{"paper_id":"e4e4cb4f-85f6-4988-be93-dcb010050576","body_text":"Smoking-attributable burden of chronic obstructive pulmonary disease from 1990 to 2021: Temporal trends and evidence from the global burden of disease study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Smoking-attributable burden of chronic obstructive pulmonary disease from 1990 to 2021: Temporal trends and evidence from the global burden of disease study Tianqi Ma, Xianfeng Yue, Shuyu Rong, Rongqian Sun, Junyao Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7797060/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Chronic obstructive pulmonary disease (COPD) is a leading cause of death and disability worldwide, with smoking being the primary contributor. This study aims to assess the temporal and spatial trends in the burden of smoking-related COPD from 1990 to 2021 and project future trajectories. The results aim to provide insights and methodological references for COPD prevention and control strategies. Methods The data were sourced from the Global Burden of Disease (GBD) 2021 database, incorporating estimates and uncertainty intervals ( UI ) for deaths, disability-adjusted life years (DALYs), and age-standardized rates (ASRs) of smoking-related COPD across 204 countries and regions worldwide. The present study employed a variety of statistical methodologies, including estimated annual percentage change (EAPC) to measure trend shifts, frontier analysis to assess the association between the Socio-demographic Index (SDI) and COPD disease burden, decomposition analysis to clarify the impacts of population aging, population growth, and epidemiological changes, and Bayesian age-period-cohort (BAPC) modeling for future disease burden projections. Results From 1990 to 2021, global smoking-related COPD deaths increased from 10,538 (95% UI : 8,724 − 12,339) hundred to 13,350 (95% UI : 10,533 − 15,966) hundred, and DALYs rose from 23,601 (95% UI : 19,648 − 27,495) thousand to 27,795 (95% UI : 22,234 − 32,884) thousand. However, both age-standardized mortality rates (ASMR) and age-standardized disability-adjusted life years rates (ASDR) demonstrated a decline across most regions. The largest decreases were observed in High-middle SDI regions. Males consistently bore a higher burden than females. The burden increased with age, peaking at 70–74 years. Aging and population growth were the main contributors to the rise in DALYs, while epidemiological changes had a negative effect. Projections indicate a continued decline in ASMR and ASDR through 2040. Conclusions Despite global progress in reducing the ASRs burden of smoking-related COPD, the absolute burden continues to rise. The findings underscore the necessity for targeted public health interventions in these regions, with a focus on enhancing tobacco control policies, improving healthcare access, and addressing age- and gender-specific risk factors. Chronic obstructive pulmonary disease smoking Global Burden of Disease socio-demographic index age-standardized rates Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Chronic obstructive pulmonary disease (COPD) is a preventable and treatable lung disorder characterized by persistent respiratory symptoms and spirometry-verified progressive airflow limitation [ 1 , 2 ]. Development of COPD has been associated with an aberrant pulmonary inflammatory response to inhaled noxious particles and gases [ 3 ]. Recent global estimates attribute more than three million deaths per year to COPD, underscoring its substantial contribution to global mortality [ 4 ]. As prevalence continues to climb, COPD has moved from the fourth to the third leading cause