Understanding disparities in cardiovascular toxicity among breast cancer survivors in Arkansas

PROJECT SUMMARY Cardiovascular disease (CVD) is a rapidly growing public health concern for female breast cancer (BC) survivors. This is due, in part, to cardiovascular (CV) toxicities from common cancer therapies that are associated with CVD. CV toxicities are more common among marginalized racial and ethnic groups. They are

also more common among individuals at lower levels of socioeconomic and insurance status, as well as those living in disadvantaged neighborhoods or rural areas. Yet, the complex role of these factors—social determinants of health (SDOH)—in CV toxicity disparities among BC survivors is unknown, and few studies

have considered the intersections among race, ethnicity, and SDOH. Given the known relationship between SDOH and cardiometabolic dysfunction, disparities in SDOH may contribute to CV toxicities among BC survivors through increased risk of comorbid cardiometabolic dysfunction. Therefore, to improve the CV health

of BC survivors, it is critical to examine CV toxicity disparities in the context of cardiometabolic dysfunction. To address this issue, the following specific aims will be completed: 1) Identify the extent of disparities in incident CV toxicity among BC survivors in Arkansas based on race, ethnicity, neighborhood socioeconomic status, and geography.

2) Develop a predictive algorithm for risk stratification in BC survivors at high risk for CV toxicity using machine learning approaches that incorporate race, ethnicity, and SDOH. Data collected from 2013–2019 in the Arkansas All-Payer Claims Database (APCD) linked to the Cancer Registry, as well as clinical and refined SDOH information from the electronic health records system at the

University of Arkansas for Medical Sciences, will be utilized in this study. A longitudinal analysis for the development of CV toxicities in women with a first diagnosis of BC (stage I–III), with passive follow-up in the claims data through 2023, will be conducted. Machine-learning methods will be used to develop an algorithm

that predicts CV toxicities among BC survivors based on race, ethnicity, complex SDOH, and other clinical factors. This K01 will: 1) provide training in social epidemiology and health disparities; 2) promote research skills using large-scale, longitudinal administrative healthcare data; 3) develop competence in advanced

analytic methods; and 4) increase understanding of BC survivorship and provide content expertise in cardio- oncology research. This study responds to the NHLBI's compelling question (5.CQ.10) to reduce cardiac morbidity and mortality in cancer survivors. By identifying factors that contribute to health disparities in CVD

among BC survivors and using them to predict CV toxicity, this research can inform targeted interventions (e.g., multidimensional intervention programs addressing race, ethnicity, and multiple SDOH) to improve the CV health of this population.