
score within the category is 0 or 1, the category score is
raised to the value 2. The scores on the categories are
averaged to construct a single total score.
Statistical analysis
The scores on the eight categories of the HAQ-DI were
used as input for the statistical analysis. The item param-
eters and the means and variances of the latent person
parameters were estimated by marginal maximum likeli-
hood, and DIF was examined using Lagrange multiplier
(LM) statistics [21]. To compute these statistics, the sam-
ple of respondents is divided into subgroups labeled g=
1,…,G. For the present application, these are the three
disease groups, that is, G=3. The statistic is based on the
difference between average observed scores on every item i
in the subgroups, that is, Sig ¼1
NgPNg
njgXni (where the
summation is over the N
g
respondents in subgroup g), and
their expectations E(S
ig
). The differences are squared and
divided by their covariance matrix (for the exact expres-
sions, refer to Glas [15]). The hypothesis thus tested is
equivalent to testing the hypothesis that the parameters of
the items are equal for the subgroups. The LM statistic has
an asymptotic chi-square distribution with G-1degrees
of freedom. Below, the statistics will be accompanied by
effect sizes dig ¼maxgSig EðSigÞ
, which show the
seriousness of the model violation. Since the effect sizes d
ig
are on a scale ranging from 0 to the maximum score m
i
,
effect sizes d
ig
\0.10 can be considered indicative of
minor, acceptable model violation. It can be noted that this
cut-off point is somewhat arbitrary, but its effectiveness
can be evaluated from whether enough DIF items are
detected and modeled to obtain a fitting overall model.
When items with DIF are identified, the next step is
trying to model the DIF in such a way that the measures
Table 1 Sample characteristics
RA Rheumatoid arthritis, OA
Osteoarthritis, HAQ-DI Health
Assessment Questionnaire
Disability Index, SF-36 Medical
Outcomes Study 36-Item Short
Form (version 2), PCS Physical
component summary, MCS
Mental component summary
Characteristics Total sample RA sample OA sample Gout sample
N846 619 125 102
Gender (%)
Female 64 69 79 18
Male 36 31 21 82
Age (years)
Mean (SD) 62 (13.6) 62 (14.2) 63 (11.5) 62 (12.6)
Disease duration (years)
Mean (SD) 13 (12.8) 13 (12.4) 14 (13.8) 10 (13.3)
HAQ-DI (range 0–3)
Mean (SD) 0.82 (0.7) 0.97 (0.7) 1.00 (0.65) 0.54 (0.67)
SF-36 PCS (range 0–100)
Mean (SD) 40 (8.4) 39 (8.1) 38 (8.3) 43 (9.2)
SF-36 MCS (range 0–100)
Mean (SD) 39 (7.0) 40 (6.8) 39 (7.3) 38 (7.0)
Qual Life Res (2010) 19:1255–1263 1257
123
obtained in the subgroups are still comparable. To this end,
DIF can be modeled by assigning these items disease-
specific parameters within a generalized partial credit
model that still pertains to all respondents. So it is assumed
that the same construct is measured in all subgroups, but
for some subgroups the item locations on the latent scale
are different. In this study, this was done in an iterative
procedure in which the item with the largest significant LM
test was given disease-specific item parameters (for more
information on this procedure, see Glas and Verhelst [16]).
These iteration steps were repeated until no items were left
with significant LM tests (P\0.01) or when the effect size
was below the set cut-off point (d
ig
\0.10). The results of
these iteration steps are presented here as results of an
analysis consisting of two steps to enhance clarity.
The final step in the statistical analyses was to assert that
the resulting model was valid in all disease groups, that is,
to assert that the same latent scale with disease-specific
item parameters for some of the items was applicable in all
disease groups. This was done again by computing LM
statistics: one targeted at the form of the item response
curves and one targeted at the assumption of local inde-
pendence. The latter assumption implies that item respon-
ses are independent given a person’s value of on the latent
variable. If this would not be the case, other, unaccounted,
variables influence response behavior and unidimension-
ality is violated.
