Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach

Abstract

Background 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, the United States, using a geospatial machine learning approach. Methods Obesity prevalence was obtained from the publicly available 500 Cities database of Centers for Disease Control and Prevention, and SDOH indicators were extracted from the US census and the US Department of Agriculture. 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. 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, the median household income, as well as the percentage of individuals who were Black, home renters, living below the poverty level, 55 years or older, unmarried, and uninsured, had a significant association with adult obesity prevalence. The grouping analysis revealed disparities in obesity prevalence among disadvantaged neighborhoods. Conclusions More research is needed to examine links between geographical location, SDOH, and chronic diseases. The findings of this study, 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 of increasing obesity prevalence.

The association between neighborhood obesogenic factors and prostate cancer risk and mortality: the Southern Community Cohort Study

“…In conclusion, we found lower nSES to be associated with a higher prostate cancer mortality overall, and a higher prostate cancer risk among Black men. This is consistent with our previous studies ( 33 – 37 ) on the impact of social determinants of health, including neighborhood characteristics, on a range of various chronic and non-chronic conditions. We also found areas with fewer healthy food choices to be associated with prostate cancer among Black men.…”

County‐level socio‐environmental factors and obesity prevalence in the United States

“…Beyond food and housing insecurity, lack of health insurance represents an additional risk factor associated with adult obesity. Analyses have revealed disparities in obesity prevalence in disadvantaged neighbourhoods and, conversely, people with obesity constitute one population that is more often uninsured 48–50 …”

County-level Socio-Environmental Factors and Obesity Prevalence in the United States

“…Analyses have revealed disparities in obesity prevalence in disadvantaged neighborhoods and conversely, people with obesity constitute one population that are more often uninsured. [33][34][35] The current regime of obesity treatment entails a "treatment pyramid". Lifestyle interventions serves as the base of the pyramid followed by pharmacological agents, endoscopic procedures, and ultimately bariatric surgeries.…”

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