Smoking-related deaths averted due to three years of policy progress
David T Levy a, Jennifer A Ellis b, Darren Mays a & An-Tsun Huang a
a. Department of Oncology, Georgetown University, 3300 Whitehaven Street NW, suite 4100, Washington, DC 20007, United States of America (USA).
b. Bloomberg Philanthropies, New York, USA.
Correspondence to David T Levy (e-mail: firstname.lastname@example.org).
(Submitted: 05 October 2012 – Revised version received: 25 March 2013 – Accepted: 27 March 2013.)
Bulletin of the World Health Organization 2013;91:509-518. doi: http://dx.doi.org/10.2471/BLT.12.113878
The World Health Organization (WHO) Framework Convention on Tobacco Control (FCTC) has been one of the most rapidly embraced treaties in the history of the United Nations. To assist countries with implementing FCTC obligations, in 2008 WHO introduced the MPOWER package of evidence-based tobacco control measures. The MPOWER package includes: monitoring tobacco use and tobacco control policies; protecting people from the dangers of tobacco smoke; offering help to quit tobacco; warning the public about the dangers of tobacco; enforcing bans on tobacco advertising, promotion and sponsorship; and raising tobacco taxes. These measures are supported by substantial evidence of their impact for reducing smoking.1–4
Since their introduction, countries have steadily implemented recommended MPOWER policies. Early leaders in adopting rigorous MPOWER measures include Brazil,5 Ireland6 and Thailand.7 As the movement to adopt MPOWER policies gains momentum, more countries have passed laws and many others have put advocacy efforts in place to support legislation. This progress has also been aided by significant support from private donors.
In 2008, WHO began systematically gathering information about MPOWER measures in every Member State to assess progress with the adoption of legislation, motivate further change and provide examples of excellence. In 2008, 2009 and 2011 WHO published reports that provide information about MPOWER implementation.8–10 Findings of the most recent report, which includes data from 2010, showed that 3.8 billion people were covered by at least one MPOWER category globally and demonstrated progress in every MPOWER category.
The goal of the MPOWER package is to reduce smoking-attributable deaths (SADs), which are projected to rise to 8.3 million annually by 2030.11 The purpose of this study is to determine the number of SADs averted as a result of the implementation of MPOWER policies. By capitalizing on the previously-validated SimSmoke model5–7,12–20 to estimate the impact of MPOWER policies for reducing SADs, we employ a simple but powerful method to estimate the long-term impact of MPOWER policy implementation in the first three years.
Assessment of incremental policy change
The WHO reports8–10 show the status of each MPOWER policy, by country or territory, for 2007, 2008 and 2010 using a five-level categorization. The highest level comprises the MPOWER measures that WHO placed in the highest category in terms of the completeness of the legislation. For example, WHO categorizes smoke-free laws at the highest level as those calling for “all public places smoke-free or ≥ 90% of the population covered by subnational smoke-free legislation”.
Using these criteria, we created a list of countries and territories that had adopted a highest-level policy after 2007, commensurate with the timing of WHO reports. We also used WHO corrigenda to clarify or correct policy status based on additional knowledge gained after publication of the reports.21 The list of countries and territories adopting highest-level policies was confirmed by representatives of the WHO Tobacco Free Initiative.
We compared 2010 data for the POWER (monitoring is excluded from this report) categories to 2007 data to determine which countries not in the highest-level category in 2007 had progressed to the highest level by 2010. For each country that had adopted a highest-level policy by 2010, policy data from earlier reports were used to determine incremental policy changes because the effect of a policy change depends upon the initial policy level (e.g. the incremental effect of a complete ban is less if a partial ban was in place than if no policy existed).
Effect size parameters
Effect sizes for incremental policy change were derived from the SimSmoke tobacco control policy model.2,22,23 The SimSmoke model has been validated for four states of the United States of America and more than 20 countries.5–7,12–20,23,24 Table 1 summarizes policies and effect sizes based on expert panels and published literature reviews.23 We provide upper and lower bounds for effect sizes based on variability estimates from our previous work.14
To determine policy effect size, we used the per cent reductions (in relative terms) in smoking prevalence resulting from a particular policy. The policy effect size is classified as being either short-term or long-term: (i) a short-term effect size is defined as the percentage change in smoking prevalence in the first three years of policy implementation; (ii) a long-term effect size is the percentage change in smoking prevalence after 40 years of implementation. In SimSmoke models, a short-term effect size is based on the change in policy by 2010 from its 2007 level, and a long-term effect size is derived by multiplying the short-term effect by a long-term multiplier and by adjustment factors for health knowledge, urbanization and enforcement. In previously-developed versions of SimSmoke, the long-term multiplier is estimated for each policy (LT-MULTp) as the ratio of the relative change in prevalence after 40 years to the relative change in short-term prevalence in the first year.
