TY - JOUR
AU - Pichon-Riviere,, Andrés
AB - Abstract Introduction The burden of disease attributable to tobacco use in Latin America is very high. Our objective was to evaluate the 10-year potential impact of current legislation related to cigarette packaging and warnings and expected effects of moving to a higher level of strategies implementing cigarette plain packaging on health and cost outcomes in Argentina, Bolivia, Brazil, Chile, Colombia, Mexico, and Peru, using a microsimulation model. Aims and Methods We used a probabilistic state-transition microsimulation model, considering natural history, costs, and quality of life losses associated with main tobacco-related diseases. We followed up individuals in hypothetical cohorts and calculated health outcomes annually to obtain aggregated long-term population health outcomes and costs. We performed a literature review to estimate effects and analyzed studies and information from ministries, relevant organizations, and national surveys. We calibrated the model comparing the predicted disease-specific mortality rates with local statistics. Results Current graphic warnings already in place in each country could avert, during 10 years, 69 369 deaths and 638 295 disease events, adding 1.2 million years of healthy life and saving USD 5.3 billion in the seven countries. If these countries implemented plain packaging strategies, additional 155 857 premature deaths and 4 133 858 events could be averted, adding 4.1 million healthy years of life and saving USD 13.6 billion in direct health care expenses of diseases attributable to smoking. Conclusions Latin American countries should not delay the implementation of this strategy that will alleviate part of the enormous health and financial burden that tobacco poses on their economies and health care systems. Implications Tobacco smoking is the single most preventable and premature mortality cause in the world. The Framework Convention on Tobacco Control, supported by the World Health Organization, introduced a package of evidence-based measures for tobacco control. This study adds evidence on the potential health effects and savings of implementing cigarette plain packaging in countries representing almost 80% of the Latin American population; findings are valuable resources for policy makers in the region. Introduction Tobacco smoking is the single most preventable, premature mortality cause in the world, with about 6 million deaths every year.1,2 Globally, the amount of health care expenditure for smoking-attributable diseases surpasses USD 400 billion, and the economic cost of smoking represents USD 1436 billion; about 40% of this cost corresponds to low- and middle-income countries.3 In Latin America, the annual consumption of tobacco per person is estimated 160–2000 cigarettes with a prevalence between 6.4% and 35.2%.4 Due to the increasing smoking-related health costs and the high toll of smoking-attributable diseases, several interventions to counter chronic diseases’ risk factors have been prioritized as “best buys” by World Health Organization (WHO), meaning they could favorably and efficiently improve population health.5,6 In 2007, WHO promoted the Framework Convention on Tobacco Control (FCTC) that included six evidence-based measures, referred as MPOWER for its acronyms; measures were Monitoring tobacco use and tobacco control measures; Protecting people from tobacco smoke; Offering help to quit tobacco; Warning people about the dangers of tobacco; Enforcing bans on tobacco advertising, promotion, and sponsorship; and Raising tobacco taxes.7 Although many MPOWER strategies have been implemented in various countries, with almost 3 billion people now covered by at least one measure at its highest level of achievement, their application in Latin America has encountered several barriers as a result of the heterogeneity in target populations and public health policies in the region, as well as to tobacco industry interference through aggressive lobbying, litigation against policies, or misinterpretation of scientific evidence, among other tactics.8 The concept of plain packaging (sometimes referred to as standardized packaging) is defined as those “measures to restrict or prohibit the use of logos, colours, brand images or promotional information on packaging other than brand names and product names displayed in a standard colour and font style (plain packaging)”.9,10 Plain packaging pursues reducing the attractiveness of tobacco products, eliminating the effects of tobacco packaging as a form of advertising and promotion, addressing design techniques directed to show that some products are less harmful than they really are, and increasing the noticeability of health warnings.9–11 Health warnings and messages on tobacco product packaging and labeling may be in the form of or include pictures or pictograms. Article 11 of FCTC on “Packaging and Labelling of Tobacco Products” stipulates that each packet and package of tobacco products and any outside packaging and labeling of such products carry health warnings describing the harmful effects of tobacco use, with other appropriate messages; such warnings shall be approved by the competent national authority, shall be rotating, shall be large, clear, visible, and legible, should be 50% or more of the principal display areas but shall be no less than 30% of the principal display areas, and may be in the form of or include pictures or pictograms. Evidence shows that health warnings and messages that contain both pictures and text are far more effective than those that are text only.12 They also have the added benefit of potentially reaching people with low levels of literacy and those who cannot read the language(s) in which the text of the health warning or message is written.9,10,13–15 Australia was the first country to implement plain packaging in 2012, followed by France in 2016, the United Kingdom and Ireland in 2017, Hungary, New Zealand, and Norway in 2018, Thailand and Uruguay in 2019, while other countries such as Canada, Singapore, Belgium, Romania, Turkey, Finland, Chile, and South Africa have taken steps toward the introduction