Optimizing Initial Screening for Colorectal Cancer Detection with Adherence Behavior
70 Pages Posted: 10 Dec 2021
Date Written: October 28, 2021
Two-stage screening programs are widely adopted for early colorectal cancer (CRC) detection. Individuals receiving positive outcomes in the first-stage (initial) test are recommended to undergo a second-stage test (colonoscopy) for further diagnosis. We study the initial test design—i.e., selecting cutoffs to report test outcomes—to balance the trade-off between screening effectiveness (i.e., cancer detection) and efficiency (i.e., colonoscopy costs), considering that not all individuals adhere to the guidelines to follow up with a colonoscopy after receiving positive outcomes. We integrate the Bayesian persuasion framework with information avoidance to model the problem. We show that under certain conditions, using a single cutoff in the initial test is optimal for follow-up maximization, and a continuous test (i.e., showing exact readings of the biomarker) is optimal for effectiveness maximization. We apply the framework to Singapore's CRC screening design and calibrate the model using various sources of data, including a nationwide survey in Singapore. Our results suggest that compared with the current practice, increasing the cutoff to the level that maximizes expected follow-ups by cancer patients can detect 969 more CRC incidences and prevent 37,820 colonoscopies. The current practice of using lower cutoffs to achieve high sensitivity can backfire and lead to excessive unnecessary colonoscopies and low adherence. Leveraging the interpretable clustering technique, we find that using a lower cutoff for males older than 60 and females older than 70 (high-risk and high-adherence groups) and a higher cutoff for the remaining screening population (low-risk and low-adherence groups) can further improve screening effectiveness and efficiency.
Funding Information: None to declare.
Declaration of Interests: None to declare.
Keywords: Cancer Screening; Cutoff Selection; Adherence; Bayesian Persuasion; Information Avoidance
Suggested Citation: Suggested Citation