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Cadernos de Saúde Pública

Print version ISSN 0102-311XOn-line version ISSN 1678-4464

Cad. Saúde Pública vol.31 no.6 Rio de Janeiro June 2015 


The burden of smoking-related diseases in Brazil: mortality, morbidity and costs

Márcia Teixeira Pinto 1  

Andres Pichon-Riviere 2  

Ariel Bardach 2  

1Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernandes Figueira, Fundação Oswaldo Cruz, Rio de Janeiro, Brasil.

2Instituto de Efectividad Clínica y Sanitaria, Buenos Aires, Argentina.


Advances in tobacco control in Brazil can be reflected in the decrease in prevalence over the past two decades. Death statistics and the occurrence of events and direct costs attributable to tobacco-related diseases have not been frequently estimated in the country. The goal of this article is to estimate the burden of smoking in 2011 regarding mortality, morbidity and medical care costs of the main tobacco-related diseases. A probabilistic microsimulation health economic model was built. The model incorporates the natural history, costs and quality of life of all the tobacco-related adult-specific diseases. Smoking was accountable for 147,072 deaths, 2.69 million years of life lost, 157,126 acute myocardial infarctions, 75,663 strokes, and 63,753 cancer diagnoses. The direct cost for the health system was of BRL 23.37 billion. The monitoring of tobacco-related burden is an important strategy to guide decision-makers and to strenghten health public policies.

Key words: Cost of Illness; Smoking; Potential Years of Life Lost; Mortality


Smoking is one of the main risk factors for non-communicable chronic diseases 1,2 and the main global cause of preventable morbidity and mortality, accounting for some 6 million deaths a year. Estimates indicate that in 2020 there will be 7.5 million deaths by smoking, or 10% of all deaths worldwide 1.

The epidemiological evidence points to a causative relationship between smoking and some 50 diseases, including cardiovascular and respiratory conditions, and cancer. Studies indicate that 45% of the deaths by coronary artery disease (acute myocardial infarction – AMI), 85% by chronic obstructive pulmonary disease (COPD), 25% by cerebrovascular disorders, and 30% by cancer could be attributed to the use of tobacco-related products 3,4,5. Second-hand smoke is also a serious public health hazard, as 40% of the children, 35% of the women, and 33% of men who do not smoke are exposed to the smoke of tobacco products. This scenario is made worse by the estimate of 603,000 deaths a year among persons exposed to tobacco smoke, of which 47% are women, 28% are children, and 26% are men 6.

The magnitude of tobacco-related costs imposes a significant burden for individuals and health systems. Conservative estimates indicate that health costs attributable to tobacco-related diseases reach some US$ 500 billion a year globally, from loss of productivity, illnesses and premature deaths 4. These costs may range from 0.1% to 1.5% of the Gross Domestic Product (GDP) in high-income countries. Furthermore, these costs range from 6% to 15% of national health expenditures 7. In countries with a less developed economy, such information is more scarcely available, but it is estimated that health care costs are as high as in those countries with industrialized economies 8.

In Brazil, Monteiro et al. 9 concluded there was a decrease in the prevalence of smoking, from 34.8% in 1989 to 22.4% in 2003. Results from a national survey indicate that in 2008 prevalence was 18.5%, showing a significant reduction when compared with the 1989 data 10. Advances in tobacco control in Brazil seen over the last decades are positive, but it is observed that prevalence among young women is growing more than among men of the same age 11, mortality is still high 12,13 and the costs are underestimated 14. In this scenario, calculation of the tobacco-related disease burden is not often made in the country.

It is worth mentioning that this investigation is part of a collaborative effort of researchers and health system managers of seven Latin-American countries (Argentina, Bolivia, Brazil, Chile, Colombia, Mexico and Peru). Its main purpose was the development of a methodological framework, and the design of a common economic model to estimate the burden of tobacco-related diseases. Thus, the current study intended to estimate the burden of tobacco for Brazil in 2011 in terms of mortality, morbidity and direct costs for the health system from the proposed economic model.

