1. MAPPING THE DIABETES HEALTH PROFILE (DHP-18) ONTO THE EQ-5D AND SF-6D GENERIC PREFERENCE BASED MEASURES OF HEALTH Brendan Mulhern 1, Keith Meadows 2, Donna Rowen 1 & John Brazier 1 1 Health Economics and Decision Science, University of Sheffield 2 DHP Research & Consultancy Ltd, London Contact: b.mulhern@sheffield.ac.uk

2. Contents Introduction Measurement of HRQL and cost utility in diabetes Methods Data Mapping techniques Results Mapping functions developed to estimate EQ-5D utility scores Mapping functions developed to estimate SF-6D utility scores Discussion 19/08/2015© The University of Sheffield

3. Introduction Diabetes interventions place a significant burden on health resources Approx 10% of the NHS budget Resource allocation informed by economic evaluation of new treatments Quality Adjusted Life Year (QALY) a recommended measure of outcome (NICE, 2008). Value for ‘quality’ derived using generic preference based measures EQ-5D and SF-6D Generic measures should be included in trials to facilitate economic evaluations but are often not used 19/08/2015© The University of Sheffield

4. Mapping Mapping used to: Predict generic measure utility values from existing condition specific measures of HRQL using regression modelling Mapping is possible when The generic measure performs well in the disease area EQ-5D and SF-6D validated in diabetes populations There is a correlation between the generic and condition specific HRQL measures This study predicts EQ-5D and SF-6D utility values scores using the Diabetes Health Profile-18 (DHP-18) 19/08/2015© The University of Sheffield

5. Generic preference based measures 19/08/2015© The University of Sheffield SF-6D Derived from SF-36/SF-12 6 dimensions physical functioning role limitations social functioning pain mental health vitality Generates 18,000 health states 249 states valued using Standard Gamble Utility score range 0.29 to 1 EQ-5D Recommended by NICE for use in cost utility analysis 5 dimensions with 3 response levels Mobility/self-care daily activities pain/discomfort anxiety/depression Generates 243 (3 5 ) health states Selection of states valued using Time Trade Off Results modelled to produce single figure utility score, range -0.594 to 1

6. Diabetes Health Profile-18 Measures HRQL in diabetes Psychological distress Barriers to activity Disinhibited eating Demonstrates reliability, validity and patient acceptability 26 translations Completed using a range of media Diabetes-specific measure selected for the UK Department of Health PROMs Pilot for Long Term Conditions in Primary Care Visit www.diabeteshealthprofile.com for more information 19/08/2015© The University of Sheffield

7. Mapping specifications 19/08/2015© The University of Sheffield Model type OLS; RE GLS; Tobit; Two part models Model performance indicators: R 2, Walt chi squared Mean absolute error and mean squared error Plots of observed and predicted scores NumberModel specification 1DHP dimension scores 2 DHP dimension scores, DHP dimension scores squared 3 DHP dimension scores, DHP dimension scores squared, DHP dimension score interactions 4 DHP dimension scores, DHP dimension scores squared, DHP dimension score interactions, Age, Gender 5 DHP item scores 6 DHP item scores, DHP item scores squared 7 DHP item scores, DHP item scores squared, Age, Gender

8. Sample UK longitudinal dataset of a community-based postal survey ≥18 years of age Data collected at baseline and 1 year Pooled data used for mapping Type 1 n=286; Type 2 n=2755 19/08/2015© The University of Sheffield CharacteristicType 1Type 2 M (SD)Range M(SD)Range Age59.65 (15.62)18-9366.19 (11.32)26-98 Male38.46%60.87% Diabetes related health complications59.44%38.05% Other health complications78.32%79.79% EQ-5D index (pooled)0.60 (0.37)-0.59 to 10.65 (0.32)-0.43 to 1 SF-6D index (pooled)0.66 (0.16)0.35 to 10.69 (0.16)0.35 to 1 DHP-18 Psychological distress (pooled)28.16 (24.7)18.61 (20.7) DHP-18 Barriers to activity (pooled)35.58 (21.2)21.90 (19.4) DHP-18 Disinhibited eating (pooled)36.43 (23.2)35.78 (22.8)

9. Results: Measure distributions 19/08/2015© The University of Sheffield EQ-5D type 2SF-6D type 2 EQ-5D type 1SF-6D type 1

10. Model specifications: EQ-5D 19/08/2015© The University of Sheffield RE GLS model 7 (DHP-18 items, items squared, age and gender) performed best Actual vs. predicted utility values Type 1 (R 2 : 0.50) Type 2 (R 2 : 0.29) Mean absolute error

11. Model specifications: SF-6D 19/08/2015© The University of Sheffield Actual vs. predicted utility values (type 1)Actual vs. predicted utility values (type 2) RE GLS model 7 (DHP-18 items, items squared, age and gender) performed best Type 1 (R 2 : 0.65) Type 2 (R 2 : 0.40)

12. Discussion (1) Mapping an increasingly popular method for deriving utility scores EQ-5D and SF-6D utility scores can now be estimated for type 1 and type 2 diabetes GLS model 7 (DHP-18 items, squared item scores and demographics) performed best Type 1 predictions perform better than type 2 predictions SF-6D predictions perform better than EQ-5D predictions Over predicts utility for severe states and under predicts utility for mild states 19/08/2015© The University of Sheffield

13. Discussion (2) Using mapped values is second best to the direct inclusion of generic measures Lack external validity if not validated on external sample Using mapped values increases the error of the estimates How can mapping function precision be improved? Analyses using predicted values should consider the precision of the estimates. 19/08/2015© The University of Sheffield

14. Any questions? Mapping algorithms available at: www.diabeteshealthprofile.com 19/08/2015© The University of Sheffield