Smoking-attributable mortality by sex in the 27 Brazilian federal units: 2019

Themed Paper eOriginal Research Smoking-attributable mortality by sex in the 27 Brazilian federal units: 2019 B. Wanderlei-Flores a , J. Rey-Brandariz a , b , * , P.C. Rodrigues Pinto Corr^ ea c , A. Ruano-Ravina a , b , d , C. Guerra-Tort a , C. Candal-Pedreira a , b , L. Varela-Lema a , b , d , A. Montes a , b , d ,M.P  erez-Ríos a , b , d a Department of Preventive Medicine and Public Health, Universidade de Santiago de Compostela, Santiago de Compostela, Spain b Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública/CIBERESP), Madrid, Spain c Escola de Medicina, Universidade Federal de Ouro Preto (UFOP), Ouro Preto, MG, Brazil d Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain article info Article history: Received 23 September 2023 Received in revised form 23 December 2023 Accepted 3 January 2024 Available online 20 February 2024 Keywords: Smoking Attributable mortality Brazil Lung cancer Cardiovascular disease Chronic obstructive pulmonary disease abstract Objectives: The aim of this study was to estimate smoking-attributable mortality (SAM) in the population aged 35 years and over in Brazil's 27 federal units by sex, in 2019. Study design: This is an attributable mortality analysis. Methods: We applied a method dependent on the prevalence of smoking, based on the population attributable fractions. Data on mortality due to causes causally related to smoking were derived from Brazil's Death Registry, data on prevalence of smoking from a survey conducted in Brazil in 2019, and data on relative risks from five US cohorts. Crude and age-adjusted SAM rates were calculated by sex. Estimates of SAM were calculated by specific causes of death and major mortality groups for each federal unit by sex. Results: In 2019, smoking caused 480 deaths per day in Brazil. Although the SAM varied among the federal units, the pattern is not clear, with the greatest difference being between Rio Grande do Sul (crude rate: 248.8/100,000 inhabitants) and Amazonas (106.0/100,000). When the rates were adjusted by age, the greatest differences were observed between Acre (271.1/100,000) and Distrito Federal (131.1/ 100,000). SAM was higher in males; however, while the main specific cause of SAM in men was ischemic heart disease, in women it was chronic obstructive pulmonary disease. The major mortality group having the greatest impact on SAM across all federal units was the cardiometabolic diseases. Conclusions: The variability in the burden of SAM in the different regions of Brazil reaffirms the need for SAM data disaggregated at the geographic level. ©2024 The Author(s). Published by Elsevier Ltd on behalf of The Royal Society for Public Health. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4. 0/). Introduction Smoking is the leading preventable cause of disease and one of the main causes of premature death worldwide, associated with more than seven million deaths per year. According to the World Health Organization (WHO), there are more than one billion smokers in the world, and approximately 80% of these live in low-to middle-income countries. 1 After recognizing smoking as a major public health problem, Brazil set up a National Smoking Control Program in 1989. Tax increases, restrictions on availability, control of marketing and sale, heightened educational activities in schools, and implementation of laws governing smoke-free zones are some of the actions by the National Smoking Control Program that have contributed to the decrease in the prevalence of smoking in Brazil since the late 1990s. 2,3 Regarding taxes, in 2012, a minimum sale price for cigarettes was established and there are currently two tax reform bills for tobacco products under revision in the National Congress. The implementation in 2011 of the Law 12.5464 was a milestone; it *Corresponding author.  Area de Medicina Preventiva y Salud Pública, Universidade de Santiago de Compostela, Santiago de Compostela, C/ San Francisco s/n, A Coru~ na CP 15782, Spain. Tel.: þ34 881 81 22 78. E-mail address: juliarey[email protected] (J. Rey-Brandariz). Contents lists available at ScienceDirect Public Health journal homepage: www.elsevier.com/locate/puhe https://doi.org/10.1016/j.puhe.2024.01.016 0033-3506/©2024 The Author(s). Published by Elsevier Ltd on behalf of The Royal Society for Public Health. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Public Health 229 (2024) 24e32 prohibits the advertising, promotion and sponsorship of tobacco products and the consumption of tobacco in closed settings. In the period 1990e2017, the prevalence of smoking in Brazil fell by 24 percentage points, sitting at 11.3% in 2017. 4 Comparing the 2013 and 2019 Pesquisa Nacional de Saúde (PNS) results, smoking prevalence in men in Brazil decreased by 3% and in women by 1.4%. When analyzing the results by region, the region with the largest decrease in smoking prevalence for both men and women was the Northeast region, with a decrease of 4.9% in men and 2.2% in women, followed by the North region with a decrease of 3.8% and 1.7%, respectively. The region with the smallest decrease in smoking prevalence in men was the South region with 2.1%, followed by the Southeast region with 2.4%. In women, the smallest decrease was the Centerwest region with a decrease of 0.4%, followed by the South region with 0.8%. However, it should be noted that based on data furnished by the 2019 PNS, smoking prevalence is seen to vary by federal unit with the northern federal units showing decreases in smoking prevalence above the Brazilian average, while smaller decreases are recorded in the southern ones. 5 Notwithstanding this fall in prevalence, Distrito Federal was the federal unit with the lowest smoking prevalence (10.9%) and Acre with the highest (17.0%). 