Exploring Drivers of "Major Global Uncertainty" in Economic Opinions by the HAS

OBJECTIVES: This study aims to review recent CEESP (Commission for Economic Evaluation and Public Health) appraisals to identify the main factors and arguments leading to the characterization of major global uncertainty in economic opinions. Understanding these factors is crucial as major global uncertainty impacts pricing conditions in France.

METHODS: We reviewed all CEESP economic opinions released since the publication of the CEESP doctrine in July 2021. We identified opinions concluding with major global uncertainty and extracted the justifications for this conclusion, along with the types and grading of methodological reservations.

RESULTS: Out of 64 economic opinions reviewed, 12 (19%) concluded with major global uncertainty. These included 4 opinions related to COVID-19 indications, 3 to oncology, 1 to obesity, and 3 to rare diseases (immunology, hematology, and genetic disorders). Of these, 7 manufacturers (58%) unsuccessfully challenged the characterization during the contradictory phase. Opinions with major global uncertainty did not differ from others in the number of major reservations but had a higher number of important reservations (7.3 on average compared to 4.5). These reservations were most frequently related to modelling aspects of the cost-effectiveness analysis (3.55 important reservations on average). For non-COVID-19 indications, key arguments included long-term treatment effect hypotheses (88%), immaturity of clinical data and unjustified modelling assumptions (75% each), and quality of life data sources and assumptions(63%), often not collected in clinical trials. In 88% of opinions, the absence or insufficiency of sensitivity analyses to balance sources of uncertainty was noted. For COVID-19 related opinions, the rapid evolution of epidemiology and disease characteristics were highlighted.

CONCLUSIONS: The conclusion of major global uncertainty is often linked to data scarcity. Conducting more comprehensive sensitivity analyses could better quantify uncertainty and address CEESP requirements more effectively.