Models for cumulative spatial-temporal assessment of non-Hodgkin lymphoma risk

描述（由申请人提供）：许多癌症已经确定或怀疑的风险因素在环境中分布不均匀。探索癌症发病率和死亡率的空间模式可以识别风险显著升高的区域，并提供潜在的病因学线索。越来越多地，在诊断或研究登记时的居住地址，甚至完整的居住史收集在癌症的流行病学研究，使更精细的空间分析比生态研究。在癌症风险的空间流行病学研究中，住宅位置通常被用作家中和周围发生的未知环境暴露的替代。然而，在流行病学研究中，当考虑风险的时间维度时，在癌症的空间分析中出现了一些方法学上的挑战。当使用诊断时的居住地点作为环境暴露的标志时，没有评估累积的环境暴露，也忽略了疾病潜伏期和人口流动性。有必要开发和评估统计方法，以模拟累积的时空癌症风险的流行病学研究与居住史，同时占人口流动性，疾病潜伏期，和已知的疾病危险因素。本研究的具体目的是：1）开发癌症风险的累积时空模型，并将其应用于具有居住史的非霍奇金淋巴瘤（NHL）的病例对照研究; 2）评价统计模型检测随时间推移的重大风险区域的准确性; 3）调查可能与任何检测到的风险显著升高的区域相关的可能暴露。NHL适合于空间模式分析，因为它是一种病因不明的癌症，其确定的风险因素仅占美国每年NHL病例总数的一小部分。本研究的预期成果将是：1）新的统计方法来模拟时空风险，考虑生命过程中的环境暴露，2）模型的准确性进行全面评估，3）在美国的四个地区（底特律，爱荷华州，洛杉矶，西雅图）的NHL在空间和时间上的重大风险的地区识别。这项研究的意义是双重的。首先，发展和评价更好地考虑生命过程环境暴露的时空风险分析新方法将推动空间分析研究领域的发展。其次，这将是NHL病例对照研究的第一个累积时空风险分析，该研究还调整了已知的环境，遗传和人口统计学风险因素。这项研究申请中的工作将作为国家癌症研究所更大规模赠款申请的基础，以模拟癌症风险的时空不确定性。评估新的和现有的方法的性能，随着时间的推移，癌症的空间风险建模将被用来指导方法的选择和完善，包括在以后的应用程序，并证明所提出的方法对其他癌症的好处。

DESCRIPTION (provided by applicant): Many cancers have established or suspected risk factors that are distributed unevenly in the environment. Exploring spatial patterns of cancer incidence and mortality can identify areas of significantly elevated risk and provide potential etiologic clues. Increasingly, residential addresses at time of diagnosis or study enrollment and even complete residential histories are collected in epidemiologic studies of cancer that enable a finer level of spatial analysis than in ecological studies. In spatial epidemiology studies of cancer risk the residential location is often used as a surrogate for the unknown environmental exposures that occur in and around the home. However, several methodological challenges arise in spatial analysis of cancer in epidemiologic studies when considering the temporal dimension of risk. Cumulative environmental exposures are not assessed and disease latency and population mobility are ignored when using the residential location at time of diagnosis as a marker for environmental exposures. There is a need for development and assessment of statistical methods to model cumulative spatial-temporal cancer risk in epidemiologic studies with residential histories while accounting for population mobility, disease latency, and known disease risk factors. The specific aims of this research are 1) to develop cumulative spatial-temporal models of cancer risk and apply them to a case-control study of non-Hodgkin lymphoma (NHL) with residential histories, 2) to evaluate the accuracy of the statistical models for detecting areas of significant risk over time, and 3) to investigate possible exposures that may be associated with any detected areas of significantly elevated risk. NHL is suitable for a spatial pattern analysis because it is a cancer with an unclear etiology and established risk factors that account for only a small proportion of the total annual NHL cases in the United States. The expected outcomes of this research will be 1) new statistical approaches to model spatial-temporal risk that consider life-course environmental exposures, 2) a thorough assessment of the accuracy of the models, and 3) identification of areas of significant risk of NHL in space and time in four areas (Detroit, Iowa, Los Angeles, Seattle) of the United States. The significance of this research is two-fold. First, the development and evaluation of new approaches to spatial-temporal risk analysis that better consider life-course environmental exposures will advance the field of spatial analysis research. Second, this will be the first cumulative spatial-temporal risk analysis of a case-control study of NHL, which also adjusts for known environmental, genetic, and demographic risk factors. The work in this research application will serve as the foundation for a larger grant application to the National Cancer Institute to model spatial-temporal uncertainty in cancer risk. The assessment of the performance of new and existing methods for modeling spatial risk of cancer over time will be used to guide the selection and refinement of methods for inclusion in the later application and to demonstrate the benefits of the proposed approach to other cancers.