of death worldwide and, by 2020, has already surpassed 400 million cases—ten years earlier than the World Health Organization’s original projection for 2030 [ 1 , 5 , 6 ]. Numerous factors contribute to COPD development, including air pollution, occupational exposures, genetic predisposition, recurrent respiratory infections, and socioeconomic factors, all of which play critical roles in disease onset and progression [ 7 ]. Major risk factors for COPD remain widespread worldwide. An estimated 2 billion people are exposed to emissions from biomass-fuel combustion, 1 billion to ambient (outdoor) air pollution, and about 1 billion are current smokers—thereby exposing a comparable number of nonsmokers to secondhand smoke [ 8 ]. Environmental and occupational hazards together with tobacco use are the predominant risk factors for chronic respiratory diseases (CRDs), with smoking constituting the single most important modifiable driver of COPD. The distribution of tobacco smoking exposures varies by geography, culture, age, and sex [ 9 ]. Tobacco smoke is a complex aerosol of particulate tar and reactive gases that deposits throughout the respiratory tract depending on particle size, with smaller particles reaching peripheral bronchioles and alveoli and initiating chronic injury of the airway and parenchyma [ 10 ]. The chemical constituents drive oxidative stress, persistent airway inflammation, mucociliary dysfunction, and defective epithelial repair, fostering infection, small airway remodeling, and the progressive airflow limitation that typifies COPD [ 11 ]. Although the effects of tobacco smoking on COPD have been widely studied, notable gaps persist—particularly the scarcity of long-term spatiotemporal analyses across diverse geographies and Socio-demographic Index (SDI) strata, as well as forward-looking burden projections [ 12 , 13 ]. Much of the literature relies on localized or short-duration data and lacks the comprehensive global and regional assessments needed to guide targeted public-health action. Leveraging data from the Global Burden of Disease (GBD) 2021 study, we quantify temporal and spatial trends in smoking-attributable COPD burden from 1990 to 2021, delineate heterogeneity by sex, age, region, and SDI level, and project future burden. The results of this study would provide policy-relevant evidence to inform the design and targeting of tobacco-control and respiratory-health interventions worldwide. 2. Methods 2.1 Overview Data were obtained from the GBD 2021 database, a comprehensive, systematic resource covering 371 diseases and injuries and 88 risk factors[ 14 ]. Estimates with 95% uncertainty intervals ( UIs ) for smoking-attributable deaths, disability-adjusted life years (DALYs), and age-standardized rates (ASRs) for COPD were extracted. ASRs include the age-standardized mortality rates (ASMR) and age-standardized disability-adjusted life years (ASDR). Analyses were performed for 5-year age groups from 30 to 95 years old, with an additional category for ≥ 95 years. 2.2 Definition of smoking and COPD According to GBD 2021, smoking is defined as current using any kind of combustible tobacco product, whether daily or occasional. COPD is defined according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria, with a forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) ratio of less than 0.7 following a spirometry test. The International Classification of Diseases (ICD) codes employed by the GBD study include J41, J42, J43, J44, and J47 (ICD-10 codes), as well as 491, 492, and 496 (ICD-9 codes), for the identification of cases related to COPD. In addition to the GOLD criteria, the GBD also takes into account a variety of alternative diagnostic standards. These include pre-bronchiectasis GOLD criteria, lower limit of normal (LLN), and European Respiratory Society (ERS) guidelines. 