Modeling Interventions for Lung Cancer Mortality

研究癌症发病率和死亡率的时间趋势可以为了解一种疾病对人群的影响提供有价值的见解，将开发一个模型，其中吸烟是这种疾病的众所周知原因，它对基于人群的肺癌发病率产生影响。年龄-时期-队列模型为开发时间趋势的统计摘要提供了一种有用的方法。在这种情况下，年龄代表衰老过程对疾病风险的影响。另一方面，期间和队列很可能反映了受进口风险因素影响的情况或监测系统的变化。虽然分析性流行病学研究提供了估计假定风险因素对疾病风险的影响的最佳方法，但定量描述暴露变化对人口比率的影响可能更具挑战性。这项研究的目的是开发一个模型，在该模型中，肺癌发病率的危险因素的趋势被用来描述观察到的肺癌发病率和死亡率的趋势。然后，这些数据将被用来评估旨在减少吸烟的干预措施对肺癌死亡率的影响。这一方法将扩展到其他危险因素，如石棉暴露、遗传和肺癌筛查的使用。这项研究的具体目标是：(1)在SEER登记处建立肺癌发病率趋势模型，并确定吸烟趋势的现有数据可用作解释变量的程度；(2)利用SEER登记处的现有数据，完整地开发描述肺癌发病率和死亡率之间关系的区隔模型；(3)开发一个模型，利用吸烟趋势的现有状态信息来解释相邻州之间癌症死亡率趋势的差异；(4)验证目标1-3的模型；(5)使用在AIMS 1-3中开发的模型来估计各种癌症控制策略对未来肺癌死亡率趋势的人口影响；(6)积极与CISNet成员合作；(7)开发开源软件代码和文件，使人们能够应用来自其他来源的数据，并将这种方法扩展到对来自其他癌症站点的数据的分析；以及(8)开发一个网站，使感兴趣的模型师可以相当详细地使用该模型的假设。

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.