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Modeling the progression of rheumatoid arthritisA two-country model to estimate costs and consequences of rheumatoid arthritis.

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Vol. 46, No. 9, September 2002, pp 2310–2319
DOI 10.1002/art.10471
© 2002, American College of Rheumatology
Modeling the Progression of Rheumatoid Arthritis
A Two-Country Model to Estimate Costs and Consequences of
Rheumatoid Arthritis
Gisela Kobelt,1 Linus Jönsson,2 Peter Lindgren,2 Adam Young,3 and Kerstin Eberhardt4
Objective. Two simulation models were developed
to analyze the cost-effectiveness of new treatments that
affect the progression of rheumatoid arthritis (RA).
Methods. We used data from 2 cohorts of patients
with early RA who had been followed up since disease
onset (up to 15 years). In the Swedish study, 183
patients were followed up for a mean of 11.3 years. In
the UK study, 916 patients were followed up for a mean
of 7.8 years. Disease progression over 10 years was
modeled as annual transitions between disease states,
defined by Health Assessment Questionnaire (HAQ)
scores. A regression model was used to estimate transition probabilities conditional on age, sex, and time since
onset of disease, in order to allow simulation of different
patient cohorts. Costs and utilities associated with
different HAQ levels were based on data from the cohort
studies and cross-sectional surveys.
Results. Costs increase and quality of life decreases as RA progresses. In Sweden, total annual costs
range from $4,900 to $33,000 per patient, compared with
$4,900 to $14,600 in the UK. Cumulative costs over 10
years for patients starting in disease state 1 (HAQ <
0.6) are $54,600 in Sweden and $26,600 in the UK. The
cumulative numbers of quality-adjusted life-years
(QALYs) are 5.5 and 5.6, respectively. Both costs and
QALYs were discounted at 3%.
Conclusion. The 2 models, which were based on
different patient cohorts, reach a similar conclusion in
terms of the effect of RA over 10 years. They appear to
accurately capture disease progression and its effects
and can therefore be useful in estimating the costeffectiveness of new treatments in RA.
The prevalence of rheumatoid arthritis (RA) is
estimated at 0.5–1% worldwide (1,2), but the progressive
nature of the disease and its onset relatively early in life
lead to a considerable social and economic impact (3–5).
The costs to society associated with RA are substantial,
because the disease may cause not only restricted joint
mobility, chronic pain, fatigue, and functional disability,
but also psychological distress (6,7). Half of patients will
be work-disabled within 10 years after disease onset
(8–10), making productivity losses the predominant economic burden of the disease (5,11–13).
Direct health care consumption represents about
one-fourth of all costs and is dominated by inpatient
care. For example, in Sweden, the total annual cost
associated with RA in 1999 was estimated at 3 billion
Swedish kronor (SEK) ($285 million; $1.00 ⫽ 10.5
SEK), and the average estimated cost per patient was
49,650 SEK ($4,730). Direct costs represent 28%, and,
of these, inpatient care represents 45% (14). In the UK,
the total annual cost associated with RA in 1992 was
estimated at £605 million ($900 million; $1.00 ⫽ 0.67£),
and the cost per patient was ⬃£2,600 ($3,880). Direct
costs represent ⬃20%, and, of these, inpatient and
community day care represent 28% and 22%, respectively (15).
In both Sweden and the UK, drugs currently
represent a minor cost: 3–4% of total costs and 13–15%
of direct costs. However, the introduction of several new
treatments of RA will change the distribution of costs
associated with this disease. Both the widely used generic
Gisela Kobelt, MBA: Health Dynamics International, Speracedes, France; 2Linus Jönsson, MD, Peter Lindgren, MSc: Stockholm Health Economics Consulting AB, Stockholm, Sweden; 3Adam
Young, MD: City Hospital, St. Albans, UK (for the Early Rheumatoid
Arthritis Study Group); 4Kerstin Eberhardt, MD: Lund University
Hospital, Lund, Sweden.
Address correspondence and reprint requests to Gisela Kobelt, Health Dynamics International, 492 Chemin des Laurens, 06530
Speracedes, France. E-mail:
Submitted for publication December 12, 2001; accepted in
revised form May 3, 2002.
disease-modifying antirheumatic drugs (DMARDs) and
nonsteroidal antiinflammatory drugs (NSAIDs) are being replaced with treatments that are more potent
and/or more tolerable but also are more expensive.
Therefore, drug budgets will increase. The interesting
economic question is whether savings in other resources
will offset the added cost of drugs, or whether overall
costs in RA will increase. If overall costs increase, the
relevant question is whether there is an associated gain
in health and whether, from a societal perspective, this
gain justifies the additional expenditure.