Results
The results of the DIF analysis before modeling for pres-
ence of DIF are given in Table 2. Three items showed DIF
according to the criteria defined earlier: Dressing and
grooming, Reach, and Activities. The LM statistics of those
items were significant, and their effect sizes were larger
than 0.10. The item Dressing and grooming was given
disease-specific item parameters first. In a second analysis,
the Activities item showed DIF. The process was repeated
until in the third analysis four items were given disease-
specific item parameters. The resulting item parameters are
shown in Table 3. It is important to note that the significant
items in Table 2(Walking, Dressing and grooming, Reach,
Activities) are not completely analogous to the items in
Table 3(Walking, Dressing and grooming, Eating, Activ-
ities). The reason is that the presence of DIF items biases
the item parameter estimates of all items, both the items
with and without DIF. This motivates the iterative nature of
the procedure where items are processed one at a time.
To clarify the interpretation of the results in the table,
the item probabilities for OA and gout are illustrated in
Fig. 1. Consider the item Dressing and grooming. The
discrimination indices (under the heading a
i
in Table 3)
show that the item has the highest loading on the latent
dimension for the patients with gout and the lowest for the
patients with OA. Further, in Table 3and Fig. 1, it can be
seen that the category intersection parameters d
ij
are higher
for the patients with gout than for the patients with OA.
This means that the expected score on Dressing and
Table 2 Outcomes of tests for DIF
HAQ-DI categories LM PAbs. DIF
Rising 3.33 0.19 0.03
Walking 25.02 0.00 0.09
Dressing and grooming 71.20 0.00 0.17
Reach 37.63 0.00 0.15
Eating 6.68 0.04 0.06
Grip 2.66 0.26 0.03
Activities 51.52 0.00 0.15
Hygiene 7.29 0.03 0.06
DIF Differential item functioning, HAQ-DI Health Assessment
Questionnaire Disability Index, LM Outcome Lagrange multiplier
test, Abs. DIF Amount of absolute DIF d
ig
Degrees of freedom =2
Table 3 Item parameters after modeling DIF
HAQ-DI categories Item parameters
aid
i1
d
i2
d
i3
Rising 3.758 -0.089 3.777 4.660
Walking
RA 3.253 0.429 3.568 6.534
OA 3.691 -0.515 3.878 6.875
Gout 3.987 0.196 3.844 9.377
Dressing and grooming
RA 3.285 -0.428 2.124 3.905
OA 2.532 0.101 2.885 3.666
Gout 3.850 3.066 4.832 4.994
Reach 2.671 -0.004 2.306 3.499
Eating
RA 2.629 -0.883 1.982 1.861
OA 3.077 -0.299 2.828 2.585
Gout 2.464 -0.079 2.786 1.673
Grip 3.824 -0.149 3.176 4.455
Activities
RA 2.915 -1.234 2.159 3.205
OA 2.431 -0.388 2.070 4.226
Gout 3.124 1.713 3.829 4.172
Hygiene 3.768 -1.557 2.089 3.242
DIF Differential item functioning, HAQ-DI Health Assessment
Questionnaire Disability Index, a
i
Discrimination parameter, d
i1
,d
i2
,
and d
i3
Category intersection parameters, RA Rheumatoid arthritis,
OA Osteoarthritis
Log likelihood =-5941.921
1258 Qual Life Res (2010) 19:1255–1263
123
grooming given a certain disability level is higher for the
patients with OA than for the patients with gout. That is,
patients with OA endorse this item more and the item
Dressing and grooming is more difficult for them than for
the patients with gout.
The next question addressed is whether the scale pre-
sented in Table 3actually fits the data. This was investi-
gated using two LM statistics [21], one targeted at the form
of the item response curves and one targeted at the
assumption of local independence. The first statistic is
defined analogous to the statistic for DIF, only this time the
subgroups are total-score level groups within the disease
groups. The observed total score is the sum score of the
responses on all items except the item targeted. Glas [21]
demonstrated that this statistic pertains to the hypothesis
that the response probabilities as a function of the latent
disability parameters are as predicted by the model. Within
the three disease groups, three total-score level groups were
formed in such a way that the numbers of respondents in
each group were approximately the same. The ranges of the
scores in the total-score level groups are given at the bot-
tom of the table. The results for the patients with RA are
shown in Table 4. The results for the two other diseases
were analogous. Note that none of the outcomes of the LM
tests were below the significance level of 1%. The last
column gives the effect sizes d
ig
. The highest effect size
was 0.05, which was well below the set criterion of 0.10.
