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Objectives To measure changes in prevalence and predictors of home smoking bans (HSBs) among smokers in four European countries after the implementation of national smoke-free legislation. Design Two waves of the International Tobacco Control Policy Evaluation Project Europe Surveys, which is a prospective panel study. Pre- and post-legislation data were used from Ireland, France, Germany and the Netherlands. Two pre-legislation waves from the UK were used as control. Participants 4634 respondents from the intervention countries and 1080 from the control country completed both baseline and follow-up and were included in the present analyses. Methods Multiple logistic regression models to identify predictors of having or of adopting a total HSB, and Generalised Estimating Equation models to compare patterns of change after implementation of smoke-free legislation to a control country without such legislation. Results Most smokers had at least partial smoking restrictions in their home, but the proportions varied significantly between countries. After implementation of national smoke-free legislation, the proportion of smokers with a total HSB increased significantly in all four countries. Among continuing smokers, the number of cigarettes smoked per day either remained stable or decreased significantly. Multiple logistic regression models indicated that having a young child in the household and supporting smoking bans in bars were important correlates of having a pre-legislation HSB. Prospective predictors of imposing a HSB between survey waves were planning to quit smoking, supporting a total smoking ban in bars and the birth of a child. Generalised Estimating Equation models indicated that the change in total HSB in the intervention countries was greater than that in the control country. Conclusions The findings suggest that smoke-free legislation does not lead to more smoking in smokers' homes. On the contrary, our findings demonstrate that smoke-free legislation may stimulate smokers to establish total smoking bans in their homes. [ABSTRACT FROM AUTHOR]

Different lifestyle patterns across Europe may influence plasma concentrations of B-vitamins and one-carbon metabolites and their relation to chronic disease. Comparison of published data on one-carbon metabolites in Western European regions is difficult due to differences in sampling procedures and analytical methods between studies. The present study aimed, to compare plasma concentrations of one-carbon metabolites in Western European regions with one laboratory performing all biochemical analyses. We performed the present study in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort among 5446 presumptively healthy individuals. Quantile regression was used to compare sex-specific median concentrations between Northern (Denmark and Sweden), Central (France, Germany, The Netherlands and United Kingdom) and Southern (Greece, Spain and Italy) European regions. The lowest folate concentrations were observed in Northern Europe (men, 10·4 nmol/l; women, 10·7 nmol/l) and highest concentrations in Central Europe. Cobalamin concentrations were slightly higher in Northern Europe (men, 330 pmol/l; women, 352 pmol/l) compared with Central and Southern Europe, but did not show a clear north–south gradient. Vitamin B2 concentrations were highest in Northern Europe (men, 22·2 nmol/l; women, 26·0 nmol/l) and decreased towards Southern Europe (Ptrend< 0·001). Vitamin B6 concentrations were highest in Central Europe in men (77·3 nmol/l) and highest in the North among women (70·4 nmol/l), with decreasing concentrations towards Southern Europe in women (Ptrend< 0·001). In men, concentrations of serine, glycine and sarcosine increased from the north to south. In women, sarcosine increased from Northern to Southern Europe. These findings may provide relevant information for the study of regional differences of chronic disease incidence in association with lifestyle. [ABSTRACT FROM PUBLISHER]

Background Finite mixture models posit the existence of a latent categorical variable and can be used for probabilistic classification. The authors illustrate the use of mixture models for dietary pattern analysis. An advantage of this approach is taking classification uncertainty into account. Methods Participants were a random sample of women from the European Prospective Investigation into Cancer. Food consumption was measured using dietary questionnaires. Mixture models identified latent classes in food consumption data, which were interpreted as dietary patterns. Results Among various assumptions examined, models allowing the variance of foods to vary within and between classes fit better than alternatives assuming constant variance (the K-means method of cluster analysis also makes the latter assumption). An eight-class model was best fitting and five patterns validated well in a second random sample. Patterns with lower classification uncertainty tended to be better validated. One pattern showed low consumption of foods despite being associated with moderate body mass index. Conclusion Mixture modelling for dietary pattern analysis has advantages over both factor and cluster analysis. In contrast to these other methods, it is easy to estimate pattern prevalence, to describe patterns and to use patterns to predict disease taking classification uncertainty into account. Owing to substantial error in food consumptions, any analysis will usually find some patterns that cannot be well validated. While knowledge of classification uncertainty may aid pattern evaluation, any method will better identify patterns from food consumptions measured with less error. Mixture models may be useful to identify individuals who under-report food consumption. [ABSTRACT FROM AUTHOR]