2.3 Socio-demographic Index (SDI) SDI is a composite measure of overall socioeconomic development, scaled from 0 to 1, with higher values indicating greater development [ 15 ]. The present study employed SDI to examine the relationship between the burden of smoking- attributable COPD and the degree of socioeconomic development. The dataset includes 204 countries and regions, stratified into five distinct regions based on SDI levels: Low (SDI ≤ 0.47), Low-middle (0.47 < SDI ≤ 0.62), Middle (0.62 < SDI ≤ 0.71), High-middle (0.71 < SDI ≤ 0.81), and High (SDI > 0.81) [ 16 ]. In addition, the classification system is segmented into 21 GBD regions, which are distinguished for geographic comparisons. 2.4 Time-trend analysis Temporal trends in ASMR and ASDR were quantified using the estimated annual percentage change (EAPC) [ 17 , 18 ]. For each location, a linear model was fitted to the natural logarithm of the ASR over calendar year, The following equation was employed to estimate the EAPC: $$\\:ln\\left(ASR\\right)=\\alpha\\:+\\beta\\:\\times\\:Year+ϵ$$ $$\\:EAPC=100\\times\\:\\left({e}^{\\beta\\:}-1\\right)$$ The 95% confidence interval ( CI ) for the EAPC was derived from the standard error of β . Specifically, if the lower limit entirely above or below 0 indicated a significant increase or decrease; otherwise, the trend was considered stable. 2.5 Frontier analysis Frontier analysis was conducted in order to examine the non-linear association between the smoking-attributable COPD burden, measured by ASDR, and the SDI. Unlike conventional regression, this approach constructs an efficiency frontier representing the lowest theoretically attainable ASDR at each SDI level. For each country or territory, the distance to the frontier quantifies the gap between the observed burden and the potential minimum, indicating the scope for further reduction. 2.6 Decomposition analysis Das Gupta decomposition method was employed to disaggregate the changes in smoking-related COPD burden between 1990 and 2021 into the effects of aging, population growth, and epidemiologic changes [ 19 , 20 ]. This methodological approach enabled the disentangling of the overall variation in disease burden and the quantification of the independent effect of each factor. A thorough examination of these trends has enabled us to develop a more profound understanding of the potential factors that may lead to changes in the global burden of smoking-induced COPD. 2.7 Bayesian Age-Period-Cohort (BAPC) To estimate and forecast the future burden of smoking-attributable COPD, the BAPC model was applied. Parameter was estimated via the Integrated Nested Laplace Approximations (INLA) method in R, incorporating the effects of age, period, and birth cohort. A comparison of the BAPC approach with traditional models reveals that it offers enhanced stability and improved long-term predictive performance, particularly in scenarios involving sparse or complex data [ 21 ]. The ASMR and ASDR projections extend through the year 2040, with the results reported as medians accompanied by their respective 95% CI. 2.8 Statistical analysis Analyses were stratified by sex, age group, SDI, country, and GBD region. The objective was to elucidate variation in the burden of smoking-induced COPD among different regions and demographics. Unless otherwise specified, point estimates were presented with 95% UIs (GBD outputs) or 95% CIs (model-based statistics such as EAPC). All analyses and graphical presentations were performed using the R software program (Version 4.4.2). 