skip to main content
Washington State Institute for Public Policy
Back Button

Anti-smoking media campaigns, adult effect

Public Health & Prevention: Population-level policies
Benefit-cost methods last updated December 2019.  Literature review updated December 2014.
Open PDF
Hopkins, et al. (2001) provides a useful definition of mass media campaigns that we use in determining whether a study fits within our meta-analysis. They define a mass media intervention as interventions “of an extended duration that use brief, recurring messages to inform and motivate individual to remain tobacco free.” We append that definition only slightly to include interventions that motivate individuals to become tobacco free (in addition to remaining tobacco free), including mass media interventions aimed at cessation as well as prevention. The effects presented in this review represent only the effects of anti-smoking media campaigns on adults.
For an overview of WSIPP's Benefit-Cost Model, please see this guide. The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2018). The chance the benefits exceed the costs are derived from a Monte Carlo risk analysis. The details on this, as well as the economic discount rates and other relevant parameters are described in our Technical Documentation.
Benefit-Cost Summary Statistics Per Participant
Benefits to:
Taxpayers $552 Benefits minus costs $1,729
Participants $926 Benefit to cost ratio $47.06
Others $185 Chance the program will produce
Indirect $103 benefits greater than the costs 87 %
Total benefits $1,766
Net program cost ($38)
Benefits minus cost $1,729

Meta-analysis is a statistical method to combine the results from separate studies on a program, policy, or topic in order to estimate its effect on an outcome. WSIPP systematically evaluates all credible evaluations we can locate on each topic. The outcomes measured are the types of program impacts that were measured in the research literature (for example, crime or educational attainment). Treatment N represents the total number of individuals or units in the treatment group across the included studies.

An effect size (ES) is a standard metric that summarizes the degree to which a program or policy affects a measured outcome. If the effect size is positive, the outcome increases. If the effect size is negative, the outcome decreases. See Estimating Program Effects Using Effect Sizes for additional information.

Adjusted effect sizes are used to calculate the benefits from our benefit cost model. WSIPP may adjust effect sizes based on methodological characteristics of the study. For example, we may adjust effect sizes when a study has a weak research design or when the program developer is involved in the research. The magnitude of these adjustments varies depending on the topic area.

WSIPP may also adjust the second ES measurement. Research shows the magnitude of some effect sizes decrease over time. For those effect sizes, we estimate outcome-based adjustments which we apply between the first time ES is estimated and the second time ES is estimated. We also report the unadjusted effect size to show the effect sizes before any adjustments have been made. More details about these adjustments can be found in our Technical Documentation.

Meta-Analysis of Program Effects
Outcomes measured Treatment age No. of effect sizes Treatment N Adjusted effect sizes(ES) and standard errors(SE) used in the benefit - cost analysis Unadjusted effect size (random effects model)
First time ES is estimated Second time ES is estimated
ES SE Age ES SE Age ES p-value
42 7 3577 -0.060 0.054 42 -0.060 0.054 43 -0.060 0.262
1In addition to the outcomes measured in the meta-analysis table, WSIPP measures benefits and costs estimated from other outcomes associated with those reported in the evaluation literature. For example, empirical research demonstrates that high school graduation leads to reduced crime. These associated measures provide a more complete picture of the detailed costs and benefits of the program.

2“Others” includes benefits to people other than taxpayers and participants. Depending on the program, it could include reductions in crime victimization, the economic benefits from a more educated workforce, and the benefits from employer-paid health insurance.

3“Indirect benefits” includes estimates of the net changes in the value of a statistical life and net changes in the deadweight costs of taxation.
Detailed Monetary Benefit Estimates Per Participant
Affected outcome: Resulting benefits:1 Benefits accrue to:
Taxpayers Participants Others2 Indirect3 Total
Regular smoking Labor market earnings associated with smoking $370 $869 $0 ($185) $1,053
Health care associated with smoking $180 $51 $185 $90 $506
Mortality associated with smoking $3 $7 $0 $217 $226
Program cost Adjustment for deadweight cost of program $0 $0 $0 ($19) ($19)
Totals $552 $926 $185 $103 $1,766
Click here to see populations selected
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $34 2012 Present value of net program costs (in 2018 dollars) ($38)
Comparison costs $0 2012 Cost range (+ or -) 20 %
Estimated weighted average per capita smoker costs based on (1) cost reported directly in the studies used in the meta-analysis and (2) cost-effectiveness studies of media campaigns. We used an average cost based on the cost effectiveness studies and estimated this as the cost of a study in the meta-analysis if no cost was reported. Costs were weighted by the size of the study and then averaged.
The figures shown are estimates of the costs to implement programs in Washington. The comparison group costs reflect either no treatment or treatment as usual, depending on how effect sizes were calculated in the meta-analysis. The cost range reported above reflects potential variation or uncertainty in the cost estimate; more detail can be found in our Technical Documentation.
Benefits Minus Costs
Benefits by Perspective
Taxpayer Benefits by Source of Value
Benefits Minus Costs Over Time (Cumulative Discounted Dollars)
The graph above illustrates the estimated cumulative net benefits per-participant for the first fifty years beyond the initial investment in the program. We present these cash flows in discounted dollars. If the dollars are negative (bars below $0 line), the cumulative benefits do not outweigh the cost of the program up to that point in time. The program breaks even when the dollars reach $0. At this point, the total benefits to participants, taxpayers, and others, are equal to the cost of the program. If the dollars are above $0, the benefits of the program exceed the initial investment.

Citations Used in the Meta-Analysis

Dwyer, T., Pierce, J.P., Hannam, C.D., & Burke, N. (1986). Evaluation of the Sydney "Quit. For Life" anti-smoking campaign. Part 2. Changes in smoking prevalence. The Medical Journal of Australia, 144 (7), 344-347.

Etter, J.F. (2007). Informing smokers on additives in cigarettes: A randomized trial. Patient Education and Counseling, 66 (2), 188-191.

Ledwith, F. (1984). Immediate and delayed effects of postal advice on stopping smoking. Health Bulletin, 42 (6), 332-44.

Meyer, A.J., Nash, J.D., McAlister, A.L., Maccoby, N., & Farquhar, J.W. (1980). Skills training in a cardiovascular health education campaign. Journal of Consulting and Clinical Psychology, 48 (2), 129-142.

Osler, M., & Jespersen, N.B. (1993). The effect of a community-based cardiovascular disease prevention project in a Danish municipality. Danish Medical Bulletin, 40 (4), 485-489.

Steenkamp, H.J., Jooste, P.L., Jordaan, P.C., Swanepoel, A.S., & Rossouw, J.E. (1991). Changes in smoking during a community-based cardiovascular disease intervention programme. The Coronary Risk Factor Study. South African Medical Journal, 79 (5), 250-253.

Sutton, S.R., & Hallett, R. (1987). Experimental evaluation of the BBC TV series "So You Want To Stop Smoking?". Addictive Behaviors, 12(4), 363-366.