Using Collaborative Simulation Modeling to Develop a Web-Based Tool to Support Policy-Level Decision Making About Breast Cancer Screening Initiation Age

Abstract

Background: There are no publicly available tools designed specifically to assist policy makers to make informed decisions about the optimal ages of breast cancer screening initiation for different populations of US women. Objective: To use three established simulation models to develop a web-based tool called Mammo OUTPuT. Methods: The simulation models use the 1970 US birth cohort and common parameters for incidence, digital screening performance, and treatment effects. Outcomes include breast cancers diagnosed, breast cancer deaths averted, breast cancer mortality reduction, false-positive mammograms, benign biopsies, and overdiagnosis. The Mammo OUTPuT tool displays these outcomes for combinations of age at screening initiation (every year from 40 to 49), annual versus biennial interval, lifetime versus 10-year horizon, and breast density, compared to waiting to start biennial screening at age 50 and continuing to 74. The tool was piloted by decision makers (n = 16) who completed surveys. Results: The tool demonstrates that benefits in the 40s increase linearly with earlier initiation age, without a specific threshold age. Likewise, the harms of screening increase monotonically with earlier ages of initiation in the 40s. The tool also shows users how the balance of benefits and harms varies with breast density. Surveys revealed that 100% of users (16/16) liked the appearance of the site; 94% (15/16) found the tool helpful; and 94% (15/16) would recommend the tool to a colleague. Conclusions: This tool synthesizes a representative subset of the most current CISNET (Cancer Intervention and Surveillance Modeling Network) simulation model outcomes to provide policy makers with quantitative data on the benefits and harms of screening women in the 40s. Ultimate decisions will depend on program goals, the population served, and informed judgments about the weight of benefits and harms.