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Background: In 2002, the National Institute for Health and Clinical Excellence (NICE), recommended atypical antipsychotics over conventional ones for first-line schizophrenia treatment, based on their lower risk of extrapyramidal symptoms.
Objective: To estimate the incremental cost-effectiveness of atypical relative to conventional antipsychotics for the treatment of schizophrenia in the UK.
Methods: A discrete event simulation (DES) model was adopted to reflect the treatment of schizophrenia in the UK. The model estimates symptoms (using the Positive and Negative Symptom Score [PANSS]), psychiatrist visits, pharmacological treatment and treatment location, number and duration of psychotic relapses, level of compliance, quality-adjusted life-years (QALYs), and side effects over a 5-year time period. Probabilistic sensitivity analyses were carried out. Following NICE's “atypical” recommendation, the cost-effectiveness of atypical versus conventional antipsychotics was estimated in a scenario analysis, assuming both groups differ only in side-effect profile.
Results: When comparing conventional and atypical antipsychotics, the model predicts that the latter would decrease 5-year costs by £1633 per patient and result in a QALY gain of 0.101. The probabilistic sensitivity analysis suggests these results are robust. The sensitivity analyses indicate that incremental costs and effects are most sensitive to the differential efficacy of atypicals and conventionals, as measured by PANSS. When it is assumed that the only differences between atypicals and conventionals are found in side-effect profiles, the incremental cost-effectiveness ratio of the atypicals is £45,000 per QALY gained.
Conclusion: According to this DES model for schizophrenia, atypical antipsychotics are cost-effective compared to the conventional antipsychotics. The assumptions used in the model need further validation through large naturalistic based studies with reasonable follow-up to determine the real-life differences between atypicals and conventional antipsychotics.
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Schizophrenia is a chronic disease with no definitive cure. The prevalence of the condition is estimated at between 0.2% and 1% in the general population [1,2]. Schizophrenia typically starts at a young age with considerable impact on the patients and their social system. As a result, the societal costs of schizophrenia are substantial. According to Mangalore and Knapp, the estimated total societal cost of schizophrenia in the UK was 6.7 billion pounds in 2004 to 2005 .
The National Institute for Health and Clinical Excellence (NICE) recommends that atypical antipsychotics should be considered first-line treatment for newly diagnosed schizophrenia patients, particularly when full discussion between clinician and patient is not possible [1,4]. This recommendation is based on the lower potential risk of extrapyramidal symptoms (EPS) on atypical antipsychotics than on conventional ones. The guidance recognizes that patients who are currently successfully treated with a conventional need not be switched to an atypical antipsychotic when symptom control is adequate and side effects are acceptable. The present study addresses the cost-effectiveness of the use of atypical antipsychotics in the early treatment of schizophrenia.
Reliable data of the long-term impact of schizophrenia and its treatment are scarce, both in terms of clinical as well as economic outcomes. Additionally such data are often of poor quality and difficult to generalize among a broad population . This hampers making well-informed decisions concerning reimbursement and treatment guidelines. A health economic model, which consolidates the available evidence in a structured manner, may, however, be able to help gain insight into the different mechanisms which influence treatment outcomes. While the outcomes of such a model should be treated with caution , they can still be of great value in serving as a starting point for further research and discussion. It may be argued that the lower the quality of the evidence is, the more closely a model should reflect clinical practice and rely on explicit assumptions and interdependencies, so that the impact of each can be individually tested and reviewed. Furthermore, this approach potentially makes the model more suitable for the inclusion of expert opinion to complement the published literature, as the level of abstraction is reduced to a minimum. This realization, along with the highly heterogeneous and time-dependent nature of the illness and its progression, makes schizophrenia a highly suitable candidate for discrete event simulation (DES) [6,7]. DES models allow for the simulation of individual patients and they are highly flexible in terms of their time frame, as well as the inclusion of a large number of interdependencies with respect to individual patient characteristics and their treatment histories.
