Objectives: Escitalopram is the S-enantiomer of citalopram and is the most discriminating of the selective serotonin reuptake inhibitors (SSRI). The aim of the current analysis was to assess the cost effectiveness of escitalopram versus the serotonin norepinephrine reuptake inhibitors (SNRI) duloxetine and generic venlafaxine as second-step treatment of major depressive disorder.
Methods: The analysis was based on a decision analytic model. Effectiveness outcomes were quality-adjusted life-years (QALYs) and remission rates; cost outcomes were direct medical costs, including impact of treating adverse events, and indirect costs associated with lost productivity. The analysis was performed from the societal perspective in Sweden over a 6-month timeframe.
Results: Estimated remission rates showed an incremental effectiveness in favour of escitalopram of 16.4 percentage points compared with both SNRI comparators. The escitalopram strategy was associated with a 0.025 increase in QALYs. Sensitivity analyses demonstrated that the model is robust and that escitalopram remains a cost-effective option when considering future predicted price reductions of generic venlafaxine.
Limitations: The main limitation in this study was the lack of data available for second-step treatment. The remission rates, which are a key input to the model, were obtained from a relatively small sample of patients on second-step treatment and there are no published relapse rates for second-step treatment. The model also assumed that there was no difference in the adverse event (AE) rates between treatments after the first 8 weeks.
Conclusions: This cost-effectiveness analysis indicates that, at a willingness-to-pay threshold of £30,000, escitalopram is the most cost-effective second-step treatment option for MDD in Sweden in over 85% cases compared with both venlafaxine and with duloxetine. Benefits for escitalopram included both increased effectiveness and reduced overall costs. The major contributing costs were those associated with productivity loss. The model was shown to have internal validity and robustness through the use of stochastic simulations and sensitivity analyses, which were conducted around the key efficacy parameters.