Ross Arena, Shuaijie Wang, Nicolaas P. Pronk, Colin Woodard, José Pacas, Tanvi Bhatt
Drivers of population-level health in the United States (U.S.) are complex. This analysis refines an ecologic framework of health, employing artificial intelligence modeling to estimate the impact of slavery on present-day, population-level lifespan. This study utilized several U.S. county-level datasets with more than 40 predictive variables representing the ecological framework of health. A non-linear artificial intelligence statistical approach was used to assess the ability of these variables to predict county-level life expectancy, years of life lost and death rate. The R 2 values demonstrated that the overall predictive performance of the models for life expectancy, years of life lost and death rates were all very strong, with mean R 2 ≥ 0.71 in all 3 prediction models. The number of predictor variables retained in the models ranged from 31 to 46, with measures from each category of the ecological framework of health being retained. The historical prevalence of slavery on a county level was a significant, repetitive interactive term. Unhealthy lifestyle behaviors are a primary driver of chronic disease, ultimately leading to diminished quality of life and lifespan. These health challenges are not insurmountable if the true root causes and forcing factors of health are acknowledged, studied, and addressed.