The current article aims to provide insight and understanding regarding the clinical and economic effects of the treatment of schizophrenia, using a nonproduct-specific DES model which represents the use of pharmacological agents in day-to-day clinical practice in the UK. The objective was to assess the cost-utility of atypical versus conventional antipsychotics based on their respective market shares in the UK treatment setting.
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The base-case estimates of costs and effectiveness outcomes are summarized in Table 3. Starting with atypicals rather than conventionals was predicted to result in cost savings of £1633 (∼3% of total costs). Drug costs increase by £1258, while hospital costs decrease by £2730. Hence, the reduction in hospitalization compensated for the higher cost of the atypical medications. It is expected that the atypicals on average avoid 16 hospital days per patient over 5 years. Table 4 shows that compared to conventionals, atypical drugs were predicted to reduce PANSS scores, increase time on first treatment, and improve quality of life. Thus, conventional antipsychotic treatment appeared to be dominated by atypicals because the latter were associated with cost savings (of £1633) and QALY gains (of 0.10). At a WTP of £30,000 per QALY gained, the expected incremental net benefit (INB = ΔE*WTP − ΔC) of the atypical treatment arm was £4668 per patient over a 5-year period.
Table 4. Overview of base-case cost and effect outcomes and incremental cost-effectiveness ratios for starting with a conventional or with an atypical
| || ||Conventional group||Atypical group||Difference|
|Discounted costs (by cost component)||Community care Intense community care||£4,282 £3,541||£4,349 £3,587||£67 £46|
|Drug costs (incl. depot cost)||£3,588||£4,846||£1,258|
|Undiscounted costs (over time)||Year 1 Year 2||£14,156 £11,376||£14,082 £11,135||−£75 −£241|
|QALY||Discounted 5-year total||3.53||3.64||0.10|
|Percentage of time spent per location||Community care Intense community care||63.3% 13.1%||64.3% 13.3%||1.0% 0.2%|
|Percentage of total cost spent per location||Community care Intense community care||7.2% 5.9%||7.5% 6.2%||0.3% 0.2%|
|Discontinuation of first treatment||Time on Tx1 (years) % still on Tx1 after 1 year||1.36 66.5%||1.51 72.4%||0.15 5.9%|
|Relapses||Number of relapses||2.67||2.64||−0.03|
|Total relapse time||2.31||2.27||−0.03|
|Average PANSS||5-year total||62.7||59.8||−2.9|
Average time on first treatment was 1.36 years on conventionals and 1.51 years on atypicals. After 5 years, about 70% of patients had experienced three treatment switches and were on clozapine treatment. Additionally the atypicals were expected to slightly reduce the number of relapses suffered (by 0.03 relapses over 5 years). This reduction in the average number of relapses realized by atypicals was smaller than expected because of the comparatively high proportion of patients on conventional depots in the UK (and thus relatively high compliance).
The uncertainty surrounding the cost per QALY gained is presented using a cost-effectiveness scatter plot (Fig. 3a). The center of the cost-effectiveness “cloud” lies below the x-axis, which indicates cost savings, and to the right of the y-axis, which implies health benefits at the same time. The information contained in the scatter plot is used to produce the acceptability curve (presented in Fig. 3b). This curve presents the probability of the “experimental” treatment being cost-effective compared to its comparator for various WTP thresholds, which are presented on the x-axis. In the UK, there is general consensus among policymakers that a WTP threshold for a QALY gained of approximately £30,000 is acceptable . At this threshold, with the given levels of uncertainty, the probability of atypicals being cost-effective compared to conventionals is 98.2%.
Figure 3. (a) Scatter plot of incremental costs and quality-adjusted life-years (QALYs) based on a 1000 runs of 10,000 patients. The larger portion of the scatter plot is found underneath the £30,000/QALY threshold, suggesting that treating patients with atypicals is likely to be cost-effective at the National Institute for Health and Clinical Excellence threshold of £30,000/QALY. (b) Acceptability curves: the probability that the new treatment option is cost-effective given various WTP thresholds for a QALY gained.
