Screening for colorectal cancer (CRC) reduces the chances of dying from CRC, yet screening rates are low, particularly in low income and racial/ethnic minority individuals, resulting in higher death rates in these groups. Compounding this inequity, patients at risk for the worst CRC outcomes are less likely to participate in research focused on improving early detection methods and “precision” prevention (i.e., development of new biological and genetic markers for CRC and its precursors in blood and stool samples).

This Team will bring together leading researchers, patient advocates, community leaders, and clinicians to compare the effectiveness and feasibility of two approaches to improve CRC screening in safety net settings and to collect data that can be used to develop new early detection methods. Each person selected to join this Dream Team brings a deep understanding of racial/ethnic minority communities and health equity regionally and nationally.

The Team will establish three SU2C Zones – Greater Boston, Los Angeles, and Tribal Nations in South Dakota- focused on critical steps necessary to reduce the burden of CRC, paving the way for national efforts to effectively address screening barriers for racial/ethnic minority communities across the country.

The Team will also design and deploy a community-based campaign to increase CRC screening rates in a demographically diverse impact zone within Los Angeles County, CA. They will recruit and deploy community health action teams (CHATs) – residents trained and supported to work as health promoters and care navigators within their own neighborhoods – to implement a locally designed and operated CRC screening campaign and, when necessary, accompany patients along the journey from screening to treatment. In parallel with the intervention, CHATs will also partner with the Dream Team on a longitudinal, integrated mixed methods research study to systematically test the effectiveness of the CHAT approach and generate learnings that can inform implementation of the model in other settings and contexts.