Predicting the Risk of Lung Cancer in Never-smokers

Despite being considered a disease of smokers, approximately 10-15% of lung cancer cases occur in never-smokers. Lung cancer risk prediction models have demonstrated excellent ability to discriminate cases from non-cases, and have been shown to be more efficient at selecting individuals for future screening than current criteria. Existing models have primarily been developed in populations of smokers, thus there was a need to develop an accurate model in never-smokers. This study focused on developing and validating a model using never-smokers from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Cox regression analysis, with six-year follow-up, was used for model building. Predictors included: age, body mass index, education level, personal history of cancer, family history of lung cancer, previous chest X-ray, and secondhand smoke exposure. This model achieved fair discrimination (optimism corrected c-statistic = 0.6645) and good calibration. This represents an improvement on existing neversmoker models, but is not suitable for individual-level risk prediction.

The impact of tobacco control policies on university students' smoking in Ontario

Objective. Despite steady declines in the prevalence of tobacco use among Canadians, young adult tobacco use has remained stubbornly high over the past two decades (CTUMS, 2005a). Currently in Ontario, young adults have the highest proportion of smokers of all age cohorts at 26%. A growing body of evidence shows that smoking restrictions and other tobacco control policies can reduce tobacco use and consumption among adults and deter initiation among youth; whether young adult university students' smoking participation is influenced by community smoking restrictions, campus tobacco control policies or both remains an empirical question. The purpose of this study is to examine the relationship among current smoking status of students on university campuses across Ontario and various tobacco control policies, 3including clean air bylaws of students' home towns, clean air by-laws of the community where the university is situated, and campus policies. Methods. Two data sets were used. The 200512006 Tobacco Use in a Representative Sample of Post-Secondary Students data set provides information about the tobacco use of 10,600 students from 23 universities and colleges across Ontario. Data screening for this study reduced the sample to 5,114 17-to-24 year old undergraduate students from nine universities. The second data set is researcher-generated and includes information about strength and duration of, and students' exposure to home town, local and campus tobacco control policies. Municipal by-laws (of students' home towns and university towns) were categorized as weak, moderate or strong based on criteria set out in the Ontario Municipal By-law Report; campus policies were categorized in a roughly parallel fashion. Durations of municipal and campus policies were calculated; and length of students' exposure to the policies was estimated (all in months). Multinomial logistic regression analyses were used to examine the relationship between students' current smoking status (daily, less-than-daily, never-smokers) and the following policy measures: strength of, duration of, and students' exposure to campus policy; strength of, duration of, and students' exposure to the by-law in the university town; and, strength of, duration of, and students' exposure to the by-law in the home town they grew up in. Sociodemographic variables were controlled for. Results. Among the Ontario university students surveyed, 7.0% currently use tobacco daily and 15.4% use tobacco less-than-daily. The proportions of students experiencing strong tobacco control policies in their home town, the community in which their university is located and at their current university were 33.9%,64.1 %, and 31.3% respectively. However, 13.7% of students attended a university that had a weak campus policy. Multinomial logistic regressions suggested current smoking status was associated with university town by-law strength, home town by-law strength and the strength of the campus tobacco control policy. In the fmal model, after controlling for sociodemographic factors, a strong by-law in the university town and a strong by-law in students' home town were associated with reduced odds of being both a less-than-daily (OR = 0.64, 95%CI: 0.48-0.86; OR = 0.80, 95%CI: 0.66-0.95) and daily smoker (OR = 0.59, 95%CI: 0.39-0.89; OR = 0.76, 95%CI: 0.58-0.99), while a weak campus tobacco control policy was associated with higher odds of being a daily smoker (OR = 2.08, 95%CI: 1.31-3.30) (but unrelated to less-than-daily smoking). Longer exposure to the municipal by-law (OR = 0.93; 95%CI: 0.90-0.96) was also related to smoking status. Conclusions. Students' smoking prevalence was associated with the strength of the restrictions in university, and with campus-specific tobacco control policies. Lessthan- daily smoking was not as strongly associated with policy measures as daily smoking was. University campuses may wish to adopt more progressive campus policies and support clean air restrictions in the broader community. More research is needed to determine the direction of influence between tobacco control policies and students' smoking.

Evaluating three self-help smoking cessation interventions for post-secondary smokers : a randomized controlled trial on six Ontario campuses

Objective. Smoking prevalence is highest among the young adult cohort. Postsecondary students are no exception. Although many students intend to quit smoking, no research has established what methods best promote reductions in, or complete abstinence from smoking. This randomized controlled trial examined the effectiveness of three self-help smoking cessation interventions. Method. On six post-secondary campuses, 483 smokers who voluntarily accessed Leave The Pack Behind (a tobacco control initiative) were randomly assigned to one of three smoking cessation interventions: One Step At A Time (a 2-booklet, *gold standard' program for adults); Smoke|Quit (a newly-developed 2-booklet program for young adult students); and usual care (a 'Quit Kit' containing a booklet on stress management, information about pharmacological quitting aides and novelty items). All participants also received one proactive telephone support call from a peer counsellor. During the study, 85 participants withdrew. The final sample of 216 students who completed baseline questionnaires and 12-week follow-up telephone interviews was representative of the initial sample in terms of demographic characteristics, and smokingquitting- related variables. Results. Whether participants quit smoking depended upon treatment condition, ^(2, N=2\6) = 6.34, p = .04, with Smoke|Quit producing more successfijl quitters (18.4%) than One Step At A Time (4.5%) or the Quit Kit (1 1.4%). On average, participants had quit 53.46 days, with no significant difference across treatments. Selfefficacy also increased. Use of the intervention or other quitting aides was not associated with treatment condition. Among the 191 participants who did not quit smoking, treatment condition did not influence outcomes. Overall, 46.2% had made a quit attempt. Significant decreases in weekly tobacco consumption and increases in self-efficacy to resist smoking were observed from baseline to follow-up. Conclusion. Post-secondary institutions represent a potentially final opportunity for age-targeted interventions. Self-help resources tailored to students' social and contextual characteristics will have considerable more impact than stage-only tailored interventions. Both reduction and abstinence outcomes should be emphasized to positively support students to stop smoking.