JMIR Publications

ABSTRACT

Background:

Obesity is a global epidemic causing at least 2.8 million deaths per year. Obesity is associated with significant socio-economic 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 machine-learning approach.

Methods:

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:

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 - black population, home renters, people below the poverty level, and people without a high school diploma 25 years and older, had a significant association with obesity prevalence. The grouping analysis revealed disparities in obesity prevalence amongst disadvantaged neighborhoods.

Conclusions:

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 more disadvantaged neighborhoods, could provide insightful geospatial health information to health officials and policymakers to facilitate informed decision-making and interventions that mitigate risk factors for increasing obesity prevalence.