A Probabilistic Neural Network Approach to Modeling the Impact of Tobacco Control Policies by Gender

This paper evaluates the impact of tobacco control policies on female and male smokers. The data used in this study are from the 2002-2006 International Tobacco Control Four Country Survey. Based on eleven smokers’ motivational attributes used in the survey, principle component analysis is used to identify tobacco control policy drivers which are labeled as personal concerns, cigarette price, environmental restrictions and social encouragement. To examine the relative impact degrees of these four policy drivers on the groups of female and male smokers for their quit attempts, probabilistic neural network models are developed using hypothetical policy impacted populations. The experimental result shows that the most significant motivator for female smokers to make a quit attempt is their personal concerns. For male smokers, social encouragement plays a dominant role for them to make a quit attempt. The result indicates that smoking restrictions in public places or at workplace can somewhat encourage them to make a quit attempt. However, increasing the cigarette price is less likely to affect the MQA rate of both female and male smokers.