This question immediately poses the problem of
how to make predictions of both costs and outcome
when no or only limited data on the use of such
treatments in clinical practice are available, and any
assessment must be based on rather short-term clinical
trials in selected patient groups. The health benefits, as
well as any potential economic benefit associated with
these new treatments, that affect disease progression
will, however, be most evident in the longer term,
because delaying the development of functional disability will lead to lower levels of resource consumption and
maintain the ability of patients to work longer. Several
studies have also shown that patients’ quality of life
decreases as RA progresses (13,16). As a consequence,
slowing disease progression can be expected to maintain
quality of life at a higher level for a longer period of
Thus, a baseline is required that represents disease progression, resource consumption, and the quality
of life in patients using current treatments, against which
the new treatments can be evaluated within a time frame
that exceeds that of the clinical trials. Epidemiologic,
clinical, and economic data must be combined, and
modeling becomes unavoidable. Economic evaluation
implies, in most cases, the use of some type of model,
and models are increasingly accepted as a good—and
often the only—way to illustrate disease processes and
their economic impact and to estimate the impact of
changes in treatment strategies.
Economic evaluations compare treatment strategies in terms of their costs (resources used) and their
effectiveness (health benefits), and results are expressed
as the extra cost for each additional unit of health
benefit gained with one treatment strategy compared
with another (17–19). A disease model that serves as a
baseline for analysis of the cost-effectiveness of new
treatments of RA used in short-term clinical trials must
incorporate good epidemiologic data over a relevant
period of time, detailed information about resource
consumption by patients at all levels of disease severity,
and an effectiveness measure that is generally available
in both epidemiologic studies and clinical trials. The
model must also be adaptable to different patient populations, because different clinical trials select patients
at different levels of disease severity or at different times
since disease onset.
We now propose a framework for costeffectiveness analysis of new treatments of RA based on
2 cohort studies in Sweden and the UK. The approach is
based partly on our earlier work in this field of study (13)
but uses a novel technique for modeling disease progression and covers a longer period of time.
Disease progression in chronic diseases is most often
illustrated using Markov models (20). Markov models classify
patients into a finite number of distinct and mutually exclusive
disease states at baseline, usually defined by disease severity.
Changes in disease are represented as transitions from one
state to another over time. Spending a given amount of time
(cycle) in a particular state is associated with a cost and a
certain level of quality of life, and the models thus calculate
expected costs and outcomes in a given cohort of patients over
a defined number of cycles. To make models credible and
useful for decisions about allocation of resources, large
amounts of different types of data are required.
We constructed 2 models that had an identical structure but were based on 2 different inception cohorts, one in
Sweden and the other in the UK (21–24). The studies were
similar in that in the late 1980s, they enrolled consecutive
patients with early RA (according to criteria of the American
College of Rheumatology; formerly, the American Rheumatism Association) (25) and conducted followup of these patients for up to 15 years.
Epidemiologic data. Table 1 shows the demographic
characteristics of the 2 cohorts included in the models, and
Figure 1 shows the development of average Health Assessment
Questionnaire (HAQ) (26) scores of the 2 patient populations.
The data cover 10 years for the Swedish cohort but only 9 years
for the UK cohort, because of the small number of patients in
the latter study for whom 10-year followup was complete at the
time of our analysis.
The Lund Study (Sweden). This prospective study of
the course and outcome of RA was conducted at the Department of Rheumatology at Lund University Hospital. Criteria
for inclusion in the study were definite RA, with duration of
symptoms less than 24 months and age 18 years or older.
Patients were enrolled irrespective of disease activity and
severity. The enrollment period lasted from 1985 until 1989,
and most of the patients were referred from primary care units
in the surrounding health districts. In total, 183 patients,
constituting ⬃50% of all new cases of RA during that period of
time, were enrolled. Table 1 shows the demographics and some
disease characteristics of these patients at the time of enrollment. Erosive disease was defined as a Larsen score of 2 in at
least 1 joint in the hands and/or feet. The patients were treated
according to routine clinical practice but had at least 1 special
Table 1.
