Geographical Patterns and Risk Factor Association of Cardio-Oncology Mortality in the United States

Cardio-oncology mortality (COM) is a complex issue that is compounded by multiple fac-tors that transcend a depth of socioeconomic, demographic, and environmental exposures. Although metrics and indexes of vulnerability have been associated with COM, advanced methods are required to account for the intricate intertwining of associations. This cross-sectional study utilized a novel approach that combined machine learning and epidemiol-ogy to identify high-risk sociodemographic and environmental factors linked to COM in United States counties. The study consisted of 987,009 decedents from 2,717 counties, and the Classification and Regression Trees model identified 9 county socio-environmental clusters that were closely associated with COM, with a 64.1% relative increase across the spectrum. The most important variables that emerged from this study were teen birth, pre-1960 housing (lead paint indicator), area deprivation index, median household income, number of hospitals, and exposure to particulate matter air pollution. In conclusion, this study provides novel insights into the socio-environmental drivers of COM and highlights the importance of utilizing machine learning approaches to identify high-risk populations and inform targeted interventions for reducing disparities in COM. & COPY; 2023 Elsevier Inc. All rights reserved. (Am J Cardiol 2023;201:150-157)

Automated summary

Cardio-oncology mortality (COM) is a complex issue with multiple factors that go beyond socioeconomic, demographic, and environmental exposures. This study used a novel approach combining machine learning and epidemiology to identify high-risk sociodemographic and environmental factors associated with COM in US counties. The study identified 9 county socio-environmental clusters closely linked to COM. Important variables included teen birth, pre-1960 housing (lead paint indicator), area deprivation index, median household income, number of hospitals, and exposure to particulate matter air pollution. This study provides valuable insights into the drivers of COM and emphasizes the need for targeted interventions to reduce disparities in affected populations.