The Impact of Slavery Legacy on the Ecological Framework of Health: A Lasting Power Dynamic Impacting Present-Day Health

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 <i>R</i> <sup>2</sup> 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 <i>R</i> <sup>2</sup> ≥ 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.

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Publication Details

Journal
American Journal of Lifestyle Medicine
Published
2026-02-17
DOI
https://doi.org/10.1177/15598276261426327
Primary Topic
Health, Environment, Cognitive Aging
Type
article
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article

The Impact of Slavery Legacy on the Ecological Framework of Health: A Lasting Power Dynamic Impacting Present-Day Health

José Pacas, Nicolaas P. Pronk, Colin Woodard, Tanvi Bhatt et al.
American Journal of Lifestyle Medicine
Health, Environment, Cognitive Aging
article

The Impact of Slavery Legacy on the Ecological Framework of Health: A Lasting Power Dynamic Impacting Present-Day Health

José Pacas, Nicolaas P. Pronk, Colin Woodard, Tanvi Bhatt, Shuaijie Wang, Ross Arena
article en

Abstract

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 <i>R</i> <sup>2</sup> 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 <i>R</i> <sup>2</sup> ≥ 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.

American Journal of Lifestyle Medicine
University of Minnesota System (US), University of Illinois System (US), Health Net (US), Constellation Technology (United States) (US), Salve Regina University (US), HealthPartners (US)
No poverty
Openalex Percentile: Top 55%
Health, Environment, Cognitive Aging
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The Impact of Slavery Legacy on the Ecological Framework of Health: A Lasting Power Dynamic Impacting Present-Day Health — José Pacas, Nicolaas P. Pronk, et al. · American Journal of Lifestyle Medicine (2026) | TGRS Research Map | TGRS