A decision model to compare health care costs of olanzapine and risperidone treatment for schizophrenia in Germany

Abstract

Second-generation atypical antipsychotics such as clozapine, olanzapine, risperidone, quetiapine, ziprasidone, amisulpride and ariprazole offer the potential to reduce the significant health care resource demands in the treatment of schizophrenia through improved levels of initial clinical response and reduced levels of long-term acute relapse. However, the optimal sequencing of these drugs remains unclear. To consider this issue from a health economic viewpoint a decision model approach was used comparing healthcare costs and clinical outcomes when treating patients with alternative sequences of atypical antipsychotic treatment. Treated patients were assumed to be in a current acute episode with at least a 10-year history of disease and to be naive to previous atypical treatments. Treatment strategies were based on either first-line olanzapine or risperidone with switching to the alternative drug as second-line treatment following an inadequate clinical response to first-line drug therapy. Clinical response data were derived from a pivotal published comparative study of both olanzapine and risperidone. Published data on the long-term use of antipsychotic drugs where used wherever possible to populate the model for relapse rates during the maintenance phase. Health care resource data were defined for Germany based on expert clinical opinion. A treatment strategy of first-line olanzapine was shown to be cost saving over a 1-year period, with additional clinical benefits in the form of avoided relapses. The model suggests that over the first year of treatment a strategy of first-line olanzapine is associated with lower risk of additional relapse (0.33 fewer acute relapses per 100 patients per year) and with cost savings (euro 35,306 per 100 patients per year). There is a need for longer term direct in-trial comparisons of atypical antipsychotics to confirm these indicative results.

Cost of treatment of schizophrenia in six European countries

“…The appropriateness of this approach is an issue of recent controversial debate (Hansen et al, 2006;Beard et al, 2006;Barbui & Lintas;2006). Follow-up studies assessing and analysing psychopharmacologic drug consumption and the associated costs from a naturalistic perspective on a patient level are still scarce.…”

The Cost of Relapse in Schizophrenia

“…The literature on economic evaluations yielded 8 de novo estimates of the cost of relapse derived either from publicly available data or expert opinion ranging from $1,895 to $48,847 (2015 PPP$) (table 3) [69][70][71][72][73][74][75][76]. Costs of hospital stay were predominantly estimated from national administrative data [70,71,74,75] or expert opinion [72,73,76] on LOS, in combination with daily reimbursement rates. Assumptions on the total inpatient stay varied from 11d [75] to 111d [70] for admitted patients.…”

“…The latter estimate was derived from UK National Health Service administrative data (Hospital Episode Statistics). With the exception of two studies [73,75] inpatient costs were supplemented with estimates of additional resource use generated predominantly from expert opinion. Two studies assumed further day hospital stay following discharge of 8d [72] and 23d [69].…”

“…Two studies included accommodation costs following discharge from hospital [69,76], one of which generated the largest overall cost estimate [69]. Two publications distinguished costs according to whether or not relapse required hospitalisation [72,74], and one study distinguished costs according to whether relapse occurred during first or second line treatment [73].The time period over which costs were estimated varied from 67 days to 1 year, although the selection of 3 months or 1 year was common. Longer periods were not necessarily associated with higher costs.…”

Evaluating Health Care using System Dynamics Modelling – a Case Study in Schizophrenia

“…In the field of health outcomes research, complex, chronic, long term diseases are most commonly evaluated through bottom up modelling approaches allowing to include a variety of data sources and to outline longer term effects that may not be directly covered in the input data. Frequently, models are based on decision trees and, depending on the nature of the disease, Markov models may be used to simulate disease processes and their outcomes [1]. Alternative approaches such as Discrete Event Simulation (DES) are becoming more and more popular [2,3].…”