JOURNAL ARTICLE
Optimizing Initial Screening for Colorectal Cancer Detection with Adherence Behavior.
Published In: Management Science (INFORMS), 2025, v. 71, n. 9. P. 7516 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Gao, Sarah Yini; He, Yan; Zhang, Ruijie; Zheng, Zhichao; Lam, Shao Wei; Tan, Emile 3 of 3
Abstract
This article focuses on optimizing the fecal immunochemical test (FIT) cutoff values in two-stage colorectal cancer (CRC) screening programs to improve detection effectiveness and manage colonoscopy demand, incorporating individuals' adherence behavior. Using an information design model based on Bayesian persuasion and information avoidance, calibrated with a nationwide survey of 3,920 Singapore residents aged 50 and above, the study finds that the current FIT cutoff in Singapore (20 μg/g) is suboptimal. Raising the cutoff to 39 μg/g in a two-sample FIT scheme could detect 21% more CRC and polyp cases, reduce colonoscopies by 27%, lower lifetime CRC risk by 11%, and save significant healthcare costs. The research also shows that reporting exact fecal-hemoglobin concentrations (continuous FIT) can maximize detection effectiveness, while a single cutoff maximizes follow-up adherence, and that customized cutoffs for subpopulations based on demographic factors further improve outcomes. These findings support adopting quantitative FIT kits with tailored cutoffs to enhance CRC screening programs, though local calibration is necessary for broader application.
Additional Information
- Source:Management Science (INFORMS). 2025/09, Vol. 71, Issue 9, p7516
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2025
- ISSN:0025-1909
- DOI:10.1287/mnsc.2023.01319
- Accession Number:188078601
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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