K L Edwards1, G P Clarke, J K Ransley, J Cade. 1. Cancer Epidemiology Group, Division of Epidemiology, Worsley Building, University of Leeds, Leeds LS2 9NL, UK. k.l.edwards@leeds.ac.uk
Abstract
BACKGROUND: Reducing childhood obesity is a key UK government target. Obesogenic environments are one of the major explanations for the rising prevalence and thus a constructive focus for preventive strategies. Spatial analysis techniques are used to provide more information about obesity at the neighbourhood level in order to help to shape local obesity-prevention policies. METHODS: Childhood obesity was defined by body mass index, using cross-sectional height and weight data for children aged 3-13 years (obesity>98th centile; British reference dataset). Relationships between childhood obesity and 12 simulated obesogenic variables were assessed using geographically weighted regression. These results were applied to three wards with different socio-economic backgrounds, tailoring local obesity-prevention policy. RESULTS: The spatial distribution of childhood obesity varied, with high prevalence in deprived and affluent areas. Key local covariates strongly associated with childhood obesity differed: in the affluent ward, they were perceived neighbourhood safety and fruit and vegetable consumption; in the deprived ward, expenditure on food, purchasing school meals, multiple television ownership and internet access; in all wards, perceived access to supermarkets and leisure facilities. Accordingly, different interventions/strategies may be more appropriate/effective in different areas. CONCLUSIONS: These analyses identify the covariates with the strongest local relationships with obesity and suggest how policy can be tailored to the specific needs of each micro-area: solutions need to be tailored to the locality to be most effective. This paper demonstrates the importance of small-area analysis in order to provide health planners with detailed information that may help them to prioritise interventions for maximum benefit.
BACKGROUND: Reducing childhood obesity is a key UK government target. Obesogenic environments are one of the major explanations for the rising prevalence and thus a constructive focus for preventive strategies. Spatial analysis techniques are used to provide more information about obesity at the neighbourhood level in order to help to shape local obesity-prevention policies. METHODS: Childhood obesity was defined by body mass index, using cross-sectional height and weight data for children aged 3-13 years (obesity>98th centile; British reference dataset). Relationships between childhood obesity and 12 simulated obesogenic variables were assessed using geographically weighted regression. These results were applied to three wards with different socio-economic backgrounds, tailoring local obesity-prevention policy. RESULTS: The spatial distribution of childhood obesity varied, with high prevalence in deprived and affluent areas. Key local covariates strongly associated with childhood obesity differed: in the affluent ward, they were perceived neighbourhood safety and fruit and vegetable consumption; in the deprived ward, expenditure on food, purchasing school meals, multiple television ownership and internet access; in all wards, perceived access to supermarkets and leisure facilities. Accordingly, different interventions/strategies may be more appropriate/effective in different areas. CONCLUSIONS: These analyses identify the covariates with the strongest local relationships with obesity and suggest how policy can be tailored to the specific needs of each micro-area: solutions need to be tailored to the locality to be most effective. This paper demonstrates the importance of small-area analysis in order to provide health planners with detailed information that may help them to prioritise interventions for maximum benefit.
Authors: D T Levy; P L Mabry; Y C Wang; S Gortmaker; T T-K Huang; T Marsh; M Moodie; B Swinburn Journal: Obes Rev Date: 2010-10-26 Impact factor: 9.213
Authors: Shannon N Zenk; Amy J Schulz; Stephen A Matthews; Angela Odoms-Young; JoEllen Wilbur; Lani Wegrzyn; Kevin Gibbs; Carol Braunschweig; Carmen Stokes Journal: Health Place Date: 2011-05-17 Impact factor: 4.078
Authors: M Pia Chaparro; Shannon E Whaley; Catherine M Crespi; Maria Koleilat; Tabashir Z Nobari; Edmund Seto; May C Wang Journal: J Epidemiol Community Health Date: 2014-07-10 Impact factor: 3.710
Authors: Kun Mei; Hong Huang; Fang Xia; Andy Hong; Xiang Chen; Chi Zhang; Ge Qiu; Gang Chen; Zhenfeng Wang; Chongjian Wang; Bo Yang; Qian Xiao; Peng Jia Journal: Obes Rev Date: 2020-07-28 Impact factor: 9.213