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危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格局可以确定风险显著升高的区域，并提供潜在的病因线索。在癌症流行病学研究中，越来越多地收集了诊断或研究登记时的居住地址，甚至完整的居住历史，这使得空间分析比生态学研究更精细。在癌症风险的空间流行病学研究中，居住地点经常被用作家庭内部和周围未知环境暴露的替代。然而，在考虑风险的时间维度时，在流行病学研究中对癌症的空间分析中出现了一些方法学上的挑战。在使用诊断时的居住地作为环境暴露的标志时，不评估累积环境暴露，忽略疾病潜伏期和人口流动性。有必要开发和评估统计方法，在考虑人口流动性、疾病潜伏期和已知疾病风险因素的同时，对具有居住史的流行病学研究中的累积时空癌症风险进行建模。本研究的具体目的是：1)建立癌症风险的累积时空模型，并将其应用于有居住史的非霍奇金淋巴瘤（NHL）的病例对照研究；2)评估统计模型在检测显著风险区域时的准确性；3)调查可能与任何检测到的显著风险升高区域相关的可能暴露。NHL适合进行空间模式分析，因为它是一种病因不明确、危险因素确定的癌症，仅占美国每年NHL病例总数的一小部分。本研究的预期结果将是：(1)考虑生命过程中环境暴露的时空风险模型的新统计方法；(2)对模型的准确性进行全面评估；(3)确定美国四个地区（底特律、爱荷华州、洛杉矶、西雅图）NHL在空间和时间上的显著风险区域。这项研究的意义是双重的。首先，更好地考虑生命过程环境暴露的时空风险分析新方法的发展和评价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、遗传和人口风险因素。这项研究申请的工作将作为国家癌症研究所更大的拨款申请的基础，以模拟癌症风险的时空不确定性。随着时间的推移，对癌症空间风险建模的新方法和现有方法的性能进行评估，将用于指导选择和改进方法，以便在以后的应用中纳入，并证明所提出的方法对其他癌症的益处。

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.