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PP149 Features Of Accountable And Reasonable Processes For Coverage Decision-Making
- Monika Wagner, Dima Samaha, Roman Casciano, Matthew Brougham, Charles Petrie, Payam Abrishami, Bernard Avouac, Paul Kind, Lorenzo Giovanni Mantovani, Antonio Sarria-Santamera, Michael Schlander, Michele Tringali
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- Journal:
- International Journal of Technology Assessment in Health Care / Volume 34 / Issue S1 / 2018
- Published online by Cambridge University Press:
- 03 January 2019, p. 125
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Introduction:
The Accountability for Reasonableness (A4R) framework addresses the legitimacy of coverage decision processes by defining four conditions for accountable and reasonable processes: Relevance, Publicity, Appeals, Implementation. Cost-per-quality-adjusted life year (QALY) and multicriteria-centered processes may have distinct implications for meeting A4R conditions. The aim of this study was to reflect on how the diverse features of decision-making processes can be aligned with A4R conditions to guide legitimized decision-making. Rare disease and regenerative therapies (RDRTs) pose special decision-making challenges and offer a useful case study.
Methods:To support reflection on how different approaches address the A4R conditions, thirty-four features operationalizing each condition were defined and organized into a matrix. Seven experts from six countries explored and discussed these features during a panel (Chatham House Rule) and provided general and RDRT-specific recommendations for each feature. Responses were analyzed to identify converging and diverging recommendations.
Results:Regarding Relevance, panelists highlighted the importance of supporting deliberation, stakeholder participation and grounding coverage decision criteria in the legal framework, goals of sustainable healthcare and population values. Among seventeen criteria, thirteen were recommended by more than half of panelists. Although the cost-effectiveness ratio was deemed sometimes useful, the validity of universal thresholds to inform allocative efficiency was challenged. Regarding Publicity, panelists recommended communicating the values underlying a decision in reference to broader societal objectives, and being transparent about value judgements in selecting evidence. For Appeals, recommendations included clear definition of new evidence and revision rules. For Implementation, one recommendation was to perform external quality reviews of decisions. While RDRTs raise issues that may warrant special consideration, rarity should be considered in interaction with other aspects (e.g. disease severity, age, budget impact).
Conclusions:Improving coverage decision-making towards accountability and reasonableness involves supporting participation and deliberation, enhancing transparency, and more explicit consideration of multiple decision criteria that reflect normative and societal objectives.
VP100 Disease Modelling Approaches In Multiple Sclerosis
- Paolo Cortesi, Nilhan Uzman, Matteo Ferrario, Lorenzo Giovanni Mantovani
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- Journal:
- International Journal of Technology Assessment in Health Care / Volume 33 / Issue S1 / 2017
- Published online by Cambridge University Press:
- 12 January 2018, pp. 194-195
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INTRODUCTION:
In the past decades the cost-effectiveness of new effective disease-modifying therapies (DMTs) for Relapsing Remitting Multiple Sclerosis (RRMS) form was assessed through decision analytical models. Recently, new treatment option for the Primary Progressive (PPMS) form was developed. Aim of this work was assessing the similarities and differences of PPMS and RRMS and their impact in the development of decision analytical model for PPMS.
METHODS:Literature review was performed to retrieve information on natural history of PPMS and RRMS and impact of DMTs agents on the progression of these conditions. Further, a review of the published cost-effectiveness models for RRMS was performed. Based on these data, an analysis on the difference and similarities between the two MS forms that could have an impact on the development of decision analytical model for PPMS was performed.
RESULTS:Based on the analysis, similar structure model used for RRMS could be applied for PPMS. Health states of the model could be based on Expanded Disability Status Scale score as already done for RRMS. The relapse events considered for RRMS should not be included in PPMS model, and no possibility to develop another form, as the Secondary Progressive, should be included. While RRMS models should include at least a second line treatment option due to alternative DMTs available, only first treatment line should be considered for PPMS. Assessing data available to populate the model, poor data on the natural history, utility and cost associated to PPMS were available and assumption or expert opinions will be needed to overcome the lack of robust data.
CONCLUSIONS:A decision analytical model for PPMS can use a similar structure used in the models for RRMS. However, more robust data on PPMS and some structural change are needed to provide a good tool to assess cost-effectiveness of DMTS in PPMS.