Presentation (CTI)

OBJECTIVES: Epidemiology models for non-muscle invasive bladder cancer (NMIBC) remain limited, particularly those capable of projecting clinically relevant patient subgroups aligned with treatment pathways and emerging therapeutic targets, with few transparent, reproducible projections available. Robust epidemiological forecasts are needed to inform healthcare resource planning and support economic evaluation. This study describes an epidemiological model framework to estimate the burden of intermediate-risk (IR) and high-risk (HR) NMIBC in Europe.
METHODS: A cohort-based epidemiological model was developed using uniformly analyzed real-world datasets from multiple countries collected as part of a retrospective, observational cohort study, complemented by literature inputs. The model incorporates estimates for disease epidemiology, treatment distribution, time to next treatment, and survival. An epidemiological funnel approach was applied to derive IR and HR NMIBC populations and clinically relevant subgroups (e.g., tumor type), stratified by BCG exposure, FGFR status, and recurrence patterns. The model includes monthly cycles to forecast patient counts and incorporates subgroup-specific treatment pathways across up to three lines of therapy.
RESULTS: The framework generated country-specific estimates of IR- and HR-NMIBC populations across clinically relevant subgroups defined by BCG exposure, BCG response category, recurrence timing, FGFR status. These estimates were linked to treatment-specific pathways to project treatment-eligible populations, treatment utilization, and patient transitions across up to three lines of therapy. Over a 5-year horizon, the model quantified recurrence trajectories and future patient burden within clinically relevant populations, enabling evaluation of disease burden under alternative country-specific epidemiological and treatment scenarios.
CONCLUSIONS: This study presents a transparent, data-driven epidemiological modelling framework translating real-world data into clinically meaningful NMIBC population estimates. By capturing key patient segments aligned with treatment pathways and clinically relevant subgroups, the model provides a robust foundation for modelling disease burden and informing healthcare decision-making, including resource allocation and economic evaluation across Europe.