of this measure.16 An increasing number of Latin American countries have been adopting MPOWER measures with dissimilar results. Studies on the potential effects of implementing measures and on the level of current implementation through modeling are crucial for policy makers. Uruguay has been the first country in Latin America to adopt the plain packaging strategy.16 In Latin America, hurdles to policy change still exist, with persisting knowledge gaps in many aspects; however, researchers have been working to produce local high-quality information in conjunction with policy makers.17 Hence, our model was designed to provide evidence on the health and financial burden of smoking in the region and cost-effectiveness of interventions to curb the tobacco epidemic.18 Of the countries studied, only Colombia currently has a level of implementation of health warnings that cover between 30% and 49% of the surface of the pack; the other six countries have health warnings ranging between 50% and 80% of the pack. In 2009, Uruguay introduced legislation to increase the size of health warnings with significant subsequent increases in effectiveness indicators.16,19 The objective of this study is twofold: (1) to estimate the health and economic benefits that can be achieved through the current cigarette packaging policies in Argentina, Bolivia, Brazil, Chile, Colombia, Mexico, and Peru and (2) to estimate the health and financial impact of improving current strategies through the implementation of cigarette plain packaging in these seven Latin American countries. Methods The model used in this study is an individual-based Markov model (first-order Monte Carlo technique)18 that has been previously validated and applied in several studies to estimate the tobacco burden of disease, and the expected impact of tobacco tax increases and other tobacco control interventions.20–24 Through the model, the health and economic impact of tobacco under the present conditions in each country is estimated (status quo) and compared to hypothetical scenarios of reduced smoking prevalence as a consequence of the tobacco control interventions being evaluated (in the present paper, we assess graphic warnings and plain packaging policies). The model considers the natural history, costs, and quality of life losses associated with main tobacco-related diseases (coronary and non-coronary heart disease, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), pneumonia, influenza, lung cancer, and nine other neoplasms). Simulating each individual’s lifetime, we followed up individuals in hypothetical cohorts and calculated health outcomes on an annual basis to obtain aggregated long-term population health outcomes and costs. For acute events, we calculated age and gender-specific absolute risks based on national mortality rates and the lethality of the event. Then, the baseline risk in nonsmokers is calculated based on the smoking prevalence in each age and sex group, and the relative risk of smoking associated with each event. For cancers, we obtained incidence statistics for each age and sex with GLOBOCAN for each country.25 The model updates input parameters for each subject in yearly cycles and calculates individual lifetime risks of occurrence of each event, disease progression, and death, based on demographic attributes, smoking status, and clinical conditions based on the underlying risk equations. The main outcomes are life years, quality-adjusted life years, disease events, hospitalizations, disease incidence, and disease costs. We calculated the years of life lost due to smoking-related diseases at a population level as the sum of years of life lost due to premature death, and years of life lost due to living with a poor quality of life. As the model does not directly calculate the consequences of passive smoking and perinatal effects, based on the results of previous studies, we assumed that these causes impose an additional burden of 13.6% for men and 12% for women.26 Modeling of Policy Effect Tobacco control policies have an effect mediated by a reduction in consumption. This lower consumption at the country level is a consequence of both a reduction in the number of cigarettes smoked per smoker, and lower tobacco prevalence due to an increase in quitting rates (short term) and lower tobacco initiation rates in the medium and long term. To estimate the impact of implementing plain tobacco packaging, the smoking prevalence postintervention was calculated as follows: Prevalancepost=Prevalancepre−(Em∗Ip∗Prevalencepre) Where Prevalencepre is the prevalence of smokers before the intervention, Em is the effectiveness of the intervention expressed as relative reduction in tobacco consumption, and Ip is the proportion of variation in consumption that impacts smoker prevalence. Different studies have estimated that, in the short and medium term, approximately half of the reduction in consumption is a consequence of reduced prevalence and the other half is explained by reduced consumption of continuing smokers.27–31 Model Scenarios To estimate the potential impact of tobacco control policies, we analyzed three scenarios in each country. Short-term scenario: we assumed that 50% of the reduction in consumption would have an impact on prevalence (Ip = 0.5) and that the reduction in prevalence led to an increase in former smokers. This conservative scenario is more likely to occur in the short term, as it does not include effects that the intervention may have in preventing people from starting to smoke or the health benefits of smoking fewer cigarettes for those who continue smoking. Mid-term scenario: similar to the previous scenario, but it also incorporates the potential effects associated with a reduction in the number of cigarettes smoked in continuing smokers. It is assumed that low-intensity smokers have in average 75% less excess disease risk than high-intensity smokers (82% less for lung cancer, 57% less for ischemic heart disease, and 80% less for COPD),32 and consequently the reduction in the number of cigarettes smoked is modeled as a proportional reduction in the 75% of the excess risk difference between a