SES, Environmental Factors and Colorectal Cancer Risk

描述（由申请人提供）： 除了在个人层面上测量的风险因素，如饮食，癌症家族史和癌症筛查行为，在群体或地区层面上测量的因素可能会影响健康。为了研究女性结直肠癌发病率、社会经济地位和环境危险因素之间的关系，我们建议将公开的数据集与最近完成的病例对照研究中收集的危险因素信息联系起来。特别是，我们假设（1）结直肠癌发病率的地区差异可以通过个人水平的社会经济地位指标和其他既定的风险因素（包括筛查史）来解释;（2）饮用水中的污染物-特别是三卤甲烷，硝酸盐和农药-与结直肠癌风险增加有关。为了解决这些假设，我们建议对1997-2000年诊断的833例女性结直肠病例和2,123例基于人群的对照进行地理编码（分配纬度和经度）。所有病例和对照组均可获得结构化电话访谈的综合风险因素问卷信息。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组放置在地图上。然后，我们可以将有关社会经济状况（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）的信息覆盖在这张地图上。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。该项目还将为个人风险因素历史和公开数据集之间的许多未来联系以及应用统计技术分配暴露于地理定义的潜在致癌化合物提供基础。

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.