The purpose of this project is to develop a Cancer Prevention Policy Model which can be used to estimate the health and economic consequences of cancer prevention interventions in selected target populations. These target populations will include: (a) the actual study populations of the proposed interventions studies in worksite and health care settings, (b) subsets of the U.S. population defined by ethnicity and race, and (c) the U.S. adult population as a whole. The model specifications will incorporate risk factors and natural history data on colorectal and breast cancer, and will draw upon primary data from the Nurses' Health Study and the Health Professionals Follow-up Study, as well as data from the published literature and publicly available data bases. This model will include a Primary Prevention Submodel, a Premalignant Disease Submodel, and a Detected Cancer Submodel. The Primary Prevention Submodel will model the relationship between genetic, dietary, lifestyle, and environmental risk factors and protective factors for these cancers. It will incorporate risk factor distributions directly from the intervention studies and, for the U.S. population and its subpopulations,, based on NHANES III and other sources. The mathematical relationships between risk factors and cancer incidence will be based on the Nurses' Health study, the Health Professionals Follow-up study, and other sources. Data on the effectiveness and costs of preventive interventions will be based on the proposed intervention studies, other studies in progress, and published literature. The Premalignant Disease Submodel will incorporate the incidence and progression of colorectal polyps and carcinoma in situ of the breast. It will incorporate the sensitivity, specificity, costs, and risks of screening, and the costs and risks of early intervention. The Undetected Cancer Submodel will reflect progression through disease stages, cancer mortality, effects of cancer of health-related quality of life, and costs and risks of treatment. Also included will be the sensitivity and specificity, costs, and risks of screening and the costs and risks of early intervention. The Detected Cancer Submodel will also reflect disease progression, mortality,, effects on health-related quality of life, and treatment costs. The model specifications will include linkages to outcome and cost data from the Coronary Heart Disease Policy Model, for risk factors common to both diseases (e.g., physical exercise). The Model will be programmed, calibrated to U.S. population data, and applied to cost-effectiveness analyses of cancer prevention programs, including but not limited to those included in this program-project application, in demographically diverse populations.
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