Cigarette Smoking and Cancer Risk: Modeling Total Exposure and Intensity (original) (raw)
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Re:" Modeling smoking history: A comparison of different approaches"-Reply
The impact of cigarette smoking on various diseases is studied frequently in epidemiology. However, there is no consensus on how to model different aspects of smoking history. The aim of this investigation was to elucidate the impact of several decisions that must be made when modeling smoking variables. The authors used data on lung cancer from a case-control study undertaken in Montreal, Quebec, Canada, in 1979. The roles of smoking status, intensity, duration, cigarette-years, age at initiation, and time since cessation were investigated using time-dependent variables in an adaptation of Cox's model to case-control data. The authors reached four conclusions. 1) The estimated hazard ratios for current and ex-smokers depend strongly on how long subjects are required to not have smoked to be considered "ex-smokers." 2) When the aim is to estimate the effect of continuous smoking variables, a simple approach can be used (and is proposed) to separate the qualitative difference between never and ever smokers from the quantitative effect of smoking. 3) Using intensity and duration as separate variables may lead to a better model fit than using their product (cigarette-years). 4) When estimating the effects of time since cessation or age at initiation, it is still useful to use cigarette-years, because it reduces multicollinearity. Downloaded from 816 Leffondré et al. Am J Epidemiol 2002;156:813-823 by guest on July 3, 2015 http://aje.oxfordjournals.org/ Downloaded from 820 Leffondré et al. Am J Epidemiol 2002;156:813-823
Re: "Modeling Smoking History: A Comparison of Different Approaches
American Journal of Epidemiology, 2003
The impact of cigarette smoking on various diseases is studied frequently in epidemiology. However, there is no consensus on how to model different aspects of smoking history. The aim of this investigation was to elucidate the impact of several decisions that must be made when modeling smoking variables. The authors used data on lung cancer from a case-control study undertaken in Montreal, Quebec, Canada, in 1979-1985. The roles of smoking status, intensity, duration, cigarette-years, age at initiation, and time since cessation were investigated using time-dependent variables in an adaptation of Cox's model to case-control data. The authors reached four conclusions. 1) The estimated hazard ratios for current and ex-smokers depend strongly on how long subjects are required to not have smoked to be considered "ex-smokers." 2) When the aim is to estimate the effect of continuous smoking variables, a simple approach can be used (and is proposed) to separate the qualitative difference between never and ever smokers from the quantitative effect of smoking. 3) Using intensity and duration as separate variables may lead to a better model fit than using their product (cigarette-years). 4) When estimating the effects of time since cessation or age at initiation, it is still useful to use cigarette-years, because it reduces multicollinearity.
Modeling smoking history: a comparison of different approaches
American journal of epidemiology, 2002
The impact of cigarette smoking on various diseases is studied frequently in epidemiology. However, there is no consensus on how to model different aspects of smoking history. The aim of this investigation was to elucidate the impact of several decisions that must be made when modeling smoking variables. The authors used data on lung cancer from a case-control study undertaken in Montreal, Quebec, Canada, in 1979-1985. The roles of smoking status, intensity, duration, cigarette-years, age at initiation, and time since cessation were investigated using time-dependent variables in an adaptation of Cox's model to case-control data. The authors reached four conclusions. 1) The estimated hazard ratios for current and ex-smokers depend strongly on how long subjects are required to not have smoked to be considered "ex-smokers." 2) When the aim is to estimate the effect of continuous smoking variables, a simple approach can be used (and is proposed) to separate the qualitative...
Modelling smoking history using a comprehensive smoking index: application to lung cancer
The mathematical representation of smoking history is an important tool in analysis of epidemiological and clinical data. Hoffmann and colleagues recently proposed a single aggregate measure of smoking exposure that incorporates intensity, duration, and time since cessation. This comprehensive smoking index (CSI), which may be incorporated in any regression model, depends on a half-life ( ) and a lag ( ) parameters that have to be fixed a priori, or estimated by maximizing the fit. The CSI has not previously been used for analysis of cancer data. Following some preliminary results on smoking and lung cancer, the authors proposed a new version of the CSI for lung cancer. The aim of this study was to investigate the performance of the original and the new versions of the CSI in the analysis of three data sets from two case-control studies of lung cancer undertaken in Montreal, in 1979Montreal, in -1985Montreal, in in males, and in 1996Montreal, in -2000 in both males and females. The estimates of and for both versions of the CSI were similar across data sets. The new version of the CSI fitted the three data sets systematically although moderately better than the original version, and at least as well as other representations of lifetime smoking history that used separate variables for time since cessation and cumulative amount of cigarettes smoked. The results suggest that the CSI may be an attractive and parsimonious alternative to conventional modelling of different aspects of smoking history for lung cancer. THE COMPREHENSIVE SMOKING INDEX FOR LUNG CANCER 4133 1. INTRODUCTION
Can we trust national smoking prevalence figures? Discrepancies between …
… Epidemiology Biomarkers & …
Background: National smoking prevalence estimates are the primary basis for assessing progress in tobacco control across the world. They are based on surveys of self-reported cigarette smoking. It has been assumed that this is sufficiently accurate for policy purposes, but this assumption has not been adequately tested. Methods: We report data from the 2003 Health Survey for England, the U.S. National Health and Nutrition Examination Survey for 2001-2002, and the 2004 national smoking behaviors survey in Poland as examples of countries at different stages in the ''tobacco epidemic.'' Self-reported cigarette and total tobacco smoking prevalence were assessed by means of the standard questions used in each country. In subsamples, specimens were collected for analysis of cotinine (saliva, N = 1,613 in England; serum, N = 4,687 in the United States; and saliva, N = 388 in Poland) providing an objective means of determining active smoking. A cut point of 15 ng/mL was used to discriminate active smoking from passive smoke exposure. Results: Self-reported cigarette smoking prevalence using the standard methods underestimated true tobacco smoking prevalence by an estimated 2.8% in England, 0.6% in the United States, and 4.4% in Poland. Cotinine concentrations in those misclassified as nonsmokers were indicative of high levels of smoke intake. Interpretation: Underestimation of smoking prevalence was minimal in the United States but significant in England and Poland. A review of methodologies for assessing tobacco smoking prevalence worldwide is urgently needed. (Cancer Epidemiol Biomarkers Prev 2007;16(4):820 -2)
BMC Public Health, 2021
Background Cigarettes and smokeless tobacco (SLT) products are among a wide range of tobacco products that are addictive and pose a significant health risk. In this study, we estimated smoking- and SLT use-related mortality hazard ratios (HRs) among U.S. adults by sex, age group, and cause of death, for nine mutually exclusive categories of smoking and/or SLT use. Methods We used data from the public-use National Health Interview Survey Linked Mortality with mortality follow-up through 2015. We used Cox proportional hazard models to estimate mortality HRs, adjusted by race/ethnicity, education, poverty level, body mass index, and tobacco-use status. Results With never users as reference group, HRs for smoking-related diseases for male exclusive current smokers aged 35–64 and 65+ were 2.18 (95% confidence interval [CI]: 1.79–2.65), and 2.45 (95% CI: 2.14–2.79), respectively. Similar significant HR estimates were found for females and for all-cause mortality (ACM) and other-cause mort...