论文标题
用于空间不对对准健康的贝叶斯建模数据：多元会员方法
Bayesian modelling for spatially misaligned health areal data: a multiple membership approach
论文作者
论文摘要
糖尿病患病率在英国正在上升，对于公共卫生战略，相对疾病风险的估计以及随后的制图很重要。我们考虑对伦敦糖尿病患病率和死亡率的数据申请。为了提高相对风险的估计，我们分析了共同的流行率和死亡率数据，以确保对两个结果的借贷强度。可用的数据涉及两个空间框架，区域（中级超级输出区域，MSOA）和一般实践（GPS），招募了来自多个地区的患者。这引发了我们通过采用多个成员原则来处理的空间错位问题。具体而言，我们会根据居住在不同地区的GP种群的比例来解释区域空间效应，以解释GP实践的患病率。 MCAR和GMCAR的Stan稀疏实现允许对这些双变量先验进行比较，并探索对这两个结果的映射模式的不同含义。糖尿病患病率在死亡率上的必要因果优先级允许在GMCAR中具有特定的条件假设，并不总是在疾病制图的背景下存在。
Diabetes prevalence is on the rise in the UK, and for public health strategy, estimation of relative disease risk and subsequent mapping is important. We consider an application to London data on diabetes prevalence and mortality. In order to improve the estimation of relative risks we analyse jointly prevalence and mortality data to ensure borrowing strength over the two outcomes. The available data involves two spatial frameworks, areas (middle level super output areas, MSOAs), and general practices (GPs) recruiting patients from several areas. This raises a spatial misalignment issue that we deal with by employing the multiple membership principle. Specifically we translate area spatial effects to explain GP practice prevalence according to proportions of GP populations resident in different areas. A sparse implementation in Stan of both the MCAR and GMCAR allows the comparison of these bivariate priors as well as exploring the different implications for the mapping patterns for both outcomes. The necessary causal precedence of diabetes prevalence over mortality allows a specific conditionality assumption in the GMCAR, not always present in the context of disease mapping.
