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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. [ABSTRACT FROM AUTHOR]

Background: Childhood obesity is high on the policy agenda of wealthier nations, and many interventions have been developed to address it. This work describes an overview of schemes for obese and overweight children and young people in England, and the 'mapping' approach we used.Methods: Our search strategy, inclusion criteria and coding frame had to be suitable for describing a potentially large number of schemes within a short timeframe. Data were collected from key informants, scheme publicity and reports, and via a web-survey. To be included, schemes had to be based in England, follow a structured programme lasting at least two weeks, promote healthy weight, and be delivered exclusively to overweight and/or obese children and young people (age range 4-18). Data were entered into a coding frame recording similar information for each scheme, including any underpinning research evidence, evaluation or monitoring reports. Priority questions were identified in consultation with colleagues from the Department of Health and the Cross Government Obesity Unit.Results: Fifty-one schemes were identified. Some operated in multiple areas, and by using estimates of the number of schemes provided by multi-site scheme leads, we found that between 314 and 375 local programmes were running at any time. Uncertainty is largely due to the largest scheme provider undergoing rapid expansion at the time of the mapping exercise and therefore able to provide only an estimate of the number of programmes running. Many schemes were similar in their approach, had been recently established and were following NICE guidelines on interventions to promote healthy weight. Rigorous evaluation was rare.Conclusions: Our methods enabled us to produce a rapid overview of service activity across a wide geographic area and a range of organisations and sectors. In order to develop the evidence base for childhood obesity interventions, rigorous evaluation of these schemes is required. This overview can serve as a starting point for evaluations of interventions to address obesity. More generally, a rapid and systematic approach of this type is transferable to other types of service activity in health and social care, and may be a tool to inform public health planning. [ABSTRACT FROM AUTHOR]

Objective: To estimate the prevalence of underweight between 1998 and 2006 in Liverpool schoolchildren aged 9-10 years using recently published underweight cut-off points. Design and setting: Stature and body mass data collected at the Liverpool SportsLinx project's fitness testing sessions were used to calculate BMI. Subjects: Data were available on 26 782 (n 13 637 boys, 13 145 girls) participants. Results: Overall underweight declined in boys from 10?3% in 1998-1999 to 6?9% in 2005-2006, and all sub-classifications of underweight declined, in particular grade 3 underweight, with the most recent prevalence being 0?1%. In girls, the prevalence of underweight declined from 10?8% in 1998-1999 to 7?5% in 2005-2006. The prevalence of all grades of underweight was higher in girls than in boys. Underweight showed a fluctuating pattern across all grades over time for boys and girls, and overall prevalence in 2005-2006 represents over 200 children across the city. Conclusions: Underweight may have reduced slightly from baseline, but remains a substantial problem in Liverpool, with the prevalence of overall underweight being relatively similar to the prevalence of obesity. The present study highlights the requirement for policy makers and funders to consider both ends of the body mass spectrum when fixing priorities in child health. [ABSTRACT FROM AUTHOR]