Bayesian Risk Modeling of Racial-spatial Interactions Among Childhood Cancer Hist

儿童癌症患者种族空间相互作用的贝叶斯风险模型

• 批准号: 8139272
• 负责人: JAMES A THOMPSON
• 金额: $ 7.11万
• 依托单位: TEXAS A&M UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8139272
• 关键词: Address; Cancer Cluster; Cancer Etiology; Characteristics; Child; Complex; Databases; Development; Disease; Environmental Exposure; Epidemiologic Studies; Epidemiologist; Epidemiology; Etiology; Evaluation; Exposure to; Fetus; Genetic; Genetic Predisposition to Disease; Geographic Factor; Goals; Hispanics; Individual; Investigation; Knowledge; Lead; Link; Location; Malignant Childhood Neoplasm; Malignant Neoplasms; Markov Chains; Methods; Modeling; Mutagens; Outcome; Pathway interactions; Pattern; Pollution; Predisposition; Publishing; Race; Reproduction; Research; Risk; Risk Estimate; Risk Factors; Science; Source; Surface; Testing; Texas; Toxic Environmental Substances; Toxicant exposure; Toxicology; Training; Validation; Work; abstracting; base; cancer risk; cancer type; college; environmental toxicology; experience; high risk; innovation; interest; model development; novel; novel strategies; prenatal; programs; public health relevance; racial difference; reproductive

DESCRIPTION (provided by applicant): Project Summary/Abstract. Identification of the causes of childhood cancer has been elusive with two exceptions. Geographic risks, like proximity to pollution sources, are often implicated and racial differences in risk have been profound. Interpreting how geographic exposures and individual characteristics, like race, act and interact among multiple cancer types is the current impasse. To overcome this obstacle, we need a risk modeling approach that incorporates both geographic-level and individual-level risks and can facilitate the objective pooling or parsing of cancer types. Two recent modeling developments present a promising opportunity. First, it is now possible to disentangle individual risk factors from geographic risk factors utilizing hierarchical modeling. Second, it is now possible for a single model to incorporate risks for multiple outcomes, like cancer histotypes. This application calls for developing a childhood cancer risk model that capitalizes on these two recent advances. The objective of this application is to establish a modeling approach that is both fully hierarchical and inclusive of multiple childhood cancer histotypes. The central hypothesis is that race, as the most important example of individual-level risk factors, and geographic-level exposures combine to cause multiple childhood cancer histotypes. This hypothesis will be tested by two specific aims: Specific Aim 1 is to model the small-scale spatial patterns of childhood cancer risks. The results of this specific aim will be detailed risk surfaces capable of demonstrating risk patterns and risk clusters. These modeling results will enable focused investigation, including focused risk modeling, for high-risk locations. Specific Aim 2 is to identify the spatially specific racial risks for childhood cancer. This specific aim will utilize recently developed risk modeling for spatially varying risks. The results of this specific aim will be multiple path analyses and will clarify causal pathways for childhood cancer. This application is innovative because it will exploit two new developments, fully conditional hierarchical modeling and multivariate modeling. PUBLIC HEALTH RELEVANCE: Project Narrative. The proposed research is significant because what is needed most to advance our understanding of the causes of childhood cancer is a risk model that incorporates both geographic risks and individual risks and applies to the multiple types of childhood cancer. The study will validate a risk modeling approach that will enable the elucidation of the causal mechanisms for childhood cancer.

描述（由申请人提供）：项目摘要/摘要。除了两个例外，儿童癌症的病因一直难以确定。地理风险，如接近污染源，往往涉及和种族差异的风险已经深刻。解释地理暴露和个人特征，如种族，如何在多种癌症类型之间起作用和相互作用，是目前的僵局。为了克服这一障碍，我们需要一种风险建模方法，该方法将地理层面和个人层面的风险结合起来，并有助于客观汇集或分析癌症类型。最近的两个建模发展提供了一个很有希望的机会。首先，现在可以利用分层建模将个体风险因素与地理风险因素分开。其次，现在可以用一个模型来整合多种结果的风险，比如癌症组织分型。这项申请要求开发一个儿童癌症风险模型，利用这两个最新的进展。本申请的目的是建立一种建模方法，该方法既完全分层又包含多种儿童癌症组织型。中心假设是种族，作为个人水平风险因素的最重要例子，和地理水平暴露联合收割机结合导致多种儿童癌症组织类型。这一假设将通过两个具体目标进行检验：具体目标1是模拟儿童癌症风险的小规模空间模式。这一具体目标的结果将是能够展示风险模式和风险群的详细风险面。这些建模结果将有助于对高风险地点进行重点调查，包括重点风险建模。具体目标2是确定儿童癌症的空间特异性种族风险。这一具体目标将利用最近开发的空间变化风险的风险建模。这一具体目标的结果将是多路径分析，并将阐明儿童癌症的因果途径。这个应用程序是创新的，因为它将利用两个新的发展，完全条件分层建模和多变量建模。 公共卫生相关性：项目叙述。这项研究意义重大，因为我们最需要的是一个风险模型，它结合了地理风险和个体风险，并适用于多种类型的儿童癌症。这项研究将验证一种风险建模方法，从而能够阐明儿童癌症的因果机制。