Project 2

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PROJECT SUMMARY – Project 2 Underserved breast cancer screening populations, including those that are predominantly composed of racial/ethnic minorities, lower income, lower educated, and rural populations, continue to have a higher breast cancer morbidity and mortality burden than their counterparts. These populations tend to have lower follow-up rates after abnormal screening, more missed cancers, and more advanced stage disease at the time of diagnosis. Drivers of inequities are likely multi-factorial and include not only woman-level enabling factors but also neighborhood-level social determinants of health and facility-level factors that influence access to and use of high quality screening, timely diagnostic evaluation, and treatment. The National Institute of Minority Health and Health Disparities, on behalf of the NIH, reports that a major barrier in achieving health equity is that prior disparities research efforts have focused on individual enabling factors rather than neighborhood or healthcare delivery factors. Understanding the impact of breast imaging facility-level drivers of inequities is particularly important as new screening technologies, including artificial intelligence (AI), are rapidly adopted in clinical practice. If newer technologies do not diffuse equitably across communities, persistent breast cancer disparities may be further exacerbated. Our overall project objective is to identify modifiable breast imaging facility-level factors that drive breast cancer screening disparities. Using an observational cohort study design and simulation modeling, we will explore how targeted facility-level changes that aim to increase access to and use of routine screening and targeted use of AI for improved imaging interpretation accuracy can promote greater equity in screening outcomes. We will leverage the robust, longitudinal, multi-level Breast Cancer Surveillance Consortium data across eight regional U.S. breast imaging registries to pursue the following specific aims: Aim 1) Perform multi-level analyses to identify facility-level factors (e.g., on-site technologies) that drive disparities in screening performance and outcomes. Aim 2) Using a retrospective matched case control design and five commercially available AI technologies, evaluate whether commercially available AI tools for automated mammography interpretation can aid low-performing facilities to meet or exceed national mammography performance benchmarks. Aim 3) Using three established microsimulation models and results from Aims 1 and 2, estimate the long-term, population-level benefits, harms, and costs of enacting facility-level quality-of-care interventions (e.g., AI for higher performance) for the overall U.S. screening population and for underserved subpopulations. Elevating the quality-of-care at low-performing facilities has the potential to tip the balance towards greater screening benefits and less harms at the population-level, while also promoting health equity.