3. Results 3.1 Global burden and trends of smoking-related COPD From 1990 to 2021, the global deaths and DALYs attributable to smoking-related COPD exhibited a gradual upward trajectory, increased by 27% and 18%, respectively (Fig. 1 A and B). Deaths rose from 10,538 (95% UI : 8,724 − 12,339) hundred in 1990 to 13,350 (95% UI : 10,533 − 15,966) hundred in 2021, while DALYs increased from 23,601 (95% UI : 19,648 − 27,495) thousand to 27,795 (95% UI : 22,234 − 32,884) thousand (Table 1 ). Despite the rising counts, both ASMR and ASDR decline over time (Fig. 1 C and D). In 2021, the global ASMR and ASDR were 16.12 (95% UI : 12.68–19.29) and 325.52 (95% UI : 260.32-385.52) per 100,000 people, respectively. EAPC was − 2.26 (95% CI : -3.37 to -2.16) for ASMR and − 2.31 (95% CI : -2.39 to -2.23) for ASDR, indicating sustained downward trajectory (Table 1 ). Table 1 The global burden of COPD attributable to smoking in 1990 and 2021 and the temporal trends during 1990–2021 location 1990 2021 EAPC Death cases No.×10 2 (95% UI ) ASMR per 10 5 No. (95% UI ) DALYs No.×10 3 (95% UI ) ASDR per 10 5 No. (95% UI ) Death cases No.×10 2 (95% UI ) ASMR per 10 5 No. (95% UI ) DALYs No.×10 3 (95% UI ) ASDR per 10 5 No. (95% UI ) ASMR No. (95% CI ) ASDR No. (95% CI ) Global 10538 (8724,12339) 29.87 (24.55,35.06) 23601 (19648,27495) 619.26 (513.93,721.20) 13350 (10533,15966) 16.12 (12.68,19.29) 27795 (22234,32884) 325.52 (260.32,385.52) -2.26 (-2.37,-2.16) -2.31 (-2.39,-2.23) Sex Male 8570 (7184,10049) 58.50 (48.95,68.45) 19347 (16359,22615) 1153.6 (974.89,1350.10) 11029 (8710,13251) 31.20 (24.43,37.48) 22843 (18383,26947) 595.27 (478.16,703.87) -2.28 (-2.37,-2.18) -2.36 (-2.44,-2.29) Female 1967 (1377,2586) 9.98 (7.00,13.12) 4253 (3087,5495) 205.55 (149.09,265.86) 2320 (1636,3161) 4.94 (3.49,6.73) 4952 (3630,6491) 106.2 (77.92,138.98) -2.62 (-2.80,-2.44) -2.39 (-2.52,-2.26) SDI Regions High SDI 1405 (1148,1657) 12.31 (10.07,14.54) 3321 (2725,3899) 295.46 (242.74,346.62) 1736 (1323,2166) 7.43 (5.73,9.18) 4040 (3160,4936) 190.05 (149.49,230.91) -1.72 (-1.79,-1.66) -1.44 (-1.48,-1.40) High-middle SDI 2983 (2449,3514) 34.18 (27.66,40.46) 6302 (5250,7368) 662.96 (551.54,779.67) 2863 (2188,3522) 14.74 (11.25,18.09) 5562 (4429,6757) 281.31 (223.53,341.61) -3.30 (-3.53,-3.07) -3.30 (-3.48,-3.11) Middle SDI 3989 (3296,4726) 50.26 (41.30,59.30) 8779 (7306,10388) 953.07 (788.04,1129.47) 4816 (3679,5924) 21.02 (16.13,25.85) 9510 (7477,11518) 379.97 (297.64,460.97) -3.20 (-3.34,-3.06) -3.31 (-3.42,-3.19) Low-middle SDI 1764 (1333,2188) 36.26 (27.54,45.56) 4225 (3206,5215) 757.26 (579.41,938.32) 3290 (2615,4005) 27.47 (21.81,33.52) 7193 (5755,8715) 542.25 (432.86,659.20) -0.75 (-0.87,-0.63) -0.99 (-1.08,-0.91) Low SDI 392 (282,498) 23.09 (16.64,29.70) 960 (702,1211) 475.7 (351.51,602.07) 639 (479,803) 16.97 (12.71,21.25) 1475 (1136,1842) 335.21 (255.29,419.43) -0.79 (-0.97,-0.61) -1.07 (-1.18,-0.95) GBD Regions Andean Latin America 7 (5,9) 3.95 (2.99,5.01) 14 (11,18) 75.99 (57.88,95.34) 12 (8,16) 2.16 (1.53,2.9) 25 (18,32) 42.91 (31.06,56.7) -1.56 (-1.68,-1.43) -1.55 (-1.64,-1.46) Australasia 28 (22,35) 11.59 (9.12,14.30) 64 (51,77) 264.05 (208.46,319.57) 27 (20,36) 4.6 (3.35,6.03) 57 (43,73) 104.24 (78.53,132.94) -3.17 (-3.39,-2.95) -3.16 (-3.36,-2.97) Caribbean 14 (11,18) 6.01 (4.68,7.44) 34 (27,41) 132.83 (105.02,161.49) 31 (23,39) 5.69 (4.25,7.17) 70 (53,87) 128.60 (98.71,160.48) -0.37 (-0.61,-0.13) -0.29 (-0.52,-0.07) Central Asia 49 (40,57) 11.11 (9.11,13.07) 125 (104,145) 264.51 (219.27,307.91) 48 (38,58) 6.61 (5.28,8.04) 128 (103,153) 157.67 (126.36,190.87) -1.93 (-2.17,-1.68) -2.10 (-2.37,-1.83) Central Europe 190 (156,225) 13.06 (10.65,15.60) 481 (402,560) 319.05 (265.76,371.73) 145 (112,177) 6.23 (4.83,7.59) 380 (298,456) 174.27 (137.45,208.09) -2.17 (-2.29,-2.05) -1.76 (-1.87,-1.65) Central Latin America 77 (62,94) 11.71 (9.20,14.19) 160 (129,192) 215.18 (172.68,258.59) 119 (89,151) 5.08 (3.80,6.45) 240 (185,302) 99.13 (76.45,125.12) -2.97 (-3.19,-2.75) -2.82 (-3.03,-2.61) Central Sub-Saharan Africa 13 (9,18) 7.12 (4.72,9.84) 37 (25,51) 169.46 (114.78,231.53) 21 (14,29) 4.57 (3.13,6.37) 64 (45,87) 116.40 (83.45,158.43) -1.43 (-1.61,-1.25) -1.20 (-1.37,-1.03) East Asia 5457 (4464,6542) 93.29 (74.65,111.22) 11404 (9353,13624) 1583.70 (1290.64,1885.68) 5757 (4272,7285) 30.56 (22.68,38.48) 10446 (8000,12958) 508.68 (387.70,633.16) -4.07 (-4.27,-3.88) -4.09 (-4.25,-3.93) Eastern Europe 360 (307,410) 13.08 (11.09,14.98) 893 (769,1016) 313.29 (269.19,356.86) 171 (139,203) 4.74 (3.87,5.63) 452 (372,532) 129.52 (106.65,152.50) -4.20 (-4.59,-3.80) -3.82 (-4.23,-3.42) Eastern Sub-Saharan Africa 51 (38,65) 9.12 (6.67,11.64) 131 (99,164) 194.05 (146.05,243.35) 64 (47,82) 4.88 (3.57,6.36) 180 (135,229) 113.96 (85.12,145.17) -2.25 (-2.34,-2.16) -1.93 (-2.02,-1.85) High-income Asia Pacific 125 (102,145) 6.92 (5.65,8.10) 300 (248,351) 154.94 (128.12,181.40) 170 (127,217) 2.68 (2.05,3.38) 375 (292,469) 73.13 (57.66,90.80) -3.27 (-3.45,-3.08) -2.44 (-2.52,-2.36) High-income North America 478 (384,576) 12.92 (10.39,15.54) 1291 (1039,1537) 363.95 (293.81,431.59) 796 (593,1007) 11.32 (8.50,14.27) 2024 (1565,2509) 303.03 (235.42,374.47) -0.50 (-0.68,-0.32) -0.61 (-0.76,-0.45) North Africa and Middle East 180 (141,219) 13.22 (10.27,16.18) 469 (373,562) 294.16 (232.55,356.10) 320 (247,391) 8.79 (6.71,10.72) 851 (668,1033) 199.59 (156.74,242.97) -1.25 (-1.38,-1.12) -1.24 (-1.32,-1.16) Oceania 9 (6,12) 36.84 (25.39,50.63) 23 (16,32) 818.89 (570.83,1114.45) 15 (11,20) 24.23 (17.43,32.73) 40 (30,54) 549.31 (402.65,734.15) -1.53 (-1.62,-1.44) -1.45 (-1.54,-1.36) South Asia 1941 (1408,2440) 44.25 (32.42,55.84) 4671 (3458,5793) 911.90 (672.62,1136.61) 3703 (2875,4588) 30.26 (23.48,37.23) 8051 (6283,9895) 591.70 (464.04,727.28) -1.06 (-1.21,-0.91) -1.31 (-1.41,-1.21) Southeast Asia 543 (428,655) 27.30 (21.39,33.00) 1275 (1015,1522) 555.44 (439.24,666.67) 922 (743,1109) 17.12 (13.84,20.63) 2159 (1768,2588) 353.74 (288.47,423.88) -1.68 (-1.82,-1.53) -1.62 (-1.73,-1.50) Southern Latin America 40 (31,48) 8.72 (6.82,10.75) 100 (80,119) 213.15 (170.12,254.80) 53 (39,68) 5.91 (4.37,7.50) 121 (92,149) 138.28 (105.51,170.25) -1.12 (-1.41,-0.83) -1.31 (-1.54,-1.08) Southern Sub-Saharan Africa 35 (28,42) 15.41 (12.30,19.09) 91 (73,110) 355.30 (285.70,428.86) 45 (36,56) 9.06 (7.02,11.40) 129 (103,158) 228.28 (180.35,280.24) -2.03 (-2.40,-1.66) -1.67 (-1.98,-1.36) Tropical Latin America 167 (136,198) 22.20 (17.91,26.79) 383 (316,444) 449.05 (367.72,524.07) 202 (152,257) 8.12 (6.08,10.37) 450 (343,564) 176.20 (133.86,221.14) -3.79 (-4.08,-3.49) -3.60 (-3.89,-3.32) Western Europe 747 (609,877) 12.18 (9.92,14.31) 1579 (1294,1840) 266.31 (218.94,309.97) 695 (520,870) 6.39 (4.88,7.90) 1440 (1115,1756) 151.56 (118.88,183.30) -2.07 (-2.15,-1.99) -1.78 (-1.85,-1.71) Western Sub-Saharan Africa 27 (19,35) 3.71 (2.60,4.88) 75 (54,98) 88.25 (63.65,115.68) 35 (26,45) 2.21 (1.58,2.85) 113 (83,142) 57.95 (42.45,73.13) -1.52 (-1.60,-1.44) -1.26 (-1.32,-1.20) ASMR, age-standardized mortality rate; ASDR, age-standardized DALYs rate; DALYs, disability-adjusted life years; EAPC, estimated annual percentage change; UI, Uncertainty interval; SDI, Socio-demographic Index; CI, Confidence interval 3.2 Regional burden and trends of