261967 Using Markov Chain Modeling and Event History Analysis to determine predictors of enactment of smokefree laws using the American Nonsmokers' Rights Foundation's Tobacco laws database

Tuesday, October 30, 2012 : 9:10 AM - 9:30 AM

Ashley Sanders-Jackson, PhD , Center for Tobacco Control Research and Education, University of California San Francisco, San Francisco, CA
Anna Song, PhD , Psychological Sciences, School of Social Sciences, Humanities, and Arts, University of California, Merced, Merced, CA
Mariaelena Gonzalez, PhD , Center for Tobacco Control Research & Education, University of California, San Francisco, San Francisco, CA
Brandon Zerbe, PhD , Institute for Human Genetics, University of California San Francisco, San Francisco, CA
Stan Glantz, PhD , Department Medicine, School UCSF School of Medicine, San Francisco, CA
Smokefree laws in the United States have significantly increased since they started passing in the early 1980s. However, diffusion of laws has been uneven and little is known about how they cluster geographically or the sequence in which venues (workplaces vs hospitality sector coverage) are regulated. This study outlines two statistical approaches for analyzing diffusion: event history analysis and Markov Chain modeling. We use these two methods to analyze the spread of smokefree laws in terms of the sequence and strength of the law. The database contains many types of US smokefree laws (government workplace, private workplace, restaurant, bars, outdoor, sales, advertising, and more) at three levels of government (state, county, and city). We focus on workplace, restaurant, and bar laws, and explore the sequences of smokefree laws passage for venues (workplace vs hospitality) both overall and across different periods of time. We use both Hidden Markov Modeling and simple Markov Chain methods allowing for the simultaneous passage of laws. Using event history analysis, we explore the time between the passage of weaker and stronger laws at each level of government including the effect of state-level pre-emption on the passage of county and city-level laws. The use of Markov Chain modeling and event history analysis may be applicable to other areas of law and policy. Analyzing the diffusion of regulation is crucial to understanding public policy trends and more effectively targeting these population-level interventions.

Learning Areas:
Biostatistics, economics
Public health or related laws, regulations, standards, or guidelines
Systems thinking models (conceptual and theoretical models), applications related to public health

Learning Objectives:
1. Demonstrate the application of Markov Chain Modeling and Event History Analysis as health policy analysis tools. 2. Explain the diffusion of smokefree laws within the United States.

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have a Ph.D. from the Annenberg School for Communication with a focus in health communication where I studied cancer communication messaging. I am a postdoctoral fellow on an NIH funded training grant at the Center for Tobacco Control Research and education at UCSF. I have written the majority of the abstract and have worked on the analysis.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.