ORIGINAL RESEARCH article
Front. Public Health
Sec. Environmental Health and Exposome
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1649851
This article is part of the Research TopicArtificial Intelligence Algorithms and Cardiovascular Disease Risk AssessmentView all 7 articles
Spatio-Temporal Distribution of Influencing Factors of Cardiovascular Disease in the United States
Provisionally accepted
- 1Lanzhou University, Lanzhou, China
- 2Lanzhou University Second Hospital Department of Cardiac Surgery, Lanzhou, China
- 3Lanzhou University Second Hospital Department of Nursing, Lanzhou, China
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Introduction Cardiovascular disease (CVD) is a major global health issue, contributing significantly to mortality and morbidity worldwide. The American Heart Association highlights primary prevention as a crucial strategy for mitigating the burden of CVD. This research aims to identify essential CVD drivers and support primary prevention efforts. Methods: This study analyzed data on CVD incidence across 48 states in the United States (US) from 1991 to 2020, using data obtained from the Global Burden of Disease database. To investigate the spatial-temporal heterogeneity and drivers of CVD, we employed Global Moran's I, hot spot analysis, GeoDetector, and Geographically Weighted Neural Network Weighted Regression (GTNNWR). Results: Global Moran's I analysis revealed significant clustering (Z-score > 2.58) of CVD rates across regions. The hotspot analysis identified significant clusters in the northeastern US. Factor detection indicated that population density, ambient particulate matter pollution, diet low in fruit, diet low in whole grain, diet high in sodium, and tobacco influenced CVD incidence. In contrast, total GDP was not statistically significant (P > 0.05). Interaction detection demonstrated that factors did not act independently; most interactions exhibited bilinear enhancement (q(X1, X2) > max(q(X1), q(X2))). Conclusion: Our article reveals significant spatial clustering of CVD in the US, with population density, air pollution, poor dietary patterns, and smoking emerging as major contributors. The study provides important evidence for designing geographically targeted public health interventions. Keywords: Cardiovascular disease, US, hot spot analysis, Moran’s I, GeoDetector, Getis-Ord Gi*, GTNNWR.
Keywords: cardiovascular disease, US, Hot spot analysis, moran's I, Geodetector, Getis-Ord Gi*, GTNNWR
Received: 19 Jun 2025; Accepted: 28 Aug 2025.
Copyright: © 2025 lou, zhang, zhang, Wu, Li and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yongnan Li, Lanzhou University, Lanzhou, China
Yanhua Zhang, Lanzhou University Second Hospital Department of Cardiac Surgery, Lanzhou, China
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