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Kc S, Lin LW, Bayani DBS, Zemlyanska Y, Adler A, Ahn J, Chan K, Choiphel D, Genuino-Marfori AJ, Kearney B, Liu Y, Nakamura R, Pearce F, Prinja S, Pwu RF, Akmal Shafie A, Sui B, Suwantika A, Tunis S, Wu HM, Zalcberg J, Zhao K, Isaranuwatchai W, Teerawattananon Y, Wee HL. What, Where, and How to Collect Real-World Data and Generate Real-World Evidence to Support Drug Reimbursement Decision-Making in Asia: A reflection Into the Past and A Way Forward. Int J Health Policy Manag 2023; 12:6858. [PMID: 37579427 PMCID: PMC10461954 DOI: 10.34172/ijhpm.2023.6858] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 01/28/2023] [Indexed: 08/16/2023] Open
Abstract
BACKGROUND Globally, there is increasing interest in the use of real-world data (RWD) and real-world evidence (RWE) to inform health technology assessment (HTA) and reimbursement decision-making. Using current practices and case studies shared by eleven health systems in Asia, a non-binding guidance that seeks to align practices for generating and using RWD/RWE for decision-making in Asia was developed by the REAL World Data In ASia for HEalth Technology Assessment in Reimbursement (REALISE) Working Group, addressing a current gap and needs among HTA users and generators. METHODS The guidance document was developed over two face-to-face workshops, in addition to an online survey, a face-to-face interview and pragmatic search of literature. The specific focus was on what, where and how to collect RWD/ RWE. RESULTS All 11 REALISE member jurisdictions participated in the online survey and the first in-person workshop, 10 participated in the second in-person workshop, and 8 participated in the in-depth face-to-face interviews. The guidance document was iteratively reviewed by all working group members and the International Advisory Panel. There was substantial variation in: (a) sources and types of RWD being used in HTA, and (b) the relative importance and prioritization of RWE being used for policy-making. A list of national-level databases and other sources of RWD available in each country was compiled. A list of useful guidance on data collection, quality assurance and study design were also compiled. CONCLUSION The REALISE guidance document serves to align the collection of better quality RWD and generation of reliable RWE to ultimately inform HTA in Asia.
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Affiliation(s)
- Sarin Kc
- Health Intervention and Technology Assessment Program (HITAP), Ministry of Health, Nonthaburi, Thailand
| | - Lydia Wenxin Lin
- Saw Swee Hock School of Public Health, National University of Singapore (NUS), Singapore, Singapore
| | | | - Yaroslava Zemlyanska
- Saw Swee Hock School of Public Health, National University of Singapore (NUS), Singapore, Singapore
| | - Amanda Adler
- The Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, UK
| | | | - Kelvin Chan
- Sunnybrook Odette Cancer Centre, Toronto, ON, Canada
- Sunnybrook Research Institute, Toronto, ON, Canada
- Canadian Centre for Applied Research in Cancer Control, Toronto, ON, Canada
| | - Dechen Choiphel
- Essential Medicine and Technology Division, Department of Medical Services, Ministry of Health, Thimphu, Bhutan
| | | | - Brendon Kearney
- Faculty of Medicine, University of Adelaide, Adelaide, SA, Australia
- Health Policy Advisory Committee on Technology, Brisbane, QLD, Australia
| | - Yuehua Liu
- China Health Technology Assessment Centre, National Health Development Research Centre, Ministry of Health, Beijing, China
| | - Ryota Nakamura
- Hitotsubashi Institute for Advanced Study, Hitotsubashi University, Tokyo, Japan
| | - Fiona Pearce
- Agency for Care Effectiveness, Ministry of Health, Singapore, Singapore
| | - Shankar Prinja
- Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Raoh-Fang Pwu
- Taiwan National Hepatitis C Program Office, Ministry of Health and Welfare, Taipei, Taiwan
| | - Arsul Akmal Shafie
- Discipline of Social and Administrative Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, Malaysia
| | - Binyan Sui
- China Health Technology Assessment Centre, National Health Development Research Centre, Ministry of Health, Beijing, China
| | - Auliya Suwantika
- Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia
| | - Sean Tunis
- Center for Medical Technology Policy (CMTP), Baltimore, MD, USA
| | - Hui-Min Wu
- Taiwan National Hepatitis C Program Office, Ministry of Health and Welfare, Taipei, Taiwan
| | - John Zalcberg
