The neighbourhood matters: studying exposures relevant to childhood obesity and the policy implications in leeds, UK.

Auteur(s) :
Cade JE., Ransley JK., Edwards KL., Clarke GP.
Date :
Mar, 2010
Source(s) :
Adresse :
Cancer Epidemiology Group, Division of Epidemiology, Worsley Building, University of Leeds, Leeds LS2 9NL, UK.

Sommaire de l'article

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.

Source : Pubmed