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.
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.
LisaAnn S. Gittner, Barbara J. Kilbourne, Ravi Vadapalli, Hafiz M.K. Khan, Michael A. Langston, "A multifactorial obesity model developed from nationwide public health exposome data and modern computational analyses", Obesity Research & Clinical Practice, Volume 11, Issue 5, 2017, Pages 522-533, ISSN 1871-403X, https://doi.org/10.1016/j.orcp.2017.05.001.