Mapping the ADDQoL to the EQ-5D-5L and SF-6Dv2 among Chinese Patients with Type 2 Diabetes Mellitus
Author(s)
Haoran Fang, BSc1, Tianqi Hong, PhD2, Xinran Liu, MS1, Chang Luo, MSc1, Yuanyuan Hou, MS1, Shitong Xie, PhD1.
1Tianjin University, Tianjin, China, 2McMaster University, Hamilton, ON, Canada.
1Tianjin University, Tianjin, China, 2McMaster University, Hamilton, ON, Canada.
Presentation Documents
OBJECTIVES: The Audit of Diabetes-Dependent Quality of Life (ADDQoL) is a widely used instrument for assessing quality of life in Type 2 Diabetes Mellitus (T2DM). However, it does not directly yield health utility values essential for economic evaluations. This study developed mapping algorithms to predict EQ-5D-5L and SF-6Dv2 utility values from ADDQoL scores in T2DM patients in China.
METHODS: Cross-sectional data from 800 T2DM patients in China, stratified by age, sex, and geographical region, were divided into development (80%) and validation (20%) groups. Pearson correlation analyses were conducted to assess the conceptual overlap between ADDQoL and the EQ-5D-5L and SF-6Dv2. Six predictor sets and six regression methods were explored to map ADDQoL scores to EQ-5D-5L and SF-6Dv2 utility values, respectively. Model performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), and intraclass correlation coefficient (ICC).
RESULTS: For the development group, the mean (SD) ADDQoL Average Weighted Impact (AWI) score was -2.426 (1.052), and the mean (SD) utility values for EQ-5D-5L and SF-6Dv2 were 0.928 (0.092) and 0.791 (0.133), respectively. Among all 36 alternative mapping models each for EQ-5D-5L and SF-6Dv2, the best performance was consistently observed in the two-part models that included the ADDQoL AWI, the first overview item, and their squared terms. For the algorithm mapping to EQ-5D-5L utility values, it achieved a MAE of 0.067, a RMSE of 0.095, and an ICC of 0.414; For the algorithm mapping to SF-6Dv2 utility values, the corresponding metrics were an MAE of 0.099, an RMSE of 0.120, and an ICC of 0.517.
CONCLUSIONS: This study provides a mapping framework to estimate EQ-5D-5L and SF-6Dv2 utility values from ADDQoL scores. These algorithms could be used to support economic evaluations, specifically tailored for Chinese T2DM populations.
METHODS: Cross-sectional data from 800 T2DM patients in China, stratified by age, sex, and geographical region, were divided into development (80%) and validation (20%) groups. Pearson correlation analyses were conducted to assess the conceptual overlap between ADDQoL and the EQ-5D-5L and SF-6Dv2. Six predictor sets and six regression methods were explored to map ADDQoL scores to EQ-5D-5L and SF-6Dv2 utility values, respectively. Model performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), and intraclass correlation coefficient (ICC).
RESULTS: For the development group, the mean (SD) ADDQoL Average Weighted Impact (AWI) score was -2.426 (1.052), and the mean (SD) utility values for EQ-5D-5L and SF-6Dv2 were 0.928 (0.092) and 0.791 (0.133), respectively. Among all 36 alternative mapping models each for EQ-5D-5L and SF-6Dv2, the best performance was consistently observed in the two-part models that included the ADDQoL AWI, the first overview item, and their squared terms. For the algorithm mapping to EQ-5D-5L utility values, it achieved a MAE of 0.067, a RMSE of 0.095, and an ICC of 0.414; For the algorithm mapping to SF-6Dv2 utility values, the corresponding metrics were an MAE of 0.099, an RMSE of 0.120, and an ICC of 0.517.
CONCLUSIONS: This study provides a mapping framework to estimate EQ-5D-5L and SF-6Dv2 utility values from ADDQoL scores. These algorithms could be used to support economic evaluations, specifically tailored for Chinese T2DM populations.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
PCR252
Topic
Patient-Centered Research
Topic Subcategory
Health State Utilities, Patient-reported Outcomes & Quality of Life Outcomes
Disease
No Additional Disease & Conditions/Specialized Treatment Areas, SDC: Diabetes/Endocrine/Metabolic Disorders (including obesity)