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The neighbourhood matters: studying exposures relevant to childhood obesity and the policy implications in Leeds, UK
  1. K L Edwards1,
  2. G P Clarke2,
  3. J K Ransley1,
  4. J Cade1
  1. 1 Division of Epidemiology, University of Leeds, United Kingdom;
  2. 2 School of Geography, University of Leeds, United Kingdom
  1. * Corresponding author; email: k.l.edwards{at}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, thus and? are a constructive focus for preventative 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 twelve 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.

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