smoking-related COPD In 1990, the burden of smoking-related COPD was primarily concentrated in Middle and High-middle SDI region (Table 1 and Fig. 1 ). By 2021, deaths and DALYs increased in all SDI regions except for the High-middle SDI region. The most substantial increase was observed in Low-middle SDI region, which rose to rank second, following Middle SDI region (Table 1 and Fig. 1 A and B). The highest ASMR and ASDR in 2021 were observed in Low-middle SDI region, with values of 27.47 (95% UI : 21.81–33.52) and 542.25 (95% UI : 432.86–659.20) per 100,000 population, respectively (Table 1 and Fig. 1 C and D). From 1990 to 2021, the EAPCs of ASMR were negative in all SDI regions. The most pronounced decline was observed in High-middle SDI region, with EAPC of -3.30 (95% CI : -3.53 to -3.07) (Table 1 ). Among the 21 GBD regions, both in 1990 and 2021, East Asia and South Asia exhibited a substantially higher burden of COPD attributable to smoking compared to other regions (Table 1 and Fig. 2 A and B). In 2021, East Asia reported the highest deaths (5,757 hundred; 95% UI : 4,272-7,285) and DALYs (10,446 thousand; 95% UI : 8,000–12,958), followed by South Asia with 3,703 (95% UI : 2,875-4,588) hundred deaths and 8,051 (95% UI : 6,283-9,895) thousand DALYs. With respect to ASR, East Asia exhibited the highest ASMR and ASDR in 1990 (Table 1 ). By 2021, East Asia maintained its position as the region with the highest ASMR, while South Asia surpassed East Asia in terms of ASDR. Conversely, Andean Latin America consistently exhibited the lowest burden in both 1990 and 2021, across all indicators (Table 1 ). The EAPCs for both ASMR and ASDR were negative in all GBD regions. East Asia demonstrated the most significant decrease in ASDR, exhibiting an EAPC of -4.09 (95% CI : -4.25 to -3.93). This was followed by Eastern Europe (Table 1 ). 3.3 National burden and trends of smoking-related COPD With respect to national burden, China and India had the highest smoking-related COPD deaths and DALYs in 1990 and remained consistent in 2021. In 2021, DALYs reached 101,874 (95% UI : 77,472 − 126,914) thousand in China and 66,816 (95% UI : 51,993 − 83,099) thousand in India (Table S2). Relative to 1990, China experienced a 9.4% decrease in DALYs, whereas India showed an 82.7% increase. For ASMR, China had the highest ASMR in 1990 (96.37 per 100,000; 95% UI : 77.06-115.04), followed by Nepal (88.39 per 100,000; 95% UI : 61.21-118.16) and Myanmar (73.92 per 100,000; 95% UI : 53.54–97.36). By 2021, Nepal ranked first in ASMR, followed by Myanmar and Kiribati (Table S6 and Fig. 2 A). Most countries exhibited declines in ASMR and ASDR over time (Table S1 and Fig. 2 C and D). The largest reductions were observed in Singapore, Ukraine, and Belarus, whereas Georgia experienced the largest increase, indicating substantial heterogeneity in global progress (Table S1 0 and S11). 3.4 Patterns of smoking-related COPD by sex and age Burden differed by sex throughout 1990 to 2021, with males consistently exceeding females in deaths and DALYs (Table 1 and Fig. 3 A and B). Males deaths increased from 8,570 (95% UI : 7,184 − 10,049) hundred to 11,029 (95% UI : 8,710 − 13,251) hundred. Females deaths rose modestly from 1,967 (95% UI : 1,377-2,586) hundred to 2,320 (95% UI : 1,636-3,161) hundred. ASMR and ASDR decreased in both sexes, with steeper declines in females, as reflected by larger absolute EAPCs (Table 1 ). Age patterns showed that DALYs increased gradually after the age of 40, peaking at 70–74 years and the declining. Similarly, both ASMR and ASDR demonstrated an increase with age, reaching a peak in the 90–94 age group (Fig. 3 C and D). 