Global, local and focused geographic clustering for case-control data with residential histories.

Global, local and focused geographic clustering for case-control data with residential histories.
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DOI:
10.1186/1476-069x-4-4
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发表时间:
2005-03-22
期刊:
Environmental health : a global access science source
影响因子:
--
通讯作者:
Nriagu, Jerome
Nriagu, Jerome
中科院分区:
其他
文献类型:
--
作者:
Jacquez, Geoffrey M;Kaufmann, Andy;Nriagu, Jerome

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背景:本文介绍了一种新的方法来评估聚类的病例对照数据,占居住历史。虽然已经提出了许多统计评估本地,集中和全球聚类的健康结果,很少,如果有的话,存在用于评估集群时,个人是移动的。方法:本地,全球和集中的测试住宅历史的基础上开发的矩阵最近邻关系,反映不断变化的拓扑结构的情况和控制。暴露痕迹被定义为考虑暴露和疾病表现之间的潜伏期,并且使用持续时间可能不同的暴露窗口。几个这样推导出的方法被应用到评估集群的居住史在密歇根州东南部的膀胱癌的病例对照研究。这些数据仍在收集和分析进行演示purposesonly.RESULTS:统计学显着聚集的居住历史的情况下被发现,但可能是由于延迟报告的情况下,由医院参与研究之一:结论:数据与居住历史是最好的,当致病暴露和疾病潜伏期发生在一个足够长的时间跨度,人类的流动性问题。为了分析这些数据,需要考虑居住历史的方法。
BACKGROUND: This paper introduces a new approach for evaluating clustering in case-control data that accounts for residential histories. Although many statistics have been proposed for assessing local, focused and global clustering in health outcomes, few, if any, exist for evaluating clusters when individuals are mobile.METHODS: Local, global and focused tests for residential histories are developed based on sets of matrices of nearest neighbor relationships that reflect the changing topology of cases and controls. Exposure traces are defined that account for the latency between exposure and disease manifestation, and that use exposure windows whose duration may vary. Several of the methods so derived are applied to evaluate clustering of residential histories in a case-control study of bladder cancer in south eastern Michigan. These data are still being collected and the analysis is conducted for demonstration purposes only.RESULTS: Statistically significant clustering of residential histories of cases was found but is likely due to delayed reporting of cases by one of the hospitals participating in the study.CONCLUSION: Data with residential histories are preferable when causative exposures and disease latencies occur on a long enough time span that human mobility matters. To analyze such data, methods are needed that take residential histories into account.