The cost-effectiveness of infliximab (Remicade®) in the treatment of rheumatoid arthritis in Sweden and the United Kingdom based on the ATTRACT study

G. Kobelt, L. Jönsson1, A. Young2 and K. Eberhardt3

HDI France, Spéracèdes, France,
1 Stockholm Health Economics Consulting AB, Uppsala, Sweden,
2 Department of Rheumatology, City Hospital, St Albans, UK (for the Early RA Study group) and
3 Department of Rheumatology, Lund University Hospital, Lund, Sweden


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Objective. The cost per quality-adjusted life-year (QALY) of infliximab (Remicade®) treatment in rheumatoid arthritis (RA) was estimated on the basis of a clinical trial comparing infliximab plus methotrexate with methotrexate alone in 428 patients with advanced disease [Anti-Tumour Necrosis Factor Trial in Rheumatoid Arthritis with Concomitant Therapy (ATTRACT)].

Methods. The effect of infliximab on disease progression and related costs and utilities was estimated using two disease progression models based on epidemiological cohorts followed for up to 15 yr in Sweden and the UK. The clinical trial data were used directly in the model and extrapolated to 10 yr using a cohort from the epidemiological studies matched for gender, age, time since onset of RA and disease severity.

Results. One to two years of treatment with infliximab treatment reduced direct and indirect resource consumption in both countries, thereby partly offsetting the treatment cost. In the base case, including both direct and indirect costs, the cost per QALY gained was SEK 32 000 (€3440) in Sweden and GBP 21 600 (€34 800) for 1 yr of treatment. The respective QALY gains were 0.248 and 0.298. With 2 yr of treatment, the costs per QALY gained were SEK 150 000 (€16 100) and GBP 29 900 (€48 200).

Conclusions. Although 1–2 yr of treatment with infliximab will lead to savings in both direct and indirect costs, these will not offset the drug cost. However, the cost-effectiveness ratios remain within the usual range for treatments to be recommended for use.

KEY WORDS: RA, Cost-effectiveness, Utility, QALY, Cost, Infliximab.


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
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 [35]. The costs to society associated with RA are considerable, as the disease may lead to restricted joint mobility, chronic pain, fatigue and functional disability, but also to psychological distress [6, 7]. Half of the patients will be work-disabled within 10 yr after disease onset [810], making productivity losses the predominant economic burden of the disease [5, 1113].

Direct health-care consumption represents about one-quarter of all costs in Sweden and the UK and is dominated by in-patient care [14, 15]. Drugs represent currently a minor fraction of total costs (3–4% of total and 13–15% of direct costs). However, several new treatments have recently been introduced, and both the widely used and typically generic disease-modifying anti-rheumatic drugs (DMARDs) and non-steroidal anti-inflammatory drugs (NSAIDs) are being replaced with more potent and/or more tolerable, but also more expensive, treatments. Thus, the economic question is how the added benefit of these treatments compares with the increased drug costs.

When compared with methotrexate alone, infliximab (Remicade®) in combination with methotrexate has been shown in a 1 yr double-blind clinical trial [Anti-Tumour Necrosis Factor Trial in Rheumatoid Arthritis with Concomitant Therapy (ATTRACT)] to reduce disease symptoms significantly and to slow disease progression in patients with RA who are not adequately controlled on DMARDs including methotrexate [16]. In the open extension, this effect was maintained [16]. The objective of the present analysis was to estimate the cost-effectiveness of infliximab plus methotrexate vs methotrexate alone in this patient population.

The key issue when performing cost-effectiveness analyses of new treatments in chronic diseases is that clinical trials are generally short compared with the duration of the disease, and limited data on the use of the new treatments in clinical practice are available. Health benefits, as well as the potential economic impact, of treatments that affect the progression of RA will, however, be most evident in the longer term. If the development of functional disability is delayed, the resource consumption of patients can be expected to be lower, their ability to work maintained longer, and consequently their quality of life (QoL) increased. Several studies in RA patients have shown that resource consumption increases as the disease progresses, while the patients' QoL decreases [13, 17, 18].

Thus, we require a baseline against which we can evaluate new treatments within a time-frame that exceeds that of the clinical trials, making modelling unavoidable. 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 unit of additional health benefit obtained with one treatment strategy compared with another [1921]. A baseline disease model must therefore incorporate good epidemiological data over a relevant time-frame, detailed resource consumption for patients at all levels of disease severity, and an effectiveness measure that is generally available in both epidemiological studies and clinical trials. The model must also be adaptable to different patient populations, as different clinical trials select patients at different levels of disease severity or at different times since disease onset. We have proposed such a framework earlier, on the basis of two cohort studies in Sweden and in the UK [17], and the present analysis of infliximab uses these two disease models.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Disease models
Disease progression in chronic diseases is most often illustrated using Markov models [22, 23]. In Markov models, patients are distributed across a finite number of distinct and mutually exclusive disease states at baseline, usually defined by disease severity. The development of the disease is then represented by the movement of patients between these states over time, and changes due to treatment are calculated as the changes in the transitions between states. Markov states are associated with costs and QoL, and total expected costs and outcome for a cohort of patients over a defined number of cycles are calculated.

