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Comparison of spatial approaches to assess the effect of residing in a 20-minute neighbourhood on body mass index.
Yang, Guannan; Thornton, Lukar E; Daniel, Mark; Chaix, Basile; Lamb, Karen E.
Afiliação
  • Yang G; Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia.
  • Thornton LE; Department of Marketing, Faculty of Business and Economics, University of Antwerp, Antwerp, Belgium; Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, Victoria, Australia.
  • Daniel M; Health Research Institute, Faculty of Health, University of Canberra, Canberra, Australia; Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Fitzroy, Victoria, Australia.
  • Chaix B; INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique, Nemesis Research Team, Sorbonne Université, Paris F75012, France.
  • Lamb KE; Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia; Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, Victoria, Australia. Electronic address: klamb@unimelb.edu.au.
Spat Spatiotemporal Epidemiol ; 43: 100546, 2022 11.
Article em En | MEDLINE | ID: mdl-36460452
Beliefs that neighbourhood environments influence body mass index (BMI) assume people residing proximally have similar outcomes. However, spatial relationships are rarely examined. We considered spatial autocorrelation when estimating associations between neighbourhood environments and BMI in two Australian cities. Using cross-sectional data from 1329 participants (Melbourne = 637, Adelaide = 692), spatial autocorrelation in BMI was examined for different spatial weights definitions. Spatial and ordinary least squares regression were compared to assess how accounting for spatial autocorrelation influenced model findings. Geocoded household addresses were used to generate matrices based on distances between addresses. We found low positive spatial autocorrelation in BMI; magnitudes differed by matrix choice, highlighting the need for careful consideration of appropriate spatial weighting. Results indicated statistical evidence of spatial autocorrelation in Adelaide but not Melbourne. Model findings were comparable, with no residual spatial autocorrelation after adjustment for confounders. Future neighbourhoods and BMI research should examine spatial autocorrelation, accounting for this where necessary.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Índice de Massa Corporal Tipo de estudo: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: Oceania Idioma: En Revista: Spat Spatiotemporal Epidemiol Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Índice de Massa Corporal Tipo de estudo: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: Oceania Idioma: En Revista: Spat Spatiotemporal Epidemiol Ano de publicação: 2022 Tipo de documento: Article