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A cross sectional study examining the pattern of childhood obesity in Leeds: affluence is not protective
  1. Kimberley L Edwards*,
  2. Janet E Cade,
  3. Joan K Ransley,
  4. Graham P Clarke
  1. 1 University of Leeds, United Kingdom
  1. Correspondence to: Kimberley L Edwards, Division of epidemiology and biostatistics, University of Leeds, Room 8.49 Worsley Building, University of Leeds, Leeds, LS2 9NL, United Kingdom; k.l.edwards{at}


Background: The aim of this paper was to investigate variations in childhood obesity globally and spatially at the micro-level across Leeds.

Methods: Body mass index data from three sources were used. Children were aged 3-13 years. Obesity was defined as above the 98th centile (British reference dataset). The data were analysed by age group and gender, then tested for significant micro-level hot spots of childhood obesity using a spatial scan statistic and a 2-level multi-level model.

Results: Older children (13y) were 2.5 times (95%CI: 2.1 to 3.1) more likely to be obese than younger children (3y). Childhood obesity was significantly associated with both deprived and affluent areas. ‘Blue collar communities’, ‘Constrained by circumstances’, and ‘Multicultural’ had significantly higher (relative risk: 1.1, 1.2, 1.2; 95%CI: 1.0 to 1.2, 1.1 to 1.2, 1.1 to 1.3, respectively), ‘Typical traits’ and ‘Prospering suburbs’ had significantly lower (RR: 0.9, 0.8; 95%CI: 0.8 to 1.0, 0.7 to 0.9, respectively), obesity levels. In the unadjusted model, obesity ‘hot spots’ were found in deprived (RR 1.5) and affluent (RR 6.1) areas. After adjusting for demographic covariates, hot spots were found only in affluent areas (RR 1.6 to 1.9), and cold spots in affluent (RR 1.3 to 4.4) and deprived (RR up to 1.1) areas.

Conclusion: These results suggest there is either a spread of obesity across socio-economic groups and/or something special about the high/low prevalence areas that affects the likelihood of obesity. The micro-level spatial analyses displayed the variations in obesity across Leeds thoroughly, identifying high-risk populations.

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