Characteristics of cohorts at the time of inclusion*
Female, %
Age, years, mean ⫾ SD
Duration of symptoms, months, mean ⫾ SD
Rheumatoid factor positive, %
Functional class, no. of patients
Erosive disease, %
HAQ score, mean ⫾ SD
Years of followup, mean ⫾ SD
HAQ score at 9/10-year followup, mean ⫾ SD
Lund cohort, Sweden
(n ⫽ 183)
ERAS cohort, UK
(n ⫽ 916)
52.4 ⫾ 12.5
11.1 ⫾ 6.1
54.8 ⫾ 13.6
8.2 ⫾ 6.1
0.93 ⫾ 0.6
11.3 ⫾ 2.7
1.10 ⫾ 0.7
1.11 ⫾ 0.7
7.8 ⫾ 2.1
1.26 ⫾ 0.9
* Ten-year followup data were used for the Sweden cohort, and 9-year followup data were used for the UK
cohort. ERAS ⫽ Early Rheumatoid Arthritis Study; HAQ ⫽ Health Assessment Questionnaire.
annual followup assessment using standardized measures at a
team care unit. Two- and 5-year followup data have been
published previously (21,22). Recently, a 10-year followup was
performed, and this data set was used to build the disease
model (23).
The Early Rheumatoid Arthritis Study (ERAS), UK.
ERAS started in 1987 with the primary aims of recruiting
patients with early RA prior to their use of second-line drugs
and following up these patients for at least 10 years, in order to
characterize the disease on the basis of age, sex, disease
pattern and severity, and treatment response (9,24). Currently,
ERAS includes 1,473 patients; of these, 916 with minimum
5-year followup data have been used for the model. From the
late 1980s until the early 1990s, it was normal practice in the
Figure 1. Development of mean Health Assessment Questionnaire
(HAQ) scores in the epidemiologic cohorts in Sweden (n ⫽ 183) and
the UK (n ⫽ 916). HAQ scores improved or remained stable in the
first years after diagnosis and entry into the studies, likely illustrating
the effect of early treatment, particularly in the UK. The average
increase in HAQ score over 10 years is limited, because rheumatoid
arthritis (RA) progresses slowly in a majority of patients and possibly
because of the use of early intervention, which is now standard practice
in RA management.
UK to initiate treatment with DMARDs early in the course of
RA, using sulfasalazine first, followed by methotrexate for
nonresponders or patients who experienced adverse effects.
The Markov model. Figure 2 illustrates the structure of
the Markov model. Markov states are defined according to
functional disability measured with the HAQ (26). Since its
inception, the HAQ has been widely used in cohort studies and
is included routinely in all clinical studies. It has also been
Figure 2. Structure of the Markov model of disease progression.
Patients can start in any of the Health Assessment Questionnaire
(HAQ)–derived states except “dead.” After each cycle (1 year), the
model redistributes patients in the different states, depending on
whether their HAQ scores improved, remained stable, or worsened
during that cycle, or whether the patient died during the cycle. [⫹]
indicates that this redistribution, as illustrated for state 1, takes place
at each state every year.
shown to correlate better than radiologic scores with quality of
life (utility) and resource utilization (13), making it the ideal
measure to use in economic evaluation. Six disease states were
defined previously by a panel of rheumatologists (13). Each
state represents 0.5 on the HAQ scale, with cutoffs at 0.6, 1.1,
1.6, 2.1, and 2.6; a state for death was included. The model was
developed using DATA Pro (TreeAge Software, Williamstown, MA).
Transition probabilities. Disease progression is modeled as transitions between states over time. Transition probabilities between states can vary over time and depend on
patient characteristics but not on previous events (transitions).
The cycle length in this model is 1 year.
Ordered probit regressions were used to estimate
probabilities of transition between HAQ-derived states, controlling for the age and sex of the patient and the time since
onset of disease. Using this technique, it is possible to adjust
the Markov model to match a clinical trial population in terms
of age, sex, time since disease onset, and disease severity. Thus,
although the model was originally designed for use in patients
with early RA, it is possible to use the transitions between
HAQ states that occurred in the later years of cohort studies to
match a clinical trial in which patients are, for instance, in their
seventh or eighth year of disease. Simulations will then use this
hypothetical cohort over a defined time frame (e.g., the
duration of the clinical trial or longer). In the ordered probit
regression, a latent variable, y, determines the current HAQderived state of a patient. This latent variable is explained by
the following regression equation:
y ⫽ ␤0 ⫹ ␤1 䡠 age ⫹ ␤2 䡠 sex ⫹ ␤3 䡠 distime ⫹ ␥2 䡠 s2
⫹ ␥3 䡠 s3 ⫹ ␥4 䡠 s4 ⫹ ␥5 䡠 s5 ⫹ ␥6 䡠 s6 ⫹ ␧
where ␤0 is a constant, age is the age of the patient at the time
of disease onset, distime is the time since onset of disease, s2–s6
are dummy variables for the HAQ state in the preceding
period (1 year earlier), and ␧ is the (normally distributed) error
term. Thus, the model predicts the probability of the patient
being in each HAQ state during the current period, conditional
upon the HAQ state during the previous period (among other
factors). This is equal to the transition probability between
different states over 1 year.
