ArticleScientific reports2023
Risk factors and geographic disparities in premature cardiovascular mortality in US counties: a machine learning approach.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 24 citations in OpenAlex.
- Research opportunities to advance cardiovascular health through a planetary health lens.American journal of preventive cardiology · 2026Review
- Disparities in County-Level Vulnerability to Cardiovascular Mortality Associated With Extreme Heat Exposure.JACC. Advances · 2026Article
- How do counties' industrial structures shape geographic disparities in cardiovascular disease mortality?Social science & medicine (1982) · 2026Article
- Machine Learning Based County Level Phenotypes Related to Diabetes Prevalence.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Development and evaluation of a machine learning model predicting out-of-hospital cardiac arrest using environmental factors.NPJ digital medicine · 2025Article
- A social determinants-based county-level cardiovascular mortality index to identify high-risk counties in the USA.BMC medicine · 2025Article
- The association between county-level social determinants of health and cardio-kidney-metabolic disease attributed all-cause mortality in the US: A cross sectional analysis.The American journal of the medical sciences · 2025Article
- Spatio-temporal distribution of influencing factors of cardiovascular disease in the United States.Frontiers in public health · 2025Article
- County-level socio-environmental factors and obesity prevalence in the United States.Diabetes, obesity & metabolism · 2024Article
- The Exposome and Cardiovascular Health.The Canadian journal of cardiology · 2023Review
- Geographical Patterns and Risk Factor Association of Cardio-Oncology Mortality in the United States.The American journal of cardiology · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 5 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.