Materials and methods

Description of the model

This economic model used a first-order Monte Carlo (probabilistic microsimulation of individual subjects) that includes the natural history, medical care costs and quality of life loss associated with the main tobacco-related disease. The selected diseases were: ischemic heart diseases, unstable angina, other heart diseases, COPD (including chronic bronchitis and pulmonary emphysema), pneumonia and influenza, stroke, and the following types of cancer: lung, oral cavity and pharinx, oesophagus, stomach, pancreas, kidney and renal pelvis, larynx, bladder, cervical cancer, and myeloid leukemia. Further details of the methodological development of the model and coding of the disease according to the 10th revision of the International Classification of Diseases (ICD-10) may be found in Pichon-Riviere et al. 15,16.

The subjects were followed in hypothetical cohorts during annual cycles until the end of their lives. For each cycle, the individual risk for the occurrence of outcomes was calculated, which included disease-related events, its progression, and death, and the costs of medical care. From the calculation of the individual risk, aggregated results for the outcomes were obtained. The likelihood of occurrence was based on demographic data (sex and age) (Instituto Brasileiro de Geografia e Estatística. Contagem da população 2007., accessed on 10/Oct/2011), life tables (Instituto Brasileiro de Geografia e Estatística. Tábuas completas de mortalidade, 2010., accessed on 10/Nov/2011), status of the subject in regards to smoking (smoker, former smoker, non-smoker) 10, medical status, and risk equations. Figure 1 presents acute events, chronic conditions and cause of death used in the model, as well as the equations for the calculation of their likelihood of occurrence.

Figure 1 Acute events, chronic conditions, causes of death and calculation of likelihood. 

The model captured the frequency of outcomes considering that a subject could present none, one, or multiple events, as acute events (such as AMI) and chronic medical conditions (like cancer) were not mutually exclusive. The likelihood of occurrence of acute events in each annual cycle, and of the chronic conditions that last a lifetime were calculated. In addition, the use of health resources and quality-adjusted life-year (QALY) were also calculated throughout life.

Risk calculation of acute events was estimated from a specific individual risk per age and sex in non-smokers (baseline incidence) per annual cycle. This risk was multiplied by the relative risk (RR) of the disease in smokers, calculated with the tool Smoking-Attributable Mortality, Morbidity, and Economic Costs (SAMMEC. Department of Health and Human Services, Centres for Disease Control and Prevention, Atlanta, U.S.A.). For COPD, the risk of becoming ill and disease progression to more advanced stages according to smoking were considered 10. Cancer incidence was estimated from the risk per age and sex in non-smokers multiplied by smoking-related RR of each type of cancer 15,16.

The risk of death was also calculated per individual per time cycle and was associated with the events and medical conditions they could experience over this cycle. Overall mortality of the population per age and sex was incorporated in the death risk estimates presented in Figure 1. Data on deaths were obtained from Mortality Information System, Brazilian Unified National Health System (SIM-SUS) (Departamento de Informática do SUS;

The model was programmed in Excel (Microsoft Corp., U.S.A.), with Visual Basic macros (Microsoft Corp., U.S.A.), and a software to randomly generate numbers was used.

Methods for the selection of data sources and incorporation of parameters

Baseline risks for each medical condition in non-smokers were estimated from the incidence of each disease in the population, calculated from mortality data. Due to the lack of good quality information on the incidence in the population of the diseases included in the model, a SIM-SUS-supported methodology was established. The selection of this methodology, relating incidente to mortality data is common adopted in economic and epidemiological models 17,18,19,20,21,22. Thus, there are different perspectives for the estimation of risks of acute events or chronic conditions. For each acute event, the absolute risk per age and sex was calculated against its mortality rate and lethality, given by:

Where Rdeath is the specific mortality per age and sex, and L is lethality. Once this absolute risk was obtained, the risk in non-smoking individuals was calculated from the specific prevalence of the habit of smoking, per age and sex 10. Specific smoking RR from each disease was calculated by the following equation:

Where Rnonsmk is the annual incidence of the event of reference in non-smokers, Rpop.event is obtained from Equation 1, and RRsmk and RRfrsmk are the RRs of the event to occur in smokers and former smokers, compared to non-smokers. The fsmk, ffrsmk and fnonsmk parameters are the specific proportions per age and sex of smokers, former smokers and non-smokers 10.