5 Estimating the burden of mortality attributable to a risk factor such as tobacco consumption makes it possible to ascertain its impact on population health. This estimate is crucial for planning, implementing, and assessing the impact of smoking control programs, whether at the city, state, or country level. Since 2008, seven studies have estimated smoking-attributable mortality (SAM) in Brazil. Four of these studies estimated attributable mortality for Brazil as a whole, 6e9 one estimated it for federal units, 4 one for 16 Brazilian capitals 10 and another for a single federal unit. 11 However, the study that estimated SAM in the federal units did not perform a detailed analysis by cause of death. In addition, in 2019, a health survey making smoking prevalence at the federal unit level available was conducted. Therefore, it would be appropriate to update the SAM estimates and to have a more detailed analysis of these estimations. Accordingly, the aim of this study was to estimate SAM in the population aged 35 years and over by sex in the Brazil's 27 federal units, in 2019. Methods Brazil is a country of continental dimensions, made up of five large regions, North, North-east, Center-west, South-east, and South, divided into 27 federal units. The North region (N) includes the states of Roraima, Amazonas, Acre, Rond^ onia, Amap a, Par a and Tocantins; the North-east (NE) includes Maranh~ ao, Piauí, Cear a, Rio Grande do Norte, Paraíba, Pernambuco, Alagoas, Sergipe y Bahía; the Center-west (CW) Mato Grosso, Mato Grosso do Sul, Goi as, and Distrito Federal; the South-east (SE) Minas Gerais, Espírito Santo, Rio de Janeiro, and S~ ao Paulo; and the South (S) Paran a, Rio Grande do Sul, and Santa Catarina (Supplementary Fig. S1). Brazil population is estimated at 213.3 million. The three most heavily populated states lie in the South-east region of the country, with S~ ao Paulo being the first of these with 46.6 million inhabitants, accounting for 21.9% of the country's total population. It is followed by Minas Gerais with 21.4 million, and Rio de Janeiro with 17.5 million. The least populous state is Roraima, situated in the North region, with 652,713 inhabitants. 12 Calculation procedure We applied a method dependent on the prevalence of smoking, based on the calculation of population attributable fractions (PAFs). 13 This method estimates SAM as the product of observed mortality by PAF. The PAF is estimated as: ½P0þP1RR1þP2RR21 ½P0þP1RR1þP2RR2; where P is the prevalence of never smokers (0), smokers (1), and ex-smokers (2); and RR is the risk posed to smokers (1) and exsmokers (2) of dying due to smoking-related diseases, taking never smokers as the reference group. The SAM estimates were calculated in accordance with the STREAMS-P tool guidelines. 14 Data source Observed mortality in the country's 27 federal units in 2019 was based on data derived from the Mortality Information System of Brazil's Unified Health System. Death data due to underlying causes codified by the 10th edition of the International Classification of Diseases (ICD-10) were available. The observed mortality figures and the smoking-related causes of death included in this analysis, along with their ICD-10 rubrics, are shown in Supplementary Tables S1 and S2. Causes of death were grouped into cardiovascular diseases and diabetes mellitus (hereinafter ‘cardiometabolic diseases’), cancer, and respiratory diseases. The prevalence of smokers, ex-smokers, and never smokers in each federal unit, with a breakdown by sex and age group (35e54; 55e64; 65e74, and 75 years and over), was obtained on the basis of microdata from Module PeLifestyles of the 2019 National Health Survey (PNS). The PNS is a nation-wide household health survey conducted by the Ministry of Health in collaboration with the Brazilian Geography and Statistics Institute (Instituto Brasileiro de Geografia e Estatística/IBGE)(Supplementary Table S3). The relative risks (RRs) applied were drawn from the follow-up of five US cohorts that included 956,756 participants: the National Institutes of Health-AARP Diet and Health Study, the American Cancer Society's CPS-II Nutrition Cohort, the Women's Health Initiative, the Nurses' Health Study, and the Health Professionals Follow-Up Study. 15 These risks are risk ratios adjusted for age, race, and educational level (Appendix 1 and Supplementary Table S4). The population for calculating global and age-specific SAM rates was derived from the IBGE and was adjusted applying the WHO Standard World Population for 2000e2025. 16 Analysis In each of Brazil's federal units, we estimated the following: PAFs and SAM for specific causes and in the three main groups of causes of death, global and by sex; crude SAM rates by sex; agespecific rates by sex; and age-adjusted rates in men and women. In addition, sex ratios for the adjusted SAM rates were calculated in each federal unit. The age-specific SAM rates and the adjusted rates broken down by sex are shown on maps. Estimates were calculated using the Stata 16.1 statistics software program, and spatial representation was performed using the QGIS 3.10 software package. Results In 2019, smoking caused 174,483 deaths in Brazil among the population aged 35 years and over, accounting for 14.5% of the country's total mortality for that year in the same population. A total of 109,161 deaths (62.6% of SAM) occurred in men, accounting for 16.9% of the total mortality in Brazil in men aged 35 years and B. Wanderlei-Flores, J. Rey-Brandariz, P.C. Rodrigues Pinto Corr^ ea et al. Public Health 229 (2024) 24e32 25 over. In women, tobacco consumption