In 2001, the USPHS Community Preventive Services Task Force's Guide to Community Preventive Services: Reducing Tobacco Use and Secondhand Smoke Exposure1 (the “Task Force”) reviewed the empirical literature on the effectiveness of tobacco control interventions. A Task Force panel of public health and prevention experts, appointed by and independent of the Centers for Disease Control and Prevention, assessed the evidence base and provided a range of effect sizes for price, mass media, smoke-free air, and health care provider interventions. Shortly thereafter, the Tobacco Control “Scorecard,” published in 2004,2 provided estimates of policy effect sizes on smoking initiation, cessation, and prevalence for a broader set of policies that included health warnings and advertising bans. Both of these reviews concluded that there was moderate to strong evidence on the effectiveness of cigarette price increases, smoke-free air laws (SFALs), and mass media campaigns (MMCs), and limited evidence on the effectiveness of cessation treatment policies. The 2004 Scorecard2 also found limited evidence regarding the effectiveness of graphic health warnings and tobacco marketing restrictions.
The Scorecard2 provides essential inputs to tobacco control policy simulation models, cost-effectiveness analyses, and other methodologies used to evaluate individual or combined tobacco control policies and their past or future impact on population health.3–9 These analyses can help guide decision making about which tobacco policies to prioritize and implement. Furthermore, because the reported effect sizes represent changes in smoking rates relative to initial levels, they can be applied to different countries using their respective smoking prevalence. In the last 13 years, however, the evidence base has grown substantially. Since the 2001 review, the Task Force has updated its review10 and other reviews have been conducted.11,12
We update the 2004 Tobacco Control Scorecard to (1) reflect newer evidence of effect sizes, with particular attention to policies in which previous evidence was limited and (2) provide credible ranges of effect sizes for each tobacco control policy. In updating the reviews conducted for the 2004 Scorecard, we include reviews and studies published after 2000 and focus on 3 policies in which previous evidence was limited: tobacco control campaigns, graphic health warnings, and marketing bans. As in the original Scorecard paper, we focus on high-income countries (HICs), where numerous reviews and studies were available.
We confine the review to analyses of interventions traditionally used to reduce cigarette demand, including cigarette taxes, SFALs, marketing restrictions, comprehensive tobacco control programs, media campaigns, graphic health warnings, and cessation treatment policies. These policies have received the most attention in the tobacco control literature and are explicitly recognized in the World Health Organization MPOWER Reports.13,14
We conducted a search of the PubMed database for reviews and articles published from January 1, 2000, to June 30, 2016. We also included articles from Task Force reviews and other reviews obtained from our search.12 We used the following key word search terms: (“cigarette,” or “smoking,” or “tobacco control”) and (“effectiveness,” or “evaluation,” or “impact”) and descriptors for a particular policy (eg, “price,” “tax,” “smoke-free air,” “clean air,” etc). Eligible studies included experimental, quasi-experimental, and population-based evaluations (including case control, cohort and cross-sectional studies). To determine average policy effect sizes, strongest weight was given to population-level evaluations with at least 1 smoking outcome: initiation, cessation, prevalence, or quantity smoked. Because standardizing to prepolicy levels is useful in translating results to populations with different smoking rates, we estimate average effect sizes in terms of relative changes from the initial smoking prevalence. Since Task Force estimates are generally provided in terms of (absolute) percentage point (PP) estimates, we convert their estimates to relative terms, that is, the absolute change relative to the initial smoking prevalence, using a smoking prevalence of 25% as a representative level. Although US smoking prevalence rates are currently below 25%,15 we adopt the 25% initial smoking prevalence as a conservative estimate of the initial rates during the time period when most evaluation studies were conducted.
Policy effect sizes are reported as the estimated percentage change in smoking prevalence over a 5-year (short-term) or 40-year (long-term) time horizon. The short-term effects rely most heavily on studies that examine changes in smoking prevalence following policy implementation, while the long-term effects reflect the reduced initiation and increased cessation if newly implemented policies are maintained over time. We suggest credible ranges for effect sizes based on the number of studies conducted, variation in results, and strength of evidence.
The short-term and long-term effect sizes for each policy type are summarized in the Table, where we provide upper and lower bounds on these effects and policy implementation and enforcement issues.
Increasing cigarette excise taxes raises the purchase price, thereby reducing cigarette consumption. Consumer responsiveness is generally estimated by the price elasticity, which measures the percentage change in quantity demanded corresponding to a 1% price increase.
The Task Force1 (103 studies from 2 systematic reviews16,17 combined with 13 more recent studies from January 2009 to July 2012) obtained a price elasticity for overall cigarette consumption of −0.37 (a 3.7% decrease in quantity demanded resulting from a 10% price increase), with −0.18 attributed to reduced prevalence and −0.19 to the reduced quantity of cigarettes consumed. The Task Force also obtained a price elasticity of +0.38 for adult cessation and −0.42 for initiation. Higher prevalence elasticities were found for youth, young adults, and low-income individuals.