Mortality. The evidence regarding the effect of RA on
life expectancy is conflicting. Several epidemiologic studies
have shown increased mortality in patients with severe RA,
and the mean standardized mortality ratio has been estimated
at 1.87 (27). A link between functional status (HAQ score) and
mortality has also been reported (28). However, more recent
studies have not demonstrated any effect of RA on mortality
(29,30). Because data for estimating an age-specific and sexspecific mortality effect at different HAQ levels are not readily
available, the model includes only normal age- and sexadjusted mortality in the population.
Outcome measures. Economic evaluation requires that
effectiveness be expressed with one measure, in order to
estimate the cost per additional unit of measure gained by
using an intervention. In chronic progressive diseases such as
RA, there is no obvious single outcome measure, because
there is a continuous effect on several functions over a long
period of time. The quality-adjusted life-year (QALY), which
combines quality of life with time, is therefore the measure of
effectiveness that is used most often. In addition, use of the
QALY allows comparison of outcomes in different diseases,
which is a key requirement if such economic evaluations are to
be used to support decisions about resource allocation.
QALYs are calculated by adjusting life-years with a
quality weight (utility). Utility is defined as the preference
patients and/or the general population have for given states of
health. It is expressed as a value on a scale between 0 (death)
and 1 (full health). Therefore, a year spent at a utility of 0.5 is
equal to spending half a year at full health. Utilities are
measured using interview-based techniques from decision analysis or preference-based quality of life instruments, such as the
EuroQol-5D (EQ-5D) (31–33). The EQ-5D is selfadministered and easy to use, particularly in large populations,
and has been validated in a large number of studies, including
studies in RA (13,16). Utilities measured with the EQ-5D have
also been shown to correlate with HAQ scores (13). We
therefore use country-specific average utilities measured with
the EQ-5D for each disease state in the models.
In a cross-sectional study in Sweden, individuals in a
subsample of the Lund cohort (n ⫽ 103) were asked to
complete the EQ-5D and the HAQ (13). These utilities have
been updated with additional data from the cohort study, and
the mean utility and standard deviations for the Markov states
were estimated. Similarly, in the UK, EQ-5D and HAQ scores
were collected cross-sectionally from a subsample (n ⫽ 107) of
the ERAS cohort. In addition, to decrease variability of the
data, scores obtained in a large study investigating healthrelated quality of life in patients with RA (n ⫽ 240) were
available and were included in the model (16). For the purpose
of modeling, the utility scores are estimated for a given
HAQ-derived state (controlled for age and sex) rather than for
a defined patient population; therefore, combining the 2 data
sets does not present any problem.
Resource utilization and unit costs. The use of health
care resources and the loss of work capacity were estimated
based on information from the cohort studies, and unit costs
were obtained from hospital accounting and official price lists.
In Lund, all utilization data had been collected throughout the
study, and complete information on hospitalizations, surgical
interventions, outpatient visits, medication, and drug monitoring were available in the database. The ERAS database
contained similar data but was less complete in terms of
outpatient visits, because in the UK patients were not followed
up exclusively at the rheumatology clinic, as was the case in
Sweden. Information on medical visits and the use of community services was therefore collected in the same crosssectional survey as that used to collect utility data, based on
recall of utilization in the past 1–6 months.
All observations for patients in a given state, at any
year in the followup period, are used to calculate the average
annual cost and standard deviations for each state. One
limitation is that in both cohorts, the number of patients with
very severe disability (HAQ score ⬎2.6) was rather limited;
therefore, the direct costs associated with state 6 must be
considered with caution. However, it is interesting to note that
in neither country did patients in state 6 undergo surgical
interventions, which may partially explain the lower direct
Direct costs included were hospitalizations, surgical
interventions, ambulatory and community care, and RA med-
ication. NSAIDs were not included, because almost all patients
used them, and our earlier work in Sweden showed that use of
NSAIDs did not differ significantly between patients in the
various states. Also, NSAIDs represented a very low cost,
because the more expensive cyclooxygenase-2 (COX-2) inhibitors were not yet available during these followup studies. Also
excluded were nonmedical direct costs and costs of informal
care, because this information was not included in the data
Hospitalization was based on the number of inpatient
days in different wards. The cost of surgical interventions
(when not included in the hospital day) was based on the type
of intervention and its duration multiplied by the cost per
minute for use of the operating room. Outpatient care was
based on the number of visits to different health care professionals. The cost of RA drugs was calculated from the number
of months of usage of each drug, associated with the cost of
standard drug-monitoring protocols in place in the rheumatology departments participating in the cohort studies.
