Case-only Cancer Clustering for Mobile Populations

描述(由申请人提供)：当附近的病例几乎同时发生时，癌症病例中就会出现时空相互作用，并可能归因于感染病因或暴露导致地理上局部风险增加。但是，现有的检测相互作用的技术并没有考虑居民的流动性，也没有评估对诱导期和潜伏期的敏感性。这对癌症来说是一个重要的问题，癌症的潜伏期长达十年或更长时间。初步研究评估了使用模拟数据评估I类(假阳性)和II类(假阴性)错误的方法的统计行为，并将这些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的374例病例。这些初步研究表明，这些方法能够在个体水平上定位时空相互作用，并准确估计模拟中使用的潜伏期和感应期。该项目将以这些初步结果为基础，正式评估新的集群技术的可行性，这些技术考虑了住宅流动性、潜伏期和感应期、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.创建住宅流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。目的2.评估地理编码误差对新交互统计量的影响。目的3.使用密歇根州膀胱癌研究的完整数据集在应用环境中进一步验证方法。这将使用病例对照研究中可获得的关于癌症风险的额外信息来进一步验证使用新的仅病例方法得出的推断。目的4.评估癌症登记处和癌症控制专业人员日常使用的住宅流动性数据的可用性。在第二阶段，我们将全面开发该软件，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，以快速传播知识和工具。与公共健康相关：居住史数据越来越多，这增加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，这种监测方式考虑了个人的流动性，并纳入了癌症潜伏期和诱因的模型。这些新技术提供了一种机制，用于在给定协变量和地理流动人口中的风险因素的情况下，确定与癌症风险增加相关的地理位置和时间，超过预期。这项研究的科学和技术创新有望彻底改变我们做出合理癌症监测和控制决策的能力，这种能力是基于对时空癌症发病率和死亡率模式的准确理解，并以合理的癌症诱导期和潜伏期为前提。

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.