Health and Financial Costs of Unequal Care: Colorectal Cancer as a Case Study - ABSTRACT There is increasing awareness of systematic racism in US health care. Clinical algorithms that include race, which are often incorporated into electronic health systems meant to facilitate guideline care, are a striking example of overt and systemic differences in recommended care. Differences in care and reasons for these differences are often more difficult to identify and address. Colorectal cancer (CRC) care provides a case study. There is ample evidence of racial disparities in every step of the colorectal cancer care continuum, from early detection to treatment, especially for differences between Black patients and White patients. Compared to White people, Black people are less likely to be screened for CRC, less likely to receive endoscopic tests at high-quality facilities, more likely to be diagnosed at a later stage, less likely to receive curative treatment – even after accounting for stage at diagnosis, and have 40% higher mortality and shorter stage-specific survival. These differences in care have both health and economic consequences. In addition to worse outcomes, Black people have higher CRC-attributable treatment costs. We propose to extend and apply CRC-SPIN, an established microsimulation model for CRC, to synthesize the available evidence related to racial and ethnic differences in CRC risk, CRC care, and costs of care. We will extend CRC-SPIN, which currently simulates the overall US population, to simulate the natural history of CRC among specific racial and ethnic groups represented in SEER data (American Indian/Alaska Native, Asian or Pacific Islander, Black, White and Hispanic/non-Hispanic). Extension of CRC-SPIN to include race/ethnicity will focus on incorporating available information about differences in CRC risk. We will also extend CRC-SPIN to simulate patient characteristics beyond race/ethnicity that are related to screening, such as insurance status, which mediate the relationship between race/ethnicity and CRC care. We will use the resulting model to simulate the overall impact of disparate care on CRC outcomes, and to identify elements of the care process that have the largest impact on outcomes to better guide health policy. Health outcomes will include life years lost, disease-free life years lost, excess CRC, and excess late-stage CRC. Financial outcomes will assess societal costs, including screening costs, treatment costs and lost income both overall and for specific racial/ethnic groups. We will use robust decision making approaches to address uncertainty in the differential risk of CRC and differential CRC care. We will use the model to project the impact of policy scenarios designed to reduce racial/ethnic disparities in CRC to assist in identify the most effective policy scenarios for reducing racial/ethnic disparities in CRC.
