Predicting incident cardiovascular disease among African-American adults: A deep learning approach to evaluate social determinants of health in the Jackson heart study.

Autor: Morris MC; Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.; Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, Mississippi, United States of America., Moradi H; Department of Data Science, University of Mississippi Medical Center, Jackson, Mississippi, United States of America.; Department of Computer Science, University of North Carolina Agricultural and Technical State University, Greensboro, North Carolina, United States of America., Aslani M; Department of Data Analytics, University of North Texas, Denton, Texas, United States of America., Sims M; Department of Social Medicine, Population, and Public Health, University of California, Riverside, California, United States of America., Schlundt D; Department of Psychology, Vanderbilt University, Nashville, Tennessee, United States of America., Kouros CD; Department of Psychology, Southern Methodist University, Dallas, Texas, United States of America., Goodin B; Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, Texas, United States of America.; Department of Anesthesiology, Washington University in St. Louis, St. Louis, Missouri, United States of America., Lim C; Department of Health Psychology, University of Missouri, Columbia, Missouri, Texas, United States of America., Kinney K; Department of Psychology, Vanderbilt University, Nashville, Tennessee, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Nov 10; Vol. 18 (11), pp. e0294050. Date of Electronic Publication: 2023 Nov 10 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0294050
Abstrakt: The present study sought to leverage machine learning approaches to determine whether social determinants of health improve prediction of incident cardiovascular disease (CVD). Participants in the Jackson Heart study with no history of CVD at baseline were followed over a 10-year period to determine first CVD events (i.e., coronary heart disease, stroke, heart failure). Three modeling algorithms (i.e., Deep Neural Network, Random Survival Forest, Penalized Cox Proportional Hazards) were used to evaluate three feature sets (i.e., demographics and standard/biobehavioral CVD risk factors [FS1], FS1 combined with psychosocial and socioeconomic CVD risk factors [FS2], and FS2 combined with environmental features [FS3]) as predictors of 10-year CVD risk. Contrary to hypothesis, overall predictive accuracy did not improve when adding social determinants of health. However, social determinants of health comprised eight of the top 15 predictors of first CVD events. The social determinates of health indicators included four socioeconomic factors (insurance status and types), one psychosocial factor (discrimination burden), and three environmental factors (density of outdoor physical activity resources, including instructional and water activities; modified retail food environment index excluding alcohol; and favorable food stores). Findings suggest that whereas understanding biological determinants may identify who is currently at risk for developing CVD and in need of secondary prevention, understanding upstream social determinants of CVD risk could guide primary prevention efforts by identifying where and how policy and community-level interventions could be targeted to facilitate changes in individual health behaviors.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Morris et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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