Based on the Task Force findings, the short-term price prevalence elasticity is −0.18, with a credible range of 25% above and below the effect size (−0.135 to −0.225) to reflect the large number of studies and their variability across countries, for example, consumption elasticities average −0.4 ranging between −0.3 and −0.5.1,16 Based on the Task Force estimates that initiation and cessation elasticities are approximately double those of prevalence elasticities,1,16,17 the long-term prevalence elasticity is estimated to double to −0.36 (−0.27 to −0.45). These elasticity estimates can be multiplied by the projected relative change in cigarette prices to obtain prevalence effect sizes.
In addition to the price elasticity, the public health impact of raising cigarette taxes depends on the magnitude of the tax increase and the extent to which that tax is passed on to consumers as an increase in the price of cigarettes.18 For specific (per unit) taxes, studies generally indicate that cigarette prices increase by at least the amount of the tax,16 while ad valorem taxes create more price dispersion, thereby creating more opportunities for more price-sensitive smokers to trade down to cheaper brands.19,20 The impact of tax policies may be reduced through substitution to roll-your-own,21,22 cigars, smokeless tobacco,23,24 e-cigarettes, or water pipe if taxes on these noncigarette tobacco products are relatively low,25,26 or through tax avoidance (eg, through cross-border and duty-free shopping by smokers) and tax evasion (eg, smuggling), especially if neighboring jurisdictions have lower tax rates.16 In addition, since price effects depend on cigarette affordability (price relative to income),25–27 the effectiveness of tobacco tax policies may diminish if taxes do not increase commensurately with income.
Smoke-free air laws
Comprehensive SFALs are public sector regulations that prohibit smoking in worksites and designated public areas such as restaurants, bars, shopping areas, and transit.
Based on a 2010 Task Force review28 (50 studies) and 82 more recent studies, the Task Force obtained strong evidence for a 2.7 PP (−4.7 to −1.5 PP; 11 studies) reduction in smoking prevalence from comprehensive SFALs. With 25% initial smoking prevalence, the 2.7 PP drop translates into a 10% relative reduction. Smoking bans also showed a median absolute increase in smoking cessation of 3.8 PP, a 1.2 drop in cigarettes per day, and odds of smoking lowered by 15% among youth and young adults. Similar results have been obtained in other reviews.11,28,29
Based on the Task Force estimates of prevalence effects, comprehensive SFALs that cover worksites, restaurants, and bars are associated with a short-term relative reduction in smoking prevalence of 10% (5%-15%) compared with no SFALs. Based on the Task Force estimates of initiation and cessation effects, these effects increase to a long-term reduction of 12.5% (7%-19%) through continued increases in smoking cessation (including from reduced quantity smoked) and lower initiation rates. However, SFALs may have smaller effects if smoke-free policies are already prominent in private worksites or if there is low compliance with SFALs due to weak enforcement or a lack of antitobacco social norms.30
Comprehensive tobacco control programs
Comprehensive tobacco control programs are coordinated efforts that implement multiple population-level interventions to denormalize smoking, reduce secondhand smoke exposure, increase cessation, and prevent initiation. A recent study31 found that a large percentage of the expenditures of US campaigns in 2011 are dedicated to community-based interventions (40%), MMCs (20%), providing cessation services (quit lines and low-cost pharmacotherapies; 20%), and surveillance and administration (20%).
The Task Force recently reviewed 61 studies (through August 2014) of comprehensive programs, with 56 evaluating programs directed at cigarette use. Comprehensive campaigns implemented over a median of 9 years were associated with an overall median decrease of 3.9 PP (−5.6 to −2.6 PP; 16 studies) in adult smoking prevalence, with US studies showing a median decrease of 2.8 PP (−3.5 to −2.4 PP; 12 studies). For US studies, this implies a 10% to 15% relative reduction in smoking prevalence assuming an initial 25% prevalence. Comprehensive campaigns implemented for a median of 8 years resulted in an overall median decrease of 4.6 PP (−8.4 to −1.1 PP; 10 studies) in prevalence of tobacco use among young people (<25 years of age, 14 studies). Several recent studies examine the impact of state tobacco control expenditures across states and over time. One study32 over the time period of 1991 to 2006 found a 5% to 10% reduction in smoking rates for those states that shifted from unfunded tobacco control programs to funding at Centers for Disease Control and Prevention–recommended expenditure levels; another study33 found 3% to 4% lower current and established smoking prevalence (ages 18-25 years), with a doubling of cumulative state tobacco control funding (from 14% to 28% of the Centers for Disease Control and Prevention–recommended level) between 2002 and 2009; and another study34 found a 6% reduction in youth initiation with a doubling of program funding between 2002 and 2008.
Based on the recent Task Force review and recent studies, comprehensive tobacco control programs lead to an 8% (4%-12%) short-term relative reduction, increasing to a 12% (6%-18%) long-term relative reduction in smoking prevalence through the greater impact on youth smoking.
