{Reference Type}: Journal Article
{Title}: Societal Preferences in Health Technology Assessments for Rare Diseases and Orphan Drugs: A Systematic Literature Review of New Analytic Approaches.
{Author}: Vásquez P;Hall L;Merlo G;
{Journal}: Value Health Reg Issues
{Volume}: 44
{Issue}: 0
{Year}: 2024 Jul 25
暂无{DOI}: 10.1016/j.vhri.2024.101026
{Abstract}: <B>OBJECTIVE: </B>This systematic literature review aimed to explore experiences worldwide of societal preferences integration into health technology assessments (HTAs) for rare diseases (RDs) and orphan drugs (ODs) through the implementation of multicriteria decision analysis (MCDA), discrete choice experiments (DCEs), and person trade-off (PTO) methods, among others.<BR><B>METHODS: </B>A systematic search of the literature was conducted in April 2021 using PubMed, Cochrane, Embase, and Scopus databases. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach was used for the review phases. Finally, the Promoting Action on Research Implementation in Health Services framework was used to discuss the implementation of these instruments in the RD context.<BR><B>RESULTS: </B>A total of 33 articles met the inclusion criteria. The studies measured societal preferences for RD and OD as part of HTA using MCDA (n = 17), DCE (n = 8), and PTO (n = 4), among other methods (n = 4). These found that patients and clinicians do not prioritize funding based on rarity. The public is willing to allocate funds only if the OD demonstrates effectiveness and improves the quality of life, considering as relevant factors disease severity, unmet health needs, and quality of life. Conversely, HTA agency experts preferred their current approach, placing more weight on cost-effectiveness and evidence quality, even though they expressed concern about the fairness of the drug review process.<BR><B>CONCLUSIONS: </B>MCDA, PTO, and DCE are helpful and transparent methods for assessing societal preferences in HTA for RD and OD. However, their methodological limitations, such as arbitrary criteria selection, subjective scoring methods, framing effects, weighting adaptation, and value measurement models, could make implementation challenging.