Estimation of Utilities in Attention-Deficit Hyperactivity Disorder for Economic Evaluations

Andrew Lloyd (), Paul Hodgkins, Rahul Sasane, Ron Akehurst, Edmund Sonuga-Barke, Patrick Fitzgerald, Annabel Nixon, Haim Erder and John Brazier ()

The Patient: Patient-Centered Outcomes Research, 2011, vol. 4, issue 4, 247-257

Abstract: Background: Attempts to estimate the cost effectiveness of attention-deficit hyperactivity disorder (ADHD) treatments in the past have relied on classifying ADHD patients as responders or non-responders to treatment. Responder status has been associated with a small gain in health-related quality of life (HR-QOL) [or utility, as measured using the generic QOL measure EQ-5D] of 0.06 (on a scale from 0 being dead to 1.0 being full health). Objectives: The goal of the present study was to develop and validate several ADHD-related health states, and to estimate utility values measured amongst the general public for those states and to re-estimate utility values associated with responder status. Methods: Detailed qualitative interview data were collected from 20 young ADHD patients to characterize their HR-QOL. In addition, item-by-item clinical and HR-QOL data from a clinical trial were used to define and describe four health states (normal; borderline to mildly ill; moderately to markedly ill; and severely ill). ADHD experts assessed the content validity of the descriptions. The states were rated by 100 members of the UK general public using the time trade-off (TTO) interview and visual analog scale. Statistical mapping was also undertaken to estimate Clinical Global Impression-Improvement (CGI-I) utilities (i.e. response status) from Clinical Global Impression-Severity (CGI-S) defined states. The mapping work estimated changes in utilities from study baseline to last visit for patients with a CGI-I score of ≤2 or ≤3. Results: The validity of the four health states developed in this study was supported by in-depth interviews with ADHD experts and patients, and clinical trial data. TTO-derived utilities for the four health states ranged from 0.839 (CGI-S state ‘normal’) to 0.444 (CGI-S state ‘severely ill’). From the mapping work, the change in utility for treatment responders was 0.19 for patients with a CGI-I score of ≤2 and 0.15 for patients with a CGI-I score of ≤3. Conclusions: The present study provides utilities for different severity levels of ADHD estimated in a TTO study. This approach provides a more granular assessment of the impact of ADHD on HR-QOL than binary approaches employed in previous economic analyses. Change in utility for responders and non-responders at different levels of CGI-I was estimated, and thus these utilities may be used to compare health gains of different ADHD interventions. Copyright Adis Data Information BV 2011

Date: 2011
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DOI: 10.2165/11592150-000000000-00000

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