Case-only Cancer Clustering for Mobile Populations

描述（由申请人提供）：当附近病例几乎同时发生时，癌症病例中的时空相互作用就会出现，并且可能归因于感染性病因或由于暴露导致地理上局部的风险增加。但是现有的检测相互作用的技术并没有考虑到居民的移动性，也没有评估对诱导期和潜伏期的敏感性。对于癌症来说，这是一个重要的问题，因为癌症的潜伏期为十年或更长时间。初步研究使用模拟数据评估方法的统计行为，以评估I型（假阳性）和II型（假阴性）误差，并将该方法应用于密歇根州东南部11个县正在进行的膀胱癌研究中的374例。这些初步研究证明了该方法在个体水平上定位时空相互作用的能力，并准确估计了模拟中使用的延迟和诱导期。该项目将以这些初步结果为基础，正式评估新的聚类技术的可行性，这些技术考虑了住宅流动性、潜伏期和诱导期、相关协变量（如年龄）和风险因素（如吸烟）。第一阶段研究的目标是：目标1。创建住宅移动性可视化的原型软件，计算新的聚类技术，并将结果可视化。目标2。评估地理编码错误对新交互统计的影响。目标3。使用来自密歇根州膀胱癌研究的完整数据集进一步验证应用环境中的方法。这将利用从病例对照研究中获得的关于癌症风险的额外信息，进一步验证使用新的仅限病例方法得出的推论。目标4。评估癌症登记处和癌症控制专业人员日常使用的居住流动数据的可用性。在第二阶段，我们将全面开发软件，建立针对公共卫生专业人员的教育工具，并举办一系列培训讲习班，以迅速传播知识和工具。公共卫生相关性：居住史数据越来越多，提高了癌症登记处和癌症控制专业人员进行常规监测的可能性，这种监测的方式考虑了个人的流动性，并纳入了癌症潜伏期和诱发模型。这些新技术提供了一种机制，用于识别与癌症风险增加相关的地理位置和时间，这些地理位置和时间超出了在地理流动人群中给定协变量和风险因素的预期。这项研究的科技创新有望彻底改变我们的能力，使我们能够基于对癌症时空发病率和死亡率模式的准确理解，并以合理的癌症诱发期和潜伏期为前提，做出合理的癌症监测和控制决策。

DESCRIPTION (provided by applicant): Space-time interaction in cancer cases arises when nearby cases occur at about the same time, and may be attributable to an infectious etiology or from exposures that cause a geographically localized increase in risk. But available techniques for detecting interaction do not account for residential mobility, nor do they evaluate sensitivity to induction and latency periods. This is an important problem for cancer, where latencies of a decade or more occur. Preliminary research evaluated the statistical behavior of the methods using simulated data to assess type I (false positives) and type II (false negatives) error, and applied the methods to 374 cases from an ongoing study of bladder cancer in 11 counties in southeastern Michigan. These preliminary studies demonstrated the ability of the methods to localize space-time interaction at the individual-level, and to accurately estimate the latency and induction periods used in the simulations. This project will build on these preliminary results to formally evaluate the feasibility of the new clustering techniques that account for residential mobility, latency and induction periods, relevant covariates (such as age) and risk factors (such as smoking). The Aims of the Phase I research are to: Aim 1. Create prototype software for visualization of residential mobility, calculation of the new clustering techniques, and visualization of the results. Aim 2. Evaluate the impacts of geocoding error on the new interaction statistics. Aim 3. Further validate the methods in an applied setting using the complete dataset from the study of bladder cancer in Michigan. This will use the additional information on cancer risk available from the case-control study to further validate the inferences drawn using the new case-only methods. Aim 4. Assess the availability of residential mobility data for routine use by cancer registries and cancer control professionals. In Phase II we will fully develop the software, establish educational tools targeting public health professionals, and conduct a series of training workshops to quickly disseminate knowledge and tools. PUBLIC HEALTH RELEVANCE: Residential history data are increasingly available, raising the possibility of routine surveillance in cancer registries and by cancer control professionals in a manner that accounts for individual mobility and that incorporates models of cancer latency and induction. These new techniques provide a mechanism for identifying those geographic locations and times associated with increases in cancer risk above and beyond that expected given covariates and risk factors in geographically mobile populations. The scientific and technologic innovations from this research are expected to revolutionize our ability to make sound cancer surveillance and control decisions based on an accurate understanding of space-time cancer incidence and mortality patterns, and premised on plausible cancer induction and latency periods.