The investigation of perceived geographical disease clusters serves as a preliminary step that expedites subsequent etiological studies and analysis of epidemicity. With the identification of disease clusters of statistical significance, to determine whether or not the detected disease clusters can be explained by known or suspected risk factors is a logical next step. The models allowing for confounding variables permit the investigators to determine if some risk factors can explain the occurrence of geographical clustering of disease incidence and to investigate other hidden spatially related risk factors if there still exist geographical disease clusters, after adjusting for risk factors. We propose to develop statistical methods for differentiating incidence intensity of geographical disease clusters of peak incidence and low incidence in a hierarchical manner, adjusted for confounding variables. The methods prioritize the areas with the highest or lowest incidence anomalies and are designed to recognize hierarchical (in intensity) disease clusters of respectively high-risk areas and low-risk areas within close geographic proximity on a map, with the adjustment for known or suspected risk factors. The data on spatial occurrence of sudden infant death syndrome with a confounding variable of race in North Carolina counties were analyzed, using the proposed methods. The proposed Poisson model appears better than the one based on SMR, particularly at facilitating discrimination between the 13 counties with no cases. Our study showed that the difference in racial distribution of live births explained, to a large extent, the 3 previously identified hierarchical high-intensity clusters, and a small region of 4 mutually adjacent counties with the higher race-adjusted rates, which was hidden previously, emerged in the southwest, indicating that unobserved spatially related risk factors may cause the elevated risk. We also showed that a large geographical cluster with the low race-adjusted rates, which was hidden previously, emerged in the mid-east. With the information on hierarchy in adjusted intensity levels, epidemiologists and public health officials can better prioritize the regions with the highest rates for thorough etiologic studies, seeking hidden spatially related risk factors and precisely moving resources to areas with genuine highest abnormalities.

中文翻译：

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将异常疾病强度与空间中的混杂变量区分开来。

对感知到的地理疾病集群的调查是加速后续病原学研究和流行病分析的初步步骤。随着具有统计意义的疾病集群的识别，确定检测到的疾病集群是否可以用已知或可疑的风险因素来解释是合乎逻辑的下一步。允许混杂变量的模型允许研究人员确定某些风险因素是否可以解释疾病发病率的地理聚类的发生，并在调整风险因素后调查其他隐藏的空间相关风险因素（如果仍然存在地理疾病聚类）。我们建议开发统计方法，以分层方式区分高峰发病率和低发病率的地理疾病集群的发病强度，并针对混杂变量进行调整。这些方法优先考虑具有最高或最低发病率异常的区域，并旨在识别地图上地理接近的高风险区域和低风险区域的分级（强度）疾病集群，并根据已知或疑似情况进行调整风险因素。使用所提出的方法分析了北卡罗来纳州各县具有种族混杂变量的婴儿猝死综合征的空间发生数据。提出的泊松模型似乎比基于 SMR 的模型更好，特别是在促进 13 个没有病例的县之间的歧视方面。我们的研究表明，活产儿种族分布的差异在很大程度上解释了先前确定的 3 个等级高强度集群，以及具有较高种族调整率的 4 个相互相邻县的小区域，这在以前是隐藏的，在西南地区出现，表明未观察到的空间相关风险因素可能导致风险升高。我们还表明，在中东出现了一个以前隐藏的低种族调整率的大型地理集群。借助调整后强度级别的等级信息，流行病学家和公共卫生官员可以更好地优先考虑发生率最高的区域，以进行彻底的病因学研究，寻找隐藏的空间相关风险因素，并将资源精确地转移到真正异常最高的区域。