Mass-reach health communication interventions
Mass-reach health communication interventions target large audiences through television and radio broadcasts, print, digital media, and out-of-home placements (eg, billboards, point-of-sale). Messages are typically developed through formative testing and may aim to reduce smoking initiation among young people, increase cessation, or educate the public on the harms of tobacco use and secondhand smoke.12
The Task Force identified 70 studies (January 2000 to July 2012) evaluating MMCs, with 64 assessing television as the primary communication medium. Mass media campaigns reduced adult smoking prevalence by a median of 5.0 PP (−5.2 to −1.9 PP; 4 studies), implying a 20% drop with 25% initial prevalence. For young people through 24 years of age, they obtained a median decrease of 3.4 PP (−5.3 to −1.6 PP; 11 studies), a 14% relative drop (3.4/25). Mass media campaigns were associated with a 3.5 PP increase in cessation rates (2.0-5.0 PP; 12 studies), translating into a 14% relative increase (3.5 PP/25%). Studies also showed dose-responsiveness to MMC exposure.
Findings from the Task Force are broadly consistent with previous reviews,11,12 although the quality of evidence has raised concerns.35 Since these reviews were published, a New York MMC36 was associated with a 13% relative reduction in smoking prevalence and 35% increase in quit attempts, and the Centers for Disease Control and Prevention's Tips campaign was associated with a 13% increase in quit attempts.37–39 Other recent studies40–43 also supported a dose-response relationship between MMC exposure and smoking prevalence.
Studies on MMC effectiveness indicate reductions in smoking prevalence of at least 10%, but research on comprehensive tobacco control programs, which often include such campaigns, suggests effect sizes of 10% or less. A high-intensity MMC is estimated to reduce smoking rates by 8% (4%-12%) in the short term, increasing to 10% (6%-14%) long term. The dose-response relationship suggests gains from increased exposure to media campaigns. The effects are also likely to depend on the focus of the campaign, premarket testing of messages, sustained exposure, and the use of multiple forms of media. With the rapid growth in the use of alternative media, campaigns through social media may be needed to reach youth.44
Health warnings on cigarette packages are designed to warn consumers about the risks of smoking. These warning labels vary in size (percentage of package covered), may rotate labels over time, and may be text only or pictorial (depicting health hazards with graphic photos).
Five reviews12,45–48 consider pictorial warning labels (PWLs) and all but one46 found consistent benefits in terms of smoking behaviors. Two long-term studies of the introduction of PWLs in Canada49,50 attribute a 12% to 20% relative reduction in smoking over a 6- to 8-year period to PWLs. A meta-analysis of longitudinal studies47 reported that PWLs were associated with a 13% relative reduction in adult smoking prevalence. Between one-fifth and two-thirds of youth in Canada, the United Kingdom, and Australia report that PWLs helped prevent them from initiating smoking.45
Based on the reviews and a recent analysis taking into account observed reductions in smoking prevalence relative to earlier changes in trends,51 replacing small text warnings with large (at least 50% of pack) graphic warnings contribute to a 5% (2%-8%) short-term relative reduction in smoking prevalence and a 10% (5%-15%) long-term reduction through greater cessation and reduced initiation. Unless updated on a regular basis with new content, the effectiveness of graphic warning labels may wane over time as consumers become too accustomed to their appearance.52–54 However, MMCs accompanying health warnings can have reinforcing effects.55 Pictorial warning labels have been accompanied by plain packaging in some countries, although evidence of its effectiveness is more limited.56–59
Tobacco marketing restrictions include bans on direct advertising, such as TV, radio, magazine, newspaper, billboard, and retail point-of-sale advertising, and bans on indirect marketing, such as free distribution of products, promotional discounts, the appearance of tobacco products in TV or films, sponsorship of sports and music occasions, and the distribution of nontobacco products identified with tobacco brand names. Evaluations have focused on direct advertising bans.
While marketing bans were not reviewed by the Task Force, 5 reviews12,17,60–62 have considered advertising bans and all but one60 found evidence for the effectiveness of comprehensive advertising bans. These reviews, however, note methodological limitations, including problems of causality, failure to control for other policies, and failure to indicate the extent of ban coverage. After controlling for price and other factors across a broad range of countries from 1990 to 2005, Blecher63 found that comprehensive advertising bans reduced per capita consumption by 7% in relative terms, similar to an earlier study.64 A recent update12 of this work obtained overall reductions of 12%, and, consistent with the original study, found much larger effects in low- to middle-income countries than in HICs. A study of 18 European countries65 found that advertising bans were associated with higher quit ratios for highly educated groups. In addition, a comprehensive review66 found that youth are particularly susceptible to advertising, suggesting that marketing restrictions have the potential to reduce smoking initiation over time. Two recent studies67,68 found that awareness of tobacco advertising declines with increased restrictions.
Relying primarily on results from Blecher12,63 regarding the effect of advertising bans on total consumption, and estimating that half of the reduction in per capita consumption is attributed to reduced prevalence,16,17 a complete advertising ban (compared with no restrictions) reduces smoking prevalence by 4% (2%-6%) in the short term and 6% (3%-9%) in the long term. While evaluations of bans on indirect forms of marketing are limited, such bans may yield additional gains when coupled with direct advertising bans. Internet advertising, however, has become increasingly prevalent69,70 and may offset some gains.
