From cigarette smuggling to illicit tobacco trade

Background Tax policy is considered the most effective strategy to reduce tobacco consumption and prevalence. Tax avoidance and tax evasion therefore undermine the effectiveness of tax policies and result in less revenue for governments, cheaper prices for smokers and increased tobacco use. Tobacco smuggling and illicit tobacco trade have probably always existed, since tobacco's introduction as a valuable product from the New World, but the nature of the trade has changed. Methods This article clarifies definitions, reviews the key issues related to illicit trade, describes the different ways taxes are circumvented and looks at the size of the problem, its changing nature and its causes. The difficulties of data collection and research are discussed. Finally, we look at the policy options to combat illicit trade and the negotiations for a WHO Framework Convention on Tobacco Control (FCTC) protocol on illicit tobacco trade. Results Twenty years ago the main type of illicit trade was large-scale cigarette smuggling of well known cigarette brands. A change occurred as some major international tobacco companies in Europe and the Americas reviewed their export practices due to tax regulations, investigations and lawsuits by the authorities. Other types of illicit trade emerged such as illegal manufacturing, including counterfeiting and the emergence of new cigarette brands, produced in a rather open manner at well known locations, which are only or mainly intended for the illegal market of another country. Conclusions The global scope and multifaceted nature of the illicit tobacco trade requires a coordinated international response, so a strong protocol to the FCTC is essential. The illicit tobacco trade is a global problem which needs a global solution.

Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

Abstract Background An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. Results We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. Conclusion Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.