smoker and a former smoker. Long-term scenario: maximum effect during the 10 years period. Similar to the previous one, but here Ip = 0·75 and the entire reduction in prevalence results in an increased population of nonsmokers. The base case consisted of comparing health benefits and costs of the current packaging policy in each country to those predicted by implementing plain packaging. To estimate disease burden and costs of plain packaging strategy, we assumed a linear evolution from scenario 1 to scenario 2 within 5 years and then to scenario 3 between years 6 and 10. The burden of disease attributable to smoking was estimated for these scenarios based on these estimates of changes in smoking prevalence and new proportions of smokers, former smokers, and nonsmokers. Health impact was calculated as the observed difference between baseline burden (status quo) and the plain packaging strategy estimates, in terms of deaths, disease events, years lived, disability, and health costs. More information about the model can be found in the publications in which it was described, evaluated, or used and in the technical reports with findings on the burden of disease (available from http://www.iecs.org.ar/tabaco).18,20–24 Policies Argentina, Bolivia, Brazil, Chile, Mexico, and Peru currently have health warnings covering between 50% and 80% of pack surface, whereas Colombia has warnings covering 30%–49% of the cigarette package7 (Figure 1). Figure 1. Open in new tabDownload slide Current implementation level of policies related to health warnings and plain packaging and estimated effect in prevalence reduction in seven countries in Latin America. Figure 1. Open in new tabDownload slide Current implementation level of policies related to health warnings and plain packaging and estimated effect in prevalence reduction in seven countries in Latin America. Information Sources for the Model Epidemiological Information To populate the simulation model, we obtained data through a review of the literature on MEDLINE, Embase, CENTRAL, SocINDEX, EconLit, LILACS, NBER, CRD, and Cost Effectiveness Analysis Registry, the International Tobacco Health Conference Paper Index, and Cochrane Tobacco Addiction Review Group register. Also, we reviewed gray literature from ministries of health or of finance, Pan American Health Organization, and regional congresses proceedings. We obtained updated information on tobacco use from tobacco GATS surveys and national risk factor surveys. Researchers from participating countries provided information from civil registrations, vital statistics, and hospital discharge databases to estimate specific case fatality rates. Cost Information We performed a literature search to identify reported costs of events and developed a common costing methodology to estimate costs through a micro-costing or macro-costing approach, depending on the information availability. Then, we used a spreadsheet for each event, with frequency, use rate, and unit cost of health resources. We constructed ad-hoc micro-costing exercises, based on experts’ opinions, clinical guidelines, and a review of health care facility records. The costs of malignancies other than lung cancer were based on the cost of each cancer relative to lung cancer costs and consensus using a Delphi method exercise with oncology experts from studied countries. Where local information was unavailable, we extrapolated the model to approximate the costs of events. In those cases, we used the average proportion that represents event cost divided by per capita gross domestic product (GDP) in Argentina, Chile, and Mexico; then, on this average proportion, the per capita GDP of the country of interest was applied to obtain estimates. All costs were first estimated in the local currency; later, consumer price indices, published by the statistics institutes of each country, were used for cost adjustments and finally, costs were converted to US dollars using the exchange rates published by each country’s central bank. The exchange rates used were the average 2015 exchange rates, as follows: USD 1.00 was equivalent to Argentina ARS 9.27, Bolivia BOB 6.91, Brazil BRL 3.34, Chile CLP 654.07, Colombia COP 2743.39, Mexico MXN 15.84, and Peru PEN 3.18. Estimation for Intervention Impact To obtain data on the benefits of implementing health warnings and the plain packaging of tobacco products to populate the simulation model, we performed a three-stage systematic review. First, we performed a review of documents published by relevant international organizations, then an overview of systematic reviews on the effectiveness of graphic warnings and plain packaging interventions at the global level, and, finally, a systematic review of this specific intervention in the seven Latin American countries included in this study (Argentina, Bolivia, Brazil, Chile, Colombia, Mexico, and Peru). See Supplementary Figure 1 for the detailed methods. We incorporated the best assumption on effectiveness for the case base, agreed upon by the group of authors. A sensitivity analysis with the extreme values of the range of effectiveness reported in the literature was additionally performed. Calibration and Validation of the Model We applied the International Society for Pharmacoeconomics and Outcomes Research criteria for model development and reporting to calibrate the model in each country, compared mortality rates predicted by the model with the national statistics for 16 conditions (excluding COPD mortality, which is widely underestimated in national statistics).33 Sex- and age-specific model outputs were compared to the source and deviations from the expected values were analyzed. Predicted rates were accepted if within 10% of references. In the case of greater deviation, risk equations were modified until the parameter was within an acceptable range. Goodness of fit was assessed by plotting predicted versus observed values outcomes, fitting a linear curve through the points with the intercept set at zero, and obtaining a squared linear correlation coefficient. We externally validated the model comparing results of other epidemiological and clinical studies not