Presentation (CTI)

OBJECTIVES: Gene and cell therapies (GCTs) offer the potential to cure diseases that are currently often managed through lifelong, costly treatment. Robust quantification of the societal disease burden that could be offset by these therapies is essential to inform value-based discussion about the potential and limitations of the GCT landscape. The aim of this study is to quantify the costs of GCT-addressable indications across three disease groups in Germany.
METHODS: A three-stage, prevalence-based burden-of-illness (BOI) model was developed from a societal perspective. Stage 1: Full BOI analyses were conducted for one representative use-case indication per predefined disease group: Haemophilia A (rare diseases), Type 1 Diabetes Mellitus (high-prevalence diseases), and Non-Small Cell Lung Cancer (oncology). Direct, indirect and intangible costs were adapted from available literature and converted to the German context. Stage 2: Per-patient costs were extrapolated to the other indications within each group, using a utility-inverse ratio method. Three scenarios captured parameter uncertainty. Stage 3: Extrapolated per-patient costs were multiplied by German prevalence estimates and aggregated to group and total level.
RESULTS: Utility-based extrapolation produced robust base-case estimates exhibiting moderate scenario-driven variability. Prevalence-weighted aggregation of cases across all indications yielded a total annual societal burden in the high double- to triple-digit billion-euro range for Germany. Rare diseases were found to carry the highest per-patient costs; while high-prevalence diseases contributed the largest aggregate burden. Oncology and high-prevalence diseases offer better data availability and within-group comparability than rare diseases.
CONCLUSIONS: This study provides the first systematic, model-based estimate of the aggregate societal economic burden of GCT-relevant diseases in Germany. The estimated costs are of sufficient magnitude to support the case for value-based assessment of curative GCTs. Although broadly transferable, the method can only provide an interim solution to address the discrepancy between the current data availability and the necessary data infrastructure for robust policy research.