Modeling Interventions for Lung Cancer Mortality

点击翻译按钮获取中文摘要

The study of time trends in cancer incidence and mortality can provide valuable insights into the effect that a disease is having on the population, A model will be developed in which the effect that smoking cigarettes, a well known cause of this disease, has on population based lung cancer rates. Age-period-cohort models have offered one useful way of developing a statistical summary of temporal trends. In this case, age represents the effect of the aging process on a disease risk. Period and cohort, on the other hand, are likely to reflect changes in the exposure to import risk factors or in the surveillance system. While analytical epidemiologic studies offer the best way to estimate the effect of putative risk factors on disease risk, quantitative descriptions of the way in which changes in exposure can affect population rates can be much more challenging. The purpose of this research is to develop a model in which trends in risk factors for lung cancer incidence are used to describe observed trends in incidence and mortality for the disease. These will then be used to estimate the effect on lung cancer mortality of interventions designed to reduce cigarette smoking. This approach will be extended to other risk factors, such as asbestos exposure, genetics and use of lung cancer screening. The specific aims of this research are to: (1) Complete development of a model for lung cancer incidence trends among SEER registries and determine the extent to which available data on smoking trends can be used as explanatory variables; (2) Complete development of a compartment model that describes the relationship between lung cancer incidence and mortality using available data from SEER registries; (3) Develop a model that uses available state information on cigarette smoking trends to explain the variation in cancer mortality trends among contiguous states; (4) Validate the model from aims 1-3; (5) Use the model developed in aims 1-3 to estimate the population effect of various cancer control strategies on future lung cancer mortality trends; (6) Actively collaborate with members of CISNET; (7) Develop open source software code and documentation that will allow one to apply data from other sources, as well as to extend this approach to the analysis of data from other cancer sites; and (8) Develop a web site that will make the assumptions of the model available to interested modelers in considerable detail.