The overall conclusion is that the model fitted very well,
and the hypothesis that the same latent scale pertained to
the three diseases was not rejected.
The second test pertained to local independence. The
test is also sensitive to violations of unidimensionality. The
test targets the dependence between responses on pairs of
items. In the present case, responses to consecutive items
were evaluated, but this choice is not essential. The test
statistic is based on the evaluation of the average scores on
some item given the scores on some other item. With this
alternative definition of score groups, the test statistic is
defined analogous to other LM statistics. The results for the
patients with RA are displayed in Table 5. As with the tests
for DIF and the form of the item response curves, the
results for the two other diseases were analogous and none
of the outcomes of the LM tests were below the signifi-
cance level of 1%. The columns labeled ‘0’ to ‘3’ give the
observed and expected average scores on some item igiven
that the score on item i-1was ‘0’ to ‘3’, respectively. That
is, the average score on the item i=2, i.e., Walking, for
patients scoring ‘0’ on item i-1, i.e., Rising, was 0.18.
The associated expected score was 0.23. The last column
gives the effect sizes d
ig
. The highest effect size was 0.10,
which just attained the criterion of 0.10. So again, the
predictions by the model were quite acceptable.
More easy
to perform
Walki ng
Dressing and grooming
Walking
Activities
Rising Rising
Grip Grip
Dressing and grooming
Activities
hcaeRhcaeR
Eating
Eating
Hyg Henei ygiene
More difficult
to perform
1
3
4
5
Osteoarthritis Gout
2
Fig. 1 An illustration of the item difficulty locations (average of the
3 category intersection parameters) of the Health Assessment
Questionnaire Disability Index on the IRT latent scale in patients
with osteoarthritis and gout
Table 4 Outcomes of tests for model fit in score level groups for
patients with RA
HAQ-DI
categories
Total-score level groups
Level 1 Level 2 Level 3 d
LM PObs. Exp. Obs. Obs. Obs. Exp.
Rising 2.77 0.25 0.21 0.22 0.81 0.77 1.56 1.51 0.03
Walking 7.26 0.03 0.13 0.16 0.68 0.62 1.20 1.22 0.04
Dressing and
grooming
0.22 0.89 0.32 0.32 1.03 1.02 1.84 1.84 0.01
Reach 7.05 0.03 0.25 0.27 0.76 0.83 1.55 1.58 0.04
Eating 1.53 0.47 0.47 0.47 1.22 1.19 2.12 2.12 0.01
Grip 4.63 0.10 0.20 0.23 0.88 0.83 1.68 1.67 0.03
Activities 4.70 0.10 0.53 0.51 1.05 1.12 1.90 1.86 0.05
Hygiene 0.70 0.71 0.50 0.50 1.25 1.27 2.21 2.19 0.01
RA Rheumatoid arthritis, HAQ-DI Health Assessment Questionnaire
Disability Index, LM outcome Lagrange multiplier test, Obs.
Observed scores, Exp. Expected scores by the model, The observed
total score is the sum score of the responses on all items, deffect size
Level 1: total scores 0–4, Level 2: total scores 5–8, Level 3: total
scores 9–23
Qual Life Res (2010) 19:1255–1263 1259
123
The last question addressed concerns the impact of the
DIF for inferences concerning differences between the
three diseases on the latent scale. As mentioned previously,
the item parameters and the means and variances of the
latent person parameters were estimated by marginal
maximum likelihood. The obtained mean values of dis-
ability for each disease are presented in Fig. 2, together
with 99% confidence intervals. The mean for the patients
with gout was set equal to zero to identify the latent scale.