was associated with 65,322 deaths (37.4% of SAM), accounting for 11.6% of the total mortality in Brazil in women aged 35 years and over. In the population under 75 years of age, tobacco consumption caused 108,371 deaths (37.9% of SAM), representing 10.2% of the total mortality in Brazil in the 35e74 age group. The highest SAM burden was due to cardiometabolic diseases (72,172 deaths, 41.4% of SAM), followed by respiratory diseases (59,301 deaths, 34.0% of SAM), and cancer (43,010 deaths, 24.6% of SAM). Within the SAM due to cancer, 54.8% were due to lung cancer (23,558 deaths). The specific cause of death which accounted for the highest SAM (36,934 deaths) was chronic obstructive pulmonary disease (COPD). In relation to federal units, federal-SAM over total-SAM ranged from 0.2% in Roraima (N) and Amap a (N) to 26.1% in S~ ao Paulo (SE). S~ ao Paulo (SE) was the federal unit with the highest SAM percentage in men (44.3%) and in women (25.5%). Regardless of federal unit or cause of death, SAM was in all cases higher in men, i.e. for each attributable death in women, there were 1.7 in men (sex ratio: 1.7:1). The highest sex ratios were observed in Mato Grosso (CW) (2.3:1), and Roraima (N) (2.2:1) (Tables 1 and 2). In Brazil, the SAM by specific cause of death varied between sex and between federal units. Among men, the leading cause of smoking-related death was ischemic heart disease, which caused 19.5% of total SAM, with variations between federal units. Hence, ischemic heart disease accounted for 27.0% of SAM among men in Rio Grande do Norte (NE) vs. 14.2% of SAM among men in Minas Gerais (SE). COPD was the second leading cause to which most mortality was attributed in men, accounting for 19.2% of total attributable deaths in men; the SAM for COPD varied between 31.6% of SAM in Acre (N) and 11.7% in Rio Grande do Norte (NE). Influenza-pneumonia-tuberculosis was the third leading cause of death in men, accounting for 13.5% of total SAM in men, while lung cancer was the fourth leading cause, accounting for 12.8% of total SAM in men. Attention should be paid to the differences in terms of SAM burden in men due to lung cancer between Rio Grande do Sul (S), where it was associated with 19.7% of SAM, and Roraima (N), where it accounted for 8.1% (Table 1 or Fig. 1). Among women, the leading cause of SAM was COPD, which caused 24.5% of total SAM in women, with variations between federal units. Thus, in Goi as (CW) COPD accounted for 34.5% of SAM in women and in Rio Grande do Norte (NE) it accounted for 16.3%. Ischemic heart disease was the second leading cause to which most mortality was attributed, causing 17.7% of total SAM in women. This percentage of SAM ranged from 24.4% in Rio Grande do Norte (NE) to 12.2% in Amazonas (N). Lung cancer was the third leading cause of SAM in women, causing 14.7% of total SAM. In terms of the SAM burden in women due to lung cancer, there were differences between Rio Grande do Sul (S), where it was associated with 20.0% of SAM in women, and Tocantins (N), where it accounted for 10.1% (Table 2). The crude rate of SAM in men ranged from 139.3 deaths per 100,000 inhabitants in Amazonas (N) to 328.2 deaths in Rio Grande do Sul (S). In women, the crude SAM rate ranged from 73.1 deaths per 100,000 inhabitants in Amazonas (N) to 177.7 in Rio Grande do Sul (S) (Table 3). The age-specific SAM rate increased with age in all of Brazil's federal units, both among men and women (Fig. 2). The age-adjusted SAM ranked Acre (N) as the federal unit having the highest rate, followed by Rio Grande do Sul (S). The federal units registering the lowest age-adjusted SAM rates were Bahía (NE) and Distrito Federal (CW). The breakdown by sex showed that Acre was also the federal unit with the highest age-adjusted SAM rates for both men and women (Table 3). Furthermore, while Santa Catarina (S) and Espírito Santo (SE) were the federal units where the sex ratios were the highest (2.4:1), Tocantins (N) was the unit where the sex ratio was the lowest (1.6:1) (Fig. 3). Discussion In 2019, smoking caused one death every 3 minutes in Brazil. The results of this study show differences in SAM among federal units with the greatest differences between death rates in Rio Grande do Sul and Amazonas, and with the rates of the former being twice as high as those of the latter. When the rates were adjusted by age, the greatest differences were observed between Acre and Distrito Federal, with the rates of the former being twice as high as those of the latter. The group of causes which had greatest SAM burden in all federal units was that of cardiometabolic diseases, followed by respiratory diseases and cancer. The leading specific cause of smoking-related death was ischemic heart disease in men, and COPD in women. Regarding sex, despite federal unit, SAM was higher in males; however, among men ischemic heart disease was the main specific cause of SAM, while in women, it was COPD. When assessing the impact of tobacco consumption on lung cancer mortality, it should be noted that it was greater among women, accounting for 14.7% of the SAM, while in men it represented 12.8%. To our knowledge, this is the second study to estimate SAM in Brazil's federal units using common data sources and methodology. The results yielded by this study should not be directly compared with those of the previous study, 4 since the age groups analyzed and causes included are different. That said, however, as in the previous study, our study shows that there are major differences in SAM rates among federal units. The distribution of SAM does not show a clear geographical pattern. Two states, Acre and Rio Grande do Sul, show the highest burden of SAM; and a higher burden is observed in the North-east, Center-west (except Mato Grosso) and South. The results of the study by Malta et al. 4 point to the federal units of the South (Rio Grande do Sul, Santa Catarina and Paran a) and South-east (S~ ao Paulo, Rio de Janeiro, Espírito Santo, and Minas Gerais) as being the units with highest rate of SAM. Similar results were found by our study, with Rio Grande do Sul being the federal unit with the highest crude SAM rate and the highest SAM burden due to lung cancer. Rio Grande do Sul is the federal unit with the second most aged population in Brazil, with two out of every ten persons over 60 years old. 12 Furthermore, 45% of the population reported that they smoke or smoked at some time in their lives. 