Men reported smoking about twice as many cigarettes per day mean Select Format Select format. Cigarette smoking increases the risk of CVD by 2 to 4 times and the risk of lung cancer by 25 times, in addition to having a causal relationship with at least 11 additional types of cancer.
Assumptions about the nature of the interaction had little effect on the smoking risk estimates. With the generalized multiplicative model, the gender-averaged ERR associated with smoking 20 cigarettes per day for 50 years i. This was only slightly lower than that of 5. While the pack-year effect was highly statistically significant, the negative coefficients for the duration effects imply that the increase in rates is not linear in years smoked and that this departure from linearity becomes more marked at longer durations.
Under this duration-modified pack-year model, smoking had little impact on lung cancer rates for the first 20 years of smoking, after which the effect of smoking increases dramatically Figure 1A. This model implies a reduced potency at higher smoking intensities Figure 1B like the pattern suggested in 12 , 17 , where the departures from a linear pack-years effect were attributed to modifying effects of intensity and not duration. Smoking-related excess relative risk ERR as a function of duration and intensity.
Panel A. ERR for a 50 pack-year smoker versus smoking duration. The upper axis indicates the smoking intensity packs per day required to reach pack-years for the duration indicated on the lower axis. The fitted risk for a model that is linear in pack years with a log-quadratic duration effect is indicated with the solid line.
The fitted ERR for the pack-years-only model is indicated by the dashed line. The points are estimates of the risk in smoking duration categories. Panel B. Variation in the lung cancer ERR with smoking intensity cigarettes per day for fixed numbers of pack years. The estimated decline for past smokers relative to that for non-smokers of the same age was approximately proportional to one over the square root of time since quitting. While the smoking ERR declines following smoking cessation Figure 2A , lung cancer rates for past smokers never return to the level for non-smokers Figure 2B.
Gender-specific smoking effects on the excess relative risk Panel A and absolute rate Panel B as a function of age. The darker curves are for men and the lighter ones for women. The solid curves illustrate the modeled lung cancer risks for a person who smokes 20 cigarettes one pack per day from age The long-dashed lines indicate the risk for an individual who stopped smoking at age The short-dashed lines in Panel B indicate the risk for non-smokers.
The curves correspond to risk for an unexposed person born in The table also includes results from a model in which the radiation-effect parameters were estimated without allowance for smoking effects radiation-only as in most LSS reports 3 , 7. Both the deviance and AIC values suggest that the generalized-interaction models fit better than the simple interaction models and that the generalized multiplicative model described the data somewhat better than the generalized additive model.
Models in which the generalized interaction was modeled in terms of pack-years or years smoked were also considered, but did not describe the data any better than the smoking-intensity models given in Table 2B. The plots compare the gender-averaged risk estimates for three joint effect models. The generalized multiplicative model for non-smokers is indicated by the dark solid line while the additive model is indicated by the long-dashed line.
For both of these models the ERR is relative to the risk for non-smokers. The short-dashed line is for a model with no adjustment for smoking. In this model the ERR is relative to the risk for an unexposed cohort member without regard to smoking status.
Furthermore, the gender-averaged dose response slope for the restricted range 0. Figure 4 illustrates how the ERR changes with smoking intensity and dose under three interaction models. The points in this figure are category-specific estimates from a generalized multiplicative model in which smoking-intensity categories replaced the linear-quadratic function of log intensity in the radiation model.
The left panel describes the joint effect of radiation and smoking relative to the rates for non-smokers with no radiation exposure. Variation of the excess relative risk ERR with smoking intensity. The gender-averaged risk estimates at age 70 following radiation exposure at age Smoking was assumed to start at age 20 so that smoking duration was fixed at 50 years in this figure.
Panel A describes the joint effect of radiation and smoking relative to the baseline rate for a non-exposed non-smoker. The thin long dashed line is the fitted ERR for a person with no radiation exposure. The solid line is the fitted ERR following exposure to 1 Gy under the generalized multiplicative model, the thick dashed line is the fitted risk under a simple multiplicative model, and the short-dashed line is the fitted ERR under a simple additive joint effect model.