- Cancer Research Program, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- Department of Medical Oncology, Alfred Hospital, Melbourne, VIC, Australia
| | - Kun Zhao
- China Health Technology Assessment Centre, National Health Development Research Centre, Ministry of Health, Beijing, China
| | - Wanrudee Isaranuwatchai
- Health Intervention and Technology Assessment Program (HITAP), Ministry of Health, Nonthaburi, Thailand
- Centre for Excellence in Economic Analysis Research, St. Michael’s Hospital, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Yot Teerawattananon
- Health Intervention and Technology Assessment Program (HITAP), Ministry of Health, Nonthaburi, Thailand
- Saw Swee Hock School of Public Health, National University of Singapore (NUS), Singapore, Singapore
| | - Hwee-Lin Wee
- Saw Swee Hock School of Public Health, National University of Singapore (NUS), Singapore, Singapore
- Department of Pharmacy, Faculty of Science, National University of Singapore (NUS), Singapore, Singapore
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Miguel RTD, Rivera AS, Cheng KJG, Rand K, Purba FD, Luo N, Zarsuelo MA, Genuino-Marfori AJ, Florentino-Fariñas I, Guerrero AM, Lam HY. Estimating the EQ-5D-5L value set for the Philippines. Qual Life Res 2022; 31:2763-2774. [PMID: 35532835 PMCID: PMC9356948 DOI: 10.1007/s11136-022-03143-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/05/2022] [Indexed: 11/25/2022]
Abstract
Background The Philippines has recommended the use of Quality-Adjusted Life Years (QALYs) in government health technology assessments (HTA). We aimed to develop a value set for the EQ-5D-5L based on health preferences of the healthy general adult population in the Philippines. Methods Healthy, literate adults were recruited from the Philippine general population with quota targets based on age, sex, administrative region, type of residence, education, income, and ethnolinguistic groups. Each participant’s preference was elicited by completing Composite Time Trade-Off (C-TTO) and Discrete Choice Experiment (DCE) tasks. Tasks were computer-assisted using the EuroQol Valuation Technology 2.0. To estimate the value set, we explored 20- and 8-parameter models that either use c-TTO-only data or both c-TTO and DCE (also called hybrid models). Final model choice was guided by principles of monotonicity, out-of-sample likelihood, model fit, and parsimony. Results We recruited 1000 respondents with demographic characteristics that approximate the general population such as 49.6% Female, 82% Roman Catholic, 40% in urban areas, and 55% finished high school. None of the 20-parameter models demonstrated monotonicity (logical worsening of coefficients with increasing severity). From the 8-parameter models, the homoscedastic TTO-only model exhibited the best fit. From this model, mobility and pain/ discomfort had the highest effect on utilities. Conclusion The selected model for representing the Philippine general population preferences for EQ-5D-5L health states was an 8-parameter homoscedastic TTO-only model. This value set is recommended for use in QALY calculations in support of HTA-informed coverage decisions in the Philippines. Supplementary Information The online version contains supplementary material available at 10.1007/s11136-022-03143-w.
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Affiliation(s)
- Red Thaddeus D Miguel
- Institute of Health Policy and Development Studies, National Institutes of Health, University of the Philippines Manila, Manila, Philippines
| | - Adovich S Rivera
- Institute for Public Health and Management, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - Kent Jason G Cheng
- Social Science Department, Maxwell School of Citizenship and Public Affairs, Syracuse University, Syracuse, NY, USA
| | - Kim Rand
- Health Services Research Centre, Akershus University Hospital, Lorenskog, Norway
| | - Fredrick Dermawan Purba
- Department of Developmental Psychology, Faculty of Psychology, Universitas Padjadjaran, Jatinangor, Indonesia
| | - Nan Luo
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Ma-Ann Zarsuelo
- Institute of Health Policy and Development Studies, National Institutes of Health, University of the Philippines Manila, Manila, Philippines
| | | | | | | | - Hilton Y Lam
- Institute of Health Policy and Development Studies, National Institutes of Health, University of the Philippines Manila, Manila, Philippines
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