Except for tax policies, effect sizes are based primarily on policy evaluation studies from high-income countries (HICs) due to the lack of studies for low- and middle-income countries (LMICs). Accordingly, for LMICs, the effect size is adjusted by a health knowledge variable (AWAREp > 1 in LMICs, and 1 in HICs), reflecting the ability to affect health awareness from policy p (excluding price policies) in LMICs (e.g. Blecher et al.26 found that advertising bans have larger effects in LMICs). Some policies are also subject to the percentage of urban population in country i, URBANi, measured as (1 minus the percentage employed in agriculture).27 The urban adjustment incorporates the ability to reach and influence the population and those not covered by smoke-free-air laws (i.e. those not working indoors).
For smoke-free air (SFA) effects and impact of smoke-free air laws, we posit that half of the effects will occur automatically through passage of the law (e.g. due to a change in norms), while the remaining half of the effects will depend on enforcement (ENF, 0 ≤ ENF ≤ 1, using the MPOWER index = 1,…10 divided by 10), and a publicity index based on tobacco control spending (PUB = 0.5 if low [< 0.05 United States dollars (US$) per capita], = 0.75 if medium [≥ US$ 0.05 and < US$ 0.50 per capita], and = 1 if high [≥ $US 0.50 per capita]). Letting ST-SFA be the short-term effect size for a HIC with high enforcement and publicity (PUB), the long-term SFA effect (LT-SFAi) for country i is: (1)where ST-SFAi is country i’s short-term effect size from the change in policy if it were a HIC.
The marketing restrictions (MR) equation omits the URBAN and PUB effect (i.e. URBAN = PUB = 1), so the long-term MR effect for country i is:
For cessation treatment policies (CTP), the long-term effect (LT-CTPi) is: (3)where ST-CTPi is the short-term CTP effect size in a HIC from the change in CTP policy.
For health warnings (HWs), URBAN is set equal to one. The long-term HW effect is:
The effect for price policies (PRICE) depends on standard economic demand elasticities, which we assume are constant over price changes and are applied to the smoking prevalence. The formula for the prevalence elasticity, E, is defined in terms of the inflation-adjusted price, Pr, and prevalence, PREV. Using an arc elasticity formula, price change is measured as the price change between 2007 and 2010 relative to the average price, i.e. (P2010 – P2007)/[0.5(P2010 + P2007)]. The short-term prevalence elasticity is negative and written as:
Since the change in prevalence can be calculated as: (6)and assuming constant price elasticities,25 the price effect size for country i is calculated as:
Based on previous reviews,28 overall short-term elasticities were set at −0.15 for HICs, −0.2 for MICs and −0.2 for LICs, with a long-term multiplier of 2.
Reduction in smokers and smoking-attributable deaths
Adjusted smoking prevalence for those 15 years of age or older, by sex, for 2007 or a previous year were obtained from the 2011 WHO report for all countries except Colombia, whose adjusted prevalence was obtained from InfoBase. Prevalence reported is for “current smoking” and includes people who smoke cigarettes and other tobacco products, such as kreteks and bidis, every day and some days. The number of smokers of each sex was obtained by multiplying the respective smoking prevalence by population data for sex from the World population prospects, 2008 revision.17 The number of smokers, by sex, for country i is obtained as: (8)where PREV is smoking prevalence and Pop is populations (ages 15+), both distinguished by male (m) and female (f).
Applying the relevant policy long-term effect size to the number of smokers, we calculated the reduction in smokers in country i as a result of policy p, ΔSMOKERSi,p, as: (9)where LT-EFFECT SIZEi,p is the long-term policy effect size of a specific policy p in country i.