Cessation treatment policies
Cessation treatment policies aim to increase the use of evidence-based behavioral treatments and pharmacotherapies for smoking cessation. These policies may involve specific recommendations by a government agency or involve specific interventions fostered by a federal agency or a state or local health department or agency, and may include (1) requiring financial coverage of evidence-based cessation treatments, (2) providing state-run telephone-based quit lines, and (3) recommending health care provider interventions that encourage patients to quit. Their effect at a population level depends on treatment effectiveness, increases in treatment use, and changes in quit attempts and relapse.71
Governments may provide financial coverage of evidence-based smoking cessation treatments. The updated Task Force1 (through July 2012; 13 studies) concluded that financial interventions that make evidence-based treatments (including medication, counseling, or both) more affordable increased quit rates among tobacco users at follow-up (>3.5 months) by 4.3 PP (0.2-6.0 PP; 12 studies) and quit attempt rates by 2.8 PP (−0.6 to 9.1 PP; 6 studies). A 2012 Cochrane Review72 found that completely subsidized financial interventions directed at smokers increased abstinence with a relative risk (RR) of 2.45 (1.17-5.12 RR; 4 studies) and increased quit attempts (RR: 1.11) and treatment use (RR: nicotine replacement therapy 1.83; bupropion: 3.22; behavioral therapy: 1.77). With unassisted quit rates of 4% and quit attempt rates of 40%, the Cochrane Review results are roughly consistent with those of the Task Force. Applying the methodology in the study by Levy et al73 with an initial 40% quit attempt rate, 60% relapse rate and that 50% of treatment use are new quit attempts, completely subsidized cessation treatment yields a 2.0% (0.75%-3.25%) short-term relative reduction in smoking prevalence increasing to 4% (2%-6%) long term.73,74
Telephone quit lines provide behavioral counseling and support to help smokers who want to quit. Based on a 2013 Cochrane review75 (77 studies), the Task Force concluded that quit lines are effective. Using 49 studies comparing active with passive quit lines, they estimated that quit lines yield a median 3.1 PP (0.5-3.3 PP; 12 studies) increase in quitting and a 4.2 PP increase when promoted through mass-reach health communication interventions. Slightly higher estimates were suggested by West et al.76 When quit lines offered free NRT or other pharmacotherapy, the Task Force found a 396% (134%-1132%; 9 studies) median increase in call volume and a 9.8 PP (7.4-15.7 PP; 11 studies) median absolute increase in cessation rates.
With an estimated 5% of smokers calling quit lines each year,73 and 50% of calls as new quit attempts,73 we estimate that active quit lines without NRT coverage yield a 0.75% (0.25%-1.25%) short-term relative reduction in smoking prevalence increasing to 1.5% (0.75%-2.25%) long term. For quit lines that provide NRT at no cost to the smoker, the impact is estimated as 3% (1%-5%) in the short term increasing to 6% (2%-10%) in the long term.
Government policies may recommend health care providers to ask patients about smoking, advise them to quit, and refer them to treatment alternatives. These interventions may range from brief one-time assessments to more extensive interventions involving patient follow-up with behavioral and/or prescribed pharmacotherapies.77 West et al76 estimated that brief interventions increase quit rates by 2 PP, mostly through increased quit attempts.78 With 10% of smokers receiving extensive interventions each year, a 40% quit attempt rate and 60% relapse rate,77 health care provider interventions reduce smoking prevalence by 1.6% (0.8%-2.4%) in the short term increasing to 3.2% (1.6%-4.8%) long term.
Applying the same analyses for each of the 3 types of cessation treatment interventions, comprehensive cessation treatment policies yield a 5.5% (2.75%-8.25%) short-term relative reduction in smoking prevalence, increasing to 11% (5.5%-18.75%) long term. A limitation of the studies, however, is that they focus on the cessation intervention itself, rather than the impact of government policies on the intervention, for example, the ability to effectively recommend health care provider interventions. Nevertheless, the estimated impacts may fail to incorporate synergistic effects when implemented with other tobacco policies. Levy et al79 provide evidence that comprehensive cessation treatment policies primarily affect quit success, while taxes and SFALs increase quit attempts, implying synergistic effects when cessation treatment interventions are combined with other policies.
The policy effect sizes presented in the Table update the 2004 Tobacco Control Scorecard with findings from a rapidly accumulating evidence base over the past 15 years. The estimates of policy impact can be used to rank the relative effectiveness of different policies for HICs.
Raising cigarette taxes; implementing comprehensive SFALs; banning all tobacco advertising, promotions, and sponsorships; and funding comprehensive tobacco control programs, particularly those that include media campaigns, are highly effective strategies for reducing smoking prevalence. Cessation treatment policies and prominent graphic health warnings are likely to be especially effective in increasing quit success when combined with other policies that increase quit attempts. The Scorecard effect sizes are broadly consistent with recommendations previously issued by the Task Force10 and those reported in the previous Scorecard analysis2 but now reflect the larger evidence base evaluating the impact of health warnings and advertising bans.
