Modeling Interventions for Lung Cancer Mortality

描述(由申请人提供)对癌症发病率和死亡率的时间趋势的研究可以为疾病对人群的影响提供有价值的见解。将开发一个模型，其中吸烟对基于人群的肺癌发病率的影响，吸烟是众所周知的这种疾病的原因。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。这项研究的具体目的是： 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在多大程度上用作解释变量； 2.利用SEER登记处的现有数据，建立描述肺癌发病率和死亡率之间关系的分室模型； 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；以及， 4.使用目标1-3中开发的模型来估计不同反吸烟运动策略对未来肺癌死亡趋势的人口影响。

DESCRIPTION (provided by applicant)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 know 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. The specific aims of this research are to: 1. Develop 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. Develop 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; and, 4. Use the model developed in aims1-3 to estimate the population effect of various anti-smoking campaign strategies on future lung cancer mortality trends.