F-5 THE USE OF MAPPING IN ESTIMATES OF COST-EFFECTIVENESS: HELPFUL OR HARMFUL?

Monday, October 25, 2010: 5:30 PM
Grand Ballroom West (Sheraton Centre Toronto Hotel)
Paul McNamee1, Zahid Quayyum1, Corri Black1, Christine Clar1, Rob Henderson2, Campbell Maceachran3, Pam Royle1 and Sian Thomas1, (1)University of Aberdeen, Aberdeen, United Kingdom, (2)NHS Highland, Inverness, United Kingdom, (3)Raigmore Hospital, Inverness, United Kingdom

Purpose: Mapping is a widely used method to convert scores from condition-specific instruments to utility values.  There is limited knowledge of the extent to which use of different generic preference-based measures for mapping gives rise to differences in estimates of cost-effectiveness.  We compared estimates of cost-effectiveness derived from two different generic preference-based measures for a proposed therapy in the management of knee osteoarthritis.

Method: An economic model was used to estimate cost-effectiveness of glucosamine sulphate therapy for knee osteoarthritis, using evidence from a systematic review of randomized controlled trials of clinical effectiveness, together with data from observational studies.  The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was the condition-specific instrument used in clinical effectiveness trials, and scores were converted to utility weights using published mapping algorithms for the Health Utilities Index Mark 3 (HUI3) and the EQ-5D measures.  The mapped values were then used to estimate quality of life gains associated with therapy, and to predict cost-effectiveness relative to current care.

Result: Using the HUI3 mapping algorithm, trials showed that therapy was associated with an annual quality of life gain of 0.005.  Taking a lifetime horizon, the incremental cost per quality adjusted life year (QALY) gain for adding glucosamine sulphate to current care was approximately £21,000.  At a cost per QALY gain threshold of £20,000, the likelihood that glucosamine sulphate was more cost-effective than current care was 0.43, whilst at a threshold of £30,000, the probability rose to 0.73. Using the EQ-5D mapping algorithm however, therapy was associated with an annual quality of life loss of 0.006.   In this situation, current care was predicted to be more cost-effective than therapy at standard cost-effectiveness thresholds.

Conclusion: Cost-effectiveness estimates were highly sensitive to the choice of generic-based measure used in mapping.  This suggests that future studies should be encouraged to use generic-preference based instruments in the first instance rather than rely on mapping as a means to estimate cost-effectiveness.