17 It should be borne in mind that Brazil is the second leading tobaccoproducing country in the world and the largest exporter 18 with Rio Grande do Sul being Brazil's biggest tobacco producer, responsible for 41% of total national production. 19 However, when adjusted by age, Acre was the federal unit with the highest SAM burden. This could be related with its geographic situation, in that it lies in an area bordering Peru and Bolivia, which gives Acre access to cheaper cigarettes through cross-border trading or smuggling. We should be cautious when analyzing the impact of tobacco consumption on the major groups of causes of mortality such as cancer, cardiovascular or respiratory diseases. The concurrence of competing risks or socioeconomic differences could partly explain the results obtained. The population living in less developed areas has less access to health services, which could translate into higher mortality from causes such as cardiovascular or respiratory diseases or into poorer diagnostic capacity for cancer. The former would result in higher mortality from cardiovascular and respiratory diseases, and the latter in lower mortality attributed to cancer, since it would not be coded as a cause of death because it was not diagnosed. Regarding lung cancer, studies in North America and Europe show that lung cancer is the leading cause of death from tobacco consumption in both men and women. 20,21 However, in Brazil, it is the fourth leading cause of death from tobacco use in men and the third in women. In a study analyzing the evolution of SAM in Brazil between 1996 and 2019, it was observed that the B. Wanderlei-Flores, J. Rey-Brandariz, P.C. Rodrigues Pinto Corr^ ea et al. Public Health 229 (2024) 24e32 26 Table 1 Smoking-attributable deaths in relation to attributable mortality in men aged 35 years, by cause of death, in Brazil's federal units in 2019. Federal unit Cancer Cardiometabolic diseases Respiratory diseases Total Lung cancer Other cancer Ischemic heart disease Other heart diseases Cerebrovascular disease Other vascular diseases Diabetes mellitus COPD Influenzapneumoniatuberculosis n%n%n%n%n%n%n%n%n% Acre 37 9.4 33 8.5 69 17.8 15 3.8 29 7.5 6 1.6 14 3.7 122 31.6 63 16.2 387 Alagoas 113 8.4 107 7.9 322 23.8 94 7.0 151 11.1 39 2.9 108 8.0 235 17.4 185 13.6 1354 Amap a27 11.4 24 10.0 43 18.2 14 6.0 22 9.3 5 1.9 15 6.4 55 23.3 32 13.6 237 Amazonas 147 14.7 122 12.2 171 17.2 44 4.4 93 9.3 15 1.5 86 8.6 181 18.2 137 13.7 995 Bahía 572 9.8 823 14.0 1090 18.6 436 7.4 707 12.1 153 2.6 380 6.5 1074 18.3 626 10.7 5860 Cear a560 12.8 553 12.7 967 22.1 337 7.7 409 9.4 145 3.3 142 3.2 528 12.1 730 16.7 4371 Distrito Federal 132 15.0 140 15.8 156 17.7 40 4.5 74 8.4 34 3.8 42 4.7 178 20.2 87 9.9 883 Espírito Santo 274 13.3 318 15.4 406 19.7 91 4.4 168 8.1 83 4.0 101 4.9 412 20.0 210 10.2 2063 Goi as 482 12.5 395 10.3 706 18.4 250 6.5 270 7.0 129 3.4 166 4.3 1048 27.3 394 10.3 3839 Maranh~ ao 229 9.4 211 8.6 614 25.1 147 6.0 314 12.8 54 2.2 220 9.0 363 14.9 295 12.0 2446 Mato Grosso 167 11.8 157 11.1 254 18.0 71 5.0 108 7.6 36 2.6 74 5.3 400 28.3 146 10.3 1413 Mato Grosso do Sul 160 10.4 179 11.6 367 23.8 70 4.5 116 7.5 31 2.0 58 3.7 319 20.7 243 15.8 1543 Minas Gerais 1360 12.4 1479 13.5 1558 14.2 788 7.2 877 8.0 291 2.7 488 4.5 2531 23.1 1597 14.6 10,970 Par a274 9.9 282 10.2 583 21.1 153 5.5 291 10.5 35 1.2 198 7.2 490 17.7 462 16.7 2767 Paraíba 206 9.9 245 11.7 502 24.1 136 6.5 173 8.3 58 2.8 139 6.6 324 15.6 303 14.5 2085 Paran a945 14.5 888 13.6 1074 16.5 334 5.1 522 8.0 187 2.9 294 4.5 1517 23.2 765 11.7 6526 Pernambuco 468 10.7 457 10.4 1099 25.0 225 5.1 422 9.6 153 3.5 248 5.6 801 18.3 517 11.8 4389 Piauí 152 10.3 137 9.3 351 23.9 81 5.5 179 12.2 37 2.5 98 6.7 202 13.7 233 15.9 1470 Rio de Janeiro 1205 12.5 1096 11.4 2016 21.0 707 7.4 754 7.8 225 2.3 539 5.6 1405 14.6 1660 17.3 9608 Rio Grande do Norte 210 12.8 197 12.1 443 27.0 79 4.8 124 7.6 46 2.8 89 5.5 192 11.7 257 15.7 1637 Rio Grande do Sul 1807 19.7 1314 14.3 1347 14.7 375 4.1 652 7.1 243 2.6 445 4.8 2275 24.7 735 8.0 9193 Rond^ onia 72 11.1 87 13.5 108 16.7 45 6.9 47 7.3 14 2.1 34 5.3 171 26.4 70 10.8 648 Roraima 15 8.1 24 13.1 34 18.8 14 7.7 23 12.8 3 1.9 13 7.4 37 20.5 18 9.8 182 Santa Catarina 731 18.5 524 13.3 636 16.1 216 5.5 239 6.0 133 3.4 146 3.7 927 23.5 399 10.1 3951 S~ ao Paulo 3464 12.0 3926 13.6 6039 20.9 2030 7.0 2092 7.2 1153 4.0 1012 3.5 4839 16.7 4396 15.2 28,950 Sergipe 96 12.6 86 11.4 139 18.3 46 6.1 75 9.9 24 3.2 54 7.1 140 18.4 100 13.1 761 Tocantins 73 11.6 64 10.2 142 22.5 36 5.7 59 9.4 17 2.7 52 8.2 143 22.7 44 7.0 631 Brazil 13,974 12.8 13,868 12.7 21,238 19.5 6873 6.3 8989 8.2 3350 3.1 5255 4.8 20,911 19.2 14,703 13.5 109,161 COPD: chronic obstructive pulmonary disease. B. Wanderlei-Flores, J. Rey-Brandariz, P.C. Rodrigues Pinto Corr^ ea et al. Public Health 229 (2024) 24e32 27 Table 2 Smoking-attributable deaths in relation to attributable mortality in women aged 35 years, by cause of death, in Brazil's federal units in 2019. Federal unit