In the case of lung cancer, estimates of diagnosis likelihood per age and sex were obtained from the annual mortality rate provided by SIM-SUS, and the annual estimated survival rate from the moment of diagnosis 22,23. The specific risk of diagnosis per age and sex was given by:

Where Rdxi is the risk of being diagnosed with the disease at age i; Rmi+n is the risk of death of the overall population by the disease at age i+n; Pn is the conditional likelihood of an individual dying in a given year n after diagnosis within a 10-year period; and S10 is the proportion of individuals who survive after 10 years. It was assumed that individuals who survive 10 years after diagnosis return to present the same risk of death of the overall population. From the results obtained through the calculation of risks, a transition model between health conditions (Markov model), with the purpose of adjusting such estimates. Next, Equation 2 was applied for the calculation of baseline risk in non-smokers. For the other types of cancer it was decided that Globocan’s specific incidence and prognostic data was to be used 22,23,24. Globocan data was selected for it provides information on types of cancer in Brazil, and because it is a common source of data for all countries participating in the project.

It is acknowledged internationally that national statistics underestimated COPD mortality 25,26, thus, the risk, incidence and progression of the disease were based on international literature 25,27,28.

It is known that the etiology of pneumonia originates from infection, for smokers and non-smokers alike, and smoking is not its cause. The inclusion of pneumonia is justified by evidence in the literature about the increased risk of smokers presenting severe exacerbation episodes, and the higher mortality among them compared to non-smokers.

Second-hand smoke and the main smoking-related perinatal causes (low weight or low size at birth, respiratory distress syndrome, and sudden infant death syndrome) were included in the analysis, but were not assessed by the model in a direct manner. A literature-based approximation was used to calculate mortality, years of life lost (YLL) and costs 29. The additional burden considered for these causes was 13.6% in men and 12% in women.

• Model calibration and validation process

The criteria suggested by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 30 for the development of models and their presentation in research studies and reports were adopted.

• Internal validation

The model was reviewed and tested after its completion to identify possible errors related to the inclusion of data and the programming syntax of the software used. It was thus ensured that all mathematical calculations were correct and consistent with all specifications established by the model.

• Calibration

The specific mortality rates for each disease predicted by the model were compared with national statistics from the SIM-SUS. Sixteen parameters were selected 16, with the exclusion of COPD mortality, as this is underestimated in national statistics, as previously mentioned. The results for age and sex were also compared with the rates collected from original sources of information, and the assessment of the deviations was performed. The mean rates of events simulated by the model that were 10% above or below the mean rate of reference events (statistics and national bases) were considered acceptable. In the case of major deviations, the risk equation of this specific event was modified (lethality and survival rates varied from 15% above to 15% below) to provide a better adjustment of results. Only when the rates were within the expected 10% limit the adjustment was finalized.

The process to calculate basal risk parameters and the variation allowed during calibration are presented in Pichon-Riviere et al. 16.

To ensure that the results simulated by the model were within the established variation limits, charts with the total number and the incidence of events of each parameter were designed. The resulting curves of observed (statistics and databases) and expected (model) data indicated whether or not the adjustment was adequate. The squared linear correlation coefficient (R2) was also used to check for this adequacy.

• External validation

For the external validation process the model-generated results were validated through comparison with epidemioligic and medical studies 23,31,32,33,34,35,36 that not use the same sources of information for the estimation of equations.

