Space-Time Clustering of Non-Hodgkin's Lymphoma Using Residential Histories

描述(由申请人提供)： 这项拟议的研究项目的主要目标是在美国一项大型、多中心、以人群为基础的研究中，通过调查病例和对照的当前和过去居住地的时空聚集性，产生关于非霍奇金淋巴瘤(NHL)病因学的有价值的见解。尽管全世界每年确诊的非霍奇金淋巴瘤病例超过30万例，约占所有癌症的2.8%，但人们对非霍奇金淋巴瘤的病因知之甚少。作为一个整体，已确定的和推定的风险因素只占每年发生的NHL病例总数的一小部分，需要新的技术来解开这个谜团。我们的研究小组最近开发了Q统计和时空信息系统(STISTM)技术，使时空集群分析成为可能。其他分析癌症聚集性的方法通常忽略居住地的流动性，几乎只使用诊断时或死亡时居住地的静态空间点分布。此外，很少有空间技术能够充分考虑已知的风险因素和协变量。我们的方法通过利用被代表为生命线的参与者的居住历史来满足每一种需求，从而评估相对于对照的居住历史，在案例的居住历史的生命过程中的任何时刻的时空聚集性。此外，Q统计量取代了广泛使用的(但通常是不适当的)空间随机性零假设，它可以根据每个人的已知风险因素和协变量纳入每个人成为病例的概率。 该项目将应用创新的Q-统计和STISTM技术来识别非霍奇金淋巴瘤的空间聚集，在案例的居住历史相对于对照的居住历史的生命过程中的任何时刻。这将有史以来第一次允许流行病学家使用现在和过去的住所来评估NHL可能的地理和时间聚集性。这项拟议的研究是对一个大型的、仔细收集的预先存在的多中心数据集(1321例，1057名对照)的二次分析，目的是产生与NHL相关的潜在病因的新假设，以便在未来的研究中进行调查。

DESCRIPTION (provided by applicant): The primary objective of the proposed research project is to generate valuable insights concerning the etiology of Non-Hodgkin's lymphoma (NHL) by investigating space-time clustering of current and past residences of cases and controls in a large, multi-center, population-based study in the United States. Despite more than 300,000 cases of NHL diagnosed each year worldwide, accounting for approximately 2.8% of all cancers, relatively little is known about the etiology of NHL. The established and presumptive risk factors, taken as a whole, account for only a small proportion of the total NHL cases that occur annually, and novel techniques are needed to solve this enigma. Our research team has recently developed Q-statistics and Space-Time Information System (STISTM) technology that enable space-time cluster analyses. Other methods for analyzing cancer clusters typically ignore residential mobility, and almost exclusively work with static spatial point distributions of place-of-residence at time of diagnosis or time of death. In addition, few spatial techniques adequately account for known risk factors and covariates. Our approach addresses each of these needs by utilizing the residential history of the participants represented as a life-line, and thus evaluates space-time clustering at any moment in the life-course of the residential histories of the cases relative to the residential histories of the controls. In addition, in place of the widely used (but often inappropriate) null hypothesis of spatial randomness, Q-statistics can incorporate each individual's probability of being a case based on his/her known risk factors and covariates. This project will apply the innovative Q-statistics and STISTM technology to identify spatial clustering of NHL at any moment in the life-course of the residential histories of the cases relative to the residential histories of the controls. This will, for the first time ever, allow epidemiologists to assess possible geographic and temporal clusters of NHL incidence using current and past residences. The proposed research is a secondary analysis of a large, carefully collected pre-exiting multi-center dataset (1321 cases, 1057 controls), with the intended purpose of generating new hypotheses about potential etiologic factors associated with NHL to be investigated in future studies.