Note that average disability of the respondents for each
disease decreases after the introduction of disease-specific
item parameters. Patients with OA had the highest average
disability level in all analyses. Patients with gout had the
lowest disability. From the confidence intervals, it can be
inferred that conclusions from statistical tests would not
change. However, after modeling DIF, absolute score dif-
ferences clearly decreased.
Discussion
An item response theory (IRT)-based method is presented
that can be used to make HAQ-DI disability scores better
comparable across different rheumatic diseases, and the
results of the application of this method suggest that the
HAQ-DI can function as a generic instrument.
By now, there is extensive literature on the evaluation of
construct validity using factor analyses and IRT analyses
Table 5 Outcomes of tests for local independence for patients with RA: score level given the score level on the previous item
Category LM PScore on previous category
0123d
Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp.
2 8.03 0.05 0.18 0.23 0.80 0.76 1.28 1.26 1.61 1.70 0.09
3 3.65 0.30 0.53 0.51 1.28 1.30 1.91 1.97 2.83 2.79 0.06
4 2.25 0.52 0.37 0.34 0.81 0.83 1.35 1.40 1.99 2.04 0.05
5 2.50 0.48 0.61 0.64 1.38 1.34 1.95 1.95 2.57 2.49 0.06
6 7.69 0.05 0.22 0.26 0.86 0.80 1.27 1.25 1.78 1.84 0.06
7 0.34 0.95 0.57 0.57 1.18 1.19 1.82 1.82 2.40 2.30 0.10
8 1.01 0.80 0.50 0.47 1.17 1.18 1.84 1.85 2.45 2.46 0.03
RA Rheumatoid arthritis, The categories are numbered in the order as they appear in Table 4,LM outcome Lagrange multiplier test, Obs.
Observed scores, Exp. Expected scores by the model, The observed total score is the sum score of the responses on all items, deffect size
Fig. 2 Means of IRT disability
estimates (y-axis) in rheumatoid
arthritis (left panel) and
osteoarthritis (right panel)in
three analyses (x-axis) labeled
0, 1, and 2. The mean for gout
was set equal to zero to identify
the latent scale. Analysis 0 was
the initial analysis. In analyses 1
and 2, 2 and 4 items with
disease-specific item parameters
were introduced, respectively
1260 Qual Life Res (2010) 19:1255–1263
123
[22]. It is important to note that these two classes of models
are closely related. In fact, Takane and de Leeuw have
shown that under quite general assumptions, these two
models are equivalent [23]. Only the traditions of statistical
inference are different: factor analysis is usually based on a
covariance matrix, while IRT analysis is based on the
complete response patterns. This motivates the term ‘‘full-
information factor analysis’’ used for multidimensional
IRT by Bock, Gibbons, and Muraki [24]. Both approaches
have their advantages and disadvantages. One of the
advantages of the IRT approach is that it uses more
information in the data and, therefore, assumptions such as
the form of the item response curves and local indepen-
dence can be investigated. However, the results obtained
using both approaches are closely related. In that sense, the
correlated residuals reported by Cole [25,26] can be
interpreted as an indication for lack of local independence
and multidimensionality, which can be further investigated
in detail using IRT-based techniques. Although both factor
analysis and IRT can be used to assess the construct
validity of the HAQ-DI, it is important to note that con-
struct validity is not so much a property of an instrument,
but a property of inferences made using the instrument
[27]. In the present study, it was shown that when a number
of disease-specific item parameters are used and the HAQ-
DI is scored using h-estimates, these h-estimates relate to
the same unidimensional scale. Therefore, these scores can
support the construct validity of the HAQ-DI for inferences
across diseases.
