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Background Smoking is one of the leading causes of preventable mortality and morbidity worldwide, with the European Region having the highest prevalence of tobacco smoking among adults compared to other WHO regions. The Belgian Health Interview Survey (BHIS) provides a reliable source of national and regional estimates of smoking prevalence; however, currently there are no estimates at a smaller geographical resolution such as the municipality scale in Belgium. This hinders the estimation of the spatial distribution of smoking attributable mortality at small geographical scale (i.e., number of deaths that can be attributed to tobacco). The objective of this study was to obtain estimates of smoking prevalence in each Belgian municipality using BHIS and calculate smoking attributable mortality at municipality level. Methods Data of participants aged 15 + on smoking behavior, age, gender, educational level and municipality of residence were obtained from the BHIS 2018. A Bayesian hierarchical Besag-York-Mollie (BYM) model was used to model the logit transformation of the design-based Horvitz-Thompson direct prevalence estimates. Municipality-level variables obtained from Statbel, the Belgian statistical office, were used as auxiliary variables in the model. Model parameters were estimated using Integrated Nested Laplace Approximation (INLA). Deviance Information Criterion (DIC) and Conditional Predictive Ordinate (CPO) were computed to assess model fit. Population attributable fractions (PAF) were computed using the estimated prevalence of smoking in each of the 589 Belgian municipalities and relative risks obtained from published meta-analyses. Smoking attributable mortality was calculated by multiplying PAF with age-gender standardized and stratified number of deaths in each municipality. Results BHIS 2018 data included 7,829 respondents from 154 municipalities. Smoothed estimates for current smoking ranged between 11% [Credible Interval 3;23] and 27% [21;34] per municipality, and for former smoking between 4% [0;14] and 34% [21;47]. Estimates of smoking attributable mortality constituted between 10% [7;15] and 47% [34;59] of total number of deaths per municipality. Conclusions Within-country variation in smoking and smoking attributable mortality was observed. Computed estimates should inform local public health prevention campaigns as well as contribute to explaining the regional differences in mortality.

Background While Italy has implemented some tobacco control policies over the last few decades, which resulted in a decreased smoking prevalence, there is still considerable scope to strengthen tobacco control policies consistent with the World Health Organization (WHO) policy guidelines. The present study aims to evaluate the effect of past and project the effect of future tobacco control policies on smoking prevalence and associated premature mortality in Italy. Methods To assess, individually and in combination, the effect of seven types of policies, we used the SimSmoke simulation model of tobacco control policy. The model uses population, smoking rates and tobacco control policy data for Italy. Results Significant reductions of smoking prevalence and premature mortality can be achieved through tobacco price increases, high intensity media campaigns, comprehensive cessation treatment program, strong health warnings, stricter smoke-free air regulations and advertising bans, and youth access laws. With a comprehensive approach, the smoking prevalence can be decreased by as much as 12% soon after the policies are in place, increasing to a 30% reduction in the next twenty years and a 34% reduction by 30 years in 2040. Without effective tobacco control policies, a total of almost 300 thousand lives will be prematurely lost due to smoking by the year 2040. Conclusion Besides presenting the benefits of a comprehensive tobacco control strategy, the model helps identify information gaps in surveillance and evaluation schemes that will promote the effectiveness of future tobacco control policy in Italy.