Background: Racial disparities have been reported for breast cancer and cardiovascular disease (CVD) outcomes. The determinants of racial disparities in CVD outcomes are not yet fully understood. We aimed to examine the impact of individual and neighborhood-level social determinants of health (SDOH) on the racial disparities in major adverse cardiovascular events (MACE; consisting of heart failure, acute coronary syndrome, atrial fibrillation, and ischemic stroke) among female patients with breast cancer.
Methods: This 10-year longitudinal retrospective study was based on a cancer informatics platform with electronic medical record supplementation. We included women aged ≥18 years diagnosed with breast cancer. SDOH were obtained from LexisNexis, and consisted of the domains of social and community context, neighborhood and built environment, education access and quality, and economic stability. Race-agnostic (overall data with race as a feature) and race-specific machine learning models were developed to account for and rank the SDOH impact in 2-year MACE.
Results: We included 4,309 patients (765 non-Hispanic Black [NHB]; 3,321 non-Hispanic white). In the race-agnostic model (C-index, 0.79; 95% CI, 0.78-0.80), the 5 most important adverse SDOH variables were neighborhood median household income (SHapley Additive exPlanations [SHAP] score [SS], 0.07), neighborhood crime index (SS = 0.06), number of transportation properties in the household (SS = 0.05), neighborhood burglary index (SS = 0.04), and neighborhood median home values (SS = 0.03). Race was not significantly associated with MACE when adverse SDOH were included as covariates (adjusted subdistribution hazard ratio, 1.22; 95% CI, 0.91-1.64). NHB patients were more likely to have unfavorable SDOH conditions for 8 of the 10 most important SDOH variables for the MACE prediction.
Conclusions: Neighborhood and built environment variables are the most important SDOH predictors for 2-year MACE, and NHB patients were more likely to have unfavorable SDOH conditions. This finding reinforces that race is a social construct.
Keywords: Machine Learning; breast cancer; cardiovascular outcomes; racial disparities; social determinants of health.