The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds, UK: SimObesity

• Published in: Social science & medicine (1982) 2009-10, Vol.69 (7), p.1127-1134
• Main Authors: Edwards, Kimberley L., Clarke, Graham P.
• Format: Article
• Language: English
• Subjects: Adolescent; Biological and medical sciences; Child; Child welfare; Child, Preschool; Childhood; Children; Childrens health; Cities; Computer Simulation; Environment Design; General aspects; Health; Health Behavior; Humans; Linear Models; Logistic Models; Medical sciences; Metabolic diseases; Miscellaneous; Models, Statistical; Obesity; Obesity - epidemiology; Obesity Obesogenic environment Small-area simulations Spatial microsimulation modelling UK Children; Obesogenic environment; Polls & surveys; Public health. Hygiene; Public health. Hygiene-occupational medicine; Regression analysis; Risk Factors; Simulation; Small-area simulations; Social Environment; Social research; Socioeconomic Factors; Spatial microsimulation modelling; Topography, Medical - methods; United Kingdom; United Kingdom - epidemiology; Validation studies
• ISSN: 0277-9536; 1873-5347
• DOI: 10.1016/j.socscimed.2009.07.037

Obesogenic environments are a major explanation for the rapidly increasing prevalence in obesity. Investigating the relationship between obesity and obesogenic variables at the micro-level will increase our understanding about local differences in risk factors for obesity. SimObesity is a spatial microsimulation model designed to create micro-level estimates of obesogenic environment variables in the city of Leeds in the UK: consisting of a plethora of health, environment, and socio-economic variables. It combines individual micro-data from two national surveys with a coarse geography, with geographically finer scaled data from the 2001 UK Census, using a reweighting deterministic algorithm. This creates a synthetic population of individuals/households in Leeds with attributes from both the survey and census datasets. Logistic regression analyses identify suitable constraint variables to use. The model is validated using linear regression and equal variance t-tests. Height, weight, age, gender, and residential postcode data were collected on children aged 3–13 years in the Leeds metropolitan area, and obesity described as above the 98th centile for the British reference dataset. Geographically weighted regression is used to investigate the relationship between different obesogenic environments and childhood obesity. Validation shows that the small-area estimates were robust. The different obesogenic environments, as well as the parameter estimates from the corresponding local regression analyses, are mapped, all of which demonstrate non-stationary relationships. These results show that social capital and poverty are strongly associated with childhood obesity. This paper demonstrates a methodology to estimate health variables at the small-area level. The key to this technique is the choice of the model's input variables, which must be predictors for the output variables; this factor has not been stressed in other spatial microsimulation work. It also provides further evidence for the existence of obesogenic environments for children.