The number of SADs averted was calculated by applying an algorithm based on Doll et al.,29,30 who concluded that “half of all regular cigarette smokers will eventually be killed by their habit”. Similar results have been obtained in studies in the United States.31,32 We applied the 50% figure to smokers who quit as a result of the policy to estimate the deaths averted, ΔSMOKERS DEATHS, as:
Table 2 presents national income status, initial smoking prevalence by sex, policy effect size, estimated reduction in the number of smokers between 2007 and 2010, and reduction in SADs by policy and country. In total, 41 countries implemented a highest-level MPOWER policy; 8 implemented more than one highest-level policy. Smoking prevalence estimates varied from < 1.0% for females in Egypt to > 50% for males in Malaysia and Turkey.
Smoke-free air policies
Of the 20 countries implementing complete SFA policies, three are classified as LICs, 12 as MICs, and five as HICs. SFA laws were already in use at worksites in Spain in 2007; restaurants and bars were added by 2010. For other countries (e.g. Turkey), SFA policies were implemented for most locations between 2007 and 2010. Countries such as Burkina Faso and Chad have large rural sectors leading to small effects, whereas Colombia, Nauru, Peru, Seychelles and Turkey have a smaller agricultural sector and hence show larger effects. Owing to their large populations, Pakistan, Thailand and Turkey had the greatest number of SADs averted. In total, 5 032 629 individuals who were smoking in 2007 quit smoking as a result of SFA policies. Consequently, an estimated 2 516 314 SADs are averted (range: 1 258 157–3 774 472).
Cessation treatment policies
Two HICs and two MICs adopted highest-level cessation policies. Effect sizes vary from −1.8% for Turkey to −5.4% for Israel depending on the country’s income status and the policies in place in 2007. Collectively, changes in cessation policies are estimated to lead to 759 150 fewer smokers and 379 575 averted SADs (range 189 768 to 759 150).
Seven countries adopted highest-level pack warning policies. All were MICs except for Djibouti. Since most countries were middle-income and their health warnings changed from weak to strong, their long-term effect sizes were −3.0%. For other countries, changes in the warnings or a change to HIC status yielded an effect size of −1.5%. These policy changes reduced the number of smokers by 1 379 580 and averted 689 790 SADs (range:344 895–1 034 685).
Highest-level bans on tobacco advertising and promotions were adopted by four countries, all of which are MIC except for Chad. Panama adopted a marketing ban without prior restrictions (effect size: −13%), whereas most other countries had several restrictions in place in 2007. Overall, advertising policy changes resulted in 306 323 SADs averted (range: 153 162–459 485).
Price and taxes
Taxes were raised to the MPOWER goal of 75% of the final retail price in 14 countries, including one LIC, five MICs and eight HICs (Table 2). Relative changes in price from 2007 levels varied. Finland had relatively high tax rates in 2007 with increases of less than 5%, as did the Czech Republic and Greece. Other countries had inflation-adjusted price changes of at least 10%: Argentina (21%), Estonia (30%), Israel (41%), Italy (10%), Latvia (102%), Lithuania (53%), Madagascar (28%), Romania (69%), Slovenia (15%) and Turkey (49%). The largest number of deaths was averted in Argentina, Italy, Romania and Turkey, a reflection of the magnitude of the price increase, of initial smoking prevalence and of the size of the total population. Price increases are estimated to lead to a reduction of 7 056 006 smokers and to the aversion of 3 528 003 SADs (range: 2 646 002–4 410 004).
The total estimated impact of each of the five MPOWER policies on the number of current smokers and of deaths prevented is shown in Table 3. The estimated number of smokers will be reduced by 14 840 011 and an estimated 7 420 006 SADs will be averted (range: 4 592 004–10 437 796) among smokers alive in 2007.
Table 3. The effect of meeting targets by 2010 on smokers and smoking-attributable deaths, by policy
The highest-level MPOWER policies adopted from 2007 to 2010 will result in 15 million fewer smokers, and 7.4 premature deaths will consequently be averted by 2050. These findings underscore the urgent need for countries to focus on adopting, implementing and enforcing MPOWER policies. Although more than 40 countries adopted one or more MPOWER policies from 2007 to 2010, nearly half of the world population is still not covered by a single MPOWER policy. The policies having the greatest impact – smoke-free air laws and taxation – are alarmingly under-adopted. Only 11% of the world’s population is protected by SFA laws and less than 8% resides in countries with the recommended minimum tobacco tax rates.
