Smoking addiction is an insidious, dynamic, and seemingly selective health risk factor contributing to at least 400,000 premature deaths per year. But this addiction and its accompanying premature deaths appear to be more prevalent in lower socioeconomic, minority, and female segments of the population. As smoking addiction spreads through a population, much like a plague, historical indicators of prevalence and incidence shed light on the risk to the public. However, robust predictions over time, across and within a demographic group, are best made using a dynamic population simulation model that incorporates feedback loops to reflect recidivism.
Smokenomics is such a model. Within a given population, the model’s structure addresses smoking prevalence, prevention, cessation, and recidivism by ethnicity, gender, socioeconomic status, and age group over 5 years in three months increments. These characteristics facilitate the analysis of disparities in health outcomes dynamically extrapolated into the future. For each three-month period the number of smokers, nonsmokers, and never smokers are tallied for two situations, with and without access to an intervention program. Thus, equal health opportunities can be examined in the context of both deficiencies in prevention and shortfalls in cessation. These findings lead to projections of cancer, heart disease, and other chronic disorders.
Learning Objectives: LEARNING OBJECTIVES: The role of dynamic population models in the analysis of equal health opportunities will be shown. Insight will be provided into projections of the apparent unequal distribution of smoking addiction, prevention, and cessation across ethnic, socioeconomic, gender, and age groups.
Keywords: Equal Access, Tobacco Policy
Presenting author's disclosure statement:
Organization/institution whose products or services will be discussed: None
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.