This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor 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.
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