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.

Using a Bayesian model for bankruptcy prediction : a comparative approach

The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are assessed- Bayesian, Hazard and Mixed Logit. A salient feature of the study is that the analysis includes both parametric and nonparametric bankruptcy prediction models. A sample of firms from Lynn M. LoPucki Bankruptcy Research Database in the U. S. was used to evaluate the relative performance of the three models. The choice of a cut-off point and sampling procedures were found to affect the rankings of the various models. In general, the results indicate that the empirical cut-off point estimated from the training sample resulted in the lowest misclassification costs for all three models. Although the Hazard and Mixed Logit models resulted in lower costs of misclassification in the randomly selected samples, the Mixed Logit model did not perform as well across varying business-cycles. In general, the Hazard model has the highest predictive power. However, the higher predictive power of the Bayesian model, when the ratio of the cost of Type I errors to the cost of Type II errors is high, is relatively consistent across all sampling methods. Such an advantage of the Bayesian model may make it more attractive in the current economic environment. This study extends recent research comparing the performance of bankruptcy prediction models by identifying under what conditions a model performs better. It also allays a range of user groups, including auditors, shareholders, employees, suppliers, rating agencies, and creditors' concerns with respect to assessing failure risk.