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In Table 5, all variables are presented, which in the OLS analysis had a significant impact (P-value < 0.05) on incremental costs and/or effects that were generated in the multivariate sensitivity analysis. These variables have been sorted in descending order of their impact on the incremental outcomes. The last three columns of the table provide information about the influence of the worst case values. The values of the worst case and base-case values for a variable have been reproduced in columns 4 and 5. The product of the difference between these values multiplied by the regression coefficient is presented in column 6.
Table 5. Regression analysis of incremental costs and effects (QALY) based on 1000 runs
|Variables||Estimate||P-value||BC||Worst case||New ΔC BC = −£1633|
|A. Incremental costs regression|
|PANSS reduction on atypicals (relapse)||15,090||0.000||0.80||0.93||£435|
|PANSS-risk regression beta 0||−1,197||0.000||−5.131||−6.802||£368|
|PANSS-risk regression beta 1||−121,400||0.000||0.0403||0.0299||−£366|
|Days in hospital||−35||0.000||40||13||−£668|
|Hospital location cost||−0.04||0.000||63,350||42,391||−£887|
|Compliance rate between relapses community treatment atypical oral||−3,361||0.000||0.65||0.43||−£907|
|PANSS episode partial recovery patients||−125||0.000||47.0||42.5||−£1,067|
|Compliance rate during relapses community treatment atypical oral||−2,153||0.000||0.60||0.40||−£1,210|
|PANSS reduction on atypicals (TBR)||4,371||0.000||0.95||1.044||−£1,220|
|Compliance rate between relapses community treatment conventional oral||2,104||0.000||0.60||0.79||−£1,235|
|Percentage partial recovery patients||−2,324||0.000||0.62||0.45||−£1,236|
|Compliance rate during relapses community treatment conventional oral||1,562||0.000||0.55||0.72||−£1,375|
|Probability of switching because of somnolence||987||0.007||0.30||0.51||−£1,425|
|Percentage of nonsevere patients||710||0.041||0.10||0.33||−£1,468|
|Shortening of TBR because of noncompliance atypicals||412||0.001||3.70||4.05||−£1,489|
|Variables||Estimate||P-value||BC||Worst case||New ΔE BC = 0.101|
|B. Incremental effects regression*|
|PANSS Reduction on atypicals (relapse)||−0.306||0.000||0.80||0.93||0.059|
|QALY–PANSS regression beta||−7.363||0.000||−0.0043||−0.0023||0.086|
|Utility score EPS||−0.253||0.000||0.89||0.95||0.086|
|Incidence weight gain olanzapine||−0.034||0.000||0.41||0.78||0.088|
|Utility score weight gain||0.390||0.000||0.96||0.93||0.089|
|Incidence EPS flupentixol depot||0.028||0.000||0.58||0.17||0.089|
|Compliance rate between relapses community treatment atypical oral||0.051||0.000||0.65||0.43||0.090|
|PANSS reduction on atypicals (TBR)||−0.109||0.000||0.95||1.04||0.091|
|Percentage partial recovery patients||0.056||0.000||0.62||0.45||0.091|
|Compliance rate during relapses community treatment atypical oral||0.048||0.000||0.60||0.40||0.092|
|Incidence somnolence olanzapine||−0.030||0.000||0.30||0.59||0.092|
|Incidence somnolence quetiapine||−0.020||0.000||0.55||0.93||0.093|
|Zuclophentixol depot EPS||0.020||0.000||0.58||0.19||0.093|
|Utility score somnolence||−0.123||0.000||0.91||0.97||0.094|
|Incidence EPS haloperidol oral||0.017||0.000||0.58||0.14||0.094|
|Utility score TD||−0.149||0.000||0.86||0.91||0.094|
|Incidence somnolence flupentixol depot||0.020||0.000||0.49||0.14||0.094|
|Probability of switching after side-effect EPS||−0.037||0.000||0.30||0.48||0.094|
|Incidence EPS olanzapine||−0.043||0.000||0.16||0.30||0.095|
|Incidence EPS risperidone oral||−0.026||0.000||0.27||0.49||0.095|
|Incidence somnolence risperidone oral||−0.024||0.000||0.27||0.50||0.096|
|Incidence somnolence amisulpride||−0.019||0.000||0.27||0.54||0.096|