Epidemiological data
The RA models have six disease states based on functional disability (state 1 represents least disability and state 6 worst disability) measured with the Health Assessment Questionnaire (HAQ) [24] and one state for death. Disease progression is based on changes in annual HAQ scores in two cohort studies in Sweden (Lund) [2528] and the UK [Early RA Study (ERAS)] [29, 30]. The Lund study followed 183 patients for up to 16 yr (mean 11.3 yr), and the ERAS study currently includes 1473 patients followed for up to 15 yr (mean 7.8 yr). The basic demographics of the two cohorts are shown in Table 1Go and disease progression is illustrated in Fig. 1Go.


View this table:
[in this window]
[in a new window]
 
TABLE 1. Epidemiological cohorts

 


View larger version (24K):
[in this window]
[in a new window]
 
FIG. 1. Development of the average HAQ score in the Lund and ERAS cohorts. HAQ scores improved or remained stable in the first years after diagnosis and entry into the studies, probably illustrating the effect of the introduction of treatment. The average HAQ increase over 10 yr was limited, as the disease progresses slowly in most patients, but possibly also as a result of early treatment intervention in the study, particularly in ERAS, which has currently become standard practice in RA management.

 

Transition probabilities
The basic structure of the models is shown in Fig. 2Go. The cycle length is 1 yr, in line with the annual follow-up data in the epidemiological studies, and the models run for 10 yr. Transition probabilities between states were estimated using an ordered probit regression model that allowed controlling for the age and gender of the patient and the time since onset of disease. The regression function can be used to generate transition probabilities for a cohort that will match the patients included in ATTRACT in terms of age, gender and time since onset of RA. In the ordered probit regression model, a latent variable, y, determines the current HAQ state of a patient. This latent variable is explained by the regression equation


where ß0 is a constant, age is the age of the patient at onset of the disease, distime is the time since onset of disease, s2s6 are dummy variables for the HAQ state in the preceding period (1 yr earlier), and {varepsilon} is the (normally distributed) error term. Thus the regression function 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 yr. Transitions to the state ‘death’ are based on normal age- and gender-adjusted mortality in each country.



View larger version (17K):
[in this window]
[in a new window]
 
FIG. 2. Structure of the Markov model. Patients start at baseline in different states, according to their HAQ scores. State 1 represents least disability (HAQ<0.6) and state 6 worst disability (HAQ>=2.6). After each cycle (1 yr), the model redistributes patients in the different states, depending on whether their HAQ scores have improved, remained stable or worsened during that cycle, or whether the patient died during the cycle.

 

Outcome measure
Economic evaluation requires that effectiveness be expressed with one measure, in order to estimate the cost per additional ‘health unit’ gained by an intervention. RA, like many other chronic diseases, has a continuous effect on several functions over a long period of time, and several disease measures are used. In such cases, the effectiveness measure most used in economic evaluation is the quality-adjusted life-year (QALY). The QALY combines quality of life with time by adjusting life-years with a quality weight, measured as utility. Utility is defined as the preference patients and/or the general population have for given states of health, expressed as a value on a scale between 0 (death) and 1 (full health), and is measured using interview-based techniques from decision analysis or preference-based QoL instruments, such as the EQ-5D (EuroQol) [3133]. This instrument has been shown to be sensitive to disease severity in RA [13, 18] and was therefore used to estimate country-specific utilities for the different Markov states, as shown in Fig. 3Go [17].



View larger version (30K):
[in this window]
[in a new window]
 
FIG. 3. Comparison of utility scores estimated with the EQ-5D in the two countries. Utilities were measured as a score on a scale between 0 (equal to death) and 1 (equal to full health), and represent preferences that patients and the general population have for certain health states. Differences in utilities between the states were significant in both countries, confirming that the EQ-5D is able to discriminate between relatively small differences in disability.

 

Resource utilization and costs
Resource consumption was estimated from the cohort studies and from a cross-sectional subsample of patients in ERAS [17]. All observations for patients in a given state, at any year in the follow-up, were used to calculate the average annual cost for each state. One limitation is that in both cohorts, the number of patients with very severe disability (HAQ >2.6) was rather limited, and direct costs in state 6 should therefore be considered with caution. Direct resources included were hospitalization, surgical interventions, ambulatory and community care and RA medication. NSAID usage was not included, as most patients used them and usage did not differ significantly between the states. Also excluded were non-medical direct costs and informal care, as these were not collected in the data sets.