While we have focused on the effects of implementing individual policies, the impact of a new intervention depends on the existing tobacco control environment and on whether any other policies are simultaneously implemented. Interventions implemented in settings with strong existing tobacco control legislation and strong antitobacco social norms may yield smaller gains than those implemented in settings that have limited or no existing tobacco policies. For this reason, simultaneously implemented policies may have overlapping effects. To estimate the combined effect of implementing more than 1 policy intervention, we recommend applying effect sizes as constant relative reductions, that is, for policy and with effect sizes and would be applied to the current smoking prevalence. This formulation confines the resulting smoking prevalence to positive levels and also implies slightly smaller absolute reductions for each policy when implemented in combination with other policies than if implemented alone. Because of limited evidence about the nature and extent of tobacco policy interactions, wider credible ranges should be applied to the effect sizes of combined policies.
Previous simulation modeling and empirical studies that evaluate the impact of combined tobacco control policies have obtained results consistent with the constant relative reduction formulation. The SimSmoke tobacco control policy simulation model has applied effect sizes similar to those suggested previously with a constant relative reduction assumption in several US states80–82 and in HICs such as Ireland83 and the United Kingdom.84
The Tobacco Control Scorecard reports policy effect sizes directed at cigarette-smoking prevalence. However, policies directed at noncigarette nicotine delivery products, such as smokeless tobacco, water pipe, and e-cigarettes, may influence the effectiveness of cigarette-oriented policies. Policies directed at reducing the use of alternative nicotine delivery products could discourage smoking by promoting stronger antitobacco norms, or they could dissuade smokers from substituting their cigarettes for other products and thereby encourage continued smoking.85
The effect sizes for demand reduction policies indicate the potential for substantial reductions in smoking prevalence, as much as 60%.6,86 Nevertheless, there may be an upper limit to the combined impact of demand reduction policies, beyond which the reduction to smoking prevalence could be minimal. Efforts that restrict the supply of cigarettes, such as policies that address smuggling, raising and enforcing minimum purchase age laws, limiting the number and location of retailers (eg, through licensing), or regulating the content (eg, levels of nicotine, toxic constituents, or flavors) of tobacco products, may be needed to dramatically reduce population smoking.12 Such supply-oriented approaches, when coupled with comprehensive demand reduction policies, may ultimately be necessary for countries to reach “tobacco endgame” goals.87,88
Implications for Policy & Practice
- The literature on policy effect sizes for tobacco control policies has increased substantially in the last 15 years, providing a stronger base for justifying specific policies.
- Raising cigarette taxes, implementing smoke-free air laws, comprehensive marketing bans, media campaigns, cessation treatment policies, and graphic health warnings each have important roles in reducing smoking prevalence in HICs. Large increases in cigarette taxes relative to initial prices continue to be the most potent policy.
- Studies of supply-oriented policies, such as regulating the content of tobacco products, are needed.
1. Hopkins DP, Briss PA, Ricard CJ, et al Reviews of evidence regarding interventions to reduce tobacco use and exposure to environmental tobacco smoke. Am J Prev Med. 2001;20(2 suppl):16–66.
2. Levy DT, Chaloupka F, Gitchell J. The effects of tobacco control policies on smoking rates: a tobacco control scorecard. J Pub Health Manag Pract. 2004;10(4):338–353.
3. Levy D, de Almeida LM, Szklo A. The Brazil SimSmoke policy simulation model: the effect of strong tobacco control policies on smoking prevalence and smoking-attributable deaths in a middle income nation. PLoS Med. 2012;9(11):e1001336.
4. Levy D, Rodriguez-Buno RL, Hu TW, Moran AE. The potential effects of tobacco control in China: projections from the China SimSmoke simulation model. BMJ. 2014;348:g1134.
5. Levy DT, Fouad H, Levy J, Dragomir AD, El Awa F. Application of the Abridged SimSmoke model to four Eastern Mediterranean countries. Tob Control. 2016;25(4):413–421.
6. Levy DT, Huang AT, Currie LM, Clancy L. The benefits from complying with the framework convention on tobacco control: a SimSmoke analysis of 15 European nations. Health Policy Plan. 2014;29(8):1031–1042.
7. Levy DT, Meza R, Zhang Y, Holford TR. Gauging the effect of U.S. tobacco control policies from 1965 through 2014 using SimSmoke. Am J Prev Med. 2016;50(4):535–542.
8. Mendez D, Alshanqeety O, Warner KE. The potential impact of smoking control policies on future global smoking trends. Tob Control. 2013;22(1):46–51.
9. Levy DT, Ellis JA, Mays D, Huang AT. Smoking-related deaths averted due to three years of policy progress. Bull World Health Organ. 2013;91(7):509–518.
11. Hoffman SJ, Tan C. Overview of systematic reviews on the health-related effects of government tobacco control policies. BMC Public Health. 2015;15:744.
15. Jamal A, King BA, Neff LJ, Whitmill J, Babb SD, Graffunder CM. Current cigarette smoking among adults—United States, 2005-2015. MMWR Morb Mortal Wkly Rep. 2016;65(44):1205–1211.
