SES, Environmental Factors and Colorectal Cancer Risk

描述(由申请人提供)： 除了在个人层面上衡量的风险因素，如饮食、癌症家族史和癌症筛查行为，在群体或地区层面上衡量的因素可能会影响健康。为了调查女性结直肠癌发病率、社会经济地位和环境风险因素之间的关系，我们建议将公开可用的数据集与风险因素信息联系起来，作为最近完成的病例对照研究的一部分。特别是，我们假设(1)结直肠癌发病率的地区差异是由个人层面的社会经济状况指标和包括筛查历史在内的其他既定风险因素来解释的；以及(2)饮用水中的污染物--特别是三卤代甲烷、硝酸盐和杀虫剂--与结直肠癌风险的增加有关。为了解决这些假设，我们建议对1997-2000年间确诊的833例女性结直肠癌病例和2123例基于人群的对照病例进行地理编码(分配经纬度)。来自结构化电话访谈的全面风险因素问卷信息可用于所有病例和对照。通过对这些研究参与者进行地理编码，我们可以将每个病例和对照放置在地图上。然后我们可以在这张地图上叠加有关社会经济状况(人口普查)和水质(威斯康星州自然资源和农业部、贸易部和消费者保护部)的信息。将采用多层次建模和空间分析，以充分利用这一丰富的组合数据源的机会。该项目还将为今后个人一级的风险因素历史和公开可用的数据集之间的许多联系提供基础，并将应用统计技术来分配地理上定义的潜在致癌化合物的暴露。

DESCRIPTION (provided by applicant): In addition to risk factors measured on an individual level, such as diet, family history of cancer and cancer screening behaviors, factors measured on a group or area level may influence health. To investigate associations between colorectal cancer incidence in women, socioeconomic status and environmental risk factors, we propose to link publicly-available data sets with risk factor information gathered as part of a recently completed case-control study. In particular, we hypothesize that (1) area variations in colorectal cancer incidence are explained by individual-level socioeconomic status indicators and other established risk factors including screening history; and (2) contaminants in drinking water - especially trihalomethanes, nitrates and pesticides - are associated with increased colorectal cancer risk. To address these hypotheses, we propose to geocode (assign a latitude and longitude to) 833 female colorectal cases diagnosed in 1997-2000 and 2,123 population-based controls. Comprehensive risk factor questionnaire information from structured telephone interviews is available on all cases and controls. By geocoding these study participants, we can place each case and control on a map. We can then overlay this map with information concerning socioeconomic status (Census) and water quality (Wisconsin Departments of Natural Resources and Agriculture, Trade, and Consumer Protection). Multi-level modeling and spatial analysis will be employed to take full advantage of the opportunities of this rich combined data source. This project will also provide the foundation for numerous future linkages between individual-level risk factor histories and publicly available datasets as well as the application of statistical techniques for assigning exposure to geographically defined, potentially carcinogenic compounds.