Cancer Cardiometabolic diseases Respiratory diseases Total Lung cancer Other cancer Ischemic heart disease Other heart diseases Cerebrovascular disease Other vascular diseases Diabetes mellitus COPD Influenzapneumoniatuberculosis n%n%n%n%n%n%n%n%n% Acre 31 14.9 15 7.0 33 15.9 11 5.2 15 7.5 5 2.6 7 3.6 59 28.6 30 14.7 207 Alagoas 110 11.4 58 6.0 193 19.8 61 6.3 107 11.0 35 3.6 66 6.8 212 21.8 128 13.2 971 Amap a18 12.8 12 8.8 20 14.1 7 5.2 10 7.0 1 0.8 10 7.1 42 29.7 20 14.5 140 Amazonas 86 16.4 59 11.3 64 12.2 25 4.8 45 8.5 7 1.3 36 6.9 136 25.8 68 12.9 528 Bahía 421 12.3 334 9.7 633 18.5 285 8.3 340 9.9 108 3.2 225 6.6 762 22.2 318 9.3 3427 Cear a523 15.7 281 8.4 642 19.3 233 7.0 277 8.3 110 3.3 99 3.0 654 19.6 517 15.5 3335 Distrito Federal 107 16.2 71 10.8 91 13.8 37 5.6 55 8.3 34 5.1 21 3.2 191 28.8 54 8.2 663 Espírito Santo 153 14.4 96 9.1 205 19.4 51 4.8 89 8.4 41 3.9 43 4.0 273 25.8 109 10.3 1061 Goi as 295 12.8 166 7.2 335 14.6 169 7.3 151 6.6 78 3.4 89 3.9 794 34.5 224 9.7 2301 Maranh~ ao 172 10.7 136 8.4 372 23.0 115 7.1 189 11.7 50 3.1 117 7.2 279 17.3 187 11.5 1617 Mato Grosso 77 12.3 44 7.1 99 15.9 40 6.4 47 7.5 17 2.8 37 5.9 202 32.4 60 9.6 623 Mato Grosso do Sul 111 12.2 64 7.0 193 21.2 50 5.5 63 7.0 22 2.4 36 4.0 246 27.1 123 13.6 909 Minas Gerais 823 13.2 544 8.7 815 13.0 601 9.6 458 7.3 184 2.9 262 4.2 1754 28.1 810 13.0 6250 Par a165 12.2 112 8.3 224 16.6 75 5.6 114 8.5 20 1.5 78 5.8 352 26.1 207 15.3 1347 Paraíba 169 11.6 112 7.7 322 22.1 114 7.8 127 8.7 38 2.6 75 5.2 301 20.6 198 13.6 1458 Paran a629 17.0 310 8.4 499 13.5 225 6.1 247 6.7 94 2.5 152 4.1 1216 32.9 327 8.8 3699 Pernambuco 351 12.0 224 7.7 669 22.9 161 5.5 246 8.4 121 4.1 149 5.1 706 24.2 296 10.1 2922 Piauí 121 11.9 83 8.2 203 19.9 63 6.2 124 12.2 28 2.8 56 5.5 178 17.5 163 16.0 1019 Rio de Janeiro 931 15.3 522 8.6 1215 20.0 442 7.3 466 7.7 163 2.7 298 4.9 1126 18.5 914 15.0 6077 Rio Grande do Norte 173 14.9 103 8.9 284 24.4 74 6.3 83 7.1 38 3.2 67 5.7 190 16.3 152 13.0 1164 Rio Grande do Sul 1111 20.0 523 9.4 748 13.5 319 5.7 379 6.8 142 2.5 243 4.4 1715 30.8 380 6.8 5560 Rond^ onia 55 16.9 25 7.9 44 13.7 23 7.2 20 6.3 5 1.4 16 5.0 101 31.3 33 10.4 324 Roraima 13 15.9 9 10.7 12 14.8 4 5.2 6 7.1 3 4.0 6 6.9 18 22.6 10 12.7 81 Santa Catarina 373 17.5 183 8.6 300 14.1 159 7.5 140 6.6 79 3.7 72 3.4 622 29.2 200 9.4 2128 S~ ao Paulo 2461 14.8 1430 8.6 3190 19.2 1465 8.8 1103 6.6 800 4.8 495 3.0 3657 22.0 2042 12.3 16,645 Sergipe 65 13.5 40 8.4 76 15.8 29 6.0 42 8.7 15 3.2 29 6.0 124 25.8 61 12.6 482 Tocantins 39 10.1 26 6.8 69 17.8 24 6.3 42 10.9 18 4.6 24 6.2 111 28.7 33 8.5 386 Brazil 9584 14.7 5584 8.5 11,552 17.7 4863 7.4 4984 7.6 2258 3.5 2809 4.3 16,023 24.5 7664 11.7 65,322 COPD: chronic obstructive pulmonary disease. B. Wanderlei-Flores, J. Rey-Brandariz, P.C. Rodrigues Pinto Corr^ ea et al. Public Health 229 (2024) 24e32 28 crude rate of SAM due to lung cancer in men remained stable at around 25 deaths per 100,000 inhabitants. 22 This figure is well below than that observed in a study conducted in Spain where this rate was found throughout the period 1990e2018 to be above 100 deaths per 100,000 population. 23 In Brazilian women, an upward trend of SAM rates due to lung cancer is observed and these rates are more similar to rates observed in a study conducted in Spain. 23 This evolution of the rates of SAM due to lung cancer could be explained by the evolution of smoking prevalence in Brazil, since lung cancer is considered a marker of the tobacco epidemic. Thus, from 1996 to 2019 the prevalence of tobacco consumption in Brazil went from 29% to 15.9% in men and from 19% to 9.6% in women. 5,7 However, it should be noted that based on data furnished by the 2019 PNS, smoking prevalence is seen to vary by federal unit. While Amap a, Goi as, Rond^ onia, Acre, Par a, Sergipe, Rio Grande do Norte, Alagoas, Espírito Santo, Piauí, Cear a, Maranh~ ao, Roraima, and Bahía Fig. 1. Age-adjusted smoking-attributable mortality rates in men and women aged 35 years: rates per 100,000 population, by federal unit, in 2019. Table 3 Smoking-attributable mortality rates, crude and adjusted per 100,000 inhabitants in the population aged 35 years, by federal unit, and the sex ratio of the adjusted rates, in 2019. Federal unit Crude rates Adjusted rates Sex ratio Total Men Women Total Men Women Acre 206.7 271.3 142.9 271.1 367.8 174.3 2.1 Alagoas 168.7 211.9 131.3 194.1 252.5 135.6 1.9 Amap a138.3 171.9 103.9 203.3 259.4 147.2 1.8 Amazonas 106.0 139.3 73.1 148.3 199.8 96.7 2.1 Bahía 133.5 176.0 94.4 147.1 203.2 91.1 2.2 Cear a198.6 240.9 161.5 206.2 262.8 149.6 1.8 Distrito Federal 108.7 140.3 83.7 131.1 171.0 91.2 1.9 Espírito Santo 161.2 218.7 106.7 168.4 237.8 99.0 2.4 Goi as 195.5 251.0 142.7 225.4 296.1 154.7 1.9 Maranh~ ao 155.7 196.2 118.6 178.8 233.9 123.8 1.9 Mato Grosso 137.2 187.2 85.5 163.1 227.7 98.5 2.3 Mato Grosso do Sul 199.0 256.2 144.3 215.9 283.2 148.5 1.9 Minas Gerais 164.5 216.8 115.6 159.6 217.7 101.5 2.1 Par a130.5 175.7 85.3 162.8 225.5 100.1 2.3 Paraíba 196.4 250.0 150.3 202.4 267.6 137.2 2.0 Paran a183.5 245.0 127.2 182.5 248.6 116.5 2.1 Pernambuco 173.1 225.7 128.2 190.4 258.3 122.4 2.1 Piauí 179.2 226.8 137.6 200.2 265.0 135.4 2.0 Rio de Janeiro 181.4 240.1 130.9 174.6 238.6 110.7 2.2 Rio Grande do Norte 178.5 220.5 140.7 188.5 246.9 130.1 1.9 Rio Grande do Sul 248.8 328.2 177.7 223.0 305.4 140.6 2.2 Rond^ onia 128.3 169.2 86.4 175.8 233.1 118.5 2.0 Roraima 145.9 196.8 92.4 212.2 288.9 135.5 2.1 Santa Catarina 181.0 248.2 120.5 190.5 270.2 110.9 2.4 S~ ao Paulo 200.0 267.3 139.1 201.4 279.1 123.7 2.3 Sergipe 127.9 166.6 93.6 154.4 209.8 98.9 2.1 Tocantins 162.4 199.1 124.8 186.2 231.4 141.0 1.6 Brazil 179.0 235.1 127.9 177.6 251.2 123.5 2.0 B. Wanderlei-Flores, J. Rey-Brandariz, P.C. Rodrigues Pinto Corr^ ea et al. Public Health 229 (2024) 24e32 29 display decreases above the Brazilian mean, S~ ao Paulo, Pernambuco, Minas Gerais, Rio de Janeiro, Paran a, Distrito Federal, Mato Grosso do Sul, Rio Grande do Sul, Mato Grosso, and Santa Catarina register smaller decreases. 