Diabetes prevalence is on the rise in the United Kingdom, 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 involve two spatial frameworks, areas (Middle Layer 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 areal spatial effects to explain GP practice prevalence according to proportions of GP populations resident in different areas. A sparse implementation in RStan of both the multivariate conditional autoregressive (MCAR) and generalised MCAR (GMCAR) with multiple membership 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. Additionally, an area-locality comparison is considered to locate high versus low relative risk clusters.

中文翻译：

---

空间错位健康区域数据的贝叶斯建模：多成员方法

英国的糖尿病患病率正在上升，对于公共卫生战略而言，相对疾病风险的估计和随后的绘图很重要。我们考虑将有关糖尿病患病率和死亡率的伦敦数据应用到伦敦。为了改进对相对风险的估计，我们联合分析了患病率和死亡率数据，以确保对这两个结果的借贷强度。可用数据涉及两个空间框架，区域（中间层超级输出区域，MSOA）和从多个区域招募患者的一般实践（GP）。这提出了一个空间错位问题，我们通过采用多成员原则来处理该问题。具体来说，我们根据居住在不同地区的 GP 人口比例来转换区域空间效应来解释 GP 实践流行。RStan 中多元条件自回归 (MCAR) 和具有多个成员资格的广义 MCAR (GMCAR) 的稀疏实现允许比较这些双变量先验以及探索对两种结果的映射模式的不同影响。糖尿病患病率高于死亡率的必要因果优先级允许 GMCAR 中的特定条件假设，并不总是出现在疾病映射的背景下。此外，区域-地方比较被认为是定位高与低相对风险集群。糖尿病患病率高于死亡率的必要因果优先级允许 GMCAR 中的特定条件假设，并不总是出现在疾病映射的背景下。此外，区域-地方比较被认为是定位高与低相对风险集群。糖尿病患病率高于死亡率的必要因果优先级允许 GMCAR 中的特定条件假设，并不总是出现在疾病映射的背景下。此外，区域-地方比较被认为是定位高与低相对风险集群。