Pei Wang1,2, Gordon G Liu3, Min-Woo Jo4, Frederick Dermawan Purba5,6, Zhihao Yang5, Mihir Gandhi7,8,9, Juntana Pattanaphesaj10, Jeonghoon Ahn11, Eliza Lai-Yi Wong12, Arsul A Shafie13, Jan Jv Busschbach5, Nan Luo14. 1. a School of Public Health, Fudan University, Shanghai, China. 2. b Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China (Fudan University), Shanghai, China. 3. c National School of Development, Peking University, Beijing, China. 4. d Department of Preventive Medicine, University of Ulsan College of Medicine, Seoul, Korea. 5. e Medical Psychology, Erasmus MC, Rotterdam, Netherlands. 6. f Faculty of Psychology, Universitas Padjadjaran, Jatinangor, Indonesia. 7. g Biostatistics, Singapore Clinical Research Institute, Singapore, Singapore. 8. h Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore. 9. i Center for Child Health Research, University of Tampere, Tampere, Finland. 10. j Health Intervention and Technology Assessment Program (HITAP), Bangkok, Thailand. 11. k Department of Health Convergence, Ewha Womans University, Seoul, Korea. 12. l JC School of Public Health & Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China. 13. m Discipline of Social & Administrative Pharmacy, Universiti Sains Malaysia, Penang, Malaysia. 14. n Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Abstract
Objectives: To compare the time trade-off (TTO) utility values of EQ-5D-5L health states elicited from different general populations in Asia. Methods: We analyzed the TTO data from seven Asian EQ-5D-5L valuation studies in which utility values of 86 EQ-5D-5L health states were elicited from general population samples. An eight-parameter multiplicative regression model including five dimension parameters (mobility [MO], self-care, usual activities [UA], pain/discomfort, anxiety/depression) and three level parameters (level 2 [L2], level 3 [L3], and level 4 [L4]) was used to model the data from each of the populations. The model coefficients were compared to understand how the valuations of EQ-5D-5L health states differ. Results: For dimension parameters, Korea and Indonesia generally had the highest and lowest values among the populations, respectively; UA and MO commonly had the highest and lowest values among the parameters, respectively. For level parameters, Singapore and Korea generally had the highest and lowest values, respectively; L2 showed less variance compared to L3 and L4. Koreans, Indonesians, and Singaporeans appeared to have different health preferences compared with other populations. Conclusion: Utility values of EQ-5D-5L health states differ among Asian populations, suggesting that each health system should establish and use its own value set.
Objectives: To compare the time trade-off (TTO) utility values of EQ-5D-5L health states elicited from different general populations in Asia. Methods: We analyzed the TTO data from seven Asian EQ-5D-5L valuation studies in which utility values of 86 EQ-5D-5L health states were elicited from general population samples. An eight-parameter multiplicative regression model including five dimension parameters (mobility [MO], self-care, usual activities [UA], pain/discomfort, anxiety/depression) and three level parameters (level 2 [L2], level 3 [L3], and level 4 [L4]) was used to model the data from each of the populations. The model coefficients were compared to understand how the valuations of EQ-5D-5L health states differ. Results: For dimension parameters, Korea and Indonesia generally had the highest and lowest values among the populations, respectively; UA and MO commonly had the highest and lowest values among the parameters, respectively. For level parameters, Singapore and Korea generally had the highest and lowest values, respectively; L2 showed less variance compared to L3 and L4. Koreans, Indonesians, and Singaporeans appeared to have different health preferences compared with other populations. Conclusion: Utility values of EQ-5D-5L health states differ among Asian populations, suggesting that each health system should establish and use its own value set.
Authors: Zhihao Yang; Fredrick Dermawan Purba; Asrul Akmal Shafie; Ataru Igarashi; Eliza Lai-Yi Wong; Hilton Lam; Hoang Van Minh; Hsiang-Wen Lin; Jeonghoon Ahn; Juntana Pattanaphesaj; Min-Woo Jo; Vu Quynh Mai; Jan Busschbach; Nan Luo; Jie Jiang Journal: Qual Life Res Date: 2022-02-18 Impact factor: 3.440