In support of tobacco control efforts, the long term goals of this research are to develop and demonstrate the utility of (a) measures of tobacco use initiation, and (b) analytic methods for evaluation of trends in use and extrapolation/prediction from trends. In this project we propose to demonstrate the utility of three population-based measures of initiation (risk interval, resistant population, late initiation), and to explore the potential of a markov chain model for extrapolation of trends. Risk interval is defined as the limits and distribution of ages in which initiation occurs; resistant population is defined as that proportion of a population not initiating tobacco use; and late initiation is defined as initiation to use at age 20 or beyond. The utility of these measures is to be demonstrated through the analysis of 2 datasets, the NHIS of 1987-1988 and the Current Population Survey (smoking supplements) of 1992-1993. Estimates generated in these analyses will be used to construct the markov model. The specific aims of this project, then, are to : (1) Analyze NHIS and CPS data to estimate risk interval, resistant population, and magnitude of late initiation. Age at initiation of use, the primary outcome of interest, will be treated as time-to-event data, and Kaplan-Meier estimates of "proportion survivng" past age 19 and past the risk interval will be taken as estimates of late initiation and resistant population respectively. In estimating risk interval, the minimum and maximum initiation ages and the distribution of these will be analyzed. (2) Construct markov chain models, and estimate their stationary distribution using parameters developed in the above analysis. For each age (10-80 years) 5-states will be represented in the markov chain, (1) never-user, (2) initiator, (3) current user - formerly initiated, (4) former user, and (5) death at attained age. Details of the complete model are described in methods, where it is shown that the transition probabilities needed to implement the model are available. (3) Assess correlation of ecological-level variables (e.g. tobacco advertising expenditures and content, the volume of tobacco manufacture) following literature review and data abstraction.
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