OBJECTIVES: Prevalence of the clinical stages of bladder cancer is not well characterized in Canada, particularly for locally advanced/metastatic urothelial carcinoma (la/mUC). This knowledge gap contributes to uncertainty of the population-level burden of disease associated with la/mUC. Our objective was to estimate the annual prevalence of patients with la/mUC who undergo treatment in Canada, which has important implications for decision-makers and providers.

METHODS: A dynamic simulation model was developed combining incidence, treatment patterns, and clinical efficacy to predict the flow of UC patients from initial diagnosis (any stage disease) through death. Cohorts were continuously introduced to the model every monthly cycle based on the reported incidence (12,200 cases/year) of newly-diagnosed bladder cancer from the 2020 Canadian Cancer Statistics (Canadian Cancer Society). To populate the model, the treatment pattern inputs were varied over time based on the evolving nature of the treatment landscape. For each monthly cycle of the model, those with disease progression transitioned to the next line of therapy or discontinued, informed by real-world treatment utilization and treatment-specific clinical trial data for progression and survival. From this, we estimated annual treated prevalence for 2021, defined as the proportion of the prevalent population receiving treatment consistent with their disease stage.

RESULTS: The model estimated the overall annual prevalence of the 2021 treated la/mUC population in Canada at 3,248. Annual treated prevalence was estimated at 2,296 for first-line la/mUC. Among those patients treated first-line, 1,376 were predicted to survive and progress to receive second-line treatment. Among the prevalent patients treated second-line, 482 were estimated to survive and progress to receive third-line treatment.

CONCLUSIONS: The majority of patients with la/mUC do not receive second- and third-line treatment in Canada, demonstrating the clinical need for new, effective therapies. Simulation modeling serves as a useful tool to generate population-level estimates of country-specific disease burden.