Indirect costs were calculated as the loss of work
capacity of patients in the more advanced disease states
compared with that of patients in state 1 (HAQ score ⬍0.6).
The rationale behind this approach is that only productivity
losses attributable to RA should be included. Hence, instead of
absolute work capacity, the relevant measure is the difference
in work capacity as disability worsens. In the Swedish cohort,
patients in state 1 had the same average work capacity as that
of healthy persons with the same age and sex distribution (13).
We therefore considered that for patients with HAQ scores
⬍0.6 (state 1), only short-term sick leave represented a loss of
work capacity.
In economic evaluations, the loss of work capacity is
generally calculated using the human capital approach, by
which an individual’s productivity is valued at the market price
(i.e., at the gross income including employers’ contribution)
until retirement. Indirect costs in the models are thus calculated as the number of productive years lost at each Markov
state compared with state 1, multiplied by the average gross
annual income including employers’ contributions (SEK
327,000 [US $31,145] in Sweden and £17,658 [US $26,355] in
the UK).
Table 2. Comparison of modeled and actual distribution (%) of
patients in the different disease states*
Sweden cohort
0.6 ⬍
1.1 ⬍
1.6 ⬍
2.1 ⬍
UK cohort
10-year followup
Baseline Actual
9-year followup
Modeled Baseline Actual Modeled
* Only patients who were alive at followup were included. HAQ ⫽
Health Assessment Questionnaire.
Table 2, where the cohort distributions in the epidemiologic studies are compared with the distribution resulting from the models, at 10 and 9 years for Sweden and
the UK, respectively.
Utilities. The average utility scores in the 2
countries (according to disease state) confirmed earlier
findings that the EQ-5D is able to discriminate between
relatively small differences in disability. As shown in
Table 3, utility decreases as functional disability increases.
Costs. Table 4 shows mean annual costs for the
different disease levels. Costs in all categories of resources increased as patients’ functional disability increased, but these costs were dominated by productivity
losses. Indirect costs were much higher in Sweden than
in the UK, and the difference was predominantly because far fewer patients remained in the work force in
Sweden, as shown in Figure 3. Also, the variability in all
cost categories was very high. This is generally the case
in data sets of this size, and it is therefore important to
use the full range of all costs for the simulations and
Disease progression. The results of the probit
regressions were similar for both cohorts. All coefficient
estimates were significant at the 1% level. Longer time
since onset of disease and older age at onset were
associated with higher rates of disease progression. The
progression rates among men were lower than those
among women. As expected, the coefficient was higher
for patients starting in more severe disease states; i.e.,
being in a severe state at the beginning of a cycle
increased the risk of being in a severe state at the end of
a cycle (1 year later).
The models accurately reproduced the disease
progression in the epidemiologic cohorts, as shown in
Table 3. Utility scores according to Health Assessment Questionnaire (HAQ) level*
Utility score
HAQ score
Sweden cohort
UK cohort
0.6 ⬍
1.1 ⬍
1.6 ⬍
2.1 ⬍
0.7274 ⫾ 0.1609
0.6358 ⫾ 0.2298
0.6111 ⫾ 0.2317
0.4223 ⫾ 0.3034
0.2370 ⫾ 0.3639
0.2245 ⫾ 0.0502
0.7459 ⫾ 0.1402
0.6491 ⫾ 0.2053
0.4692 ⫾ 0.2678
0.4419 ⫾ 0.2873
0.2556 ⫾ 0.2908
0.2538 ⫾ 0.3514
* Utilities are measured on a scale between 0 (death) and 1 (full
health) and represent preferences that patients and the general
population have for certain health states. Values are the mean ⫾ SD.
Table 4. Mean annual direct, indirect, and total costs according to
disease severity level, in US dollars*
0.6 ⬍
1.1 ⬍
1.6 ⬍
2.1 ⬍
Sweden cohort
UK cohort
* $1.00 ⫽ 10.50 Swedish kronor ⫽ £0.67. HAQ ⫽ Health Assessment
calculations of total costs over a given time frame, rather
than the mean costs.