|PANSS episode partial recovery patients||0.001||0.000||47||42||0.096|
|Probability of switching after side-effect somnolence||−0.024||0.000||0.30||0.51||0.096|
|Incidence weight gain quetiapine||−0.020||0.000||0.33||0.58||0.096|
|Compliance rate during relapses community treatment conventional oral||−0.028||0.000||0.55||0.72||0.096|
|Incidence somnolence haloperidol oral||0.013||0.000||0.49||0.16||0.097|
|Incidence EPS trifluoperazine||0.010||0.000||0.58||0.14||0.097|
|Incidence TD flupentixol depot||0.122||0.000||0.05||0.02||0.097|
|Incidence EPS amisulpride||−0.012||0.000||0.33||0.65||0.097|
|Incidence EPS chlorpromazine||0.016||0.000||0.33||0.08||0.097|
|Incidence somnolence sulpiride||0.010||0.000||0.49||0.13||0.097|
|Incidence TD zuclophentixol depot||0.104||0.000||0.054||0.019||0.097|
|Compliance hospital atypical oral||0.011||0.000||0.80||0.45||0.097|
The results in Table 5 (part A) demonstrate that incremental costs were sensitive to changes in the values used of the parameters associated to whether or not the patient was at risk. Differences in the risk regression transformed the difference in PANSS reduction between conventionals and atypicals into a difference in hospitalization costs. Also, the difference between conventionals and atypicals in terms of PANSS reduction, compliance, and time between relapses had a significant impact on incremental costs.
From Table 5 (part B), it can be concluded that incremental effects are most sensitive to changes in the parameters of the QALY–PANSS relationship and the utility weights used in the calculations. The coefficients for EPS, tardive dyskinesia, and sedation were negative, because atypicals reduced these side effects, while coefficients hold for weight gain and diabetes, which are side effects more common in patients on atypicals. Side effects, especially those associated with serious consequences for quality of life such as tardive dyskinesia and diabetes, evidently had an important influence on incremental effects. In terms of effect size, the worst case value for the difference in PANSS reduction between atypicals and conventionals during episodes had the greatest impact on the incremental QALYs.
Table 6 presents the cumulative nature in which the mechanisms which are made explicit in the model affect incremental costs, effects, and the ICUR. The more the model incorporates specific advantages of atypicals, the higher the incremental effects of the atypicals are and the lower incremental costs are. When assuming, similar to the NICE recommendations, only differences in the side-effect profiles of both types of antipsychotics, the ICUR is £45,205/QALY gained.
Table 6. Sensitivity analysis: Analysis of the influence of the assumed differences between conventionals and atypicals
|Included differences||Incremental costs||Effects||ICUR|
|Side effects only||£1,917||0.042||£45,205|
|+ Time between relapses||£845||0.048||£17,778|
|+ PANSS reduction||−1,633||0.101||Dominant|
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The aim of this study was to assess the cost-utility of atypical antipsychotics in the treatment of schizophrenia compared to conventionals in the UK. On the basis of our calculations, it can be concluded that starting treatment of schizophrenia with an atypical instead of a conventional is associated with cost savings and with improved quality adjusted health over a 5-year period. Sensitivity analyses have shown that this conclusion of cost-effectiveness was quite robust to changes in input variables. The probabilistic analyses showed that atypicals will be cost-effective in a society where the WTP for a QALY is £30,000.