Estimation of the smoking-related disease burden

An assessment of the differences in the occurrence of events, deaths and direct costs associated with an hypothetical cohort of non-smokers and former smokers compared to a cohort in which the prevalence of smokers and former smokers was included. Considering that one of the aims of the study was to assess smoking-related YLL on a populational level, two components were estimated: potential years of life lost due to premature death (PYLL) e potential years of life lost for living with poor quality of life (YLL-QL). PYLLs were calculated by means of standardized methodology 37, and used health status utility measures for each disease in order to estimate the YLL-QL. The sum of these components is part of the total YLL. A discount rate of 5% was applied 38.

Methodological aspects related to data collection

The epidemiological parameters used in the model match the Brazilian demographic structure and in the individual risk of death according to cause, age and sex. These data were complemented by lethality per age and sex, estimated from the ratio between deaths by the disease and hospital admissions due to this condition. The Hospital-Based Information System of the Brazilian Unified National Health System (SIH-SUS) (Departamento de Informática do SUS; provided the number of admissions. The lethality information generated by the model for specific diseases, such as AMI, angina, and stroke, was compared with national estimates available for coronary artery disease or cerebrovascular diseases. As the cost assessment also considered the private health insurance plans, and given the lack of information on the number of annual hospital admissions, the correction proposed by Azambuja et al. 39 was made so the all hospital admissions in Brazil in 2011 could be estimated.

The information about the overall population (population per age group and sex), the disease and hospital admissions include the Brazilian population with ages between 35 and 100 years, with particulars of age and sex (Instituto Brasileiro de Geografia e Estatística. Contagem da população 2007., accessed on 10/Oct/2011).

Two corrections in the SIM-SUS databank were made: (i) the application of a death coverage adjustment fator, as proposed by the World Health Organization (WHO) for Brazil, of 1.16 (World Health Organization. WHO mortality data and statistics., accessed on 05/Jul/2011), flatly adopted for all regions of the country, and (ii) correction of death underreporting through indirect techniques (Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz. Projeto Carga de Doença., accessed on 05/Jan/2012), that redistributes “garbage codes” and unspecific codes that do not allow the accurate classification of the causes of death and of ill-defined causes.

Due to lack of local data on health status utility measures, we used sources from the international literature 15 to assess the quality of life.

Calculation of costs

The perspective of the study is the health system, and it aggregates the costs for the SUS and the private health insurance plans. The direct cost of diagnosis and treatment for each disease was calculated and included in the model, which, by simulating the likelihood of occurrence of each event over the life of the individual, totalled its cost.

The costs included: medical visits, tests, hospital stay, surgical and non-surgical prodecures. To identify and quantify cost items, five specialist practitioners were consulted in the areas of oncology, cardiology, neurology, and pneumology who work in the private health insurance plans and in the SUS. These practitioners also gave information about the likelihood of each patient to use health resources. For both, the quantification of cost items and the likelihood, a value was provided for the case of reference, and minimum and maximum variation values.

To provide value to the resources the SIGTAP table (Departamento de Informática do SUS. Sistema de gerenciamento da tabela de procedimentos, medicamentos e OPM do SUS., accessed on 20/Dec/2011) and the Health Price Database (Ministério da Saúde. Banco de Preços em Saúde., accessed on 20/Dec/2011) were consulted. The cost for the SUS to provide care to lung, larynx, and oesophagus cancer was obtained in the literature 14. The costs for the private health insurance plans were obtained from companies that market health insurance and plans in selected capital cities. The cost included in the model is the mean medical care unitary cost of 2011, presented in Brazilian Reais (BRL). No adjustments for inflation or discount rates were applied.

This study was approved by the Brazilian National Ethics Research Committee (CONEP), and by the Ethics Research Committee of the Fernandes Figueira Brazilian National Institute of Women, Child and Adolescent’s Health, Oswaldo Cruz Foundation (CEP IFF/Fiocruz), according to authorizations 16.457/2011 and 08/2011, respectively. The informed consent form was signed by the specialists prior to the collection of information for the cost analysis stage.