16. Chaloupka FJ, Straif K, Leon ME; Working Group IAfRoC. Effectiveness
of tax and price policies in tobacco control. Tob Control. 2011;20(3):235–238.
17. Wilson LM, Avila Tang E, Chander G, et al Impact of tobacco control interventions on smoking initiation, cessation, and prevalence: a systematic review
. J Environ Public Health. 2012;2012:961724.
18. Chaloupka FJ, Kostova D, Shang C. Cigarette excise tax structure and cigarette prices: evidence from the global adult tobacco survey and the U.S. National Adult Tobacco Survey. Nicotine Tob Res. 2014;16(suppl 1):S3–S9.
19. Shang C, Chaloupka FJ, Fong GT, Thompson M, O'Connor RJ. The association between tax structure and cigarette price variability: findings from the ITC Project. Tob Control. 2015;24(suppl 3):iii88–iii93.
20. Shang C, Chaloupka FJ, Zahra N, Fong GT. The distribution of cigarette prices under different tax structures: findings from the International Tobacco Control Policy
Evaluation (ITC) Project. Tob Control. 2014;23(suppl 1):i23–i29.
21. Carbajales AR, Curti D. [Fiscal policy, affordability and cross effects in the demand for tobacco products: the case of Uruguay]. Salud Publica Mex. 2010;52(suppl 2):S186–S196.
22. White JS, Ross H. Smokers' strategic responses to sin taxes: evidence from panel data in Thailand. Health Econ. 2015;24(2):127–141.
23. Adhikari BB, Zhen C, Kahende JW, Goetz J, Loomis BR. Price responsiveness of cigarette demand in US: retail scanner data (1994-2007). Econom Res Int. 2012;2012(148702):10.
24. Nargis N, Hussain AK, Fong GT. Smokeless tobacco product prices and taxation in Bangladesh: findings from the International Tobacco Control Survey. Indian J Cancer. 2014;51(suppl 1):S33–S38.
25. Blecher EH, van Walbeek CP. An international analysis of cigarette affordability. Tob Control. 2004;13(4):339–346.
26. Guindon GE, Tobin S, Yach D. Trends and affordability of cigarette prices: ample room for tax increases and related health gains. Tob Control. 2002;11(1):35–43.
27. Kostova D, Chaloupka FJ, Yurekli A, et al A cross-country study of cigarette prices and affordability: evidence from the Global Adult Tobacco Survey. Tob Control. 2014;23(1):e3.
28. Callinan JE, Clarke A, Doherty K, Kelleher C. Legislative smoking bans for reducing secondhand smoke exposure, smoking prevalence and tobacco consumption. Cochrane Database Syst Rev. 2010;(4):CD005992.
29. Frazer K, Callinan JE, McHugh J, et al Legislative smoking bans for reducing harms from secondhand smoke exposure, smoking prevalence and tobacco consumption. Cochrane Database Syst Revi. 2016;2:CD005992.
30. Nesoff ED, Milam AJ, Bone LR, et al Tobacco policies and on-premise smoking in bars and clubs that cater to young African Americans following the Maryland Clean Indoor Air Act of 2007. J Ethn Subst Abuse. 2017;16(3):328–343.
31. Huang J, Walton K, Gerzoff RB, et al State tobacco control program spending—United States, 2011. MMWR Morb Mortal Wkly Rep. 2015;64(24):673–678.
32. Rhoads JK. The effect of comprehensive state tobacco control programs on adult cigarette smoking. J Health Econ. 2012;31(2):393–405.
33. Farrelly MC, Loomis BR, Kuiper N, et al Are tobacco control policies effective in reducing young adult smoking? J Adolesc Health. 2014;54(4):481–486.
34. Farrelly MC, Loomis BR, Han B, et al A comprehensive examination of the influence of state tobacco control programs and policies on youth smoking. Am J Public Health. 2013;103(3):549–555.
35. Bala MM, Strzeszynski L, Topor-Madry R, Cahill K. Mass media interventions for smoking cessation in adults. Cochrane Database Syst Rev. 2013;6:CD004704.
36. Davis KC, Farrelly MC, Duke J, Kelly L, Willett J. Antismoking media campaign and smoking cessation outcomes, New York State, 2003-2009. Prev Chronic Dis. 2012;9:E40.
37. Huang LL, Thrasher JF, Abad EN, et al The U.S. national tips from former smokers antismoking campaign: promoting awareness of smoking-related risks, cessation resources, and cessation behaviors. Health Educ Behav. 2015;42(4):480–486.
38. McAfee T, Davis KC, Alexander RL Jr, Pechacek TF, Bunnell R. Effect of the first federally funded US antismoking national media campaign. Lancet. 2013;382(9909):2003–2011.
39. Xu X, Alexander RL Jr, Simpson SA, et al A cost-effectiveness
analysis of the first federally funded antismoking campaign. Am J Prev Med. 2015;48(3):318–325.
