Model of Surveillance of California Tobacco Control Policies
Principal Investigator: Harold D. Holder, Ph.D.
Project Manager: David Levy, Ph.D.

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Computer models can help state and local policymakers make the best decisions. A computer model analyzes many different types of data. It also applies the results of scientific studies to that data. The resulting model can predict the effect of a change in policy. The SimSmoke computer model predicts smoking rates and deaths for the United States. SimSmoke also predicts how changes in law, medicine, taxes or mass media will affect smoking and death rates.

This project will create a California SimSmoke computer model. California has a very diverse population. The state is also a leader in policies to reduce the harm caused by tobacco. For these two reasons, California is an ideal place to develop a state-level computer model.

California SimSmoke will track smokers by age, gender, and racial/ethnic group. The model will predict smoking and smoking related deaths based on population subgroups and public policies in effect. The model will include the effects of policies such as taxes, mass media, clean air laws, treatment to stop smoking, and youth access to tobacco.

Californians could use this computer model to see how different policies affect smoking, which in turn affect smoking-related deaths. The model examines the effect of past policies and develops predictions on the effect of future policies. They would be able to monitor the value of each policy, and show how past policies have been effective and where they have not been effective. They could then use the model to shape future policies.

Californians could even see the effect of policies on the smoking rates and deaths to specific age, gender and racial/ethnic groups. They could discern which age groups and racial groups are currently being affected by tobacco control policies in the state of California, and determine policies to improve the health of these groups.

Developing this computer model will help leaders understand about past policies and make better decisions about future policies. They could also see how the effect of policies depends on the way in which they are implemented, and the other policies already in place. Better decisions can improve health for everyone.


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