Model calibration

The set of data for the calibration process followed six hypothetical cohorts (three for males, three for females) of 8 million smokers, 8 million former smokers, and 8 million nonsmokers, followed up from age 35 years until death. This sample was estimated from a standard error of the parameter of highest variability (incidence of oral and pharyngeal cancer) due to the small incidence of events. Thus, 95% confidence intervals (95%CI) with variation +/- 2.5% for each cohort were obtained.

The incidence rates of the cohorts for each disease were transformed into absolute number of events per age and sex according to the age structure of the Brazilian population. After completion of the calibration process, the mean rate of events for each parameter was within the 10% of rates seen in national statistics, which ensured an excellent internal validation (Figure 2a). The correlation between the observed and the expected results seen in national statistics was higher for the events of higher incidence rates (AMI, stroke and lung cancer), and lower in less frequente diseases (leucemia, and oral and pharyngeal cancer). The charts generated from the observed vs. the expected data indicated the adequacy of the adjustment, and revealed that most values were close to the y=x line, which shows an almost perfect adjustment. The correlation assessment between observed and expected results reached R2 values ranging between 0.700 and 0.999 (perfect adjustment = 1), which indicated, once more, a high degree of correlation. Regression lines for the 16 parameters analyzed presented gradients between 0.800 and 1.350 (Figure 2b).

Figure 2 Calibration and validation of the model. 

The results of external validation showed a favorable correlation between the model’s predictive values and those observed in selected studies, as shown in Figures 2c, 2d, 2e, 2f and 2g.

Smoking-related deaths and events

In 2011, smoking was accountable for 147,072 preventable deaths or 403 deaths per day, which corresponded to 14.7% of the total deaths in Brazil (1,000,490 deaths). Deaths due to lung cancer and COPD corresponded to 81% and 78%, respectively, while 21% of deaths due to heart disease and 18% of deaths from stroke were also associated to this risk factor. Within the set of neoplasms, 31% of deaths were due to the use of tobacco-related products. Second-hand smoke and perinatal causes totaled 16,920 deaths. Smoking is associated to 1,147,037 PYLLs a year, concentrated in AMI (239,456), lung cancer (187,865), COPD (177,329), and stroke (164,618) (Table 1).

Table 1 Deaths, acute events and chronic conditions, and potential years of life lost due to premature death attributable to smoking, per sex, in Brazil. 

Totals (A) Attributable to smoking
Men (B) Women (C) Total (D = B + C) % (D/A)
n % n %
AMI 114,363 17,397 18 6,680 19 24,077 21
Coronary artery disease (except AMI) 33,391 4,212 4 1,540 4 5,752 17
Cardiovascular disease * (non ischemic) 50,536 5,439 6 1,419 4 6,858 14
Stroke 83,619 8,571 9 6,534 19 15,104 18
Lung cancer 27,024 15,543 16 6,363 18 21,906 81
Pneumonia 54,221 6,372 7 2,044 6 8,416 16
COPD 31,600 19,355 20 5,401 16 24,756 78
Oral and pharyngeal cancer 4,318 2,554 3 417 1 2,971 69
Esophageal cancer 9,633 5,457 6 1,127 3 6,584 68
Stomach cancer 17,594 3,355 4 523 2 3,878 22
Pancreatic cancer 8,857 1,137 1 777 2 1,914 22
Kidney and renal pelvis cancer 2,625 686 1 48 0 734 28
Larynx cancer 4,724 3,509 4 392 1 3,901 83
Myeloid leukemia 4,717 606 1 178 1 783 17
Bladder cancer 3,681 1,254 1 234 1 1,488 40
Cervical cancer 8,084 - - 1,033 3 1,033 13
Passive smoking and perinatal causes 16,920 - - - - 16,920 -
Total 475,906 5,445 100 34,707 100 147,072 31

  Acute events and chronic conditions
Totals (A) Attributable to smoking
Men (B) Women (C) Total (D = B + C) % (D/A)
n % n %