5 In contrast to what is observed in Europe, 20 in Brazil, COPD is the specific cause of death to which most mortality is attributed to tobacco use. This may be due to the fact that in Brazil treatment of this disease is relatively recent, since it was not until 2013 that the federal government began to guarantee cost-free distribution of drug treatment. 24 Additionally, account must be taken of the way in which socio-economic differences in the regions of Brazil are associated with COPD mortality. For instance, COPD mortality was lower in the South and South-east regions, where socioeconomic conditions are better and social inequality is less. 24,25 Furthermore, these better socio-economic conditions could be associated with a lower exposure to COPD risk factors other than smoking, such as less use of fossil fuel or biomass such as firewood, charcoal, or animal dung to heat homes and cook food. 26,27 A previous study undertaken in Brazil identified changes in risk of COPD-related death by birth cohort, though such changes show differences by sex, decreasing in men and increasing in women. 28 Goi as is the federal unit which registers the highest burden of COPD-SAM in women (34.5%) and ranks fourth in the use of energy obtained from biomass. 29 In men, the federal unit registering the highest burden of COPD-SAM was Acre (31.6%). This federal unit is situated in the North of the country, an area with less favorable conditions in terms of Mortality Information System coverage, due to its territorial isolation. 30 Ischemic heart disease is responsible for a high SAM. This result also differs from what has been reported by other studies conducted in European countries such as Spain or in the USA, 20,31 where cancer causes the highest number of smoking-attributable deaths. Differences in life expectancy could partially explain the difference in the causes of death. Also, this difference may be due to the fact that Brazil is a country of continental dimensions, with marked social inequalities. Rio Grande do Norte is the federal unit in which the SAM due to ischemic heart disease is highest in both sexes; whereas the North-west region is noteworthy for the rise in ischemic heart disease mortality since 1996. The South and Southeast regions, the most developed regions in Brazil, display a downward trend; and the North and Center-west regions show a stabilization. 32 The fact that wealthier regions have lower SAM due to ischemic heart disease may be due to better access to health services, which has led to improved secondary and tertiary prevention of cardiovascular disease. The presence of lower SAM associated with cardiovascular disease in wealthier regions was also observed in a study conducted in Portugal. 33 In Portugal, mortality from cardiovascular disease was reduced by approximately 40% due to improvements in treatment and speed of diagnosis. 34 In Brazil, as in most countries, men register higher smokingrelated mortality rates. This is related to a higher prevalence of smokers in men than in women. 4 Although in comparison with European countries such as Spain, the difference in smoking prevalence between men and women in Brazil is smaller. This is also to be seen from the male/female SAM rates ratio, which in Brazil rises to a peak of 2.4 in the federal units of Santa Catarina and Espirito Santo, as compared to Spain where the maximum of 12.1 is observed in Fig. 2. Specific smoking-attributable mortality rates, by sex (men and women) and age group (35e54, 55e64, 65e74 and 75 years), in each federal unit in 2019: rates per 100,000 population. Fig. 3. Sex ratio of the age-adjusted smoking-attributable mortality rates in the Brazilian population aged 35 years, by federal unit: 2019. B. Wanderlei-Flores, J. Rey-Brandariz, P.C. Rodrigues Pinto Corr^ ea et al. Public Health 229 (2024) 24e32 30 Extremadura (a region located in the west of Spain). 13 Comparison of these results with those derived from studies analyzing prevalence or SAM at the regional level in South America would be of value; however, detailed regional analyses are scarce. 35 This study has limitations, including those specific to the method of estimation used. It should be borne in mind that the RRs were drawn from cohort studies conducted on the US population, where the evolution of the smoking epidemic is different from that in Brazil. Even so, these risks are the best evidence available when assessing excess risk of death associated with smoking, by virtue of the fact that they derive from the follow-up of five cohorts which included 956,756 participants. 15 In this study, the PAF was used to calculate the attributed mortality. However, there are some problems in the definition and interpretation of the PAF that should be considered when interpreting the results. This is because different definitions of PAF have been used in the literature that include three different concepts. These three concepts are the excess fraction, the etiological fraction, and the incidence-density fraction. Therefore, some authors indicate that the term PAF requires the separation of these three concepts. 36 Due to the lack of data, no account was taken of the length of smoking among smokers or the time elapsed since ex-smokers had quit smoking. Regarding observed mortality, the quality and coverage of the death registry must be taken into consideration. The quality of Brazil's Death Registry has improved in recent years: 6,37 death registry coverage is estimated to have risen from 83.2% in the period 1980e1991 to 89.7% in the period 2000e2010. 