Our model calculations provide support for the NICE treatment guidelines which suggest that atypical antipsychotics should be considered for the first-line treatment of schizophrenia . This confirmation is not unequivocal. The argumentation behind the NICE recommendation relied mainly on EPS being avoided by atypicals compared to conventionals, while in the base case the model included additional differences that are believed to exist between the treatments. The scenario analysis which reflects the NICE recommendation, i.e., assumes only differences in side effects between atypicals and conventionals, indicates that the use of atypicals would be associated with an ICUR of approximately £45,000/QALY. This ICUR decreases dramatically if a difference in compliance, the time between relapses and PANSS reduction, is also assumed to a dominant ICUR in the last scenario, which is also the base case.
The debate in the literature of the cost and effects of atypicals and conventionals is inconclusive [5,14,39,46–51,68–73]. For instance recently, Jones et al. concluded that in people with schizophrenia whose medication is changed for clinical reasons that there is no disadvantage across 1 year in terms of quality of life, symptoms, or associated costs of care in using conventional antipsychotics rather than atypical antipsychotics (nonclozapine) . Moreover, in a 12-month study, Rosenheck et al. found that olanzapine demonstrated no advantages over haloperidol (with prophylactic benztropine against EPS) in compliance, symptoms, EPS, and quality of life . Furthermore, Lewis et al. argue that conventional antipsychotic drugs still have a place in the treatment of patients unresponsive to, or intolerant of, current medication . Kane et al. and Mahmoud et al. however, found that clinically relevant quality of life gains were more frequent with an atypical than with a conventional antipsychotic [70,71]. Further, Schooler et al. also showed that the time between relapses is longer on risperidone than on haloperidol . The length of study, the choice of (conventional) comparator (and potential concomitant treatments), patient selection, dosing used, outcome measures used, and the trial setup may explain this variation in the evidence [5,74,76].
The version of the model used for the current article was an upgrade of an earlier one [11–13]. This version of the model is suitable to carry out second-order Monte Carlo simulation and therefore has the capacity to investigate uncertainty across populations rather than across individuals. This is important because reimbursement decisions have to be taken on the basis of uncertainty around incremental cost-effectiveness ratios at the population level. NICE guidelines  indicate that such a probabilistic analysis is mandatory for all applications to be evaluated by NICE.
As much as was feasible, data from the literature have been used in the model calculations. Nevertheless, because of the lack of long-term data in schizophrenia, it is still unavoidable to make a few assumptions which have been substantiated by expert opinion. Therefore, predicted costs per patient, treatment location, and disease progression were compared with published sources. The modeled PANSS scores over time reflected the observations from Mahmoud et al.  and from the ongoing University Medical Center Utrecht cohort study at model entry and after 1 and 5 years, respectively. The average patient in the model was in relapse for 27.7 months over 5 years, which was 46% of the modeled 5 years. This is in line with the findings from Mason et al.  and Lenoir et al. . Compared to that of Knapp et al., the probability in the model to be treated in a certain treatment setting appeared to be modeled quite conservatively with the average patient spending approximately 78% of time in the community (of which 15% in intense community care), 14% in a staffed hostel, and 8% in hospital (in the conventional treatment arm) . The shift from hospital to community care reflects efforts that have been made since Knapp's publication to reduce the number of long-term psychiatric inpatients. Additionally, similar to Guest and Cookson, the total annual (undiscounted) direct care costs in the model were estimated at approximately £10,000 per patient . The model predicted that time on first-line treatment was about 15% longer for atypicals than for conventionals. These differences were also found in a large observational study , but not in CATIE .
Finally, with respect to the variables differing between the conventionals and the atypicals, the individual drugs within the group of atypicals and within the group of conventionals were assumed to have an identical effect. This was necessary because relevant comparative studies between the different atypicals were not available. A number of sources suggest that some differences do exist between individual drugs. Tiihonen et al., for example, found significant differences in the incidence of relapses between different atypical and conventional drugs in an observational study, even when corrected for patient characteristics . Further research into any efficacy differences that might exist between individual drugs is needed to test this assumption, which can then be incorporated into the model together with more detailed drug-specific cost-effectiveness estimates.