AMI 567,214 116,318 20 40,808 16 157,126 28
Coronary artery disease (except AMI) 417,747 78,739 14 23,412 9 102,151 24
Cardiovasculas disease * (non ischemic) - - - - - - -
Stroke 392,978 41,577 7 34,086 14 75,663 19
Lung cancer 29,125 17,192 3 6,561 3 23,753 82
Pneumonia 490,904 62,550 11 42,529 17 105,080 21
COPD 434,118 220,504 39 97,060 38 317,564 73
Oral and pharyngeal cancer 10,666 6,610 1 882 0 7,492 70
Esophageal cancer 10,340 5,858 1 1,210 0 7,068 68
Stomach cancer 26,087 5,082 1 756 0 5,838 22
Pancreatic cancer 9,011 1,169 0 785 0 1,953 22
Kidney and renal pelvis cancer 5,546 1,379 0 115 0 1,494 27
Larynx cancer 8,776 6,780 1 505 0 7,285 83
Myeloid leukemia 6,912 897 0 257 0 1,154 17
Bladder cancer 11,947 4,444 1 599 0 5,043 42
Cervical cancer 20,667 - - 2,674 1 - -
Passive smoking and perinatal causes - - - - - - -
Total 2,442,038 569,098 100 252,238 100 821,336 34

  PYLL (with a 5% discount)  
Men Women Total  
n % n % n %  

AMI 162,970 21 76,486 20 239,456 21  
Coronary artery disease (except AMI) 33,876 4 15,473 4 49,349 4  
Cardiovascular disease * (non ischemic) 43,405 6 15,237 4 58,642 5  
Stroke 79,909 10 84,709 22 164,618 14  
Lung cancer 119,276 16 68,589 18 187,865 16  
Pneumonia 39,019 5 18,248 5 57,267 5  
COPD 127,873 17 49,456 13 177,329 15  
Oral and pharyngeal câncer 25,516 3 4,609 1 30,125 3  
Esophageal cancer 47,060 6 11,161 3 58,221 5  
Stomach cancer 27,420 4 5,434 1 32,854 3  
Pancreatic cancer 9,624 1 7,429 2 17,053 1  
Kidney and renal pelvis cancer 5,963 1 534 0 6,497 1  
Larynx cancer 31,034 4 4,145 1 35,180 3  
Myeloid leukemia 5,967 1 2,250 1 8,218 1  
Bladder cancer 8,836 1 2,149 1 10,986 1  
Cervical câncer - - 13,377 4 13,377 1  
Passive smoking and perinatal causes - - - - - -  
Total 767,479 100 379,288 100 1,147,037 100  

AMI: acute myocardial infarction; COPD: chronic obstructive pulmonary disease; PYLL: potential years of life lost due to premature death.

*The matematic model does not include non-ischemic events, only the deaths.

Tobacco-related diseases were accountable for 157,126 AMI and 75,663 strokes. A total of 63,753 individuals were diagnosed with a type of cancer included in the model. Among men, the number of events (569,098) was more than twice when compared to women (252,238), being concentrated in COPD (220,504), AMI (116,318), pneumonia (62,550) and stroke (41,577) (Table 1).

Smoking-related years of life lost and quality of life

Women who smoke have a life expectancy 4.47 years shorter than non-smoker women, and 1.32 year shorter compared to former smokers. Smoking men have a life expectancy 5.03 years shorter than non-smokers, and live 2.05 years less compared to former smokers. In addition to the impact in life expectancy, smoking-related diseases also interfere in the quality of life of individuals. Because of that, reduction of life expectancy is more significant when assessed in terms of QALY. For men, this difference is of 6.25 years between smokers and nonsmokers, and for women, of 5.72 years. Figure 2h shows the survival of smokers and nonsmokers compared to the results of the investigation by Doll et al. 34 for a cohort of British medical practitioners.