The cost of hospitalization was based on the number of in-patient days in different wards and ward-specific daily costs, while the cost of surgical interventions was calculated from the type of intervention and its duration multiplied by the cost per minute of operating theatre use. Out-patient 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 use of each drug, associated with the cost of standard drug-monitoring protocols in place in the rheumatology departments participating in the cohort studies. Unit costs were taken from hospital accounting data and official price lists [3438].

Indirect costs were calculated as the loss of work capacity of patients in the more advanced disease states compared with patients in state 1, i.e. a HAQ score below 0.6. This is based on the finding that although these patients reported short-term sick leave, their overall work capacity was not different from that of the general population [13, 17]. Economic evaluation in RA should only include productivity losses due to the disease, and it is hence the difference in work capacity in the more severe disease states compared with a baseline (state 1 in our analysis) that is relevant, rather than the absolute work capacity. Therefore, for patients below HAQ 0.6, only short-term sick leave was included. When patients reach the average retirement age in each country, they are excluded from these calculations. Indirect costs were calculated using the human capital approach, in which an individual's productivity is valued at the market price, i.e. at the gross income including employers' contribution, until retirement. For the models, the total number of productive years lost at each stage (Markov states) was compared with the number for state 1, and the difference multiplied by the average gross annual income, including employers' contributions [SEK 327 000 (US$31 145) in Sweden and GBP 17 658 (US$26 355) in the UK] [39, 40].

Direct and indirect costs by disease states are shown in Fig. 4Go.



View larger version (44K):
[in this window]
[in a new window]
 
FIG. 4. Increase in costs with increasing disability in Sweden and the UK. Differences in costs between the disease states were significant. As expected, indirect costs, i.e. loss of work capacity, represent the major part of the costs.

 
To calculate the cost of infliximab, we used the official list price for 100 mg [SEK 4699 (€505) in Sweden and GBP 451.20 (€728) in the UK] and the dose prescribed in clinical practice, 3 mg/kg every 8 weeks with an initial loading dose at weeks 0, 2 and 6 (i.e. 24 mg/kg/yr). Although mean body weight in the ATTRACT trial was 75 kg (median 72 kg), we based the UK calculations on the mean weight of the UK patients in the trial (70 kg) and the Swedish calculations on the average weight of this population (65 kg). Thus, the annual cost for a compliant patient was estimated at SEK 73 304 (€7882) in Sweden and GBP 7580 (€12 226) in the UK. Adjusting for compliance as seen in the clinical trial (88.75% of possible treatment months), the annual cost was SEK 65 050 (€6995) and GBP 6727 (€10 850) respectively. Drug administration and monitoring costs were estimated from protocols in place in the Lund and ERAS centres, and the additional cost was estimated at SEK 6744 (€725) and GBP 889 (€1434) per year. As the price of infliximab varies within Europe, we present a sensitivity analysis for both countries using the Swedish, UK and Swiss prices, representing the lower, middle and higher price ranges.

Effectiveness of infliximab
ATTRACT included 428 patients from 34 study sites. Patients received four different active drug regimens (3 or 10 mg/kg, every 4 weeks or every 8 weeks) or placebo, in addition to methotrexate. After a double-blind period of 54 weeks, patients were given the option of continuing on their current treatment for another year, in an open extension.

The main economic evaluation was based on 54 weeks of double-blind treatment, as the number of patients continuing into the second year was limited, reducing the number of patients in each of the Markov states. More importantly, the methotrexate group could not be used reliably as a comparison in the second year as these patients were able to convert to active drug as soon as all patients in the trial had completed the double-blind phase and the blind was broken. We included all patients with follow-up data at 54 months in order to calculate transition probabilities, regardless of whether they had discontinued treatment, as non-compliance is incorporated into the cost of treatment. Patients with missing values were excluded, and transition probabilities were calculated on the basis of 287 patients in the treatment group and 58 patients in the comparator group. As there was no statistically significant difference in efficacy between the four dosage arms of infliximab at the end in the double-blind phase, we merged the groups for the purpose of this analysis. The demographics of patients in ATTRACT are shown in Table 2Go. There was no statistical difference in the demographics of the full trial population and the patients used in the economic evaluation.


View this table:
[in this window]
[in a new window]
 
TABLE 2. ATTRACT study

 

Basic model
In the basic model, both groups from the clinical trial data were used directly for the first year. For the following years, the Lund and ERAS data were used to simulate a 10-yr follow-up, controlling for the difference in the population in terms of age, sex, disease duration and methotrexate use. In order to model the second year of treatment, a different approach had to be used because of the limited number of patients remaining on methotrexate alone (11), and matched patients from the Lund and ERAS cohorts were used for comparison. These calculations assumed that treatment was stopped after 1 or 2 yr, when no further clinical data were available, and no further treatment costs and effects were therefore assumed; rather, both groups of the trial population followed the development of the epidemiological cohorts.

