We present an approach to estimate distance-dependent heterogeneous associations between point-referenced exposures to built environment characteristics and health outcomes. By estimating associations that depend non-linearly on distance between subjects and point-referenced exposures, this method addresses the modifiable area-unit problem that is pervasive in the built environment literature. Additionally, by estimating heterogeneous effects, the method also addresses the uncertain geographic context problem. The key innovation of our method is to combine ideas from the non-parametric function estimation literature and the Bayesian Dirichlet process literature. The former is used to estimate nonlinear associations between subject's outcomes and proximate built environment features, and the latter identifies clusters within the population that have different effects. We study this method in simulations and apply our model to study heterogeneity in the association between fast food restaurant availability and weight status of children attending schools in Los Angeles, California.
翻译:我们提出一种方法来估计与建筑环境特征和健康结果相接触的点参照值之间的远距离差异性联系; 通过估计非线性联系取决于实验对象与点参考接触的距离,这种方法解决了建筑环境文献中普遍存在的可变地区单位问题; 此外,通过估计多种影响,这种方法还解决了不确定的地理背景问题; 我们方法的主要创新是将非参数估计文献和巴耶斯迪里赫莱进程文献中的观点结合起来; 前者用于估计实验对象结果与附近建筑环境特征之间的非线性联系,后者用于查明人口中具有不同影响的群集; 我们在模拟中研究这一方法,并运用我们的模型研究快餐餐厅供应量与加利福尼亚州洛杉矶学校儿童体重状况之间的关联。