Epidemiologists commonly use regional aggregates of health outcomes to map mortality or incidence rates and identify geographic disparities. However, to detect health disparities across regions, it is necessary to identify "difference boundaries" that separate neighboring regions with significantly different spatial effects. This can be particularly challenging when dealing with multiple outcomes for each unit and accounting for dependence among diseases and across areal units. In this study, we address the issue of multivariate difference boundary detection for correlated diseases by formulating the problem in terms of Bayesian pairwise multiple comparisons by extending it through the introduction of adjacency modeling and disease graph dependencies. Specifically, we seek the posterior probabilities of neighboring spatial effects being different. To accomplish this, we adopt a class of multivariate areally referenced Dirichlet process models that accommodate spatial and interdisease dependence by endowing the spatial random effects with a discrete probability law. Our method is evaluated through simulation studies and applied to detect difference boundaries for multiple cancers using data from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute.

翻译：流行病学家通常使用健康结果的区域汇总来绘制死亡率或发病率地图，并识别地理差异。然而，要检测区域间的健康差异，必须识别出将相邻区域（具有显著不同的空间效应）分隔开的"差异边界"。当处理每个单位的多个结果，并考虑疾病间和区域单元间的依赖关系时，这尤其具有挑战性。在本研究中，我们通过将贝叶斯成对多重比较方法扩展，引入邻接建模和疾病图依赖关系，解决了关联疾病的多元差异边界检测问题。具体而言，我们寻求相邻空间效应不同的后验概率。为此，我们采用了一类多元区域参考狄利克雷过程模型，该模型通过为空间随机效应赋予离散概率律，来容纳空间依赖和疾病间依赖。我们的方法通过模拟研究进行评估，并应用于使用美国国家癌症研究所的监测、流行病学和最终结果计划数据检测多种癌症的差异边界。