Edwards Kimberley L, Clarke Graham P
Centre of Epidemiology and Biostatistics, University of Leeds, Leeds, United Kingdom.
Soc Sci Med. 2009 Oct;69(7):1127-34. doi: 10.1016/j.socscimed.2009.07.037. Epub 2009 Aug 18.
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.
致胖环境是肥胖患病率迅速上升的主要原因。在微观层面研究肥胖与致胖变量之间的关系，将增进我们对肥胖风险因素局部差异的理解。SimObesity是一个空间微观模拟模型，旨在对英国利兹市的致胖环境变量进行微观层面的估计：该模型包含大量健康、环境和社会经济变量。它使用重新加权确定性算法，将两项全国性调查中的个体微观数据与粗略地理数据，以及2001年英国人口普查中地理尺度更精细的数据相结合。这就创建了一个具有调查和人口普查数据集属性的利兹市个体/家庭合成人口。逻辑回归分析确定合适的约束变量以供使用。该模型通过线性回归和等方差t检验进行验证。收集了利兹大都市区3至13岁儿童的身高、体重、年龄、性别和居住邮政编码数据，肥胖定义为高于英国参考数据集的第98百分位。地理加权回归用于研究不同致胖环境与儿童肥胖之间的关系。验证表明小区域估计是稳健的。绘制了不同的致胖环境以及相应局部回归分析的参数估计值，所有这些都表明存在非平稳关系。这些结果表明，社会资本和贫困与儿童肥胖密切相关。本文展示了一种在小区域层面估计健康变量的方法。该技术的关键在于模型输入变量的选择，这些变量必须是输出变量的预测因子；这一因素在其他空间微观模拟工作中并未得到强调。它还为儿童致胖环境的存在提供了进一步的证据。
