Int. J. Environ. Res. Public Health 2010, 7 2792 schooling only), secondary education (apprenticeship, vocational school and secondary school) and higher education (advanced professional training, college and university). Net monthly household income categories were: Swiss francs (CHF) 0–4,000, CHF 4,001–8,000 and CHF 8,000 and over. Many studies have shown a significant link between smoking and other health-related behaviours [15-18]. We chose to include heavy drinking and concern for healthy eating as independent variables in the models. These categories were defined by those who drank two or more alcoholic beverages per day and by a positive response to the question “Do you try to eat a healthy diet?” We also included a dummy variable that was equal to one if the individual lived in a region where tobacco control was intensive at the time of the study [there are seven regions in Switzerland (Lake Geneva Region, Mittelland, North West Switzerland, Zurich, Eastern Switzerland, Central Switzerland and Ticino) some of them have implemented more stringent tobacco control policies]. Finally we included a dummy variable for each interview year to account for potential trends. 2.3. Statistical Analysis We first conducted a simple descriptive analysis to assess the association between socioeconomic indicators and the variable of interest in each analysis (successful cessation and abstinence duration). The differences between each socioeconomic subgroup were computed in both absolute and relative terms. To evaluate the simultaneous impact of socioeconomic characteristics on successful cessation while controlling for potential confounders, we conducted multivariate logistic regressions for men and women separately. Analysis of relapse was performed using the duration analysis framework, in which the variable of interest is a time period. In our case, the dependant variable was the duration, in days, of the longest quit attempt. Observations for which no relapse was observed are said to be censored and observations associated with unsuccessful quitters are uncensored (or complete). Among the numerous models available to analyse duration and its determinants, we opted for models in which the covariates are assumed to multiply the predicted time (accelerated failure-time models or AFT-AFT models are linear models of the logarithm of the survival time), in contrast with proportional hazard models in which the covariates are assumed to multiply the chance that an event occurs. Among the suitable distributions for AFT models, the log-logistic distribution is the most commonly used. The Weibull, exponential, log-normal, gamma or inverse Gaussian distributions are also appropriate. We estimated a series of AFT models by relying on various distributional assumptions. Since the different distributional assumptions have led to very similar estimates, we only report the results obtained with the log-logistic distribution. These models were applied to men and women separately. Our specifications accounted for unobserved heterogeneity (the unobserved heterogeneity was assumed to be Gamma distributed), and all estimations were performed using Stata version 10.0 (Stata Corp., Texas, USA). 3. Results 3.1. Study Population In the 2001–2007 editions of the tobacco monitoring survey, 70,216 individuals completed the survey, among whom 63,520 were aged 18 and over. These individuals included 19,622 smokers