Alternative model (sensitivity analysis)
As it may be unrealistic to assume that the treatment effect achieved in the trial will be fully maintained when treatment is withdrawn, we present a different modelling approach which incorporates a loss of effect in the year after discontinuation, expressed as faster disease progression than that in the epidemiological cohorts. We calculated the difference in the HAQ changes between the infliximab and methotrexate groups during the clinical trial, and applied the difference (odds ratios for progression) to the Lund and ERAS cohorts for the treatment arm for the first year. Thus, treatment was compared directly with that of the epidemiological cohorts, thereby also eliminating the placebo effect in the clinical trial. The loss of effect at withdrawal during the first year in the trial was already incorporated into the calculations, as the intent-to-treat group was used. Effect loss after the trial was estimated by comparing the HAQ transitions of patients in both groups in the trial who discontinued treatment in the second year but were followed for the entire study period (i.e. with HAQ scores at 102 weeks). A total of 113 patients were available for this analysis, 77 in the active group and 36 in the methotrexate group. We defined improvement as a decline in HAQ score of more than 0.5, while deterioration was defined as an increase in HAQ score by more than 0.5 (i.e. the difference between the Markov states). The difference between the two groups was then applied to the transition probabilities in the cycle following treatment discontinuation, with the assumption that patients on methotrexate alone would not experience a loss of effect, while patients on infliximab would.


    Results
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Disease progression
The disease models have been shown to reproduce accurately the progression of disability in the epidemiological cohorts [17]. When cohorts with the same distribution across the Markov states at baseline as in the Lund and ERAS cohorts were modelled over 10 yr, the distribution at 10 yr was identical to the actual distribution in the studies.

Disease progression in the clinical trial is illustrated with the first-year transitions for the infliximab and methotrexate groups (Table 3Go). The same calculations were performed for the treatment group in the second year. Figures 5Go and 6Go illustrate the progression of average HAQ levels for the two groups when transitions from ATTRACT were used directly for 1 and 2 yr, respectively, using the basic model illustrated for Sweden.


View this table:
[in this window]
[in a new window]
 
TABLE 3. Disease progression in the first year of the ATTRACT study

 


View larger version (17K):
[in this window]
[in a new window]
 
FIG. 5. Changes in average HAQ score in the two groups during the 12 month double-blind period extrapolated to 10 yr using data from the Lund cohort. HAQ scores are based on the mid-point level of the Markov states, e.g. 1.85 in state 3 (1.6 < 2.1). Thus the mean HAQ scores are for illustrative purposes only and do not correspond exactly to mean scores in the ATTRACT trial or in the Lund cohort. This graph assumes no loss of treatment effect at treatment discontinuation. However, as can be seen, there was a strong placebo effect in the trial, and this effect was maintained beyond the trial in this simulation.

 


View larger version (17K):
[in this window]
[in a new window]
 
FIG. 6. Treatment with infliximab plus methotrexate, using the open label extension for the second year. As the methotrexate group in the second year was too small to be used in this analysis, data for treatment in the second year are compared directly with data for the Lund cohort.

 
For the alternative model, we found that infliximab treatment decreased the risk of progressing to more severe states by 56% (relative risk 0.44), using the difference in progression between the two groups. The probability of improving to a less severe disease state was increased by a factor of 3.48. For patients who discontinued treatment in the second year, there was no statistically significant difference in the probability of progressing to more severe disease states (P=0.085) between patients on infliximab plus methotrexate and those on methotrexate alone. However, the difference in the probability of improving to less severe states was significant (P=0.031). Thus, in the model, patients discontinuing active treatment had a lower probability of improving than the epidemiological cohorts, and we applied the relative risk of improving in HAQ score (0.30) to the treatment arm in the model.

In the base case, when all costs were included, the incremental cost in the treatment arm was SEK 8031 (€864) and GBP 6440 (€10 387) in Sweden and the UK, respectively. The cost of infliximab treatment for 1 yr was SEK 71 794 (€7720) and GBP 7616 (€12 284), and treatment thus reduced other costs by SEK 63 736 (€6853) and GBP 1176 (€1897). The QALY gain was 2.48 and 2.98, respectively, the difference being partly due to different discount rates in the two countries. The cost per QALY gained was SEK 32 000 (€3440) and GBP 21 600 (€34 800). When treatment was given for 2 yr, the cost per QALY gained increased to SEK 150 000 (€16 000) and GBP 299 000 (€48 200).

Table 4Go shows the cost-effectiveness calculations for the 1- and 2-year treatment scenarios in the basic model, and Table 5Go gives the results of the alternative model with a loss of effect at treatment discontinuation. The sensitivity analysis on the cost of infliximab is presented in Table 6Go.


