Building effective intervention models utilizing big data to prevent the obesity epidemic. Issue 2 (March 2023)

• Record Type: Journal Article
• Authors: Tu, Brittany; Patel, Radha; Pitalua, Mario; Khan, Hafiz; Gittner, Lisaann S.
• Is Part Of: Obesity research & clinical practice. Volume 17:Issue 2(2023)
• Page Start: 108
• Page End: 115
• Publication Date: 2023-03
• Subjects: Obesity -- Exposome -- Hot-spot -- Cold-spot -- Modeling -- Population -- Case specific -- Factors; Obesity -- Research -- Periodicals; Obesity -- Treatment -- Periodicals; Obesity -- Periodicals; Obésité -- Recherche -- Périodiques; Obésité -- Traitement -- Périodiques; Obesity -- Research; Obesity -- Treatment; Electronic journals; Periodicals; 616.398
• DOI: 10.1016/j.orcp.2023.02.005
• Languages: English
• ISSNs: 1871-403X

Abstract

Abstract: Introduction: The exposome consists of factors an individual is exposed to across the life course. The exposome is dynamic, meaning the factors are constantly changing, affecting each other and individuals in different ways. Our exposome dataset includes social determinants of health as well as policy, climate, environment, and economic factors that could impact obesity development. The objective was to translate spatial exposure to these factors with the presence of obesity into actionable population-based constructs that could be further explored. Methods: Our dataset was constructed from a combination of public-use datasets and the Center of Disease Control's Compressed Mortality File. Spatial Statistics using Queens First Order Analysis was performed to identify hot- and cold-spots of obesity prevalence; followed by Graph Analysis, Relational Analysis, and Exploratory Factor Analysis to model the multifactorial spatial connections. Results: Areas of high and low presence of obesity had different factors associated with obesity. Factors associated with obesity in areas of high obesity propensity were: poverty / unemployment; workload, comorbid conditions (diabetes, CVD) and physical activity. Conversely, factors associated in areas where obesity was rare were: smoking, lower education, poorer mental health, lower elevations, and heat. Discussion: The spatial methods described within the paper are scalable to large numbers of variables without issues of multiple comparisons lowering resolution. These types of spatial structural methods provide insights into novel variable associations or factor interactions that can then be studied further at the population or policy levels.