Gender-specific trends in cigarette smoking and lung cancer incidence: A two-stage age-stratified Bayesian joinpoint model.

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.