MPOWER policy implementation is likely to yield additional benefits beyond the nearly 7.5 million estimated SADs averted. The estimates do not include the beneficial effects of adopted policies that have strong components but do not qualify as being of the highest level in the WHO reports. Our estimates also exclude smokers who may have initiated smoking after 2007 in the absence of strong policies. However, smokers who were included in our estimates and who may have quit smoking later despite the absence of such policies potentially offset some of the benefits of policy implementation that were omitted from our analysis.
We used data from WHO reports and an extensively validated statistical model to estimate MPOWER policy impact. This supports the validity of our estimates. Despite these strengths, the findings should be interpreted in light of the limitations of this work. The estimation method does not incorporate dynamic aspects of changing demographics and smoking rates and the effects of policies over time. To explicitly consider the impact of incorporating these dynamic aspects, we applied the SimSmoke simulation model in a separate analysis to nine countries that are reaching MPOWER goals (data not shown). For the nine countries we compared the effects on smoking prevalence and SADs with the effects reported in the previous section and found that they were reasonably close to the reported findings. This suggests that the dynamic aspects of policy change that were not taken into account in our analysis did not substantially bias our findings.
The policy data used for our analysis were reported by WHO, which adheres to a specific set of policies described by the FCTC. To collect the data, WHO reviews actual legislation in its original language and catalogues policy status. The categorization of the policy for inclusion in the report is then reviewed at the regional and national levels by various entities, including each country’s health ministry. Although these are the best available data that could be ascertained for our analyses, the characteristics of a policy as implemented in a specific location may not be fully captured by WHO reports. For example, the effects of smoking cessation policies may depend on financial reimbursement and the involvement of health-care workers, but these data are not collected. More specific national-level data may allow for more precise estimation of the impact of FCTC policy on smoking-related morbidity and mortality. Another limitation lies in the ability to model the combined effects of more than one policy simultaneously. Although there may be synergies, policies may also offset one another. The literature provides little guidance on the potential synergistic or offsetting effects of multiple concurrent policies.
Finally, our analysis depended on estimates of relative risks of death based on data for HICs. Studies indicate that LMICs have lower relative risks: e.g. 1.35 for China33–35 and 1.6 for the Republic of Korea and Taiwan, China,36–38 compared with ≥ 2.0 for the United States.39–41 These differences may reflect initiation at older ages or a higher background risk of premature death from other causes. To consider these effects, we multiplied the SAD estimates for LMICs (using World Bank classifications) by 0.6, based on results from previous SimSmoke models that show 40% fewer deaths in MICs.7,42 The total estimated number of SADs averted was reduced to 5 130 076 (range: 3 139 571–7243, 484). Of these averted SADs, 2 298 131 resulted from taxes, 1 610 787 from SFA laws, 791 657 from health warnings, 245 707 from cessation treatment and 183 794 from marketing restrictions. However, smoking deaths in LMICs, especially those approaching high-income status (e.g. Mexico and Turkey), may increase as the intensity and duration of smoking increase and risks not related to smoking decline.43
Our findings may have implications beyond the outcomes examined in this study. The effect of newly implemented policies could be extended to reductions not only in SADs, but also to benefits in other smoking-related outcomes (e.g. reduced adverse birth outcomes related to maternal smoking, such as low birth weight,44 better quality of life, lower health-care costs, less productivity loss). Moreover, evidence suggests that additional deaths may be averted among non-smokers because of reductions in exposure to second-hand smoke.45 Finally, we have not considered the possible effects of the MPOWER policies on reductions in smokeless tobacco prevalence, which would likely bring additional public health benefits.46,47
In conclusion, we found a substantial projected impact on SADs resulting from the adoption of MPOWER policies from 2007 to 2010. Our main finding – that nearly 7.5 million smoking-related deaths will be averted – shows that evidence-based MPOWER tobacco control measures have an enormous potential to reduce premature smoking-related mortality. The 41 countries that adopted such policies from 2007 to 2010 represent a cumulative population of nearly 1 billion people (one seventh of the 2008 world population of 6.9 billion). If the progress attained by these 41 countries were extended globally, tens of millions of smoking-related deaths could be averted. It is imperative that the public health community continue to advocate for MPOWER policies of the highest level.
David Levy has received funding from the International Union Against Tuberculosis and Lung Disease to conduct this study and has received funding from the Cancer Intervention and Surveillance and Modeling Network (CISNET of DCPS, NCI under grant U01-CA97450-02 for general development of the SimSmoke model).
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