Predicting the Population Health Economic Impact of Current and New Cancer Treatments for Colorectal Cancer: A Data-Driven Whole Disease Simulation Model for Predicting the Number of Patients with Colorectal Cancer by Stage and Treatment Line in Australia

• Published in: Value in health Vol. 27; no. 10; pp. 1382 - 1392
• Main Authors: Degeling, Koen, To, Yat Hang, Trapani, Karen, Athan, Sophy, Gibbs, Peter, IJzerman, Maarten J., Franchini, Fanny
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2024
• Subjects: Aged; Australia; colorectal cancer; Colorectal Neoplasms - drug therapy; Colorectal Neoplasms - economics; Computer Simulation; Cost-Benefit Analysis; disease projection; Female; health technology assessment; Humans; Internal Medicine; Male; Middle Aged; Models, Economic; Neoplasm Staging; Population Health; Public Health; real-world data; treatment utilization; whole-disease model; Australia; health technology assessment; real-world data; colorectal cancer; disease projection; treatment utilization; whole-disease model
• ISSN: 1098-3015; 1524-4733; 1524-4733
• DOI: 10.1016/j.jval.2024.06.006

Effective healthcare planning, resource allocation, and budgeting require accurate predictions of the number of patients needing treatment at specific cancer stages and treatment lines. The Predicting the Population Health Economic Impact of Current and New Cancer Treatments (PRIMCAT) for Colorectal Cancer (CRC) simulation model (PRIMCAT-CRC) was developed to meet this requirement for all CRC stages and relevant molecular profiles in Australia. Real-world data were used to estimate treatment utilization and time-to-event distributions. This populated a discrete-event simulation, projecting the number of patients receiving treatment across all disease stages and treatment lines for CRC and forecasting the number of patients likely to utilize future treatments. Illustrative analyses were undertaken, estimating treatments across disease stages and treatment lines over a 5-year period (2022-2026). We demonstrated the model’s applicability through a case study introducing pembrolizumab as a first-line treatment for mismatch-repair-deficient stage IV. Clinical registry data from 7163 patients informed the model. The model forecasts 15 738 incident and 2821 prevalent cases requiring treatment in 2022, rising to 15 921 and 2871, respectively, by 2026. Projections show that over 2022 to 2026, there will be a total of 116 752 treatments initiated, with 43% intended for stage IV disease. The introduction of pembrolizumab is projected for 706 patients annually, totaling 3530 individuals starting treatment with pembrolizumab over the forecasted period, without significantly altering downstream utilization of subsequent treatments. PRIMCAT-CRC is a versatile tool that can be used to estimate the eligible patient populations for novel cancer therapies, thereby reducing uncertainty for policymakers in decisions to publicly reimburse new treatments. •Accurate forecasting of patient numbers at specific cancer stages and treatment lines is vital for efficient healthcare planning and resource distribution. However, current predictions are typically limited to overall and stage-specific incidence forecasting. To ensure effective healthcare planning, novel tools are needed for Australia’s colorectal cancer (CRC) landscape.•The Predicting the Population Health Economic Impact of Current and New Cancer Treatments-CRC model, grounded in real-world Australian data, offers unparalleled granularity in predicting CRC patient counts segmented by disease stage, cancer type, and treatment utilization. The model’s ability to simulate the introduction of new treatments, exemplified through the pembrolizumab case study, demonstrates its adaptability and relevance for both present and future healthcare scenarios.•The Predicting the Population Health Economic Impact of Current and New Cancer Treatments-CRC model sheds light on Australia’s current CRC treatment trajectory. As a dynamic tool, it may assist policymakers in assessing the impact of introducing new cancer treatments by forecasting potential patient populations. Its adaptability ensures proactive healthcare planning, for various interventions throughout the patient pathway.