View this table:
[in this window]
[in a new window]
 
TABLE 4. Basic model: incremental cost per QALY with 1 or 2 yr of infliximab treatment

 

View this table:
[in this window]
[in a new window]
 
TABLE 5. Alternative model: incremental cost per QALY with 1-year infliximab treatment, including effect loss at discontinuation

 

View this table:
[in this window]
[in a new window]
 
TABLE 6. Sensitivity analysis on drug price (basic model, 1 yr of treatment)

 


    Discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
In chronic progressive diseases such as RA, changes in disease progression achieved in short-term trials will affect outcome in the long term, as some or all of the effect will last beyond the trial, and the cost of treatment must therefore be seen in relation to the long-term benefit. This is of particular importance if a new treatment is considerably more expensive than currently available therapies, as is the case for infliximab. We constructed disease models for Sweden and the UK that related disease progression to costs and quality of life, and allowed the simulation of cohorts with the same baseline characteristics (gender, age, age at disease onset, severity of disease) as patients included in clinical trials such as ATTRACT. Disease progression in the model was measured with HAQ, and there have been questions as to whether the HAQ is appropriate for this purpose. While initial disability has been shown to be a very strong predictor of final disability [26, 30, 4143], it is also highly variable and difficult to use for prognosis in individual patients [44]. However, this variability is not an issue in economic evaluation, in which analysis involves groups of patients rather than individual patients. We have shown earlier that the HAQ correlates well with health-care consumption, loss of work capacity and QoL, while radiological scores, for instance, do not [13]. This may be explained by the suggestion that functional disability is influenced by different factors early and late in disease [45]: in early disease disability is mainly due to disease activity and symptoms, whereas in late disease joint damage becomes more important. Consequently, although radiological scores have been used as the main efficacy measure in ATTRACT, and despite indications of the predictive value of early radiography on the course of the disease, HAQ is better adapted to estimating the effects of disease progression on costs and QoL in a model like ours.

Economic evaluation requires that effectiveness be expressed with a single measure, and we therefore used the QALY as the effectiveness measure in the model. The advantage of the QALY is its ability to capture health improvements in terms of both life expectancy and QoL. Thus, the QALY allows comparison of outcomes in different disease areas, a prerequisite for resource allocation. Most reimbursement agencies in countries where economic evaluation is used formally or informally for decision-making therefore prefer evaluations using the QALY. Although the QALY remains somewhat controversial, the difficulty lies less in the concept than in 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 RA in particular, we have shown earlier [13, 17] that the EQ-5D is able to discriminate between patients with differences in HAQ within the same ACR group.

The QALY gain over 10 yr is between 0.2 and 0.3 in Sweden and between 0.3 and 0.4 in the UK, the major reason for this difference being the different discount rates used (3 and 1.5% respectively); for instance, 0.3 discounted QALYs would be 0.4 and 0.35 undiscounted QALYs in Sweden and the UK, respectively, using these rates. The total gain of 0.3–0.4 QALYs over 10 yr may appear small, even undiscounted. However, the gain has to be considered in relation the total number of QALYs that an average RA patient in this group has over 10 yr, i.e. about 4.5. Undiscounted, this would be a total of 6 QALYs, which compares to about 8 QALYs for a healthy individual of a similar age. Hence, considering an average loss of 2 QALYs over 10 yr due to the disease, a gain of 0.4 QALYs with treatment appears very important.

Costs in RA are clearly driven by the loss in work capacity (indirect costs). We calculated the loss of productivity as the difference in the ability to work of patients with more advanced disease and patients with very limited disability (HAQ <0.6). Thus, productivity losses due to other causes were excluded. Work capacity in the models is based on the cohort studies, and one issue could be that the population in the clinical trial was different. We verified total work capacity (all patients, regardless of age) in ATTRACT by grouping patients into the disease states at baseline and estimating work capacity in the same way as in the cohort studies. The values were very similar, ranging from 60 to 7% in the trial compared with 57 to 0% in the Lund cohort and 54 to 8% in the ERAS cohort. The slightly higher values overall can probably be explained by the higher average age in the cohort studies compared with ATTRACT. However, in the model this has no impact, as age is controlled for.

No indirect costs for premature mortality due to RA were included in these calculations, as evidence on the effect of RA on life expectancy is conflicting. Several epidemiological studies have shown increased mortality in patients with severe RA and the mean standardized mortality ratio has recently been estimated to be 1.87 [46]. Earlier studies showed rates as high as 2.26 [47]. Also, a link between functional status (HAQ) and mortality has been reported [48]. However, more recent studies have not found any effect of RA on mortality [50, 51]. Most importantly, however, the data available do not allow the calculation of an annual age- and gender-specific mortality risk for each of the six disease states in the model.