40. Emery S, Kim Y, Choi YK, Szczypka G, Wakefield M, Chaloupka FJ. The effects of smoking-related television advertising on smoking and intentions to quit among adults in the United States: 1999-2007. Am J Public Health. 2012;102(4):751–757.
41. Nonnemaker JM, Allen JA, Davis KC, Kamyab K, Duke JC, Farrelly MC. The influence of antismoking television advertisements on cessation by race/ethnicity, socioeconomic status, and mental health status. PLoS One. 2014;9(7):e102943.
42. Nonnemaker JM, Dench D, Homsi G, MacMonegle A, Duke J. The effect of exposure to media campaign messages on adult cessation. Addict Behav. 2015;49:13–19.
43. Sims M, Salway R, Langley T, et al Effectiveness
of tobacco control television advertising in changing tobacco use in England: a population-based cross-sectional study. Addiction. 2014;109(6):986–994.
44. Davis KC, Shafer PR, Rodes R, et al Does digital video advertising increase population-level reach of multimedia campaigns? Evidence from the 2013 tips from former smokers campaign. J Med Internet Res. 2016;18(9):e235.
45. Hammond D. Health warning messages on tobacco products: a review
. Tob Control. 2011;20(5):327–337.
46. Monárrez-Espino J, Liu B, Greiner F, Bremberg S, Galanti R. Systematic review
of the effect of pictorial warnings on cigarette packages in smoking behavior. Am J Public Health. 2014;104(10):e11–e30.
47. Noar SM, Francis DB, Bridges C, Sontag JM, Ribisl KM, Brewer NT. The impact of strengthening cigarette pack warnings: systematic review
of longitudinal observational studies. Soc Sci Med. 2016;164(suppl C):118–129.
48. Noar SM, Hall MG, Francis DB, Ribisl KM, Pepper JK, Brewer NT. Pictorial cigarette pack warnings: a meta-analysis of experimental studies. Tob Control. 2016;25(3):341.
49. Azagba S, Sharaf MF. The effect of graphic cigarette warning labels on smoking behavior: evidence from the Canadian experience. Nicotine Tob Res. 2013;15(3):708–717.
50. Huang J, Chaloupka FJ, Fong GT. Cigarette graphic warning labels and smoking prevalence in Canada: a critical examination and reformulation of the FDA regulatory impact analysis. Tob Control. 2014;23(suppl 1):i7–i12.
51. Levy DT, Mays D, Yuan Z, Hammond D, Thrasher JF. Public health benefits from pictorial health warnings on U.S. cigarette packs: a SimSmoke simulation. Tob Control. 2017;26(6):649–655.
52. Li L, Borland R, Yong HH, et al Reported exposures to anti-smoking messages and their impact on Chinese smoker's subsequent quit attempts. Int J Behav Med. 2014;21(4):667–676.
53. Hitchman SC, Driezen P, Logel C, Hammond D, Fong GT. Changes in effectiveness
of cigarette health warnings over time in Canada and the United States, 2002-2011. Nicotine Tob Res. 2014;16(5):536–543.
54. Green AC, Kaai SC, Fong GT, Driezen P, Quah AC, Burhoo P. Investigating the effectiveness
of pictorial health warnings in Mauritius: findings from the ITC Mauritius survey. Nicotine Tob Res. 2014;16(9):1240–1247.
55. Nagelhout GE, Osman A, Yong HH, Huang LL, Borland R, Thrasher JF. Was the media campaign that supported Australia's new pictorial cigarette warning labels and plain packaging policy associated with more attention to and talking about warning labels? Addict Behav. 2015;49:64–67.
56. McKeganey N, Russell C. Tobacco plain packaging: evidence based policy or public health advocacy? Int J Drug Policy. 2015;26(6):560–568.
57. Smith CN, Kraemer JD, Johnson AC, Mays D. Plain packaging of cigarettes: do we have sufficient evidence? Risk Manag Healthc Policy. 2015;8:21–30.
58. Stead M, Moodie C, Angus K, et al Is consumer response to plain/standardised tobacco packaging consistent with framework convention on tobacco control guidelines? A systematic review
of quantitative studies. PLoS One. 2013;8(10):e75919.
59. Hughes N, Arora M, Grills N. Perceptions and impact of plain packaging of tobacco products in low and middle income countries, middle to upper income countries and low-income settings in high-income countries: a systematic review
of the literature. BMJ Open. 2016;6(3):e010391.
60. Capella M, Taylor C, Webster C. The effect of cigarette advertising bans on consumption: a meta-analysis. J Advertising. 2008;37(2):7–18.
61. Henriksen L. Comprehensive tobacco marketing restrictions: promotion, packaging, price and place. Tob Control. 2012;21(2):147–153.
62. Quentin W, Neubauer S, Leidl R, Konig HH. Advertising bans as a means of tobacco control policy
: a systematic literature review
of time-series analyses. Int J Public Health. 2007;52(5):295–307.
63. Blecher E. The impact of tobacco advertising bans on consumption in developing countries. J Health Econ. 2008;27(4):930–942.
