Plain Language Summary

This systematic literature review examines how different modeling methods are used to evaluate the cost-effectiveness of colorectal cancer screening programs. Understanding these methods is important as colorectal cancer is the third most diagnosed cancer worldwide, and effective screening can significantly reduce its incidence and mortality. The review highlights that modeling methods and assumptions greatly influence the outcomes of cost-effectiveness analyses, which are crucial for healthcare decision making.

The review analyzed 78 studies, identifying 52 unique models used in cost-effectiveness analysis for colorectal cancer screening. The most common included Markov and microsimulation models, with many studies utilizing previously published models, particularly the MISCAN-Colon model. This reliance on established models is beneficial as they are often validated and calibrated with real-world data, leading to more reliable results for healthcare policy makers.

Key findings emphasize that the natural history of colorectal cancer, including its progression from polyps to cancer, should be thoroughly represented in models. This is essential for accurately assessing the healthcare resources required for screening and treatment. Additionally, the review calls for clearer descriptions of modeling assumptions to enhance transparency and reproducibility in research.

The review underscores the need for effective screening programs that utilize reliable models to ensure the best health outcomes. Healthcare decision makers are encouraged to support the development and use of models that incorporate detailed disease progression and country-specific data. Lastly, researchers are urged to improve the quality of reporting in cost-effectiveness analyses by providing comprehensive details about the methods and assumptions used.

In conclusion, advancing the methodologies of health economic evaluations can lead to better-informed decisions regarding colorectal cancer screening programs, ultimately improving patient outcomes and optimizing healthcare resources. Open-source modeling practices are recommended to foster collaboration and enhance the replicability of research findings.

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