, 2006). For the current study, a nationally representative selleckchem Temsirolimus sample of smokers participating in the ITC Netherlands Survey was surveyed at four consecutive years before and after the implementation of smoke-free hospitality industry legislation in July 2008. Although the implementation of smoke-free legislation went relatively well in restaurants, there were considerable problems with the implementation in bars (Mons et al., 2012; Nagelhout, Mons, et al., 2011). High levels of noncompliance and low levels of societal and political support eventually led to a partial reversal of the smoke-free legislation in small owner-run bars at the end of 2010. Possibly due to the problems with bars, the smoke-free hospitality industry legislation had only a small impact on smoking cessation, without significantly reducing smoking prevalence (Nagelhout, Willemsen, & De Vries, 2011).
The aim of the current study was to apply the ITC Conceptual Model on pathways of change explaining the effect of individual exposure to smoke-free legislation on smoking cessation. Based on the ITC Conceptual Model and previous literature, we hypothesize that smoke-free legislation influences smoking cessation by first increasing support and harm awareness (policy-specific variables) and in turn increasing attitudes, subjective norms, and self-efficacy for quitting (psychosocial mediators). Methods Design We used longitudinal data from four consecutive annual surveys of the ITC Netherlands Survey. The baseline survey was performed about 2 months before the implementation of the smoke-free legislation in 2008.
The follow-up surveys were performed after the implementation, respectively 1, 2, and 3 years later in 2009, 2010, and 2011. Policy-specific variables, psychosocial mediators, and smoking cessation were modeled at consecutive survey waves, while controlling for policy-specific variables and psychosocial mediators at baseline, to allow for more confident inferences about the causality of the tested pathways of change. Sample Dutch smokers aged 15 years and older were recruited from TNS NIPObase, a large probability-based web database (Nagelhout et al., 2010). Quotas on gender, geographic region, household size, and education were determined from the Dutch Continuous Survey of Smoking Habits to get a sample that was representative of Dutch smokers.
Potential respondents were identified as smokers (having smoked at least 100 cigarettes in their lifetime and currently smoking at least once per month) by means of a short screening survey in March Carfilzomib 2008. In April 2008, 2,331 smokers were invited to participate in a web survey. Of these, 1,820 participated in the 2008 survey (78.1%). In April and May 2009, all 1,820 baseline smokers were invited to participate in the 2009 survey, and 1,447 took part (79.5%).