Developing a comprehensive smoking cessation program in patients with lung cancer: The role of social smoking environments.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 75-75
Author(s):  
Lawson Eng ◽  
Jie Su ◽  
Xin Qiu ◽  
Prakruthi R. Palepu ◽  
Henrique Hon ◽  
...  

75 Background: Smoking during cancer treatment negatively impacts outcome, survival, and quality of life. Social smoking environments (SSEs) (i.e., smoking in household, peers, and spouse) influence cessation rates in non-cancer patients, but are understudied in cancer patients. Methods: Lung cancer patients, recruited from Princess Margaret Hospital (2006-2012) were given baseline and follow-up questionnaires (median of 2 years apart) evaluating changes in smoking habits and SSEs. Multivariate logistic regression and Cox-proportional hazard models evaluated the association of socio-demographics, clinicopathological and SSE factors with smoking cessation and time to quitting, respectively. Results: 721 patients completed both questionnaires. Of the 261 current smokers at diagnosis, 180 (69%) had quit by follow-up. Among 318 ex-smokers, 5 re-started smoking after diagnosis. All of the 140 never smokers remained non-smoking. Home smoke exposure (OR=9.4; 95% CI: 3.4-26.2; p=2.0 x 10E-5), spousal smoking (OR=4.7, 95% CI:1.7-12.6; p=3.0 x 10E-3) and peer smoking (OR=2.6; 95% CI:1.1-6.1; p=0.03) were each associated with reduced cessation, adjusted for a base multivariate model that included education and past history of depression. Individuals with no SSE factors had a much higher chance of quitting smoking when compared to patients with multiple areas of SSEs (0 vs. 3, OR=16.4; 95% CI: 4.1-66.7; p=7.3 x 10E-5). Similar results were seen when using time-to-quitting as the outcome (0 vs 3, OR=4.4, 95% CI=1.4-14.1, p=0.01). Time to quitting analysis found that 60% of patients with at least one SSE who did quit, did so within 6 months of diagnosis. Subgroup analysis revealed similar associations in early- and late-stage patient groups. Conclusions: SSE is a key factor in smoking cessation, where household smoke exposures reduces the chance of quitting up to 9-fold. SSEs should be a key consideration when developing smoking cessation programs in lung cancer patients, as part of quality improvement strategies. Approaches incorporating household members or spouses into the smoking cessation intervention, around the time of diagnosis, should be researched further. GL and WX are co-senior authors.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 9032-9032
Author(s):  
Lawson Eng ◽  
Jie Su ◽  
Prakruthi R. Palepu ◽  
Henrique Hon ◽  
Ehab Fadhel ◽  
...  

9032 Background: Smoking during cancer treatment negatively impacts treatment, survival and quality of life. Lung cancer patients with a smoking history often continue to smoke; some ex-smokers re-start after diagnosis. Social environment impacts cessation and recidivism rates in non-cancer patients. We assessed whether the same influences occur among lung cancer patients. Methods: Lung cancer patients, recruited from Princess Margaret Hospital, completed a baseline questionnaire about their demographics and smoking history (at diagnosis). A follow-up questionnaire was administered at a median of two years, assessing changes in smoking habits, exposure at home/work/among friends, healthcare use, social support and alcohol use since diagnosis. The relationship between each variable with cessation/recidivism was analyzed. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated. Results: 478 patients completed both questionnaires. Of the 100 current smokers at diagnosis; 52 quit by the time of the follow-up questionnaire. Among 294 ex-smokers, 15 started to smoke after diagnosis. None of the 84 never smokers at baseline started to smoke after diagnosis. Exposure to smoking at home was associated with continued smoking and relapse (OR=5.1, 95% CI: 1.8–14.3, p=0.001; and OR=3.9, 95% CI: 0.8–14.4, p=0.04, respectively). Specifically, spousal smoking was associated with both continued smoking (OR=7.3, 95% CI: 2.4–21.7, p=2.0E-04) and recidivism (OR=3.7, 95% CI: 0.6–16.6, p=0.08). Having more than a few friends who smoke is associated with continued smoking (OR=3.5, 95% CI: 1.4–8.7, p=0.005) and relapse (OR=4.8, 95% CI: 1.5–15.0, p=0.004). Not completing high school was also associated with continued smoking (OR=3.0, 95% CI: 1.2–7.6, p=0.02). Multivariate analysis identified spousal smoking as the major single predictor of continued smoking (OR=8.8, 95% CI: 2.2–34.8, p=0.002). Conclusions: Smoking cessation programs for lung cancer patients should not only target the patient but also include the immediate family, consider a patient’s peers and be tailored to the patient’s education level. Involvement of the immediate family and consideration of peers may help prevent smoking relapse.


2014 ◽  
Vol 32 (15_suppl) ◽  
pp. e18537-e18537
Author(s):  
Aritoshi Hattori ◽  
Kenji Suzuki ◽  
Keiju Aokage ◽  
Takahiro Mimae ◽  
Kanji Nagai ◽  
...  

2014 ◽  
Vol 44 (11) ◽  
pp. 1088-1095 ◽  
Author(s):  
A. Hattori ◽  
K. Suzuki ◽  
K. Aokage ◽  
T. Mimae ◽  
K. Nagai ◽  
...  

1983 ◽  
Vol 69 (5) ◽  
pp. 437-443 ◽  
Author(s):  
Claudio Modini ◽  
Mario Albertucci ◽  
Franco Cicconetti ◽  
Donatella Tirindelli Danesi ◽  
Renzo Cristiani ◽  
...  

The classification of bronchogenic carcinoma as a function of the prognosis is still an open field. The evaluation of stage, by use of the TNM system, and histologic cell type is not sufficient to guarantee a correct prognosis. The growth rate of the neoplasm is another important parameter. We propose a classification that takes into account the stage (S), histologic cell type (M), immune status (I) and the growth rate of the primary tumor (G): S.M.I.G. We studied 90 lung cancer patients according to the S.M.I.G. classification and we observed that their prognoses were directly correlated with their S.M.I.G. scores (the higher the score, the higher the 10-month mortality rate). The mortality rates within the first 10 months of follow-up were respectively 0%, 0%, 36.36%, 68%, 90.9% for the 5 groups obtained by S.M.I.G. The difference is statistically significant (P < 0.0075) and there is a linear correlation between the mortality rate and the score assigned to each group (R = 0.943; P < 0.05). The S.M.I.G. classification can predict the prognosis more efficiently than the usual classification (TNM) and histological cell type.


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