Modeling results. To illustrate costs and QALYs
in the models, we present results for patients in the
different disease states at baseline as well as for the 2
epidemiologic cohorts, using the actual sex distribution
and mean values for age and disease onset. These results
were estimated using Monte Carlo simulations (10,000
individual simulations). For costs and utilities, the actual
values observed in the data sets were used. To include
uncertainty about transition probabilities, the parameters estimated with the probit regression were varied
according to their standard errors, assuming normality.
We used the estimated standard errors of the parameters in the regression models, which permits calculation of standard deviations for our estimates. In the UK,
Figure 3. Proportion of patients (ages 65 years and younger) at
different Health Assessment Questionnaire (HAQ)–derived states
who are able to work. Participation in the work force is calculated from
the total number of years worked by patients in the different disease
states. To estimate the loss of participation attributable to rheumatoid
arthritis, state 1 is used as the reference (no impact of rheumatoid
arthritis on work, other than short-term sick absence), and the loss of
work capacity of patients in the more severe states is compared with
that of patients in state 1.
costs are generally discounted at 6%, and effects are
discounted at 1.5%; however, for comparison purposes,
we used the more general discount rate of 3% in both
Table 5 shows expected average 10-year costs and
utilities, including standard deviations, for patients starting in different disease states at baseline, for both
cohorts (costs and QALYs discounted at 3%). The total
expected costs for patients starting with an HAQ
score ⬍0.6 was SEK 573,448 ($54,614) in Sweden and
£17,824 ($26,603) in the UK. The average number of
QALYs for the same patients over 10 years was 5.5 in
Sweden and 5.6 in the UK. The corresponding expected
values for patients starting in state 3 (HAQ score 1.1 ⬍
1.6) were SEK 962,982 ($91,713) and £26,914 ($40,170)
and 4.9 QALYs in both countries. In comparison, a
group of healthy people with a similar sex distribution
and age would have about 6.1 QALYs per person during
the same time period.
Several new treatments for RA have been introduced recently, and more are in late stages of development. These products are effective in reducing symptoms and have been shown in short-term clinical trials to
have an effect on disease progression. The cost of these
new treatments is higher than that of standard
DMARDs, and it is therefore important to assess the
cost of treatments in relation to their potential long-term
benefits. Such an assessment requires disease models
that relate disease progression to costs and extend over
a longer period of time than that of the clinical trials,
and against which the new treatments can be tested.
We previously proposed a structure for such a
disease model, using 5-year followup data of a subgroup
from the Lund study. The 2 new models presented in the
current report are based on the same structure, but the
underlying data sets are much larger and cover up to 15
years of followup. Use of these data sets allows simulation of patient cohorts that are more advanced in their
disease compared with the patients at baseline in the 2
cohort studies. Using a new approach to calculate the
probabilities of transition between different states of
functional disability, the models also allow controlling
for the effect of sex, age, age at the time of disease onset,
and disease severity. Thus, the models can be used to
create a hypothetical patient population that precisely
matches, for instance, the populations of a clinical trial.
Transitions between HAQ states that occurred in the
trial can then be directly compared with transitions in
Table 5. Expected costs and QALYs for patients starting at different Health Assessment Questionnaire (HAQ) levels*
Expected total cost
Expected total cost
Swedish kronor,
mean ⫾ SD
US dollars,
Expected QALYs
mean ⫾ SD
British pounds,
mean ⫾ SD
US dollars,
Expected QALYs,
mean ⫾ SD
0.6 ⬍ 1.1
1.1 ⬍ 1.6
1.6 ⬍ 2.1
2.1 ⬍ 2.6
All scores
573,448 ⫾ 621,458
770,100 ⫾ 802,639
962,982 ⫾ 844,297
1,179,148 ⫾ 833,602
1,335,294 ⫾ 783,365
1,267,971 ⫾ 837,054
828,570 ⫾ 745,683
5.479 ⫾ 0.666
5.212 ⫾ 0.850
4.932 ⫾ 0.745
4.541 ⫾ 0.931
4.243 ⫾ 0.842
4.369 ⫾ 0.827
5.102 ⫾ 0.749
17,824 ⫾ 16,495
23,921 ⫾ 26,374
26,914 ⫾ 8,470
31,090 ⫾ 28,992
36,011 ⫾ 30,667
39,072 ⫾ 30,598
25,682 ⫾ 23,999
5.595 ⫾ 0.784
5.24 ⫾ 0.617
4.908 ⫾ 0.493
4.677 ⫾ 0.585
4.256 ⫾ 0.669
4.045 ⫾ 0.590
5.05 ⫾ 0.636
* Values were discounted at 3%. QALY ⫽ quality-adjusted life-year.
the cohort studies for the duration of the trial and
extrapolated beyond the trial using the epidemiologic
data. Two examples of this method, using data from
clinical trials with infliximab and leflunomide, have been
previously reported (34,35).
