ArticleJMIR public health and surveillance2022
Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach.
Article in JMIR public health and surveillance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Neighborhood obesogenic factors and breast cancer risk and mortality in the Southern community cohort study.Scientific reports · 2026Article
- Geospatial Determinants of Maternal Overweight, Gestational Diabetes and Large for Gestational Age Birthweight in Melbourne During and After COVID-19 Lockdowns.The Australian & New Zealand journal of obstetrics & gynaecology · 2025Article
- Obesity: Clinical Impact, Pathophysiology, Complications, and Modern Innovations in Therapeutic Strategies.Medicines (Basel, Switzerland) · 2025Review
- Neighborhood Characteristics Related to Changes in Anthropometrics During a Lifestyle Intervention for Persons with Obesity.International journal of behavioral medicine · 2025Article
- Exploring the multifaceted factors influencing overweight and obesity: a scoping review.Frontiers in public health · 2025Article
- AI-driven tools for the prediction of obesity-related vascular diseases: stakeholder perspectives and challenges.Frontiers in public health · 2025Article
- Article
- County-level socio-environmental factors and obesity prevalence in the United States.Diabetes, obesity & metabolism · 2024Article
- Enhancing Health Care Accessibility and Equity Through a Geoprocessing Toolbox for Spatial Accessibility Analysis: Development and Case Study.JMIR formative research · 2024Article
- Disparities in Breast Cancer Care-How Factors Related to Prevention, Diagnosis, and Treatment Drive Inequity.Healthcare (Basel, Switzerland) · 2024Review
- The association between neighborhood obesogenic factors and prostate cancer risk and mortality: the Southern Community Cohort Study.Frontiers in oncology · 2024Article
- The Association Between Social Determinants of Health and Population Health Outcomes: Ecological Analysis.JMIR public health and surveillance · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundObesity 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.
objectiveThe 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.
methodsObesity 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.
resultsResults 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.
conclusionsMore 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.
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