38 Moreover, it is estimated that the percentage of deaths in the population aged 35 years, classified as ‘Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified’went from 16.4% in 1996 to 5.6% in 2017. 6 On the other hand, this study has strengths. The most important one is the use of prevalence data representative at a federal-unit level to estimate SAM. Moreover, our study used the same calculation procedure and the same data-sources to estimate SAM in the federal units. The STREAMS-P tool 14 was applied to enhance the reliability of the results. Conclusions In 2019, smoking caused 14.5% of total mortality in the Brazilian population aged 35 years and over. The SAM varied between sex, such that six of every ten attributable deaths occurred in men. SAM also varied between federal units, so that the crude mortality rate was highest in Rio Grande do Sul and the age-adjusted rate was highest in Acre. In all federal units, regardless of sex, the group of causes that had the greatest impact on SAM was cardiometabolic diseases. That said, the specific cause that had the greatest impact was COPD in women and ischemic heart disease in men. The variability in the burden of SAM in the different federal units of Brazil reaffirms the need for SAM data disaggregated at the geographic level. This is especially relevant in countries, such as Brazil, with important demographic, social or economic differences between the geographic areas that compose it. The results of this study should be the basis for other analyses that assess the evolution of SAM at the regional level in Brazil and in other South American countries. Having detailed information on the burden of attributable mortality to a risk factor makes possible advances in health policies aimed at specific groups or geographic areas. Identified inequalities indicates the need for intensification of the Unified Health System's principle of equity, which is directly related with the concepts of equality and social justice, and the need to focus attention on the reinforcement of public policies in the regions most affected. Author statements Acknowledgments None. Ethical approval Not applicable. Funding This study has been funded by Instituto de Salud Carlos III (ISCIII) through the project ‘PI19/00288’and co-funded by the European Union. Competing interests None declared. Consent for publication No applicable. Availability of data and material No applicable. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.puhe.2024.01.016. References 1. World Health Organization. Report on the global tobacco epidemic: protect people from tobacco smoke. 2023. https://www.who.int/publications/i/item/ 9789240077164. [Accessed 19 August 2003]. 2. Monteiro CA, Cavalcante TM, Moura EC, Claro RM, Szwarcwald CL. Populationbased evidence of a strong decline in the prevalence of smokers in Brazil (1989e2003). Bull World Health Organ 2007;85(7):527e34. https://doi.org/ 10.2471/blt.06.039073. 3. da Silva ST, Martins MC, de Faria FR, Cotta RMM. Combate ao tabagismo no Brasil: a import^ ancia estrat egica das aç~ oes governamentais. Cien Saude Colet 2014;19(2):539e52. https://doi.org/10.1590/1413-81232014192.19802012. 4. Malta DC, Flor LS, MacHado  IE, Felisbino-Mendes MS, Brant LCC, Ribeiro ALP, et al. Trends in prevalence and mortality burden attributable to smoking, Brazil and federated units, 1990 and 2017. Popul Health Metr 2020;18(Suppl. 1):24. https://doi.org/10.1186/s12963-020-00215-2. 5. Pesquisa Nacional de Saúde. Painel de Indicadores ePNS. 2023. https://www. pns.icict.fiocruz.br/painel-de-indicadores-mobile-desktop/. [Accessed 14 March 2023]. 6. Giraldo-Osorio A, Moreira RS, Neto PA, Santiago-P erez MI, Rey J, Mourino N, et al. Mortality attributed to Tobacco consumption in Brazil, 2016. Rev Bras Epidemiol 2020;24:e210007. https://doi.org/10.1590/1980-549720210007. 7. Levy D, de Almeida LM, Szklo A. The Brazil SimSmoke policy simulation model: the effect of strong tobacco control policies on smoking prevalence and smoking-attributable deaths in a middle income nation. PLoS Med 2012;9(11): e1001336. https://doi.org/10.1371/journal.pmed.1001336. 8. Acosta LD, Molinatti F, Pel aez E. Comparison of mortality attributable to tobacco in selected Latin American countries. Poblac Salud Mesoam 2019;16(2): 154e74. https://doi.org/10.15517/psm.v0i0.34484. 9. Pinto MT, Pichon-Riviere A, Bardach A. The burden of smoking-related diseases in Brazil: mortality, morbidity and costs. Cad Saúde Públ 2015;31(6):1283e97. https://doi.org/10.1590/0102-311X00192013. B. Wanderlei-Flores, J. Rey-Brandariz, P.C. Rodrigues Pinto Corr^ ea et al. Public Health 229 (2024) 24e32 31 10. Corra PC, Barreto SM, Passos VM. Smoking-attributable mortality and years of potential life lost in 16 Brazilian capitals, 2003: a prevalence-based study. BMC Publ Health 2009;9:206. https://doi.org/10.1186/1471-2458-9-206. 11. Ferreira Oliveira A, Gonçalves Valente J, Leite IC. Clinical science the disease burden attributable to smoking in the State of Rio de Janeiro, Brazil in 2000. Clinics 2008;63(2):215e22. https://doi.org/10.1590/s1807-59322008000200010. 12. Ministerio de Saúde DATASUS. Propulaç~ ao residente, https://datasus.saude.gov. br/populacao-residente/; [accessed 23 April 2023]. 13. Rey J, P erez-Ríos M, Santiago-P erez MI, Gal an I, Schiaffino A, Varela-Lema L, et al. Smoking-attributable mortality in the autonomous communities of Spain, 2017. Rev Esp Cardiol 2022;75(2):150e8. https://doi.org/10.1016/j.rec.2020. 10.023. 