Modeling Interventions for Lung Cancer Mortality

描述（由申请人提供）癌症发病率和死亡率的时间趋势的研究可以提供有价值的见解，疾病对人口的影响。 将开发一个模型，其中吸烟（这种疾病的一个众所周知的原因）对基于人群的肺癌发病率的影响。 2004 - 2005年同期组群模型为编制时间趋势统计摘要提供了一种有用的方法。 在这种情况下，年龄代表了衰老过程对疾病风险的影响。 另一方面，时期和队列可能反映了暴露于输入性风险因素或监测系统的变化。 虽然分析流行病学研究提供了估计假定风险因素对疾病风险影响的最佳方法，但定量描述暴露变化影响人口发病率的方式可能更具挑战性。本研究的目的是建立一个模型，其中肺癌发病率的危险因素的趋势被用来描述观察到的疾病的发病率和死亡率的趋势。 这些数据将用于评估旨在减少吸烟的干预措施对肺癌死亡率的影响。 这项研究的具体目标是： 1.在SEER登记中建立肺癌发病率趋势模型，并确定现有吸烟趋势数据可用作解释变量的程度; 2.利用SEER登记处的现有数据，开发一个描述肺癌发病率和死亡率之间关系的房室模型; 3.开发一个模型，使用有关吸烟趋势的现有州信息来解释相邻州之间癌症死亡率趋势的变化;以及， 4.使用aims 1 -3中开发的模型来估计不同的反吸烟运动策略对未来肺癌死亡率趋势的人口效应。

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.