used in our model. Results Data to Populate the Model We identified all the epidemiological and cost parameters needed to populate the model and show the main results of the input parameters in Table 1. The systematic review on the effectiveness of health warnings showed that smoking prevalence could be reduced by 0.6% if nongraphic warnings covered less than one third of the pack, by 3% if they covered at least one third of the pack, and by 6% if they covered at least 50% of the pack. Due to the limited experience worldwide, there is greater uncertainty regarding the potential effect of implementing plain packaging. Available data indicate that this effect could be between an additional 3.15% and 15.2%.19,34–36 For the base case, we assumed that plain packaging would reach a relative reduction equivalent to the decrease achieved when moving from nongraphic warnings covering at least one third of the pack to graphic warnings covering at least 50% of the pack (6% reduction); and we explored the published range 3.15%–15.2% in the sensitivity analysis. Table 1. Main Inputs for the Simulation Model Characteristics . Argentina . Bolivia . Brazil . Chile . Colombia . Mexico . Peru . Population (2015) 43 416 755 10 724 705 207 847 528 17 948 141 48 228 704 127 017 224 31 376 670 Smoking prevalence1  Male 23.4 20.1 18.0 35.2 20.1 19.8 23.5  Female 18.6 17.7 11.3 31.3 9.9 6.4 15.3 Crude mortality rate (male/female per 10 000)2 Acute myocardial infarction 46.1/33.1 8.4/5.5 16.0/11.0 8.3/4.9 19.0/13.7 19.9/13.9 74.6/57.3 Other cardiovascular causes 118.7/104.5 0.9/0.5 3.8/2.9 7.4/8.4 2.3/1.7 2.2/3.1 51.8/57.2 Cerebrovascular disease 52.5/43.9 8.4/8.0 8.8/7.9 9.8/9.6 8.5/9.3 8.1/8.1 52.6/50.7 Pneumonia, influenza 104.4/72.4 17.4/15.9 9.1/8.5 4.2/4.0 3.6/3.1 4.0/ 3.1 221.0/199.0 COPD 4.3/1.9 1.1/1.3 6.6/4.5 3.7/2.8 7.9/5.8 7.5/5.6 33.2/25.3 Lung cancer 15.6/4.6 3.7/3.1 4.3/2.5 3.9/2.2 3.3/1.9 2.5/1.2 13.5/10.4 Estimated direct health costs of smoking-related conditions in USD million Acute myocardial infarction 3242 5114 5006 3944 3835 4848.6 2663 Other cardiovascular causes 2432 3835 1881 2702 1534 3190.4 1850 Annual cardiovascular follow-up 1283 2024 409 1444 34 795 1240.6 1171 Cerebrovascular disease3 4294 5232 4304 4431 2174 4119.1 5058 Pneumonia/influenza 217 276 361 235 325 1309.9 174 COPD4 4394 3969 4824 6133 3463 9236.2 4363 Lung cancer5 17 392 8862 12 279 21 727 10 499 13 792.6 14 081 Mouth cancer5 12 523 6381 9602 15 644 7560 9930.6 9251 Esophageal cancer 14 610 7444 12 161 18 251 8820 11 585.7 11 828 Stomach cancer5 14 262 7267 15 074 17 816 8610 11 309.9 11 546 Pancreatic cancer5 11 827 6026 11 616 14 774 7140 9378.9 9575 Kidney cancer5 12 523 6381 4632 15 644 7560 9930.6 10 138 Tax revenue on smoking6 1926.2 21.5 9511 1346.5 174 2237.4 73.5 GDP (2015)6 583 168.6 33 197 1 774 725 240 215.7 292 080.1 1 144 331.3 192 083.7 GDP per capita (2015)6 13 432 3095 8539 13 384 6056 9009 6122 Price elasticity of demand −0.299 −0.85 −0.48 −0.45 −0.780 −0.45 −0.7 Total health expenditure (% GDP) 4.8 6.3 8.3 7.8 7.2 6.3 5.5 Characteristics . Argentina . Bolivia . Brazil . Chile . Colombia . Mexico . Peru . Population (2015) 43 416 755 10 724 705 207 847 528 17 948 141 48 228 704 127 017 224 31 376 670 Smoking prevalence1  Male 23.4 20.1 18.0 35.2 20.1 19.8 23.5  Female 18.6 17.7 11.3 31.3 9.9 6.4 15.3 Crude mortality rate (male/female per 10 000)2 Acute myocardial infarction 46.1/33.1 8.4/5.5 16.0/11.0 8.3/4.9 19.0/13.7 19.9/13.9 74.6/57.3 Other cardiovascular causes 118.7/104.5 0.9/0.5 3.8/2.9 7.4/8.4 2.3/1.7 2.2/3.1 51.8/57.2 Cerebrovascular disease 52.5/43.9 8.4/8.0 8.8/7.9 9.8/9.6 8.5/9.3 8.1/8.1 52.6/50.7 Pneumonia, influenza 104.4/72.4 17.4/15.9 9.1/8.5 4.2/4.0 3.6/3.1 4.0/ 3.1 221.0/199.0 COPD 4.3/1.9 1.1/1.3 6.6/4.5 3.7/2.8 7.9/5.8 7.5/5.6 33.2/25.3 Lung cancer 15.6/4.6 3.7/3.1 4.3/2.5 3.9/2.2 3.3/1.9 2.5/1.2 13.5/10.4 Estimated direct health costs of smoking-related conditions in USD million Acute myocardial infarction 3242 5114 5006 3944 3835 4848.6 2663 Other cardiovascular causes 2432 3835 1881 2702 1534 3190.4 1850 Annual cardiovascular follow-up 1283 2024 409 1444 34 795 1240.6 1171 Cerebrovascular disease3 4294 5232 4304 4431 2174 4119.1 5058 Pneumonia/influenza 217 276 361 235 325 1309.9 174 COPD4 4394 3969 4824 6133 3463 9236.2 4363 Lung cancer5 17 392 8862 12 279 21 727 10 499 13 792.6 14 081 Mouth cancer5 12 523 6381 9602 15 644 7560 9930.6 9251 Esophageal cancer 14 610 7444 12 161 18 251 8820 11 585.7 11 828 Stomach cancer5 14 262 7267 15 074 17 816 8610 11 309.9 11 546 Pancreatic cancer5 11 827 6026 11 616 14 774 7140 9378.9 9575 Kidney cancer5 12 523 6381 4632 15 644 7560 9930.6 10 138 Tax revenue on smoking6 1926.2 21.5 9511 1346.5 174 2237.4 73.5 GDP (2015)6 583 168.6 33 197 1 774 725 240 215.7 292 080.1 1 144 331.3 192 083.7 GDP per capita (2015)6 13 432 3095 8539 13 384 6056 9009 6122 Price elasticity of demand −0.299 −0.85 −0.48 −0.45 −0.780 −0.45 −0.7 Total health expenditure (% GDP) 4.8 6.3 8.3 7.8 7.2 6.3 5.5 GDP = gross domestic product; COPD = chronic obstructive pulmonary disease. Key: 1. Population ≥35 years expressed in millions; 2. Mortality rate per 10 000 people; 3. Values include the first and following years, as a summary, only the first year is included in this table; 4. COPD mild, moderate, and serious included; 5. Treatment costs of the following years are included; 6. In millions of USD, the exchange rate as mean in December 2015 according to central banks in each country. Open in new tab Table 1. Main Inputs for the Simulation Model Characteristics . Argentina . Bolivia . Brazil . Chile . Colombia . Mexico . Peru . Population (2015) 43 416 755 10 724 705 207 847 528 17 948 141 48 228 704 127 017 224 31 376 670 Smoking prevalence1  Male 23.4 20.1 18.0 35.2 20.1 19.8 23.5  Female 18.6 17.7 11.3 31.3 9.9 6.4 15.3 Crude mortality rate (male/female per 10 000)2 Acute myocardial infarction 46.1/33.1 8.4/5.5 16.0/11.0 8.3/4.9 19.0/13.7 19.9/13.9 74.6/57.3 Other cardiovascular causes 118.7/104.5 0.9/0.5 3.8/2.9 7.4/8.4 2.3/1.7 2.2/3.1 51.8/57.2 Cerebrovascular disease 52.5/43.9 8.4/8.0 8.8/7.9 9.8/9.6 8.5/9.3 8.1/8.1 52.6/50.7 Pneumonia, influenza 104.4/72.4 