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A (five-)level playing field for mental health conditions?: exploratory analysis of EQ-5D-5L-derived utility values
E M Camacho1, G Shields2, K Lovell3
1Division of Population Health, Health Services Research, and Primary Care, School of Health Sciences, Centre for Health Economics, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. elizabeth.camacho@manchester.ac.uk.
Purpose:
Economic evaluations of mental health interventions often measure health benefit in terms of utility values derived from the EQ-5D. For the five-level version of the EQ-5D, there are two methods of estimating utility [crosswalk and stated preference (5L-SP)]. This paper explores potential impacts for researchers and decision-makers when comparing utility values derived from either method in the specific context of mental health.
Methods:
Baseline EQ-5D-5L data from three large randomised controlled trials of interventions for mental health conditions were analysed. Utility values were generated using each method. Mean utility values were compared using a series of t tests on pooled data and subgroups. Scenario analyses explored potential impacts on cost-effectiveness decisions.
Results:
EQ-5D data were available for 1399 participants. The mean utility value for each trial was approximately 0.08 higher when estimated using the 5L-SP approach compared to crosswalk (p < 0.0001). The difference was greatest among people reporting extreme anxiety/depression (mean utility 5L-SP 0.309, crosswalk 0.084; difference = 0.225; p < 0.0001). Identical improvements in health status were associated with higher costs to gain one QALY with the 5L-SP approach; this is more pronounced when improvements are across all domains compared to improvements on the anxiety/depression domain only.
Conclusions:
The two approaches produce significantly different utility values in people with mental health conditions. Resulting differences in cost per QALY estimates suggest that thresholds of cost-effectiveness may also need to be reviewed. Researchers and decision-makers should exercise caution when comparing or synthesising data from trials of mental health interventions using different utility estimation approaches.
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