IRT methods offer a sophisticated and robust means to
test the generic nature of an instrument by examining
whether the underlying latent scale is the same for different
groups of individuals. This can be evaluated by examining
whether the questionnaire contains items with differential
item functioning (DIF), i.e., items where the probability of
scoring in the various response categories differs between
subgroups of patients after controlling for the general dis-
ability level as estimated by the IRT model. Although IRT-
based approaches to DIF detection have been increasingly
used in health outcomes assessment, research addressing
the measurement equivalence of disability scales across
different (rheumatic) diseases is still scarce. Only one
recent study was found that examined DIF for the HAQ-DI
and the 10-item physical functioning scale (PF) of the
SF-36 between patients with RA and psoriatic arthritis
(PsA) using Rasch analysis [28]. This study found evidence
of marked DIF for three HAQ-DI items, similar to our
study, and relatively minor DIF for the SF-36 PF scale. The
authors concluded that the SF-36 PF scale is a better
instrument than the HAQ-DI for comparing disability from
PsA with disability from other diseases. However, the
study did not evaluate the impact of DIF on individual
items for inferences about total HAQ-DI score differences
between the diseases or provide guidelines on how to deal
with this DIF. Therefore, the objective of this study was
twofold: first to investigate whether the HAQ-DI functions
as a generic measure of disability across different rheu-
matic diseases by evaluating DIF and second, if not, to illus-
trate the use of IRT methods to model DIF so that disability
scores can be made comparable across diseases. For this
purpose, we used data from three common rheumatic dis-
eases with known differences in disease characteristics:
rheumatoid arthritis (RA), osteoarthritis (OA), and gout.
As would be expected, the majority of the patients with
RA and OA were women, whereas patients with gout
patients were predominantly men. Mean SF-36 physical
and mental component scores were well below the average
of 50 in the general population, suggesting that all three
diseases have a substantial impact on general health status.
Whereas disability scores between RA and OA were very
similar, mean HAQ-DI scores were clearly lower for
patients with gout and in close correspondence to a recently
reported mean HAQ-DI score of 0.59 in a cross-sectional
gout sample [29].
However, half of the HAQ-DI items displayed sub-
stantial DIF between the three diseases, possibly biasing
total score differences between the diseases. After modeling
these items by assigning them disease-specific parameters,
statistical conclusions regarding disability differences
across the 3 conditions did not change. Patients with OA
and RA still displayed higher disability scores than patients
with gout. However, absolute differences between the dis-
eases were attenuated. HAQ-DI scores based on disease-
specific item parameters fitted the data very well and
resulted in an underlying latent scale that applied to all three
diseases.
An important concern, however, is that only four items
served as anchors across the three diseases, and these items
appear to be on the ‘‘more difficult’’ end of the scale. To
minimize the standard errors of differences between dis-
ability estimates in the different disease groups, anchoring
should be preferably done in all sections of a scale. Often,
this cannot be achieved, but it should be kept in mind that
the precision of the method deteriorates with the number of
anchor items and their position on the scale. The authors do
not recommend using the method when the anchor is very
small (e.g., less than 4 items or less than 50% of the items).
It is also important to emphasize here that the present
study focused only on cross-sectional samples of patients
with OA, RA, and gout as an example for evaluating the
generic nature of the HAQ-DI. It is very well possible that
these or other items of the HAQ-DI may show DIF, pos-
sibly to a different extent, between other rheumatic con-
ditions, non-rheumatic conditions, or general population
samples. Accordingly, researchers using the HAQ-DI to
compare disability between different subgroups are
Qual Life Res (2010) 19:1255–1263 1261
123
encouraged to examine DIF before comparing total HAQ-
DI scores. The present study provides an example of how
IRT methods can be used to evaluate DIF and, if necessary,
how to model this DIF to obtain more accurate disability
estimates.
Furthermore, all analyses presented in this study are
based on so-called standard scores of the HAQ-DI, which
take into account the use of aids and devices or assistance
from another person [1,3]. Although this scoring method is
most frequently used and recommended [30], some clinical
investigations have used an alternative scoring without this
correction. Secondary analysis using the alternative scoring
method in this study showed that the IRT results obtained
with and without correction were very similar.
In summary, the results of this study showed that 4 out
of the 8 disability items displayed substantial DIF across
the 3 diseases, indicating that the HAQ-DI may not fully
function as a generic instrument for the assessment of
disability across different rheumatic diseases unless DIF is
modeled and adjustments to the scoring method are made.
Open Access This article is distributed under the terms of the
Creative Commons Attribution Noncommercial License which per-
mits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source are credited.
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