17.4/15.9 9.1/8.5 4.2/4.0 3.6/3.1 4.0/ 3.1 221.0/199.0 COPD 4.3/1.9 1.1/1.3 6.6/4.5 3.7/2.8 7.9/5.8 7.5/5.6 33.2/25.3 Lung cancer 15.6/4.6 3.7/3.1 4.3/2.5 3.9/2.2 3.3/1.9 2.5/1.2 13.5/10.4 Estimated direct health costs of smoking-related conditions in USD million Acute myocardial infarction 3242 5114 5006 3944 3835 4848.6 2663 Other cardiovascular causes 2432 3835 1881 2702 1534 3190.4 1850 Annual cardiovascular follow-up 1283 2024 409 1444 34 795 1240.6 1171 Cerebrovascular disease3 4294 5232 4304 4431 2174 4119.1 5058 Pneumonia/influenza 217 276 361 235 325 1309.9 174 COPD4 4394 3969 4824 6133 3463 9236.2 4363 Lung cancer5 17 392 8862 12 279 21 727 10 499 13 792.6 14 081 Mouth cancer5 12 523 6381 9602 15 644 7560 9930.6 9251 Esophageal cancer 14 610 7444 12 161 18 251 8820 11 585.7 11 828 Stomach cancer5 14 262 7267 15 074 17 816 8610 11 309.9 11 546 Pancreatic cancer5 11 827 6026 11 616 14 774 7140 9378.9 9575 Kidney cancer5 12 523 6381 4632 15 644 7560 9930.6 10 138 Tax revenue on smoking6 1926.2 21.5 9511 1346.5 174 2237.4 73.5 GDP (2015)6 583 168.6 33 197 1 774 725 240 215.7 292 080.1 1 144 331.3 192 083.7 GDP per capita (2015)6 13 432 3095 8539 13 384 6056 9009 6122 Price elasticity of demand −0.299 −0.85 −0.48 −0.45 −0.780 −0.45 −0.7 Total health expenditure (% GDP) 4.8 6.3 8.3 7.8 7.2 6.3 5.5 Characteristics . Argentina . Bolivia . Brazil . Chile . Colombia . Mexico . Peru . Population (2015) 43 416 755 10 724 705 207 847 528 17 948 141 48 228 704 127 017 224 31 376 670 Smoking prevalence1  Male 23.4 20.1 18.0 35.2 20.1 19.8 23.5  Female 18.6 17.7 11.3 31.3 9.9 6.4 15.3 Crude mortality rate (male/female per 10 000)2 Acute myocardial infarction 46.1/33.1 8.4/5.5 16.0/11.0 8.3/4.9 19.0/13.7 19.9/13.9 74.6/57.3 Other cardiovascular causes 118.7/104.5 0.9/0.5 3.8/2.9 7.4/8.4 2.3/1.7 2.2/3.1 51.8/57.2 Cerebrovascular disease 52.5/43.9 8.4/8.0 8.8/7.9 9.8/9.6 8.5/9.3 8.1/8.1 52.6/50.7 Pneumonia, influenza 104.4/72.4 17.4/15.9 9.1/8.5 4.2/4.0 3.6/3.1 4.0/ 3.1 221.0/199.0 COPD 4.3/1.9 1.1/1.3 6.6/4.5 3.7/2.8 7.9/5.8 7.5/5.6 33.2/25.3 Lung cancer 15.6/4.6 3.7/3.1 4.3/2.5 3.9/2.2 3.3/1.9 2.5/1.2 13.5/10.4 Estimated direct health costs of smoking-related conditions in USD million Acute myocardial infarction 3242 5114 5006 3944 3835 4848.6 2663 Other cardiovascular causes 2432 3835 1881 2702 1534 3190.4 1850 Annual cardiovascular follow-up 1283 2024 409 1444 34 795 1240.6 1171 Cerebrovascular disease3 4294 5232 4304 4431 2174 4119.1 5058 Pneumonia/influenza 217 276 361 235 325 1309.9 174 COPD4 4394 3969 4824 6133 3463 9236.2 4363 Lung cancer5 17 392 8862 12 279 21 727 10 499 13 792.6 14 081 Mouth cancer5 12 523 6381 9602 15 644 7560 9930.6 9251 Esophageal cancer 14 610 7444 12 161 18 251 8820 11 585.7 11 828 Stomach cancer5 14 262 7267 15 074 17 816 8610 11 309.9 11 546 Pancreatic cancer5 11 827 6026 11 616 14 774 7140 9378.9 9575 Kidney cancer5 12 523 6381 4632 15 644 7560 9930.6 10 138 Tax revenue on smoking6 1926.2 21.5 9511 1346.5 174 2237.4 73.5 GDP (2015)6 583 168.6 33 197 1 774 725 240 215.7 292 080.1 1 144 331.3 192 083.7 GDP per capita (2015)6 13 432 3095 8539 13 384 6056 9009 6122 Price elasticity of demand −0.299 −0.85 −0.48 −0.45 −0.780 −0.45 −0.7 Total health expenditure (% GDP) 4.8 6.3 8.3 7.8 7.2 6.3 5.5 GDP = gross domestic product; COPD = chronic obstructive pulmonary disease. Key: 1. Population ≥35 years expressed in millions; 2. Mortality rate per 10 000 people; 3. Values include the first and following years, as a summary, only the first year is included in this table; 4. COPD mild, moderate, and serious included; 5. Treatment costs of the following years are included; 6. In millions of USD, the exchange rate as mean in December 2015 according to central banks in each country. Open in new tab Model Calibration and Validation After the calibration process was completed, the average rate of each predicted event was within 10% of the rate reported by national statistics (correlation between observed and expected results yielded R2 values ranging from 0.700 to 0.999). External validation also showed a good correlation between predicted results and those in epidemiology studies. Supplementary Figure 1 describes the calibration and validation process in Argentina. Health and Economic Benefits of Current Strategies The health warnings policies that are currently in place in these seven countries (Figure 1), if properly applied and maintained, are already producing health and economic benefits thanks to their potential to avert a total of 69 369 deaths, 167 251 cardiac diseases, 47 768 cerebrovascular diseases, 86 776 COPD, and 305 836 cases of cancer, totaling 638 295 disease events, during a period of 10 years; which could add 1.2 million years of healthy life and save USD 5.3 billion in direct medical costs. In Brazil, the country with the largest population in the group, 34 121 deaths and 223 585 events could be averted, with more than 1 million healthy years lived, and USD 2.4 billion in savings. In the number of averted deaths, Argentina and Mexico come in second and third places, with 11 024 and 10 229, respectively. Moreover, Mexico could prevent 316 077 events followed by Argentina, with 44 710 events (Table 2). Table 2. Ten-Year Cumulative Benefits Obtained With Currently Implemented Strategies Country . Averted deaths . Averted events . . . . . Years lived due to prevented premature death and disability . Savings in USD millions . . . Cardiac disease . Cerebrovascular disease . COPD . Cancer . Total events . . . Argentina 11 024 17 460 6326 15 830 5094 44 710 265 013 $906 Bolivia 1534 894 1688 2444 443 5469 39 397 $92.5 Brazil 34 121 126 863 25 091 55 535 16 096 223 585 1 019 088 $2400 Chile 5467 6878 6399 12 433 2206 27 916 143 120 $545 Colombia 3465 10 936 4844 5657 1339 22 776 90 285 $196.4 Mexico 10 229 26 418 6430 3845 279 384 316 077 279 384 $934.5 Peru 3529 2140 3316 6862 1274 13 592 86 598 $183 Total 69 369 167 251 47 768 86 776 305 836 638 295 1 922 885 $5257 Country . Averted deaths . Averted events . . . . . Years lived due to prevented premature death and disability . Savings in USD millions . . . Cardiac disease . Cerebrovascular disease . COPD . Cancer . Total events . . . Argentina 11 024 17 460 6326 15 830 5094 44 710 265 013 $906 Bolivia 1534 894 1688 2444 443 5469 39 397 $92.5 Brazil 34 121 126 863 25 091 55 535 16 096 223 585 1 019 088 $2400 Chile 5467 6878 6399 12 433 2206 27 916 143 120 $545 Colombia 3465 10 936 4844 5657 1339 22 776 90 285 $196.4 Mexico 10 229 26 418 6430 3845 279 384 316 077 279 384 $934.5 Peru 3529 2140 3316 6862 1274 13 592 86 598 $183 Total 69 369 167 251 47 768 86 776 305 836 638 295 1 922 885 $5257 COPD = chronic obstructive pulmonary disease. Open in new tab Table 2. Ten-Year Cumulative Benefits Obtained With Currently Implemented Strategies Country . Averted deaths . Averted events . . . . . Years lived due to prevented premature death and disability . Savings in USD millions . . . Cardiac disease . Cerebrovascular disease . COPD . Cancer . Total events . . . Argentina 11 024 17 460 6326 15 830 5094 44 710 265 013 $906 Bolivia 1534 894 1688 2444 443 5469 39 397 $92.5 Brazil 34 121 126 863 25 091 55 535 16 096 223 585 1 019 088 $2400 Chile 5467 6878 6399 12 433 2206 27 916 143 120 $545 Colombia 3465 10 936 4844 5657 1339 22 776 90 285 $196.4 Mexico 10 229 26 418 6430 3845 279 384 316 077 279 384 $934.5 Peru 3529 2140 3316 6862 1274 13 592 86 598 $183 Total 69 369 167 251 47 768 86 776 305 836 638 295 1 922 885 $5257 Country . Averted deaths . Averted events . . . . . Years lived due to prevented premature death and disability . Savings in USD millions . . . Cardiac disease . Cerebrovascular disease . COPD . Cancer . Total events . . . Argentina 11 024 17 460 6326 15 830 5094 44 710 265 013 $906 Bolivia 1534 894 1688 2444 443 5469 39 397 $92.5 Brazil 34 121 126 863 25 091 55 535 16 096 223 585 1 019 088 $2400 Chile 5467 6878 6399 12 433 2206 27 916 143 120 $545 Colombia 3465 10 936 4844 5657 1339 22 776 90 285 $196.4 Mexico 10 229 26 418 6430 3845 279 384 316 077 279 384 $934.5 Peru 3529 2140 3316 6862 1274 13 592 86 598 $183 Total 69 369 167 251 47 768 86 776 305 836 638 295 1 922 885 $5257 COPD = chronic obstructive pulmonary disease. Open in new tab Potential Impact of Implementing a Plain Packaging Strategy So far, the studied countries have not implemented plain tobacco packaging. If the seven countries moved to health warnings of more than 80% of the pack and plain packaging, 155 857 premature deaths would be averted (range: 118 177–277 898) during a 10-year period. The implementation of this measure would also avoid 437 198 cardiac events (range: 331 267–780 290), 132 116 cerebrovascular events (range: 99 810–236 753), 117 283 COPD (range: 88 344–211 019), and 597 501 cancer diagnosis (range: 455 338–1 057 912); totaling 4 133 858 potentially avoidable disease events. A total of 4.1 million healthy years of life would be added (range: 3.1–7.3), and a total of USD 13.6 billion in direct health care expenses of diseases attributable to smoking (range: 10.1–24.6) would be saved (Table 3) in the next 10 years. In absolute values, Brazil leads in the number of deaths that could be avoided if plain packaging is implemented (120 730 deaths) followed by Argentina, with 39 007, and Mexico, with 36 193 averted deaths. Table 3. Ten-Year Cumulative Benefits to be Obtained by Implementing Plain Packaging Strategy, Including Current Warnings Benefits Country . Averted deaths, N (range) . Averted events; base case (range) . . . . . Years of life due to premature death and disability, N (range) . Savings in USD millions, N (range) . . . Cardiac disease . Cerebrovascular disease . COPD . Cancer . Total events . . . Argentina 22 048 (16 812–39 007) 34 920 (26 626–61 779) 12 651 (9647–22 382) 31 661 (24 141–56 013) 10 187 (7768–18 023) 89 419 (68 182–158 197) 530 026 (404 145–937 704) 1811 (1381–3204) Bolivia 3069 (2340–5429) 1787 (1363–3162) 3377 (2575–5974) 4888 (3727–8648) 887 (676–1569) 10 939 (8341–19 353) 78 794 (60 080–139 399) 185 (141–327) Brazil 68 241 (52 034–120 730) 253 726 (193 466–448 883) 50 182 (38 264–88 781) 11 250 (8579–19 904) 11 408 (8699–20 183) 427 821 (326 214–756 887) 2 038 177 (1 554 110–3 605 874) 4866 (3710–8609) Chile 10 934 (8337–19 344) 13 757 (10 490–24 338) 12 798 (9759–22 642) 8808 (6716–15 583) 4412 (3364–7806) 39 775 (30 329–70 369) 286 240 (218 258–506 407) 1095 (835–1937) Colombia 24 049 (17 673–44 707) 75 893 (55 772–141 082) 33 615 (24 703–62 489) 39 261 (28 852–72 985) 9290 (6827–17 269) 165 090 (121 006–307 913) 626 577 (460 453–1 164 783) 3398 (2404–6608) Mexico 20 458 (15 599–36 193) 52 836 (40 287–93 475) 12 860 (9806–22 752) 7690 (5864–13 605) 558 768 (426 060–988 553) 632 154 (482 017–1 118 385) 400 849 (305 647–709 169) 1869 (1425–3307) Peru 7059 (5382–12 488) 4279 (3263–7571) 6632 (5057–11 733) 13 725 (10 465–24 281) 2549 (1943–4509) 27 185 (20 728–48 094) 173 196 (132 062–306 412) 366 (279–647) Total 155 857 (118 177–277 898) 437 198 (331 267–780 290) 132 116 (99 810–236 753) 117 283 (88 344–211 019) 597 501 (455 338–1 057 912) 1 392 383 (1 056 817–2 479 198) 4 133 858 (3 134 755–7 369 748) 13 590 (10 175–24 639) Country . Averted deaths, N (range) . Averted events; base case (range) . . . . . Years of life due to premature death and disability, N (range) . Savings in USD millions, N (range) . . . Cardiac disease . Cerebrovascular disease . COPD . Cancer . Total events . . . Argentina 22 048 (16 812–39 007) 34 920 (26 626–61 779) 12 651 (9647–22 382) 31 661 (24 141–56 013) 10 187 (7768–18 023) 89 419 (68 182–158 197) 530 026 (404 145–937 704) 1811 (1381–3204) Bolivia 3069 (2340–5429) 1787 (1363–3162) 3377 (2575–5974) 4888 (3727–8648) 887 (676–1569) 10 939 (8341–19 353) 78 794 (60 080–139 399) 185 (141–327) Brazil 68 241 (52 034–120 730) 253 726 (193 466–448 883) 50 182 (38 264–88 781) 11 250 (8579–19 904) 11 408 (8699–20 183) 427 821 (326 214–756 887) 2 038 177 (1 554 110–3 605 874) 4866 (3710–8609) Chile 10 934 (8337–19 344) 13 757 (10 490–24 338) 12 798 (9759–22 642) 8808 (6716–15 583) 4412 (3364–7806) 39 775 (30 329–70 369) 286 240 (218 258–506 407) 1095 (835–1937) Colombia 24 049 (17 673–44 707) 75 893 (55 772–141 082) 33 615 (24 703–62 489) 39 261 (28 852–72 985) 9290 (6827–17 269) 165 090 (121 006–307 913) 626 577 (460 453–1 164 783) 3398 (2404–6608) Mexico 20 458 (15 599–36 193) 52 836 (40 287–93 475) 12 860 (9806–22 752) 7690 (5864–13 605) 558 768 (426 060–988 553) 632 154 (482 017–1 118 385) 400 849 (305 647–709 169) 1869 (1425–3307) Peru 7059 (5382–12 488) 4279 (3263–7571) 6632 (5057–11 733) 13 725 (10 465–24 281) 2549 (1943–4509) 27 185 (20 728–48 094) 173 196 (132 062–306 412) 366 (279–647) Total 155 857 (118 177–277 898) 437 198 (331 267–780 290) 132 116 (99 810–236 753) 117 283 (88 344–211 019) 597 501 (455 338–1 057 912) 1 392 383 (1 056 817–2 479 198) 4 133 858 (3 134 755–7 369 748) 13 590 (10 175–24 639) Open in new tab Table 3. Ten-Year Cumulative Benefits to be Obtained by Implementing Plain Packaging Strategy, Including Current Warnings Benefits Country . Averted deaths, N (range) . Averted events; base case (range) . . . . . Years of life due to premature death and disability, N (range) . Savings in USD millions, N (range) . . . Cardiac disease . Cerebrovascular disease . COPD . Cancer . Total events . . . Argentina 22 048 (16 812–39 007) 34 920 (26 626–61 779) 12 651 (9647–22 382) 31 661 (24 141–56 013) 10 187 (7768–18 023) 89 419 (68 182–158 197) 530 026 (404 145–937 704) 1811 (1381–3204) Bolivia 3069 (2340–5429) 1787 (1363–3162) 3377 (2575–5974) 4888 (3727–8648) 887 (676–1569) 10 939 (8341–19 353) 78 794 (60 080–139 399) 185 (141–327) Brazil 68 241 (52 034–120 730) 253 726 (193 466–448 883) 50 182 (38 264–88 781) 11 250 (8579–19 904) 11 408 (8699–20 183) 427 821 (326 214–756 887) 2 038 177 (1 554 110–3 605 874) 4866 (3710–8609) Chile 10 934 (8337–19 344) 13 757 (10 490–24 338) 12 798 (9759–22 642) 8808 (6716–15 583) 4412 (3364–7806) 39 775 (30 329–70 369) 286 240 (218 258–506 407) 1095 (835–1937) Colombia 24 049 (17 673–44 707) 75 893 (55 772–141 082) 33 615 (24 703–62 489) 39 261 (28 852–72 985) 9290 (6827–17 269) 165 090 (121 006–307 913) 626 577 (460 453–1 164 783) 3398 (2404–6608) Mexico 20 458 (15 599–36 193) 52 836 (40 287–93 475) 12 860 (9806–22 752) 7690 (5864–13 605) 558 768 (426 060–988 553) 632 154 (482 017–1 118 385) 400 849 (305 647–709 169) 1869 (1425–3307) Peru 7059 (5382–12 488) 4279 (3263–7571) 6632 (5057–11 733) 13 725 (10 465–24 281) 2549 (1943–4509) 27 185 (20 728–48 094) 173 196 (132 062–306 412) 366 (279–647) Total 155 857 (118 177–277 898) 437 198 (331 267–780 290) 132 116 (99 810–236 753) 117 283 (88 344–211 019) 597 501 (455 338–1 057 912) 1 392 383 (1 056 817–2 479 198) 4 133 858 (3 134 755–7 369 748) 13 590 (10 175–24 639) Country . Averted deaths, N (range) . Averted events; base case (range) . . . . . Years of life due to premature death and disability, N (range) . Savings in USD millions, N (range) . . . Cardiac disease . Cerebrovascular disease . COPD . Cancer . Total events . . . Argentina 22 048 (16 812–39 007) 34 920 (26 626–61 779) 12 651 (9647–22 382) 31 661 (24 141–56 013) 10 187 (7768–18 023) 89 419 (68 182–158 197) 530 026 (404 145–937 704) 1811 (1381–3204) Bolivia 3069 (2340–5429) 1787 (1363–3162) 3377 (2575–5974) 4888 (3727–8648) 887 (676–1569) 10 939 (8341–19 353) 78 794 (60 080–139 399) 185 (141–327) Brazil 68 241 (52 034–120 730) 253 726 (193 466–448 883) 50 182 (38 264–88 781) 11 250 (8579–19 904) 11 408 (8699–20 183) 427 821 (326 214–756 887) 2 038 177 (1 554 110–3 605 874) 4866 (3710–8609) Chile 10 934 (8337–19 344) 13 757 (10 490–24 338) 12 798 (9759–22 642) 8808 (6716–15 583) 4412 (3364–7806) 39 775 (30 329–70 369) 286 240 (218 258–506 407) 1095 (835–1937) Colombia 24 049 (17 673–44 707) 75 893 (55 772–141 082) 33 615 (24 703–62 489) 39 261 (28 852–72 985) 9290 (6827–17 269) 165 090 (121 006–307 913) 626 577 (460 453–1 164 783) 3398 (2404–6608) Mexico 20 458 (15 599–36 193) 52 836 (40 287–93 475) 12 860 (9806–22 752) 7690 (5864–13 605) 558 768 (426 060–988 553) 632 154 (482 017–1 118 385) 400 849 (305 647–709 169) 1869 (1425–3307) Peru 7059 (5382–12 488) 4279 (3263–7571) 6632 (5057–11 733) 13 725 (10 465–24 281) 2549 (1943–4509) 27 185 (20 728–48 094) 173 196 (132 062–306 412) 366 (279–647) Total 155 857 (118 177–277 898) 437 198 (331 267–780 290) 132 116 (99 810–236 753) 117 283 (88 344–211 019) 597 501 (455 338–1 057 912) 1 392 383 (1 056 817–2 479 198) 4 133 858 (3 134 755–7 369 748) 13 590 (10 175–24 639) Open in new tab Discussion Our results show that the graphic health warning policies currently in place in these seven Latin American countries (Argentina, Bolivia, Brazil, Chile, Colombia, Mexico, and Peru) are already producing nonnegligible health and economic benefits; 69 369 averted deaths and USD 5.3 billion saved in direct medical costs every 10 years. However, these countries are missing the opportunity to obtain much greater benefits. If plain packaging plus graphic health warnings covering at least 80% of the pack were implemented, this strategy would increase the number of averted smoking-associated deaths by 224%, to 155 857. For the seven countries, the number of averted health events would increase by 218%, to 1.4 million, and savings in direct costs to the health system would increase by 258%, reaching USD 19.4 billion. Of note, these countries represent almost 80% of the population of Latin America. We observed wide differences among countries regarding absolute values, mostly because of variations in total population (ie, 207.8 million in Brazil and 10.7 million in Bolivia) and in the prevalence of tobacco use, ranging from 6.4% in Mexican women to 35.2% in Chilean men. Colombia currently has the lowest level of warnings, for this reason, the country would obtain extra benefits in relative terms from transitioning to plain packaging. Although the estimated health and economic benefits of moving to plain packaging varied widely among the analyzed countries, these are still very high in the seven countries. There is no single strategy capable to address the tobacco epidemic; and plain packaging should be used along with other evidence-based measures such as increased taxation, product regulation, and others.37 However, with at least one MPOWER policy at its highest level of implementation, 88 countries averted 22 million premature deaths, and the three most effective strategies were taxes increase, comprehensive smoke-free laws, and graphic