A multifactorial obesity model developed from nationwide public health exposome data and modern computational analyses.
Summary 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. Method...
| Publicado en: | Obesity Research & Clinical Practice Vol. 11; no. 5; pp. 522 - 534 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Sep2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=125856278&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125856278 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1871403X 3A05 jtl: Obesity Research & Clinical Practice issn: 1871403X maglogo: N pubinfo: dt: Sep2017 vid: 11 iid: 5 pid: 1004 pub: Elsevier B.V. artinfo: ui: 125856278 125856278 125856278 10.1016/j.orcp.2017.05.001 125856278 ppf: 522 ppct: 12 formats: tig: atl: A multifactorial obesity model developed from nationwide public health exposome data and modern computational analyses. aug: au: Gittner, LisaAnn S. Kilbourne, Barbara J. Vadapalli, Ravi Khan, Hafiz M.K. Langston, Michael A. affil: Department of Political Science, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USA sug: subj: Obesity Prevention and Control Obesity Etiology Obesity Epidemiology Community Role Public Health Models, Biological Computer Simulation Human Population Characteristics Factor Analysis Social Determinants of Health Heat Stress Disorders Algorithms ab: Summary 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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