In our basic analysis, the cost per QALY of 1–2 yr of treatment with infliximab in Sweden was well within the range of generally accepted cost-effectiveness ratios. When the alternative model was used, 1 yr treatment became cost-saving, and remained cost-saving even when a loss of effect at treatment discontinuation was incorporated in the calculations. The explanation for the difference is that, in the alternative model, the trial effect in the placebo (methotrexate) group is eliminated and the infliximab treatment group is compared directly with the epidemiological cohorts. Thus the difference between the two groups increases.

In the UK, cost-effectiveness ratios were substantially higher in both models. This difference is entirely driven by indirect costs, as direct costs were almost identical in the two countries and cost-effectiveness ratios including only direct costs were therefore in similar ranges. However, in the UK, a much lower proportion of patients in the advanced HAQ states were on early retirement compared with Sweden. For instance, in state 5 (HAQ 2.1 to <2.6), only 7% of patients below 65 yr were working in the Lund cohort, while the proportion in ERAS was 50%. In state 6 (HAQ >2.6) no patient was working in Sweden, while in ERAS the proportion remained at 50%. The most likely reason for this is the limited number of patients in the Lund cohort, in which only a few patients progressed to these very severe states, and those who did were over 65 yr old. Another reason may be that Sweden had a more generous policy for according long-term illness benefits during these years. Lastly, the average annual salary in Sweden was found to be higher than in the UK. As a consequence, with progressing functional disability, the increase in indirect costs was much steeper in Sweden, thereby increasing the savings if the progression of the disease were slowed.

A further important explanation for the differences between the two countries is the discount rate. In Sweden, costs are generally discounted by 3%, while in the UK, according to guidelines from the National Institute for Clinical Excellence (NICE), a rate of 6% is used. Thus, savings achieved over the years through slower disease progression will have a lower net present value in the UK.

We included both direct and indirect costs in our main analysis. In a disease in which the main economic effect is that patients lose their ability to maintain a professional activity, we would argue that indirect costs must be considered when the impact of a treatment is assessed, despite health-care budget considerations. Direct costs are currently very low, as treatment is limited and drugs, both DMARDs and NSAIDs, are generally generic and inexpensive. We did include NSAID use in our costs, as all patients in the cohorts used them at given times regardless of the level of the disease, and costs were minimal. Clearly this will change with the use of the newer cyclooxygenase 2 inhibitors, but it is probable that costs at all levels of HAQ will increase. As Markov models are driven by the difference in costs and utilities between the Markov states, and not by absolute values within the states, our results should not have been affected by this.

The most difficult issue when results from short-term trials are extrapolated to the longer term is the assumptions made for treatment continuation, as no data are available. We therefore present only results for the period during which trial data are available and estimate the effect of the benefit achieved within the trial carried over to a longer period, including, however, a potential loss of effect at treatment discontinuation. It can be argued that it is unrealistic to assume that in clinical practice patients will only be treated for 1 or 2 yr. However, there are no data from which to estimate which patients will continue and whether the treatment will continue to improve symptoms, maintain the effect or lose some of its effectiveness over time. From the second-year extension of ATTRACT, it appears that the effect is maintained over time, and we incorporated this into the estimates, despite the impossibility of using the comparator group for comparison. Compliance observed in the trial was incorporated into the analysis, but it is impossible to predict compliance in clinical practice beyond the trial based on these data. In our opinion, therefore, it is preferable to estimate the cost-effectiveness of the treatment only for the period for which data are available. A similar approach has been taken by another group developing methodologically different models in this field, with comparable results [51, 52].

The results of our models indicate that 1–2 yr of treatment with infliximab and methotrexate, compared with methotrexate alone, will lead to offsets both in direct and indirect costs. Savings in direct costs are €1500–2000 in Sweden and up to €800 in the UK and will thus not offset the cost of infliximab. The majority of the savings will come from maintaining the patients' ability to work. However, even when only direct costs are included, the cost-effectiveness ratios remain within the usual range for a treatment to be recommended for use.


    Acknowledgments
 
The authors 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. The work was supported with an unrestricted grant from Schering Plough.