Smoking accounted for 2,699,246 YLL a year, for a scenario with a 5% discount. This total is the result of PYLL (64%) and YLL-QL (36%) combined. The higher impact was among men (1,877,198 YLL) compared to women (822,048 YLL). YLL due to second-hand smoke and perinatal causes totaled 310,533, with higher proportion of PYLL (64%) (Table 2).

Table 2 Potential years of life lost due to premature death and impairment related to smoking, per sex, in Brazil. Scenarios with and without discounts. 

  Scenario without discount
Women Men Total
  n % n % n %
PYLL 919,539 79 1.743,804 74 2,663,343 76
YLL-QL 244,963 21 607,244 26 852,207 24
Total YLL attributable to smoking 1,164,502 100 2,351,048 100 3,515,550 100
PYLL attributable to passive smoking and perinatal causes 119,540 79 226,695 74 346,235 76
YLL-QL lost due to impairment attributable to passive smoking and perinatal causes 31,845 21 78,942 26 110,787 24
Total YLL attributable to passive smoking and perinatal causes 151,385 - 305,636 - 457,022 -
Total YLL 1,315,887 - 2,656,685 - 3,972,572 100

  Scenario with a 5% discount
Women Men Total
n % n % n %

PYLL 482,513 66 1,053,993 63 1,536,507 64
YLL-QL 244,963 34 607,244 37 852,208 36
Total YLL attributable to smoking 727,476 100 1,661,237 100 2,388,715 100
PYLL attributable to passive smoking and perinatal causes 62,727 66 137,019 63 199,746 64
YLL-QL lost due to impairment attributable to passive smoking and perinatal causes 31,845 34 78,942 37 110,787 36
Total YLL attributable to passive smoking and perinatal causes 94,572 100 215,961 100 310,533 -
Total YLL 822,048 - 1,877,198 - 2,699,246 100

PYLL: potential years of life lost due to premature death; YLL: years of life lost; YLL-QL: years of life lost for living with poor quality of life.

Total costs attributable to smoking

The total cost for the health system was of BR$23,374,477,024. The highest amount went to heart diseases (BRL 7,219,651,548), followed by COPD (BRL 6,773,192,770), lung cancer (BRL 1,596,815,061), and stroke (BRL 1,557,995,266). These diseases accounted for 67% of the total cost, and were the higher costs for men and women. Second-hand smoke and perinatal conditions generated costs of BRL 2,689,099,127, representing 11.5% of the total costs (Table 3).

Table 3 Total direct costs for the health system attributable to smoking, per sex, in Brazil (BRL, 2011). 

  Costs Total (D = B + C) * % (D/A)
Men (B) Women (C)
Totais (A) Attributable to smoking Total (A) Attributable to smoking
n % n %
Heart conditions 18,277.741,703 5,529,399,893 35 9,635,358,870 1,690,251,655 7 7,219,651,548 26
Stroke 3,920,102,035 848,698,670 5 3,958,646,458 709,296,597 3 1,557,995,266 20
COPD 6,502,884,836 5,154,782,425 33 2,459,444,931 1,618,410,345 7 6,773,192,770 76
Pneumonia 292,903,047 69,545,072 0 252,897,780 47,285,283 0 116,830,355 21
Lung cancer 1,332,623,595 1,189,296,787 8 612,263,501 407,518,274 2 1,596,815,061 82
Other types of cancer 5,801,696,332 2,924,913,248 19 3,083,034,477 495,979,648 2 3,420,892,897 39
Passive smoking and other causes - - - - - - 2,689,099,127 -
Total 36,127,951,549 15,716,636,095 100 20,001,646,016 4,968,741,802 100 23,374,477,024 42

COPD: chronic obstructive pulmonary disease.

*Includes the cost with the passive smoking.


The results of this study indicate that smoking is a serious public health problem in Brazil, in terms of morbidity, mortality and costs for the health system. Brazil is signatory of WHO’s Framework Convention on Tobacco Control since 2005 40, a decision that legally strengthens the public policy for the control of the epidemics. For this purpose, information generated from researchers on the burden of smoking may contribute with policy-makers in decision-taking on the actions to be developed.