14. P erez-Ríos M, Rey-Brandariz J, Gal an I, Fern andez E, Montes A, SantiagoP erez MI, et al. Methodological guidelines for the estimation of attributable mortality using a prevalence-based method: the STREAMS-P tool. J Clin Epidemiol 2022;147:101e10. https://doi.org/10.1016/j.jclinepi.2022.03.016. 15. Thun MJ, Carter BD, Feskanich D, Freedman ND, Prentice R, Lopez AD, et al. 50Year trends in smoking-related mortality in the United States. N Engl J Med 2013;368(4):351e64. https://doi.org/10.1056/NEJMsa1211127. 16. Ahmad OB, Boschi-Pinto C, Lopez Christopher AD, Murray JL, Lozano R, Inoue M. Age standardization of rates: a new WHO standard.https://cdn.who.int/ media/docs/default-source/gho-documents/global-health-estimates/gpe_ discussion_paper_series_paper31_2001_age_standardization_rates.pdf, 2001. [Accessed 21 December 2023]. 17. da Silva PA, Meucci RD, Luzini R, Paiva LA. Tobacco smoking prevalence and associated factors among the elderly in the rural area of Rio Grande/RS, Brazil. Rural Rem Health 2019;19(4):4982. https://doi.org/10.22605/RRH4982. 18. Santos CE. Anu ario Brasileiro do Tabaco. Santa Cruz do Sul.https://www. editoragazeta.com.br/sitewp/wp-content/uploads/2021/01/TABACO_2020_ DUPLAS.pdf, 2020. [Accessed 21 December 2023]. 19. Grupo Amanh~ a. Quem s~ ao os maiores produtores de tabaco no Brasil.https:// amanha.com.br/categoria/agronegocio/quem-sao-os-maiores-produtores-detabaco-no-brasil, 2023. [Accessed 11 July 2023]. 20. P erez-Ríos M, Schiaffino A, Montes A, Fern andez E, L opez MJ, MartínezS anchez JM, et al. Smoking-attributable mortality in Spain in 2016. Arch Bronconeumol 2020;56(9):559e63. https://doi.org/10.1016/j.arbr.2020.07.005. 21. Rostron B. Smoking-attributable mortality by cause in the United States: revising the CDC's data and estimates. Nicotine Tob Res 2013;15(1):238e46. https://doi.org/10.1093/ntr/nts120. 22. Wanderley-Flores B, P erez-Ríos M, Montes A, Santiago-P erez MI, VarelaLema L, Candal-Pedreira C, et al. Attributable mortality to tobacco consumption in Brazil, 1996e2019. Gac Sanit 2023;37:102297. https://doi.org/10.1016/ j.gaceta.2023.102297. 23. Rey-Brandariz J, P erez-Ríos M, Santiago-P erez MI, Gal an I, Schiaffino A, VarelaLema L, et al. Trends in smoking-attributable mortality in Spain: 1990e2018. Eur J Public Health 2022;32(6):919e25. https://doi.org/10.1093/eurpub/ckac165. 24. Gonçalves-Macedo L, Lacerda EM, Markman-Filho B, Lundgren FLC, Luna CF. Trends in morbidity and mortality from COPD in Brazil, 2000 to 2016. J Bras Pneumol 2019;45(6):e20180402. https://doi.org/10.1590/1806-3713/e20180402. 25. Instituto Brasileiro de Geografia e Estatística (IBGE). Síntese de Indicadores Sociais. Uma an alise das Condiç~ oes de Vida da Populaç~ ao Brasileira.https:// biblioteca.ibge.gov.br/visualizacao/livros/liv101979.pdf, 2022. [Accessed 21 December 2023]. 26. Moreira MAC, Barbosa MA, Jardim JR, Queiroz MCC, Lu In acio. Doença pulmonar obstrutiva cr^ onica em mulheres expostas  a fumaça de fog~ ao  a lenha. Rev Assoc Med Bras 2013;59(6):607e13. https://doi.org/10.1016/j.ramb.2013. 09.001. 27. Salvi SS, Barnes PJ. Chronic obstructive pulmonary disease in non-smokers. Lancet 2009;374(9691):733e43. https://doi.org/10.1016/S0140-6736(09) 61303-9. 28. Torres KDP, Cunha GM, Valente JG. Tend^ encias de mortalidade por doença pulmonar obstrutiva cr^ onica no Rio de Janeiro e em Porto Alegre, 1980e2014. Epidemiol Serv Saude 2018;27(3):e2017139. https://doi.org/10.5123/S167949742018000300013. 29. Geraç~ ao das usinas a biomassa bate recorde em julho ecanal Bioenergia.https:// www.canalbioenergia.com.br/geracao-das-usinas-biomassa-bate-recorde-emjulho/, 2018. [Accessed 19 April 2023]. 30.  Area de vigil^ ancia do  obito da GCIAE/MS. Qualidade da informaç~ ao sobre  obitos em municípios do Brasil: reconhecimento das diferenças e  areas com maior precariedade. https://svs.aids.gov.br/daent/centrais-de-conteudos/eventos/ 2020/IVideoVO/qualidade-informacao-mortalidade-Brasil.pdf [accessed 20 May 2023]. 31. Jacobs EJ, Newton CC, Carter BD, Feskanich D, Freedman ND, Prentice RL, et al. What proportion of cancer deaths in the contemporary United States is attributable to cigarette smoking? Ann Epidemiol 2015;25(3):179e82. https:// doi.org/10.1016/j.annepidem.2014.11.008. 32. Da Silva Pinheiro D, Veiga Jardim PCB. Mortality due to ischemic heart disease in Brazil eNortheast disparities. Arq Bras Cardiol 2021;117(1):61e2. https:// doi.org/10.36660/abc.20210419. 33. Rey-Brandariz J, Ravara S, L opez-Vizcaíno E, Santiago-P erez MI, RuanoRavina A, Candal-Pedreira C, et al. Smoking-attributable mortality in Portugal and its regions in 2019. Pulmonology 2023. https://doi.org/10.1016/j.pulmoe. 2023.09.006. 34. De Almeida J, Gonçalo S, Augusto F, Fronteira I, Hern andez-Quevedo C. Portugal: health system review. Health Syst Transit 2017;19(2):1e184. 35. Bardach A, Perdomo HA, G andara RA, Ciapponi A. Niveles de ingreso y prevalencia de tabaquismo en Am erica Latina: revisi on sistem atica y metaan alisis. Rev Panam Salud Publ 2016;40(4):263e71. 36. Greenland S, Robins JM. Conceptual problems in the definition and interpretation of attributable fractions. Am J Epidemiol 1988;128(6):1185e97. https:// doi.org/10.1093/oxfordjournals.aje.a115073. 37. Queiroz BL, De Araujo FreireI FHM, Gonzaga MR, De Lima EEC. Estimativas do grau de cobertura e da mortalidade adulta (45q15) para as unidades da federaç~ ao no Brasil entre 1980e2010. Rev Bras Epidemiol 2017;20:21e33. https:// doi.org/10.1590/1980-5497201700050003. 38. Antunes Paes N. Avaliaç~ ao da cobertura dos registros de  obitos dos Estados brasileiros em 2000. Rev Bras Epidemiol 2005;39(6):882e90. https://doi.org/ 10.1590/S0034-89102005000600003. B. Wanderlei-Flores, J. Rey-Brandariz, P.C. Rodrigues Pinto Corr^ ea et al. Public Health 229 (2024) 24e32 32