    Notes
 
Correspondence to: G. Kobelt, HDI France, 492 Chemin des Laurens, 06530 Spéracèdes, France. E-mail: gisela.kobelt{at}easynet.fr Back


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

  1. Silman A, Hochberg MC. Epidemiology of rheumatic diseases. Oxford: Oxford University Press, 1993.
  2. Symmons D, Silman A. The epidemiology of rheumatoid arthritis. In: Wolfe F, Pincus T, eds. Rheumatoid arthritis: pathogenesis, assessment, outcome and treatment. New York: Marcel Dekker, 1994:131–50.
  3. Meenan R, Yelin E, Nevitt M, Epstain W. The impact of chronic disease: A socioeconomic profile of rheumatoid arthritis. Arthritis Rheum 1981;24:544–9.[ISI][Medline]
  4. Pincus T. The underestimated long term medical and economic consequences of rheumatoid arthritis. Drugs 1995;50:1–14.[ISI][Medline]
  5. Allaire S, Prashker M, Meenan R. The cost of rheumatoid arthritis. Pharmacoeconomics 1995;6:515–22.
  6. Katz P. The impact of rheumatoid arthritis on life activities. Arthritis Care Res 1995;8:272–8.[Medline]
  7. Pincus T, Callahan L, Sale W, Brooks A, Payne L, Vaughn W. Severe functional declines, work disability and increased mortality in seventy-five rheumatoid arthritis patients studied over nine years. Arthritis Rheum 1984;27:864–72.[ISI][Medline]
  8. Fex E, Larsson B-M, Nived K, Eberhardt K. Impact of rheumatoid arthritis on work status and social and leisure time activities in patients followed 8 years from onset. J Rheumatol 1997;25:44–50.[ISI]
  9. Young A, Dixey J, Kulinskaya E et al. Which patients stop working because of rheumatoid arthritis? Results of 5 years follow up in 732 patients from the Early RA Study (ERAS). Ann Rheum Dis 2002. In press.
  10. Barrett E, Scott D, Wiles N, Symmons D. The impact of rheumatoid arthritis on employment status in the early years of disease: a UK community-based study. Rheumatology 2000;39:1403–9.[Abstract/Free Full Text]
  11. Stone C. The lifetime economic cost of rheumatoid arthritis. J Rheumatol 1984;11:819–27.[ISI][Medline]
  12. Jönsson B, Kaarela K, Kobelt G. Economic consequences of the progression of rheumatoid arthritis. A Markov model. Stockholm: Stockholm School of Economics, 1997.
  13. Kobelt G, Eberhardt K, Jönsson L, Jönsson B. Economic consequences of the progression of rheumatoid arthritis in Sweden. Arthritis Rheum 1999;42:347–56.[CrossRef][ISI][Medline]
  14. Jonsson D, Husberg L. Socioeconomic costs of rheumatic diseases. Implications for technology assessment. Int J Technol Assess Heath Care 2000;16:1193–200.[CrossRef]
  15. McIntosh E. Clinical audit: The cost of rheumatoid arthritis. Br J Rheumatol 1996;35:781–90.[ISI][Medline]
  16. Maini R and the ATTRACT study group. Infliximab (chimeric anti-tumour necrosis factor alpha monoclonal antibody) versus placebo in rheumatoid arthritis patients receiving concomitant methotrexate: a randomized phase III trial. Lancet 1999:354:1932–9.
  17. Kobelt G, Jönsson L, Lindgren P, Young A, Eberhardt K. Modeling the progression of rheumatoid arthritis. A two-country model to estimate costs and consequences of RA. Arthritis Rheum 2002;46:2310–9.[CrossRef][ISI][Medline]
  18. Hurst N, Kind P, Ruta D, Hunter M, Stubbings A. Measuring health-related quality of life in rheumatoid arthritis: Validity, responsiveness and reliability of EuroQol (EQ-5D). Br J Rheumatol 1997;36:551–9.[CrossRef][ISI][Medline]
  19. Johannesson M. Theory and methods for economic evaluation of health care. Boston: Kluwer Academic Publishers, 1996.
  20. Gold M, Siegel J, Russell L, Weinstein M. Cost-effectiveness in health and medicine. New York: Oxford University Press,1996.
  21. Drummond M, O'Brien B, Stoddart G, Torrance G. Methods for the economic evaluation of health care. Boston: Kluwer Academic Publishers, 1997.
  22. Sonnenberg F, Beck J. Markov models in medical decision making. Med Decis Making 1993;13:322–38.[ISI][Medline]
  23. Briggs A, Sculpher M. An introduction to Markov modeling for economic evaluation. Pharmacoeconomics 1998;13:397–409.[ISI][Medline]
  24. Fries J, Spitz P, Kraines R, Holman H. Measurement of patient outcome in arthritis. Arthritis Rheumatism 1980;23:137–45.[ISI][Medline]
  25. Eberhardt K, Rydgren L, Petersson H, Wollheim F. Early rheumatoid arthritis—onset, course and prognosis over 2 years. Scand J Rheumatol 1990;17:263–71.
