Department of Mathematics and Statistics, Sam Houston State University, Huntsville, TX, USA.
Department of Mathematics and Statistics, Sam Houston State University, Huntsville, TX, USA.
Cancer Epidemiol. 2023 Jun;84:102364. doi: 10.1016/j.canep.2023.102364. Epub 2023 Apr 20.
Previous studies have explored population-level smoking trends and the incidence of lung cancer, but none has jointly modeled them. This study modeled the relationship between smoking rate and incidence of lung cancer, by gender, in the U.S. adult population and estimated the lag time between changes in smoking trend and changes in incidence trends.
The annual total numbers of smokers, by gender, were obtained from the database of the National Health Interview Survey (NHIS) program of the Centers for Disease Control and Prevention (CDC) for the years 1976 through 2018. The population-level incidence data for lung and bronchus cancers, by gender and five-year age group, were obtained for the same years from the Surveillance, Epidemiology, and End Results (SEER) program database of the National Cancer Institute. A Bayesian joinpoint statistical model, assuming Poisson errors, was developed to explore the relationship between smoking and lung cancer incidence in the time trend.
The model estimates and predicts the rate of change of incidence in the time trend, adjusting for expected smoking rate in the population, age, and gender. It shows that smoking trend is a strong predictor of incidence trend and predicts that rates will be roughly equal for males and females in the year 2023, then the incidence rate for females will exceed that of males. In addition, the model estimates the lag time between smoking and incidence to be 8.079 years.
Because there is a three-year delay in reporting smoking related data and a four-year delay for incidence data, this model provides valuable predictions of smoking rate and associated lung cancer incidence before the data are available. By recognizing differing trends by gender, the model will inform gender specific aspects of public health policy related to tobacco use and its impact on lung cancer incidence.
先前的研究已经探索了人群水平的吸烟趋势和肺癌的发病率，但没有研究同时对两者进行建模。本研究通过性别对美国成年人群的吸烟率和肺癌发病率进行了联合建模，并估计了吸烟趋势变化和发病率趋势变化之间的滞后时间。
通过美国疾病控制与预防中心（CDC）的国家健康访谈调查（NHIS）项目数据库，获得了 1976 年至 2018 年期间按性别划分的每年吸烟者总数。同时，从美国国家癌症研究所的监测、流行病学和最终结果（SEER）项目数据库中获得了相同年份按性别和五年年龄组划分的肺癌和支气管癌的人群发病率数据。采用贝叶斯连接点统计模型，假设泊松误差，探索了吸烟与肺癌发病率在时间趋势中的关系。
该模型通过调整人群中预期吸烟率、年龄和性别，对时间趋势中的发病率变化率进行了估计和预测。结果表明，吸烟趋势是发病率趋势的有力预测指标，并预测在 2023 年左右，男性和女性的发病率将大致相等，然后女性的发病率将超过男性。此外，该模型估计吸烟和发病之间的滞后时间为 8.079 年。
由于吸烟相关数据的报告存在三年延迟，而发病率数据的报告存在四年延迟，因此该模型可以在数据可用之前提供有关吸烟率和相关肺癌发病率的有价值预测。通过认识到性别之间的不同趋势，该模型将为与烟草使用及其对肺癌发病率的影响有关的公共卫生政策的性别特定方面提供信息。
