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

Brittany Tu1, Radha Patel1, Mario Pitalua2

  • 1School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA.

Obesity Research & Clinical Practice
|March 4, 2023
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Summary

The exposome, encompassing life course exposures, influences obesity. High obesity areas link to poverty and comorbidities, while low obesity areas associate with smoking and lower education, revealing distinct spatial patterns.

Keywords:
Case specificCold-spotExposomeFactorsHot-spotModelingObesityPopulation

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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.