DARTMOUTH COL COBRE: P 5: ENVIRONMENTAL EPIDEMIOLOGY OF LUNG CANCER IN NH

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子项目和研究者（PI）可能从另一个NIH来源获得了主要资金，因此可以在其他CRISP条目中表示。所列机构为中心，不一定是研究者所在机构。本计画整合流行病学中的空间分析技术与传统病例对照方法，研究新罕布什尔州肺癌的环境危险因子。我们会分三个阶段进行。首先，地理信息系统（GIS），这是空间分析的技术环境中进行，将被用来揭示空间模式和环境因素（如细颗粒空气污染）和肺癌之间的关系，在新罕布什尔州。其次，传统的以人群为基础的病例对照流行病学方法将用于研究个人水平的风险因素信息（从问卷调查、饮用水样本、脚趾甲剪下、血清和生殖系DNA中收集）。这将使我们能够建立新罕布什尔州环境因素与肺癌发病风险之间的因果关系模型。作为这种方法的一部分，我们将探索由于暴露（砷和吸烟），宿主遗传易感性，饮食因素和性别之间的协同作用，相对风险的潜在修改。我们还将使用组级（生态地理）暴露信息（例如，细颗粒空气污染）和个体水平暴露信息（例如，吸烟状况、年龄、性别、教育、职业、使用烧柴炉、水砷浓度、脚趾甲砷浓度、DNA修复基因型和其他变量）。多水平建模将使我们能够通过包括没有个体水平模拟的组水平数据（例如，暴露于细颗粒空气污染）。第三，利用第二阶段建立的空间环境数据和风险模型，我们将创建新罕布什尔州的肺癌风险地图。我们将使用从新罕布什尔州新收集的肺癌发病率数据来测试我们的环境模型和肺癌风险地图的有效性。通过这三个阶段的方法，我们预计将发现肺癌的新病因，这些信息将有助于科学家和政策制定者进行风险评估和疾病预防。该项目还将为建立一个全面的区域环境卫生信息系统奠定基础，该系统将作为新罕布什尔州未来肺部疾病和其他健康结果环境卫生研究的数据库和知识库。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. This project integrates spatial analytic techniques and traditional case-control methods in epidemiology to study environmental risk factors for lung cancer in New Hampshire. We will adopt a three-phase approach. First, a geographic information system (GIS), which is the technical environment in which spatial analyses are performed, will be used to reveal spatial patterns and relationships between environmental factors (such as fine particulate air pollution) and lung cancer in New Hampshire. Second, traditional population-based, case-control methods of epidemiology will be used to study individual-level risk factor information (collected from questionnaires, drinking water samples, toenails clippings, sera, and germ-line DNA). This will permit us to model causal relations between environmental factors and risk of incident lung cancer in New Hampshire. As part of this approach, we will explore potential modifications in relative risk due to synergy between exposures (arsenic and smoking), host genetic susceptibility, dietary factors, and gender. We also will employ multilevel modeling (hierarchical regression) of individual lung cancer risk using group-level (ecologic-geographic) exposure information (e.g., fine particulate air pollution) and individual-level exposure information (e.g., smoking status, age, gender, education, occupation, use of wood burning stoves, water arsenic concentration, toenail arsenic concentration, DNA repair genotype, and other variables). Multilevel modeling will allow us to improve estimates of individual lung cancer risk by including group-level data that have no individual-level analogue (e.g., exposure to fine particulate air pollution). Third, using spatial environmental data and risk models built in phase 2, we will create a risk map of lung cancer in New Hampshire. We will test the validity of our environmental models and our risk map of lung cancer using newly collected lung cancer incidence data from New Hampshire. Through this three-phase approach, we expect that new etiologic factors for lung cancer will be uncovered and that this information will aid scientists and policy makers regarding risk assessment and disease prevention. This project will also set the stage for a comprehensive regional environmental health information system that will serve as a database and knowledgebase for future environmental health studies of lung diseases and other health outcomes in New Hampshire.