TY - JOUR
T1 - Geographical Patterns and Risk Factor Association of Cardio-Oncology Mortality in the United States
AU - Motairek, Issam
AU - Dong, Weichuan
AU - Salerno, Pedro RVO
AU - Janus, Scott E.
AU - Ganatra, Sarju
AU - Chen, Zhuo
AU - Guha, Avirup
AU - Makhlouf, Mohamed He
AU - Hassani, Neda Shafiabadi
AU - Rajagopalan, Sanjay
AU - Al-Kindi, Sadeer G.
N1 - Publisher Copyright:
© 2023 Elsevier Inc.
PY - 2023/8/15
Y1 - 2023/8/15
N2 - Cardio-oncology mortality (COM) is a complex issue that is compounded by multiple factors 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 epidemiology 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.
AB - Cardio-oncology mortality (COM) is a complex issue that is compounded by multiple factors 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 epidemiology 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.
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U2 - 10.1016/j.amjcard.2023.06.037
DO - 10.1016/j.amjcard.2023.06.037
M3 - Article
C2 - 37385168
AN - SCOPUS:85163558139
SN - 0002-9149
VL - 201
SP - 150
EP - 157
JO - American Journal of Cardiology
JF - American Journal of Cardiology
ER -