Law Jane
School of Public Health and Health Systems, University of Waterloo, ON, Canada.
AIMS Public Health. 2016 Mar 4;3(1):65-82. doi: 10.3934/publichealth.2016.1.65. eCollection 2016.
Intrinsic conditional autoregressive modeling in a Bayeisan hierarchical framework has been increasingly applied in small-area ecological studies. This study explores the specifications of spatial structure in this Bayesian framework in two aspects: adjacency, i.e., the set of neighbor(s) for each area; and (spatial) weight for each pair of neighbors. Our analysis was based on a small-area study of falling injuries among people age 65 and older in Ontario, Canada, that was aimed to estimate risks and identify risk factors of such falls. In the case study, we observed incorrect adjacencies information caused by deficiencies in the digital map itself. Further, when equal weights was replaced by weights based on a variable of expected count, the range of estimated risks increased, the number of areas with probability of estimated risk greater than one at different probability thresholds increased, and model fit improved. More importantly, significance of a risk factor diminished. Further research to thoroughly investigate different methods of variable weights; quantify the influence of specifications of spatial weights; and develop strategies for better defining spatial structure of a map in small-area analysis in Bayesian hierarchical spatial modeling is recommended.
贝叶斯分层框架下的内在条件自回归建模已越来越多地应用于小区域生态研究。本研究从两个方面探讨了该贝叶斯框架下空间结构的具体情况：邻接性，即每个区域的邻域集合；以及每对邻域的（空间）权重。我们的分析基于加拿大安大略省65岁及以上人群跌倒受伤的小区域研究，旨在估计此类跌倒的风险并识别风险因素。在案例研究中，我们观察到数字地图本身的缺陷导致邻接信息不正确。此外，当基于预期计数变量的权重取代相等权重时，估计风险范围增加，在不同概率阈值下估计风险概率大于1的区域数量增加，且模型拟合得到改善。更重要的是，一个风险因素的显著性降低。建议进行进一步研究，以彻底调查不同的可变权重方法；量化空间权重规范的影响；并制定策略，以便在贝叶斯分层空间建模的小区域分析中更好地定义地图的空间结构。
