Hanson Craig E, Wieczorek William F
Center for Health & Social Research, Buffalo State College, NY 14222, USA.
Soc Sci Med. 2002 Sep;55(5):791-802. doi: 10.1016/s0277-9536(01)00203-9.
The identification of spatial clusters of alcohol mortality can be a key tool in identifying locations that are suffering from alcohol-related problems or are at risk of experiencing those types of problems. This study compares two methods for identifying statistically significant spatial clusters of county-level alcohol mortality rates in New York. One method utilizes a local indicator of spatial association to determine which groups of neighboring counties have rates that are significantly related to each other. The other method is a spatial scan technique that calculates a maximum likelihood ratio of cases relative to the underlying population to identify the group of counties that rejects the null hypothesis of "no clustering". The results show that because each technique bases its cluster detection on its own criteria, different counties are selected by each method. However, the overlap of the selections indicates that the two analytic methods illustrate different elements of the same clusters. Consequently, these spatial analytic techniques are seen as complimentary and are best used in tandem rather than individually. These findings suggest that multiple methods are a preferred approach to identifying clusters of alcohol-related mortality at the county level.
识别酒精死亡率的空间集群可以成为确定那些正遭受与酒精相关问题或有面临此类问题风险地点的关键工具。本研究比较了两种用于识别纽约州县级酒精死亡率具有统计学显著意义的空间集群的方法。一种方法利用空间关联的局部指标来确定哪些相邻县组的死亡率彼此显著相关。另一种方法是空间扫描技术，该技术计算病例相对于潜在人口的最大似然比，以识别拒绝“无集群”零假设的县组。结果表明，由于每种技术基于自身标准进行集群检测，每种方法选择的县不同。然而，所选结果的重叠表明这两种分析方法说明了同一集群的不同要素。因此，这些空间分析技术被视为互补的，最好串联使用而非单独使用。这些发现表明，多种方法是识别县级与酒精相关死亡率集群的首选方法。