health warnings.38 One of the strategies actively endorsed by WHO is the increase in taxes to tobacco products. In other studies, we estimated that higher benefits could be gained with a 50% price increase in tobacco through a raise of taxes compared with plain packaging. For example, in the case of Brazil, we estimated that a 50% increase in cigarette prices would avoid 136 482 deaths, 507 451 cases of cardiovascular diseases, 64 382 cases of cancer, and 100 365 cases of stroke and the estimated economic benefit would be USD 25.5 billion in the next 10 years, representing twice the expected benefit of plain packaging.21 We also estimated that if these seven countries fully implemented smoke-free air strategies, it would be possible to avert nearly 180 000 premature deaths and 1.2 million events, adding 5 million healthy years of life and saving USD 13.1 billion in direct health care costs. Our data show that the expected benefits of the implementation of plain packaging would be significant even in the most conservative scenarios, and the effects achieved through the adoption of graphic and large health warnings over the last years could be further increased by adopting plain packaging. In the Latin American region, only two observational studies assessed the decision to quit smoking after the implementation of warnings in Mexico and Uruguay.19,35 Tobacco-Free Kids reported that in Brazil, health warnings led to 67% of surveyed smokers reporting their intention to stop smoking.16 Moreover, two systematic reviews of the effects of warnings on smoking prevalence showed inconclusive evidence and a high heterogeneity on the definition of reduction of consumption, measurement of exposure, study design, population, and statistical analysis.34,39 Reports such as Tobacco Atlas have shown that in Australia, smoking prevalence diminished after the implementation of plain packaging.8,40 Levy et al.12 estimated the effects of implementing graphic warnings in the United States, where only small and text-only labels on one side of the cigarette pack are required, and showed that smoking prevalence would be reduced by 5% in the first few years and 10% in the long term through the effects on initiation and cessation. Although these figures are consistent with the scenarios explored in our analysis (3.15%–15.2%), Levy et al.12 did not include plain packaging in their model. In our systematic review, we only found one study addressing the effectiveness of plain packaging; it was performed in Australia, where the tobacco prevalence has been decreasing as a result of the implementation of several strategies. Due to the uncertainty in how to extrapolate this result to Latin America, we reached a consensus with experts and decided that the effectiveness in our region could be like that assumed with the implementation of large warnings. The tobacco industry and other opponents of health warnings and plain packaging measures argue that these strategies will increase counterfeit and illicit tobacco purchasing or decrease revenues; the industry has challenged the measures adopted in Australia and Uruguay adducing breaches of international trade agreements and intellectual property legislation.41 Another strategy used by the tobacco industry to respond to these initiatives was the introduction of new products, extended brands, or pack sizes options.42,43 Evidence suggests that plain packaging does not increase retail transaction times or the use of illicit or counterfeit tobacco; importantly, the decisions on legal cases may depend on the evidence indicating health benefits outweighing the manufacturers’ interests.41 Our study, in accordance with previous research on the effects of other measures, shows that taking this measure to its highest level would avert significantly deaths, disease events, add and save money.19,38,44–52 Our study has important limitations that should be considered. The main limitation is the scarcity of high-quality evidence addressing the effectiveness of plain packaging implementation. For this reason, a conservative base case scenario was assumed, and parameter uncertainty was addressed through sensitivity analysis. Some conditions that could be related to smoking such as kidney failure, breast cancer, or diabetes were not included, and indirect costs were not assessed. Despite having a negligible effect, this could underestimate the burden of smoking-related diseases and the benefits of the interventions. We have been conservative about the benefits of a reduction in consumption not mediated by quitting, although this remains controversial. Also, we did not include the effects of tobacco products other than cigarettes and did not differentiate the effects of graphic warnings and plain packaging on the quantity smoked, smoking cessation, and initiation. However, this limitation closely mimicks real-world scenarios as plain packaging policy and enlarged graphic warnings were introduced simultaneously.53 Moreover, the effects depend on the projections of smoking prevalence. The model relies on data provided by official institutions of the participating countries; therefore, potential inaccuracies and the lack of good-quality epidemiological and cost information in the region represent a threat. The relative risks used in our model are based on studies from other countries and could vary in Latin America. Despite these limitations, given a large number of countries and medical conditions included, our results offer a robust estimate of the benefits of implementing plain packaging strategy in Latin America. In summary, the graphic health warning policies currently in place are producing nonnegligible health and economic benefits. 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TI - Health and Economic Impact of Health Warnings and Plain Tobacco Packaging in Seven Latin American Countries: Results of a Simulation Model
JF - Nicotine & Tobacco Research
DO - 10.1093/ntr/ntaa104
DA - 2020-10-29
UR - https://www.deepdyve.com/lp/oxford-university-press/health-and-economic-impact-of-health-warnings-and-plain-tobacco-TUJa0HWDIg
SP - 2032
EP - 2040
VL - 22
IS - 11
DP - DeepDyve
ER -