The Cost-Effectiveness of Intensive National School-Based Anti-Tobacco Education: Results from the Tobacco Policy Model

Health Priorities Research Group, University of California, Irvine, California 92697-7075, USA.
Preventive Medicine (Impact Factor: 3.09). 01/2002; 33(6):558-70. DOI: 10.1006/pmed.2001.0922
Source: PubMed


School-based anti-tobacco education using the "social influences" model is known to reduce smoking among youth by 5-56%. Program effectiveness, however, dissipates in 1-4 years. Consequently, opinion leaders have questioned whether a more intensive national educational effort would be economically efficient. To address this question, we evaluated the cost-effectiveness of enhanced nationwide school-based anti-tobacco education relative to the status quo.
To estimate cost-effectiveness, we created the Tobacco Policy Model, a system dynamics computer simulation model. The model relies on secondary data and is designed to calculate the expected costs and public health gains of any tobacco policy or intervention over any time frame.
Over 50 years, cost-effectiveness is estimated to lie between $4,900 and $340,000 per quality-adjusted life-year (QALY), depending on the degree and longevity of program effectiveness. Assuming a 30% effectiveness that dissipates in 4 years, cost-effectiveness is $20,000/QALY. Sensitivity analysis reveals that cost-effectiveness varies with cost, survival, and quality-of-life estimates but cost-effectiveness ratios generally remain favorable.
Although not cost saving, a much more intensive school-based anti-tobacco educational effort would be an economically efficient investment for the nation.

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    • "Changes in return on investment over time and " tipping points " could also be investigated. Finally, in addition to simulating the impacts on prevention outcomes, such models could also help quantify the delays, costs, and logistic challenges associated with training and other aspects of going to scale (see Tengs et al. 2001). Systems science approaches may help identify aspects of real world systems that we do not fully understand but that impact on intervention decision-making. "
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