64. Saffer H, Chaloupka F. The effect of tobacco advertising bans on tobacco consumption. J Health Econ. 2000;19(6):1117–1137.
65. Schaap MM, Kunst AE, Leinsalu M, et al Effect of nationwide tobacco control policies on smoking cessation in high and low educated groups in 18 European countries. Tob Control. 2008;17(4):248–255.
66. Lovato C, Watts A, Stead LF. Impact of tobacco advertising and promotion on increasing adolescent smoking behaviours. Cochrane Database Syst Rev. 2011(10):CD003439.
67. Harris F, MacKintosh AM, Anderson S, et al Effects of the 2003 advertising/promotion ban in the United Kingdom on awareness of tobacco marketing: findings from the International Tobacco Control (ITC) Four Country Survey. Tob Control. 2006;15(suppl 3):iii26–iii33.
68. Kasza KA, Hyland AJ, Brown A, et al The effectiveness
of tobacco marketing regulations on reducing smokers' exposure to advertising and promotion: findings from the International Tobacco Control (ITC) Four Country Survey. Int J Environ Res Public Health. 2011;8(2):321–340.
69. Centers for Disease Control and Prevention (CDC). Tobacco use, access, and exposure to tobacco in media among middle and high school students—United States, 2004. MMWR Morb Mortal Wkly Rep. 2005;54(12):297–301.
70. Goel RK. Advertising media and cigarette demand. Bull Econ Res. 2011;63(4):404–416.
71. Levy D, Mabry P, Graham A, Orleans CT, Abrams D. Exploring scenarios to dramatically reduce smoking prevalence: a simulation model of the three-part cessation process. Am J Public Health. 2010;100(7):1253–1259.
72. Reda AA, Kotz D, Evers SM, van Schayck CP. Healthcare financing systems for increasing the use of tobacco dependence treatment. Cochrane Database Syst Rev. 2012(6):CD004305.
73. Levy DT, Graham AL, Mabry PL, Abrams DB, Orleans CT. Modeling the impact of smoking-cessation treatment policies on quit rates. Am J Prev Med. 2010;38(3 suppl):S364–S372.
74. Levy DT, Friend K. A simulation model of policies directed at treating tobacco use and dependence. Med Decis Making. 2002;22(1):6–17.
75. Stead LF, Hartmann-Boyce J, Perera R, Lancaster T. Telephone counselling for smoking cessation. Cochrane Database Syst Rev. 2013(8):CD002850.
76. West R, Raw M, McNeill A, et al Health-care interventions to promote and assist tobacco cessation: a review
of efficacy, effectiveness
and affordability for use in national guideline development. Addiction. 2015;110(9):1388–1403.
77. Abrams DB, Graham AL, Levy DT, Mabry PL, Orleans CT. Boosting population quits through evidence-based cessation treatment and policy. Am J Prev Med. 2010;38(3 suppl):S351–S363.
78. Aveyard P, Begh R, Parsons A, West R. Brief opportunistic smoking cessation interventions: a systematic review
and meta-analysis to compare advice to quit and offer of assistance. Addiction. 2012;107(6):1066–1073.
79. Levy DT, Mabry PL, Graham AL, Orleans CT, Abrams DB. Reaching Healthy People 2010 by 2013: a SimSmoke simulation. Am J Prev Med. 2010;38(3 suppl):S373–S381.
80. Levy DT, Hyland A, Higbee C, Remer L, Compton C. The role of public policies in reducing smoking prevalence in California: results from the California Tobacco Policy Simulation Model. Health Policy. 2007;82(2):153–166.
81. Levy DT, Ross H, Powell L, Bauer JE, Lee HR. The role of public policies in reducing smoking prevalence and deaths caused by smoking in Arizona: results from the Arizona tobacco policy simulation model. J Pub Health Manag Prac. 2007;13(1):59–67.
82. Levy DT, Boyle RG, Abrams DB. The role of public policies in reducing smoking: the Minnesota SimSmoke tobacco policy model. Am J Prev Med. 2012;43(5 suppl 3):S179–S186.
83. Currie LM, Blackman K, Clancy L, Levy DT. The effect of tobacco control policies on smoking prevalence and smoking-attributable deaths in Ireland using the IrelandSS simulation model. Tob Control. 2012;22(e1):e25–e32.
84. Levy DT, Currie L, Clancy L. Tobacco control policy
in the UK: blueprint for the rest of Europe? Eur J Public Health. 2012;23(2):201–206.
85. Levy DT, Cummings KM, Villanti AC, et al A framework for evaluating the public health impact of e-cigarettes and other vaporized nicotine products. Addiction. 2017;112(1):8–17.
86. Levy D, Currie L, Clancy L. SimSmokeFinn: how far can tobacco control policies move Finland toward tobacco-free 2040 goals? Scand J Pub Health. 2012;40(6):544–552.
87. McDaniel PA, Smith EA, Malone RE. The tobacco endgame: a qualitative review
and synthesis. Tob Control. 2016;25(5):594–604.
88. Arnott D. There's no single endgame. Tob Control. 2013;22(suppl 1):i38–i39.