  Obesity is a global epidemic causing at least 2.8 million deaths per year.
This complex disease is associated with significant socioeconomic burden,
reduced work productivity, unemployment, and other social determinants of
Health (SDoH) disparities. Objective: The objective of this study was to
investigate the effects of SDoH on obesity prevalence among adults in Shelby
County, Tennessee, USA using a geospatial machine-learning approach. Obesity
prevalence was obtained from publicly available CDC 500 cities database while
SDoH indicators were extracted from the U.S. Census and USDA. We examined the
geographic distributions of obesity prevalence patterns using Getis-Ord Gi*
statistics and calibrated multiple models to study the association between SDoH
and adult obesity. Also, unsupervised machine learning was used to conduct
grouping analysis to investigate the distribution of obesity prevalence and
associated SDoH indicators. Results depicted a high percentage of neighborhoods
experiencing high adult obesity prevalence within Shelby County. In the census
tract, median household income, as well as the percentage of individuals who
were black, home renters, living below the poverty level, fifty-five years or
older, unmarried, and uninsured, had a significant association with adult
obesity prevalence. The grouping analysis revealed disparities in obesity
prevalence amongst disadvantaged neighborhoods. More research is needed that
examines linkages between geographical location, SDoH, and chronic diseases.
These findings, which depict a significantly higher prevalence of obesity
within disadvantaged neighborhoods, and other geospatial information can be
leveraged to offer valuable insights informing health decision-making and
interventions that mitigate risk factors for increasing obesity prevalence.