Simulation models in chronic disease require a
number of different types of data that are not usually
found in one single data set. Therefore, we included
cross-sectional surveys for quality of life as well as
epidemiologic data. Combining data from different
sources is currently a well-accepted methodology in
economic evaluation, provided that the data sets are
comparable. In our study, the majority of the crosssectional data came from a subgroup of the cohort study,
and including them therefore presents no difficulty.
The number of patients included in the crosssectional study of resource utilization in the UK is very
small, and direct costs other than DMARD usage must
be interpreted with care, considering the variability in
resource consumption that is generally observed. However, direct costs in the UK study were quite similar to
those in the Swedish study, in which information for the
entire group of 183 patients was available at each annual
followup assessment.
In both cohort studies, patients were enrolled
within 2 years of the onset of RA and were followed up
annually in a similar manner. It could be argued that the
2 data sets should be combined to increase the number
of patients in each disease state. This method would be
similar to that of international clinical trials, in which it
is generally accepted that clinical effects are similar
across countries. However, there are 2 arguments
against combining the data sets.
First, because 2 similar epidemiologic studies in
the same time period are rarely available, it is more
interesting to compare the 2 disease models. Also,
economic evaluations include clinical practice as one of
the drivers of cost, and these evaluations are, therefore,
country specific. It is generally difficult to compare costs
between countries, because health care systems and both
absolute and relative prices differ. However, both countries in our study have a national health service, and the
cost of the different resources is based on accounting
data rather than on tariffs or billings. It thus appears
acceptable to draw some conclusions from the comparison.
Second, development of the HAQ states in the 2
groups differed somewhat during the first 2 years of
followup, and there may be several reasons for this. The
Lund study is population-based and recruited patients
from primary care units, irrespective of disease activity
and severity. Approximately 50% of all patients in the
region who had new RA were thus enrolled. Patients
received treatment that was conventional at that time
(1985–1989), and DMARDs were prescribed only to
patients with active disease. During the first 10 years of
the study, the majority of patients received DMARDs,
predominantly chloroquine or D-penicillamine. In the
later years, methotrexate was used. ERAS is an inception cohort that recruited patients who were referred to
9 rheumatology clinics in 9 different regions in England
during the period 1987–1995. Thus, it is likely that a
higher proportion of ERAS patients had active disease
at the time of enrollment, and a higher proportion than
in Sweden received DMARDs, both because of increased disease activity in the ERAS patients and the
study objective of early intervention using sequential
In both studies, patients switched DMARDs frequently or received no treatment for periods of time.
Therefore, integrating the effectiveness or toxicity of
individual treatments into the economic analysis is dif-
ficult, but also was not the objective. Rather, effectiveness and toxicity were captured as a consequence on
costs through increased use of health care resources, because management of toxicities and switching to a different
drug would lead to additional patient monitoring.
It is interesting to note that direct spending in the
2 countries is very similar across the disease states.
Differences in total costs are entirely attributable to
differences in the loss of work capacity. The number of
patients with severe disease who are working is far fewer
in Sweden than in the UK, which may be attributable to
the very small number of patients in these disease states.
An additional reason may be that Sweden is more
generous with disability pensions compared with the
UK. Last, the average salary in Sweden was found to be
higher than that in the UK.
Use of NSAIDs was not included in the current
analysis, because previous studies showed that such use
did not differ significantly between different levels of
disease severity, and costs were minimal. Clearly, this
will change in coming years as use of COX-2 inhibitors
increases in this patient population. However, for economic models designed to capture the effect of disease
progression, such as the model presented here, the
relevant parameter is the difference in costs between the
disease states, not the absolute costs within a disease
state. It is through this difference that the effect of
delaying progression to more severe disease (and therefore higher costs) is captured. A similar argument is
made for indirect costs, for which patients at the lowest
level of disability serve as reference. This approach
allows incorporation of indirect costs due only to the
worsening of disease rather than to other factors.
We used the QALY as the effectiveness measure
in the model. The concept of the QALY has long been
controversial, because it is based on several assumptions.
