Building effective intervention models utilizing big data to prevent the obesity epidemic.

Area of Science:

• Environmental Health
• Epidemiology
• Spatial Analysis

Background:

• The exposome comprises all environmental exposures across a lifetime, dynamically influencing health.
• Understanding the exposome's complex interplay with obesity is crucial for public health.
• Social determinants, policy, climate, and economic factors are key components of the exposome.

Purpose of the Study:

• To translate spatial exposure data of exposome factors into population-based constructs related to obesity.
• To explore the multifactorial spatial connections between the exposome and obesity prevalence.

Main Methods:

• Utilized public-use datasets and the CDC's Compressed Mortality File.
• Applied Spatial Statistics (Queens First Order Analysis) to identify obesity hotspots and coldspots.
• Employed Graph Analysis, Relational Analysis, and Exploratory Factor Analysis to model spatial relationships.

Main Results:

• High obesity propensity areas were associated with poverty, unemployment, workload, diabetes, and cardiovascular disease (CVD).
• Areas with rare obesity showed associations with smoking, lower education, poorer mental health, lower elevations, and heat.
• Identified distinct environmental and socioeconomic factor associations with obesity prevalence across different spatial areas.

Conclusions:

• Spatial methods are scalable and reveal novel variable associations for population and policy-level studies.
• The findings provide actionable insights into the complex spatial determinants of obesity.
• Understanding these spatial patterns can inform targeted public health interventions.