ObjectivesThis study aimed to evaluate the impact of genetic notification of smoking-related disease risk on smoking cessation in the general population. Secondary objectives were to assess the impact of genetic notification on intention-to-quit smoking and on emotional outcomes as well as the understanding and the recall of this notification.MethodsA systematic review of articles from inception to August 2011 without language restriction was realized using PubMed, Embase, Scopus, Web of Science, PsycINFO and Toxnet. Other publications were identified using hand search. The pooled-analysis included only randomized trials. Comparison groups were (i) high and low genetic risk versus control, and (ii) high versus low genetic risk. For the pooled-analysis random effect models were applied and sensitivity analyses were conducted.ResultsEight papers from seven different studies met the inclusion criteria of the review. High genetic risk notification was associated with short-term increased depression and anxiety. Four randomized studies were included in the pooled-analysis, which revealed a significant impact of genetic notification on smoking cessation in comparison to controls (clinical risk notification or no intervention) in short term follow-up less than 6 months (RR = 1.55, 95% CI 1.09–2.21).ConclusionsIn short term follow-up, genetic notification increased smoking cessation in comparison to control interventions. However, there is no evidence of long term effect (up to 12 month) on smoking cessation. Further research is needed to assess more in depth how genetic notification of smoking-related disease could contribute to smoking cessation.

Background: Smoking behaviour is a major public health problem worldwide. Several sources have confirmed the implication of genomic factors in smoking behaviour. These factors interact both with environmental factors and interventions to develop a certain behaviour. Objectives: Describing the environmental and genomic factors as well as the interventions influencing smoking cessation (SC) and developing a working model incorporating the different factors influencing SC were our main objectives. Methods: Two systematic reviews were conducted using articles in English from the Cochrane library, PubMed and HuGENet from January 2000 to September 2012: (1) a systematic review of systematic reviews and meta-analyses and (2) a systematic review of original research for genomic factors. The proposed working model was developed by making use of previous models of SC and applying an iterative process of discussion and re-examination by the authors. Results: We confirmed the importance of the 4 main factors influencing SC: (1) environmental factors, (2) genomic factors, (3) gene-environment interactions, and (4) evidence-based interventions. The model demonstrates the complex network of factors influencing SC. Conclusion: The working model of SC proposed a global view of factors influencing SC, warranting future research in this area. Future testing of the model will consolidate the understanding of the different factors affecting SC and will help to improve interventions in this field.

Background A central statistical assessment of the quality of data collected in clinical trials can improve the quality and efficiency of sponsor oversight of clinical investigations. Material and Methods The database of a large randomized clinical trial with known fraud was reanalyzed with a view to identifying, using only statistical monitoring techniques, the center where fraud had been confirmed. The analysis was conducted with an unsupervised statistical monitoring software using mixed-effects statistical models. The statistical analyst was unaware of the location, nature, and extent of the fraud. Results Five centers were detected as atypical, including the center with known fraud (which was ranked 2). An incremental analysis showed that the center with known fraud could have been detected after only 25% of its data had been reported. Conclusion An unsupervised approach to central monitoring, using mixed-effects statistical models, is effective at detecting centers with fraud or other data anomalies in clinical trials.

BackgroundSocial media is a recent source of health information that could disseminate new scientific research, such as the genetics of smoking.ObjectiveThe objectives were (1) to evaluate the availability of genetic information about smoking on different social media platforms (ie, YouTube, Facebook, and Twitter) and (2) to assess the type and the content of the information displayed on the social media as well as the profile of people publishing this information.MethodsWe screened posts on YouTube, Facebook, and Twitter with the terms “smoking” and “genetic” at two time points (September 18, 2012, and May 7, 2013). The first 100 posts were reviewed for each media for the time points. Google was searched during Time 2 as an indicator of available information on the Web and the other social media that discussed genetics and smoking. The source of information, the country of the publisher, characteristics of the posts, and content of the posts were extracted.ResultsOn YouTube, Facebook, and Twitter, 31, 0, and 84 posts, respectively, were included. Posts were mostly based on smoking-related diseases, referred to scientific publications, and were largely from the United States. From the Google search, most results were scientific databases. Six scientific publications referred to within the Google search were also retrieved on either YouTube or Twitter.ConclusionsDespite the importance of public understanding of smoking and genetics, and the high use of social media, little information on this topic is actually present on social media. Therefore, there is a need to monitor the information that is there and to evaluate the population’s understanding of the information related to genetics and smoking that is displayed on social media.