Modeling multiple environmental exposures and childhood leukemia risk

许多癌症的发病率被怀疑与化学物质通过环境途径有关 曝光。个人同时接触到大量不同的环境化学品， 对多种化学物质暴露的评估对于确定癌症危险因素很重要。越来越多的人， 在流行病学研究中，正在对大量化学品的暴露进行测量，以允许更多 全面评估暴露组中的癌症危险因素。然而，现有的大多数研究都是 环境中的化学物质暴露和癌症使用单一的化学方法来评估化学物质 作为独立的风险因素。现有研究中使用的传统统计方法显著 受到在许多环境化学品暴露之间观察到的典型的强烈相关性的挑战，如 以及其他环境和社会经济变量。有必要制定和评估 环境癌症风险建模的统计方法，考虑了大量不同的化学物质 对不同的化学类别有不同的影响。这项研究的具体目的是1)开发更多的 综合暴露危险模型及其在儿童白血病病例对照研究中的应用 加州包含大量不同化学物质的测量浓度，2)要说明 估算化学物质浓度低于检测限值时的不确定度 化学混合效应，以及3)考虑邻里社会经济地位和残留混杂 在估计化学物质暴露的影响时。儿童白血病的研究将受益于环境 化学风险分析，因为它是一种病因不明的癌症，已经确定的危险因素可以解释 仅占美国年度病例总数的一小部分。这样做的预期结果是 研究将是1)新的统计方法来模拟考虑环境因素的环境癌症风险 更全面的暴露，同时也考虑到与缺失数据相关的不确定性，以及2) 评估接触来自许多化学类别的化学品的影响，并确定 治疗儿童白血病的重要化学物质。这项研究的意义是双重的。首先， 开发和评估考虑多种不同因素的风险分析新的统计方法 在考虑不确定性的同时，环境暴露将推动环境流行病学领域的发展 研究。其次，这将是第一次对儿童进行病例对照研究的环境风险分析 白血病使用大量相关的化学物质来评估不同化学物质的影响 同时还根据家庭和邻里层面的已知人口风险因素进行调整 并考虑到与缺失数据相关的不确定性。在这项工作中形成的方法论方法 将适用于与不同类型的多种环境暴露相关的许多其他癌症。

Incidence for many cancers is suspected to be associated with chemicals through environmental routes of exposure. Individuals are exposed to a large number of diverse environmental chemicals simultaneously and evaluation of multiple chemical exposures is important for identifying cancer risk factors. Increasingly, exposures are being measured for a large number of chemicals in epidemiologic studies to allow for a more comprehensive assessment of cancer risk factors in the exposome. However, most existing studies of environmental chemical exposures and cancer use a single-chemical approach that evaluates chemicals independently as risk factors. Traditional statistical methods used in existing studies are significantly challenged by the typically strong correlation observed among many environmental chemical exposures, as well as other environmental and socioeconomic variables. There is a need for development and assessment of statistical methods to model environmental cancer risk that consider a large number of diverse chemicals with different effects for different chemical classes. The specific aims of this research are 1) to develop more comprehensive exposure risk models and apply them to a case-control study of childhood leukemia in California that contains concentrations measured for a large number of diverse chemicals, 2) to account for uncertainty associated with imputing chemical concentrations below the limit of detection when estimating chemical mixture effect, and 3) to account for neighborhood socioeconomic status and residual confounding when estimating chemical exposure effects. The study of childhood leukemia will benefit from environmental chemical risk 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 cases in the United States. The expected outcomes of this research will be 1) new statistical approaches to model environmental cancer risk that consider environmental exposures more comprehensively while also accounting for uncertainty related to missing data, and 2) an evaluation of the effects of exposure to chemicals from many chemical classes and an identification of the important chemicals for childhood leukemia. The significance of this research is two-fold. First, the development and evaluation of new statistical approaches to risk analysis that consider multiple diverse environmental exposures while accounting for uncertainty will advance the field of environmental epidemiology research. Second, this will be the first environmental risk analysis of a case-control study of childhood leukemia that estimates effects for different chemical classes using a large number of correlated chemical exposures while also adjusting for known demographic risk factors at the household and neighborhood level and accounting for uncertainty related to missing data. The methodological approaches developed in this work will be applicable to many other cancers associated with multiple environmental exposures of differing types.