First, it assumes that the QALY is able to capture the
concept of aggregate health improvement, thus implying
that more QALYs are always better. This assumption
appears to be reasonable for the vast majority of health
care programs, in which the objective is to increase the
quantity and/or quality of individuals’ lives. The assumption underlying economic evaluation is, therefore, that
QALYs should be maximized within the available resources, and reimbursement agencies in several countries, including the UK and Canada, are using the QALY
when making decisions about resource allocation.
The appeal of the QALY is its ability to capture
health improvements in terms of both life expectancy
and quality of life, which also implies that the 2 sources
of health benefits are considered equal. This may not be
the case for all health care programs, and society may
prefer to give priority to certain groups of patients.
However, the use of QALYs in economic evaluations
does not preclude the ability to assign priority, because
cost-effectiveness analyses only provide data; decisionmakers can then set their own priorities. A second
assumption behind the QALY is that utility weights (i.e.,
scores on a scale between 0 and 1) express overall quality
of life and can be measured accurately. Clearly, the
difficulty is less with the concept than with the methods
used to assess utility.
The EQ-5D has been shown in a very large
number of studies in all types of diseases to be a valid
instrument, and its use is therefore well accepted. In
the Swedish study (13), we previously showed that in RA
in particular, the EQ-5D can discriminate between patients within the same functional class who have different HAQ scores, and data from the UK in the current
study provide further proof of the sensitivity of this
The utility values in the 2 countries are very
similar, confirming the correlation between utilities and
HAQ-derived states, irrespective of the cohort of patients. For the purpose of the models, it is this relationship that is important, because utility values are set for
each Markov state, and a patient’s utility will thus
depend solely on his or her HAQ state. In Sweden, the
scores for the severe HAQ states are based on a very
small number of patients and should be considered with
caution. However, in view of the fact that they are
virtually identical to the UK scores, we believe that they
also accurately reflect utility values in advanced disease
An additional question is whether the HAQ is an
appropriate measure for assessing disease progression.
Several studies have shown that disability begins to
develop early in RA (36), increases over time (37,38),
but is variable among individuals (39). Initial disability
has been shown to be a very strong predictor of final
disability (40–43), but it has also been demonstrated
that using the HAQ to predict the prognosis in individual patients is difficult (39). However, although this may
be an issue in clinical management, it is not important in
economic evaluation, in which analysis always involves
groups of patients rather than individuals. It has also
been suggested that functional disability is influenced by
different factors early and late in disease (44): in early
disease, disability is mainly caused by disease activity,
and in late disease, joint damage becomes more important. This explains the finding in our earlier modeling
work that radiologic scores had a poor correlation with
quality of life and argues for use of the HAQ as the
relevant disease measure in disease progression models.
In economic evaluation of chronic diseases with
several different symptoms, the important criteria for
the choice of a disease measure are whether it correlates
well with an overall measure of quality of life and
whether it is able to distinguish between patients at
different levels of disease severity. Our data show that
the HAQ correlates well with utilities measured with the
EQ-5D, and that the difference in utility between the
different HAQ groupings in the model is significant.
Also, the HAQ has been used widely for several years
and is therefore available in both longitudinal followup
studies and clinical trials.
The ordered probit regressions estimate the
probabilities of transition to a different disease state
depending on sex, age at the time of disease onset, time
since disease onset, and HAQ score for the previous
period (1 year earlier). All of these variables were shown
to have a significant (at the 1% level) effect on progression. This is consistent with clinical findings that women
have a higher risk of disability, disability increases over
time, and HAQ scores from year to year are correlated,
particularly in the later stages of RA (39).
Recently, the use of mean values in costeffectiveness analyses has been questioned in view of the
variability of outcomes, particularly costs. Because the
data for all parameters are stochastic, it is possible to
estimate the uncertainty by using Monte Carlo analysis.
This method also allows the estimation of confidence
intervals for cost-effectiveness calculations when clinical
trial data are incorporated into the model.
The accuracy of the cohort distributions at the
end of the simulations confirms that the models reflect
disease progression as seen in these 2 cohorts. Also, the
similarity between the groups in the utility estimates and
the overall outcome (QALYs) after 10 years suggests
that the models reflect the disease and quality of life in
a general way. In contrast, costs are clearly influenced by
the health care systems and therefore are very different.
Therefore, we conclude that these disease models can be
used for cost-effectiveness analyses in countries other
than Sweden and the UK, provided that resource utilization is adjusted to reflect local conditions.
We are grateful to Nigel Hurst (Edinburgh) for making
the UK EQ-5D data available, to Simon Dixon (Sheffield) for
helping with costing, and to Elisabet Lindquist (Lund) and
Cathy Mayes (St. Albans) for support with the cohort data.
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