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Chronic Disease, the Built Environment, and Unequal Health Risks in the 500 Largest U.S. Cities

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  • Kevin M. Fitzpatrick

    (Department of Sociology and Criminology, University of Arkansas-Fayetteville, Fayetteville, AR 72701, USA)

  • Don Willis

    (Department of Sociology and Anthropology, University of Arkansas-Little Rock, Little Rock, AR 72204, USA)

Abstract

Health is increasingly subject to the complex interplay between the built environment, population composition, and the structured inequity in access to health-related resources across communities. The primary objective of this paper was to examine cardiometabolic disease (diabetes, cardiovascular diseases, stroke) markers and their prevalence across relatively small geographic units in the 500 largest cities in the United States. Using data from the American Community Survey and the 500 Cities Project, the current study examined cardiometabolic diseases across 27,000+ census tracts in the 500 largest cities in the United States. Earlier works clearly show cardiometabolic diseases are not randomly distributed across the geography of the U.S., but rather concentrated primarily in Southern and Eastern regions of the U.S. Our results confirm that chronic disease is correlated with social and built environment factors. Specifically, racial concentration (%, Black), age concentration (% 65+), housing stock age, median home value, structural inequality (Gini index), and weight status (% overweight/obese) were consistent correlates ( p < 0.01) of cardiometabolic diseases in the sample of census tracts. The paper examines policy-related features of the built and social environment and how they might play a role in shaping the health and well-being of America’s metropolises.

Suggested Citation

  • Kevin M. Fitzpatrick & Don Willis, 2020. "Chronic Disease, the Built Environment, and Unequal Health Risks in the 500 Largest U.S. Cities," IJERPH, MDPI, vol. 17(8), pages 1-8, April.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:8:p:2961-:d:349944
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    References listed on IDEAS

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    Cited by:

    1. Ben Allen & Morgan Lane & Elizabeth Anderson Steeves & Hollie Raynor, 2022. "Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity," IJERPH, MDPI, vol. 19(15), pages 1-13, August.

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