LisaAnn S Gittner1, Barbara J Kilbourne2, Ravi Vadapalli3, Hafiz M K Khan4, Michael A Langston5. 1. Department of Political Science, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USA; Department of Public Health, Texas Tech University Health Science Center, 3601 4th Street, Lubbock, TX 79430, USA; High Performance Computing Center, Information Technology Division, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USA. Electronic address: Lisa.gittner@ttu.edu. 2. Department of Sociology, Tennessee State University, 3500 John A Merritt Blvd, Nashville, TN 37209, USA. Electronic address: Bkilbourne@tstate.edu. 3. High Performance Computing Center, Information Technology Division, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USA. Electronic address: Ravi.Vadapalli@ttu.edu. 4. Department of Public Health, Texas Tech University Health Science Center, 3601 4th Street, Lubbock, TX 79430, USA. Electronic address: Hafiz.khan@ttuhsc.edu. 5. Department of Electrical Engineering and Computer Science, University of Tennessee, 1520 Middle Drive, Knoxville, TN 37996, USA. Electronic address: langston@eecs.utk.edu.
Abstract
STATEMENT OF THE PROBLEM: Obesity is both multifactorial and multimodal, making it difficult to identify, unravel and distinguish causative and contributing factors. The lack of a clear model of aetiology hampers the design and evaluation of interventions to prevent and reduce obesity. METHODS: Using modern graph-theoretical algorithms, we are able to coalesce and analyse thousands of inter-dependent variables and interpret their putative relationships to obesity. Our modelling is different from traditional approaches; we make no a priori assumptions about the population, and model instead based on the actual characteristics of a population. Paracliques, noise-resistant collections of highly-correlated variables, are differentially distilled from data taken over counties associated with low versus high obesity rates. Factor analysis is then applied and a model is developed. RESULTS AND CONCLUSIONS: Latent variables concentrated around social deprivation, community infrastructure and climate, and especially heat stress were connected to obesity. Infrastructure, environment and community organisation differed in counties with low versus high obesity rates. Clear connections of community infrastructure with obesity in our results lead us to conclude that community level interventions are critical. This effort suggests that it might be useful to study and plan interventions around community organisation and structure, rather than just the individual, to combat the nation's obesity epidemic.
STATEMENT OF THE PROBLEM: Obesity is both multifactorial and multimodal, making it difficult to identify, unravel and distinguish causative and contributing factors. The lack of a clear model of aetiology hampers the design and evaluation of interventions to prevent and reduce obesity. METHODS: Using modern graph-theoretical algorithms, we are able to coalesce and analyse thousands of inter-dependent variables and interpret their putative relationships to obesity. Our modelling is different from traditional approaches; we make no a priori assumptions about the population, and model instead based on the actual characteristics of a population. Paracliques, noise-resistant collections of highly-correlated variables, are differentially distilled from data taken over counties associated with low versus high obesity rates. Factor analysis is then applied and a model is developed. RESULTS AND CONCLUSIONS: Latent variables concentrated around social deprivation, community infrastructure and climate, and especially heat stress were connected to obesity. Infrastructure, environment and community organisation differed in counties with low versus high obesity rates. Clear connections of community infrastructure with obesity in our results lead us to conclude that community level interventions are critical. This effort suggests that it might be useful to study and plan interventions around community organisation and structure, rather than just the individual, to combat the nation's obesity epidemic.
Authors: Anne D Kershenbaum; Michael A Langston; Robert S Levine; Arnold M Saxton; Tonny J Oyana; Barbara J Kilbourne; Gary L Rogers; Lisaann S Gittner; Suzanne H Baktash; Patricia Matthews-Juarez; Paul D Juarez Journal: Int J Environ Res Public Health Date: 2014-11-28 Impact factor: 3.390
Authors: Paul D Juarez; Patricia Matthews-Juarez; Darryl B Hood; Wansoo Im; Robert S Levine; Barbara J Kilbourne; Michael A Langston; Mohammad Z Al-Hamdan; William L Crosson; Maurice G Estes; Sue M Estes; Vincent K Agboto; Paul Robinson; Sacoby Wilson; Maureen Y Lichtveld Journal: Int J Environ Res Public Health Date: 2014-12-11 Impact factor: 3.390
Authors: Joanna Drowos; Aaron Fils; Maria C Mejia de Grubb; Jason L Salemi; Roger J Zoorob; Charles H Hennekens; Robert S Levine Journal: Matern Child Health J Date: 2019-12