Our findings show that smoking-related mortality is high in Brazil, particularly among men, and is concentrated in heart diseases, COPD, stroke and lung cancer. Similar results were also found in a study developed in Argentina, in which smoking accounted for 926,878 YLL a year, representing 40,591 deaths a year that could have been prevented 15.

Our results also point to a significant burden of second-hand smoke in terms of death and illnesses. Costa et al. 41 observed that exposure to tobacco smoke accounts for 2,655 annual deaths and, according to the authors, these estimates are conservative.

The model calibration process showed that the same results from the sources of data through which the parameters were obtained were replicated. The model is based on individual simulations, which allows the occurrence of multiple events in the annual cycle. This feature is the main reason for its selection, and supports the justification of not adopting models that reflect health states, and possible transitions between these states, like the Markov chains.

The results of direct costs among men were about three times higher than the costs among women, which, along with other results, is evidence that the burden of smoking in concentrated in that population. Also worthy of mention is the cost of second-hand smoke and of perinatal conditions, particularly the former, that have been included in the calculation of economic burden in a number of countries 42,43. The comparison of our findings with those of other investigations that estimated costs for the health system is limited, due to aspects that were mentioned in a previous research 44. It is possible, however, to state that many studies lead to similar results by providing evidence the high economic burden of smoking for countries 45,46,47. One of the parameters to measure this magnitude is the GDP. In China, the economic impact of smoking represented 0.7% of the GDP in 2008 47. Our results indicate that in Brazil this impact was 0.5% of the GDP in 2011. It is also to be stressed that federal tax collection from the tobacco industry reached BRL 6.3 billion in 2011, thus, the total cost was four times higher than the amount collected in taxes 48.

There are some limitations in the interpretation of results from this study. The uniform correction of mortality rates for the whole of Brazil may account for differences in the number of deaths for particular diseases, since, in some states, death registrations are more accurate and, in others, there might be undernotification. The correction of hospital admissions for the private health insurance plans by a single adjustment factor 38 is also a limitation. However, considering the scarcity of information, which includes insufficiency of ICD-10-based data, this was the methodological alternative to calculate lethality and costs as comprehensively as possible. The model used populational data of 2007 corrected for 2008, due to unavailability of data from the 2010 Census Survey at the time parameters were defined. The growth of the Brazilian population between 2007 and 2009 was not, however, significant enough to affect the results.

Tobacco-control policies in Brazil have advanced significantly over the past 25 years, with positive results leading to a reduction in prevalence. There is an ample room for the intensification of actions already set in motion, such as increase in price and taxes, and the provision of smoking-cessation treatment, as long as their effectiveness may be monitored. Furthermore, protection to non-smokers by establishing smoking-free environments is a measure that should be more strenuously enforced in Brazil.

Finally, our findings indicate that the economic burden is underestimated, as costs with absenteeism, loss of productivity, and out-of-the-pocket family expenditures were not included. Considering the whole scenario, we have presented only a small fraction of the cost and, therefore, it is suggested that the calculation of the impact from smoking be expanded, so that the actual magnitude of this risk factor can be known.


To the International Development Research Centre (IDRC), Tobacco Control Alliance (ACTbr), and Oswaldo Cruz Foundation (Fiocruz) for the funding.


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Received: November 10, 2013; Revised: October 08, 2014; Accepted: November 17, 2014

Correspondence. M. T. Pinto. Instituto Nacional de Saúde da Mulher, da Criança e do Adolescente Fernandes Figueira, Fundação Oswaldo Cruz. Av. Rui Barbosa 716, Rio de Janeiro, RJ. 22250-020, Brasil.


M. T. Pinto and A. Bardach participated in the epidemiological data and costs data collection and analysis, and in the writing of the manuscript. A. Pichon-Riviere was responsible for the development of the economic model and the calibration process, participated in the epidemiological data collection and analysis, and in the writing of the manuscript.

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