  26. Eberhardt K, Fex E. Functional impairment and disability in early rheumatoid arthritis—development over 5 years. J Rheumatol 1995;22:1037–42.[ISI][Medline]
  27. Eberhardt K, Fex K. Clinical course and remission rate in patients with early rheumatoid arthritis: relationship to outcome after 5 years. Br J Rheumatol 1998;37:1324–9.[CrossRef][ISI][Medline]
  28. Lindkvist E, Saxne T, Geborek P, Eberhardt K. Ten-year outcome in a cohort of early rheumatoid arthritis patients—health status, disease process and damage. Ann Rheum Dis 2002;61:1055–9.[Abstract/Free Full Text]
  29. Young A, Wilkinson P, Talamo J et al. Socioeconomic deprivation and rheumatoid disease. What lessons for the health service? Ann Rheum Dis 2000;59:794–9.[Abstract/Free Full Text]
  30. Young A, Dixey J, Cox N et al. How does functional disability in early rheumatoid arthritis (RA) affect patients and their lives? Results from a 5 year follow-up in 732 patients from the Early RA study. Rheumatology 2000;39:603–33.[Abstract/Free Full Text]
  31. The EuroQol Group. EuroQol—a new facility for the measurement of health-related quality of life. Health Policy 1990;16:199–208.[ISI][Medline]
  32. Dolan P, Gudex C, Kind P, Williams A. A social tariff for EuroQol: Results from a UK general population survey. York: Centre for Health Economics, University of York, 1995.
  33. Brooks R, Group E. EuroQol: the current state of play. Health Policy 1996;37:53–72.[CrossRef][ISI][Medline]
  34. National Health Services Executive: The new NHS: 1999 reference costs. Department of Health. www.doh.gov.uk/nhsexec/refcosts.htm.
  35. Netten A. Unit costs of health and social care 1999. University of Canterbury Personal Social Services Research Unit (PSSRU). www.ukc.ac.pssru/publications/html.
  36. University Hospital Lund. Price list 2002. www.srvn.org/pris02/lund.pdf.
  37. British Medical Association. The British National Formulary of Pharmaceutical Products 2000. www.bnf.org.
  38. FASS. Läkemedel I Sverige V (Drugs in Sweden). Läkemedelsinformation AB, Stockholm, 2001, www.fas.nu/forms/ffassw/htm.
  39. Office for National Statistics. Annual Labour Force Survey 2001. www.hm-treasury.gov.uk.
  40. Statistics Sweden. Statistiska Centralbyrån. www.scb.se/publkat/sm/arbetsmarknad.asp#AM38.
  41. Wolfe F, Cathey M. The assessment and prediction of functional disability in rheumatoid arthritis. J Rheumatol 1991;18:1298–306.[ISI][Medline]
  42. Harrison B, Symmons D, Brennan P et al. Inflammatory polyarthritis in the community is not a benign disease: predicting functional disability one year after presentation. J Rheumatol 1996;23:1326–31.[ISI][Medline]
  43. Van der Heijde A, Jacobs J, Haanen H, Bijlsma J. Is it possible to predict the first year extent of pain and disability for patients with rheumatoid arthritis? J Rheumatol 1995;22:1466–70.[ISI][Medline]
  44. Wiles N, Barrett J, Barrett E, Silman A, Symmons D. Disability in patients with early inflammatory polyarthritis cannot be ‘tracked’ from year to year: An examination of the hypothesis underlying percentile reference charts. J Rheumatol 1999;26:800–4.[ISI][Medline]
  45. Guillemin F, Briancon S, Pourel J. Functional disability in rheumatoid arthritis: two different models in early and established disease. J Rheumatol 1992;19:366–9.[ISI][Medline]
  46. Guedes C. Mortality in rheumatoid arthritis. Rev Rheum Engl Ed 1999;66:1895–9.
  47. Wolfe F, Mitchell DM, Sibley JT et al. The mortality of rheumatoid arthritis. Arthritis Rheum 1994;37:481–94.[ISI][Medline]
  48. Soderlin M, Nieminen P, Hakala M. Functional status predicts mortality in a community-based rheumatoid arthritis population. J Rheumatol 1998;25:492–8.[ISI][Medline]
  49. Lindqvist E, Eberhardt K. Mortality in rheumatoid arthritis patients with disease onset in the 1980s. Ann Rheum Dis 1999;58:11–4.[Abstract/Free Full Text]
  50. Kroot E. No increased mortality in patients with rheumatoid arthritis: up to 10 years follow-up from disease onset. Ann Rheum Dis 2000;59:954–8.[Abstract/Free Full Text]
  51. Wong JB, Ramey DR, Singh G. Long-term morbidity, mortality and economics of rheumatoid arthritis. Arthritis Rheum 2001;44:2746–9.[CrossRef][ISI][Medline]
  52. Wong JB, Singh G, Kavanaugh A. Estimating the cost-effectiveness of 54 weeks of infliximab for rheumatoid arthritis. Am J Med 2002;113:400–8.[CrossRef][ISI][Medline]
Submitted 12 March 2002; Accepted 15 August 2002