Smokers\u27 Characteristics and Cluster Based Quitting Rule Discovery Model for Enhancement of Government\u27s Tobacco Control Systems

Discovery of cluster characteristics and interesting rules describing smokers’ clusters and the behavioural patterns of smokers’ quitting intentions is an important task in the development of an effective tobacco control systems. In this paper, we attempt to determine the characteristics of smokers’ clusters and simplified rule for predicting smokers’ quitting behaviour that can provide feedback to build a scientific evidence-based adaptive tobacco control systems. Standard clustering algorithm groups the data based on there inherent pattern. However, they seldom provide human understandable easy description of the clusters’. Again, standard decision tree (SDT) based rule discovery depends on decision boundaries in the feature space. This may limit the ability of SDT to learn intermediate concepts for high dimensional large datasets such as tobacco control. In this paper, we propose a cluster-based rule discovery model (CRDM) that builds conceptual groups from which a set of decision trees (a decision forest) are constructed to find smokers’ quitting rules. We also employ a re-labelling of unsupervised cluster (RLUC) approach to determine the characteristics of the clusters. RLUC approach uses re-labelling and decision tree approach to find the characteristics of the smokers’ clusters. Experimental results on the tobacco control data set show that decision rules from the decision forest constructed by CRDM are simpler and can predict smokers’ quitting intention more accurately than a single decision tree. RLUC approach finds text-based characteristics of the smokers’ clusters which are easily understandable for policy makers in the tobacco control systems

Trends in beliefs about the harmfulness and use of stop-smoking medications and smokeless tobacco products among cigarettes smokers: Findings from the ITC four-country survey

BACKGROUND: Evidence shows that smokers are generally misinformed about the relative harmfulness of nicotine, and smokeless forms of nicotine delivery in relation to smoked tobacco. This study explores changing trends in the beliefs about the harmfulness and use of stop smoking medications and smokeless tobacco in adult smokers in four countries where public education and access to alternative forms of nicotine is varied (Canada, the US, the UK and Australia). METHODS: Data are from seven waves of the ITC-4 country study conducted between 2002 and 2009 with adult smokers from Canada, the US, the UK and Australia. For the purposes of this study, data were collected from 21,207 current smokers. Using generalised estimating equations to control for multiple response sets, multivariate models were tested to look for main effects of country, and trends across time, controlling for demographic variables. RESULTS: Knowledge remained low in all countries, although UK smokers tended to be better informed. There was a small but significant improvement across time in the UK, but mixed effects in the other three countries. At the final wave, between 37.5% (US) and 61.4% (UK) reported that NRT is a lot less harmful than cigarettes. In Canada and the US, where smokeless tobacco is marketed, only around one in six believed some smokeless tobacco products could be less harmful than cigarettes. CONCLUSIONS: Many smokers continue to be misinformed about the relative safety of nicotine and alternatives to smoked tobacco, especially in the US and Canada. Concerted efforts to educate UK smokers have probably improved their knowledge. Further research is required to assess whether misinformation deters smokers from appropriate use of alternative forms of nicotine

The acceptability of nicotine containing products as alternatives to cigarettes: findings from two pilot studies

<p>Abstract</p> <p>Background</p> <p>This study aimed to explore issues that might impact on the acceptability and feasibility of offering smokers nicotine containing products either to quit nicotine use altogether by using as a short term means of quitting cigarettes or as a longer term substitute.</p> <p>Method</p> <p>Two small pilot studies, one in the UK (n = 34) involving face to face contact and direct provision of the product, the other in Australia (n = 31) conducted remotely with products sent in the mail.</p> <p>Results</p> <p>Nicotine lozenges were the most popular products, but significant minorities liked a smokeless product more. Use stimulated interest in quitting, and although many failed to use all the products provided, most were interested in future use, more often to help quit than as a planned long-term substitute.</p> <p>Conclusions</p> <p>These studies indicate an untapped interest in the use of substitutes to reduce the harmfulness of smoking. Studies of this sort do not inhibit interest in quitting nicotine altogether, and may facilitate it. The greater the range of products on offer, the more smokers are likely to try a product to quit.</p

Cluster based rule discovery model for enhancement of government's tobacco control strategy

Discovery of interesting rules describing the behavioural patterns of smokers' quitting intentions is an important task in the determination of an effective tobacco control strategy. In this paper, we investigate a compact and simplified rule discovery process for predicting smokers' quitting behaviour that can provide feedback to build an scientific evidence-based adaptive tobacco control policy. Standard decision tree (SDT) based rule discovery depends on decision boundaries in the feature space which are orthogonal to the axis of the feature of a particular decision node. This may limit the ability of SDT to learn intermediate concepts for high dimensional large datasets such as tobacco control. In this paper, we propose a cluster based rule discovery model (CRDM) for generation of more compact and simplified rules for the enhancement of tobacco control policy. The clusterbased approach builds conceptual groups from which a set of decision trees (a decision forest) are constructed. Experimental results on the tobacco control data set show that decision rules from the decision forest constructed by CRDM are simpler and can predict smokers' quitting intention more accurately than a single decision tree. © 2010 IEEE

Impact of the New Malaysian Cigarette Pack Warnings on Smokers’ Awareness of Health Risks and Interest in Quitting Smoking

The objective of this research was to compare the response of adult smokers in Malaysia to newly proposed pictorial cigarette warnings against the current text-only warnings. The study population included 140 adult male smokers who were enrolled in a randomized trial to view either the new pictorial warnings (intervention) or the old text-only warnings (control). Participants completed pre-exposure and post-exposure questionnaires that assessed their awareness of the health risks of smoking, response to the package warnings, and interest in quitting smoking. Exposure to the pictorial warnings resulted in increased awareness of the risks of smoking, stronger behavioral response to the warnings and increased interest in quitting smoking. The new warnings in Malaysia will increase smokers’ knowledge of the adverse health effects of smoking and have a positive effect on interest in quitting