Ma Haijun, Carlin Bradley P, Banerjee Sudipto
MMC 303, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455-0392, USA.
Biometrics. 2010 Jun;66(2):355-64. doi: 10.1111/j.1541-0420.2009.01291.x. Epub 2009 Jul 23.
Hospice service offers a convenient and ethically preferable health-care option for terminally ill patients. However, this option is unavailable to patients in remote areas not served by any hospice system. In this article, we seek to determine the service areas of two particular cancer hospice systems in northeastern Minnesota based only on death counts abstracted from Medicare billing records. The problem is one of spatial boundary analysis, a field that appears statistically underdeveloped for irregular areal (lattice) data, even though most publicly available human health data are of this type. In this article, we suggest a variety of hierarchical models for areal boundary analysis that hierarchically or jointly parameterize both the areas and the edge segments. This leads to conceptually appealing solutions for our data that remain computationally feasible. While our approaches parallel similar developments in statistical image restoration using Markov random fields, important differences arise due to the irregular nature of our lattices, the sparseness and high variability of our data, the existence of important covariate information, and most importantly, our desire for full posterior inference on the boundary. Our results successfully delineate service areas for our two Minnesota hospice systems that sometimes conflict with the hospices' self-reported service areas. We also obtain boundaries for the spatial residuals from our fits, separating regions that differ for reasons yet unaccounted for by our model.
临终关怀服务为绝症患者提供了一种便利且符合伦理道德的医疗保健选择。然而，在没有任何临终关怀系统服务的偏远地区，患者无法获得这种选择。在本文中，我们试图仅根据从医疗保险计费记录中提取的死亡人数来确定明尼苏达州东北部两个特定癌症临终关怀系统的服务区域。这个问题属于空间边界分析领域，尽管大多数公开可用的人类健康数据都是不规则区域（格网）数据，但该领域在统计学上似乎尚未得到充分发展。在本文中，我们提出了多种用于区域边界分析的层次模型，这些模型对区域和边缘段进行分层或联合参数化。这为我们的数据带来了概念上有吸引力的解决方案，并且在计算上仍然可行。虽然我们的方法与使用马尔可夫随机场的统计图像恢复中的类似发展并行，但由于我们格网的不规则性质、数据的稀疏性和高变异性、重要协变量信息的存在，以及最重要的是，我们对边界进行完整后验推断的愿望，出现了重要差异。我们的结果成功地划定了我们明尼苏达州两个临终关怀系统的服务区域，这些区域有时与临终关怀机构自我报告的服务区域相冲突。我们还从拟合中获得了空间残差的边界，将因我们的模型尚未解释的原因而不同的区域分开。
