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Risk factors and geographic disparities in premature cardiovascular mortality in US counties: a machine learning approach
Disparities in premature cardiovascular mortality (PCVM) have been associated with socioeconomic, behavioral, and environmental risk factors. Understanding the “phenotypes”, or combinations of characteristics associated with the highest risk of PCVM, and the geographic distributions of these phenoty...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941082/ https://www.ncbi.nlm.nih.gov/pubmed/36808141 http://dx.doi.org/10.1038/s41598-023-30188-9 |
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author | Dong, Weichuan Motairek, Issam Nasir, Khurram Chen, Zhuo Kim, Uriel Khalifa, Yassin Freedman, Darcy Griggs, Stephanie Rajagopalan, Sanjay Al-Kindi, Sadeer G. |
author_facet | Dong, Weichuan Motairek, Issam Nasir, Khurram Chen, Zhuo Kim, Uriel Khalifa, Yassin Freedman, Darcy Griggs, Stephanie Rajagopalan, Sanjay Al-Kindi, Sadeer G. |
author_sort | Dong, Weichuan |
collection | PubMed |
description | Disparities in premature cardiovascular mortality (PCVM) have been associated with socioeconomic, behavioral, and environmental risk factors. Understanding the “phenotypes”, or combinations of characteristics associated with the highest risk of PCVM, and the geographic distributions of these phenotypes is critical to targeting PCVM interventions. This study applied the classification and regression tree (CART) to identify county phenotypes of PCVM and geographic information systems to examine the distributions of identified phenotypes. Random forest analysis was applied to evaluate the relative importance of risk factors associated with PCVM. The CART analysis identified seven county phenotypes of PCVM, where high-risk phenotypes were characterized by having greater percentages of people with lower income, higher physical inactivity, and higher food insecurity. These high-risk phenotypes were mostly concentrated in the Black Belt of the American South and the Appalachian region. The random forest analysis identified additional important risk factors associated with PCVM, including broadband access, smoking, receipt of Supplemental Nutrition Assistance Program benefits, and educational attainment. Our study demonstrates the use of machine learning approaches in characterizing community-level phenotypes of PCVM. Interventions to reduce PCVM should be tailored according to these phenotypes in corresponding geographic areas. |
format | Online Article Text |
id | pubmed-9941082 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-99410822023-02-22 Risk factors and geographic disparities in premature cardiovascular mortality in US counties: a machine learning approach Dong, Weichuan Motairek, Issam Nasir, Khurram Chen, Zhuo Kim, Uriel Khalifa, Yassin Freedman, Darcy Griggs, Stephanie Rajagopalan, Sanjay Al-Kindi, Sadeer G. Sci Rep Article Disparities in premature cardiovascular mortality (PCVM) have been associated with socioeconomic, behavioral, and environmental risk factors. Understanding the “phenotypes”, or combinations of characteristics associated with the highest risk of PCVM, and the geographic distributions of these phenotypes is critical to targeting PCVM interventions. This study applied the classification and regression tree (CART) to identify county phenotypes of PCVM and geographic information systems to examine the distributions of identified phenotypes. Random forest analysis was applied to evaluate the relative importance of risk factors associated with PCVM. The CART analysis identified seven county phenotypes of PCVM, where high-risk phenotypes were characterized by having greater percentages of people with lower income, higher physical inactivity, and higher food insecurity. These high-risk phenotypes were mostly concentrated in the Black Belt of the American South and the Appalachian region. The random forest analysis identified additional important risk factors associated with PCVM, including broadband access, smoking, receipt of Supplemental Nutrition Assistance Program benefits, and educational attainment. Our study demonstrates the use of machine learning approaches in characterizing community-level phenotypes of PCVM. Interventions to reduce PCVM should be tailored according to these phenotypes in corresponding geographic areas. Nature Publishing Group UK 2023-02-20 /pmc/articles/PMC9941082/ /pubmed/36808141 http://dx.doi.org/10.1038/s41598-023-30188-9 Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Dong, Weichuan Motairek, Issam Nasir, Khurram Chen, Zhuo Kim, Uriel Khalifa, Yassin Freedman, Darcy Griggs, Stephanie Rajagopalan, Sanjay Al-Kindi, Sadeer G. Risk factors and geographic disparities in premature cardiovascular mortality in US counties: a machine learning approach |
title | Risk factors and geographic disparities in premature cardiovascular mortality in US counties: a machine learning approach |
title_full | Risk factors and geographic disparities in premature cardiovascular mortality in US counties: a machine learning approach |
title_fullStr | Risk factors and geographic disparities in premature cardiovascular mortality in US counties: a machine learning approach |
title_full_unstemmed | Risk factors and geographic disparities in premature cardiovascular mortality in US counties: a machine learning approach |
title_short | Risk factors and geographic disparities in premature cardiovascular mortality in US counties: a machine learning approach |
title_sort | risk factors and geographic disparities in premature cardiovascular mortality in us counties: a machine learning approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941082/ https://www.ncbi.nlm.nih.gov/pubmed/36808141 http://dx.doi.org/10.1038/s41598-023-30188-9 |
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