The points are based on a generalized multiplicative model in which smoking intensity categories replaced the linear-quadratic function of log intensity used in the generalized multiplicative model. Panel B presents radiation-associated excess risks for an exposure of 1 Gy relative to the risk of an unexposed person with the same smoking history. The upper portion of Table 3 summarizes the distribution of observed and fitted cases over dose categories for the generalized multiplicative model.
The fitted cases are broken down into background cases i. Regardless of the type of interaction, the total estimated numbers of radiation associated cases were generally similar — but the model with no smoking adjustment provided the largest estimate of radiation-related excess cases Supplementary Tables 2 and 3 include summaries of the estimated numbers of cases attributable to radiation and smoking stratified by smoking intensity and pack-years.
Observed and fitted cases by dose category for the generalized multiplicative model with totals for alternative models. Questions about the joint effect of radiation and smoking are generally framed in terms of a choice between simple additive and multiplicative models. The analysis by Pierce et al. With additional follow-up, data from more cohort members, and more parsimonious description of the departures from simple models, we were able to reject both the simple additive and multiplicative models.
Under our fitted generalized multiplicative model the joint effect appears to be super-multiplicative for light- to moderate-smokers smoking less than a pack of cigarettes per day but additive or even sub-additive for heavy-smokers smoking a pack or more per day. How smoking modifies the radiation-related risk of lung cancer has both biological and practical implications. Since gamma rays account for most of the dose received by atomic-bomb survivors, the LSS data may be directly relevant in the first three cases.
To the extent that the biological nature of the radiation-smoking interaction is similar for gamma-ray and radon alpha particles, information from the LSS may also be important in the fourth case. Studies of Hodgkin lymphoma patients treated with radiotherapy 23 , 24 suggested a multiplicative interaction between radiation and smoking effects on lung cancer risks.
The National Research Council reviewed data from studies of underground miners for the joint effect of smoking and radon exposure 22 , 25 and concluded that the interaction could be most consistently described as less than multiplicative, although evidence from individual studies varied considerably.
The multiplicative effect of smoking and residential radon exposure was suggested in a Swedish study 26 and a pooled analysis of European residential radon exposure studies 27 , None of those studies investigated the kind of departures from simple interaction models that we found in the LSS data.
Adjustment for smoking can impact the modifying effects of gender and age factors on the radiation-related lung cancer risk. Although it was suggested in 3 that this pattern might be a consequence of the failure to adjust for the effect of smoking, the current analyses indicate that this may not be the case.
It may be that there is a certain pool of people genetically susceptible to lung cancer and that high levels of smoking have saturated that pool so that there is little room for an additional radiation effect. Another possible explanation is that radiation exposure prior to the start of smoking may be less harmful than radiation exposure after smoking initiation.
In the LSS, age at exposure is highly correlated with whether or not radiation exposure occurred before or after smoking initiation, making it difficult to address this question. However, in an analysis in which the radiation effect was allowed to depend on whether or not one reported smoking before exposure, we found that radiation risks were not significantly higher for those who smoked before exposure and that the age-at-exposure effect became even more pronounced.
We estimated that smoking related relative risks were 4. If the smoking duration and intensity were averaged over the general population, those values would be close to the risk estimates of 4. These values are much smaller than those reported from western populations 30 , This may in part reflect the higher lung cancer rates among non-smokers in Japan and other Asian countries than in the west.
A recent study 32 suggested that lung cancer rates might be higher among Japanese non-smokers and relative risks lower among Japanese smokers, compared with the US white counterparts. Our estimates of lung cancer rates for nonsmokers were similar to those found for Japanese and Korean populations in an international comparison of lung cancer risks among non-smokers The major limitation of this study arises from the use of incomplete, historical smoking data derived from mail surveys.
Use of singly-imputed values for the age at which smoking started or intensity of smoking can lead to some underestimation of the uncertainty in risk estimates. Estimates of the effect of smoking cessation can be underestimated due to smoking recidivism, as the most recent smoking data used were obtained in the early s.
However, it is also likely that many people who reported smoking at the time of their last survey response have since quit smoking, which would tend to result in underestimation of the effect of smoking in later years. While the present analysis considered all lung cancer types together, smoking and radiation may have different effects on different subtypes of lung cancer and this will be reported in a forthcoming paper. Despite these limitations, we believe that this study provides the most comprehensive characterization of the joint effects of low-dose radiation and smoking on lung cancer in any radiation-exposed population.
The results suggest that simple additive or multiplicative models may not adequately describe the complex interaction between smoking intensity and radiation and that a similar comprehensive analytical approach may be needed in risk estimation for smokers with medical or occupational radiation exposures. We think that further efforts should be made to develop methods for using generalized interaction models in radiation risk assessment.
This study also is one of the most detailed quantitative analyses of smoking effects on lung cancer rates in a Japanese population, and whether or not the present findings are duplicated in other Japanese cohorts would be of interest as they have an important public health implication for one of the major cancer problems in Japan. National Center for Biotechnology Information , U. Radiat Res. Author manuscript; available in PMC Dec 9. Author information Copyright and License information Disclaimer.
Copyright notice. The publisher's final edited version of this article is available at Radiat Res. See other articles in PMC that cite the published article. Associated Data Supplementary Materials s1. Abstract While radiation increases the risk of lung cancer among members of the Life Span Study LSS cohort of atomic-bomb survivors, there are still important questions about the nature of its interaction with smoking, the predominant cause of lung cancer.
Keywords: radiation health effects, lung cancer, dose response, joint effect, smoking. Introduction Lung cancer is the most common cancer worldwide 1. Materials and Methods Study population and case ascertainment The LSS cohort includes , residents of Hiroshima and Nagasaki who were born prior to the atomic bombings in August and were still alive on October 1, Radiation dose and smoking information Weighted DS02 9 lung-dose estimates computed as sum of the gamma-ray dose and 10 times the neutron dose were used for these analyses.
Data organization for analyses The risk analyses were based on incidence rates computed from a table of person-years and lung cancer cases stratified by general factors, radiation-exposure-related factors, and smoking-related factors. Statistical analysis Smoking and radiation joint effects These analyses focused on the joint effects of radiation and smoking in terms of risks relative to attained age a , gender g , and birth cohort b -specific baseline rates for non-smokers with no radiation exposure.
Baseline rate zero-dose, non-smokers model The baseline rate model allows for gender-specific rates. Smoking effect model Analyses of smoking effects on lung cancer risk often describe the effects in terms of the cumulative amount of smoking which, in the simplest cases, is defined as the product of intensity and duration of smoking. Open in a separate window. Non-smoker baseline rates and smoking effects Smoking effects were modeled using ERR models and expressed relative to gender-specific baseline rates for non-smokers with allowance for radiation effects.
Figure 1. Figure 2. Figure 3. Figure 4. Table 3 Observed and fitted cases by dose category for the generalized multiplicative model with totals for alternative models. Discussion Questions about the joint effect of radiation and smoking are generally framed in terms of a choice between simple additive and multiplicative models.
Supplementary Material s1 Click here to view. References 1. Global cancer statistics, CA Cancer J Clin. United Nations, New York: Solid cancer incidence in atomic bomb survivors: — Relationship of cigarette smoking and radiation exposure to cancer mortality in Hiroshima and Nagasaki. J Natl Cancer Inst. Joint effects of radiation and smoking on lung cancer risk among atomic bomb survivors. Studies of mortality of atomic bomb survivors. Report Solid cancer and noncancer disease mortality: — Dose estimation for atomic bomb survivor studies: its evolution and present status.
Noncancer disease incidence in atomic bomb survivors, — Sposto R, Preston DL. Cigarette smoking and lung cancer: modeling total exposure and intensity. Cancer Epidemiol Biomarkers Prev. Effect of recent changes in atomic bomb survivor dosimetry on cancer mortality risk estimates. Epicure Users Guide.
Purpose: Alcohol consumption and cigarette smoking increase the risk of developing several cancers. We examined the individual and synergistic effects of these modifiable lifestyle factors on overall and site-specific cancer risk. Incident cases of cancer up to December were identified via linkage to the Alberta Cancer Registry.
Associations between alcohol consumption, cigarette smoking, and cancer risk were examined using adjusted Cox proportional hazard models. Non-linear effects were estimated using restricted cubic splines. Interactions between alcohol and tobacco were examined through stratified analyses and inclusion of interaction terms in relevant models.
Results: A total of 2, participants developed cancer during the study follow-up period.
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