3.5 Frontier analysis and decomposition analysis Frontier analysis illustrated each location’s distance to the efficiency frontier for ASDR given its SDI from 1990 to 2021. The distance generally decreased with higher SDI, implying that low SDI countries still have more potential to improvement (Fig. 4 A). In 2021, among lower-SDI countries, Somalia, Burkina Faso, Niger, Ethiopia, and Benin were closest to the frontier, indicating relatively strong performance at their development level. In contrast, among the countries with higher SDI, Denmark, the United States, the United Kingdom, the Netherlands, and Belgium are the furthest from the frontier, suggesting more room for reduction than expected for their SDI. The 15 most distant locations included Nepal, Kiribati, Myanmar, Papua New Guinea, North Korea, and others (Fig. 4 B). Decomposition analysis revealed that aging and population growth contributed to global DALYs increases by 118.70% and 417.74%, respectively (Table S12). Aging had a positive impact in all SDI regions, except Low SDI region. Population growth was positive in all five SDI regions. Conversely, epidemiological changes exerted a deleterious effect on global DALYs, contributing to a -436.43% globally, a pattern consistent across strata. All three components had the largest impacts in Middle SDI, underscoring this stratum as a priority for targeted interventions (Table S12 and Fig. 5 ). 3.6 Future projections of smoking-related COPD burden The BAPC model projected continued decline in smoking-related COPD burden over the next 19 years (Fig. 6 ). By 2040, ASMR is predicted to decline to 11.77 per 100,000 population and ASDR to 242.11 per 100,000 population (Table S13 and S14). By sex, ASDR in males is estimated to decrease by 24.3%, while females are projected to experience a 36.44% decline, indicating a greater relative improvement in women. 4. Discussion COPD is one of the leading causes of morbidity and mortality worldwide. Smoking is the primary modifiable risk factor for COPD. Despite sustained declines in ASMR and the ASDR for smoking-attributable COPD since 1990, absolute deaths and DALYs increased. Decomposition analysis attributed this divergence primarily to demographic change, population growth and population ageing. Marked heterogeneity by development level, geography, sex, and age underscores the need for context-specific prevention and control strategies. A previous GBD study assessed the burden of smoking-related COPD and reported declining ASDR in nearly all SDI regions. Although the authors did not explicitly state the global trend, the figures in their report indicate a downward trajectory [ 22 ]. Our findings are consistent with this pattern, showing a continued decline in the global ASDR of smoking-related COPD between 1990 and 2021. This study also noted that the burden of smoking-related COPD was greater in males and increased with age, peaking at 85–89 years. Similarly, our study found that males experienced a substantially higher burden. However, we observed that the global ASDR peaked earlier, at 70–74 years. This variation may be due to differences in the study period, data updates, and methodological approaches. Compared with earlier studies, our analysis incorporates the most recent data and provides a more nuanced assessment by incorporating advanced analytical approaches, including BAPC modeling and decomposition analysis, providing a more up-to-date and comprehensive view of global and regional trends. At the global level, declining ASMR and ASDR are consistent with strengthened tobacco-control policies, earlier detection, and improvements in COPD management in many settings [ 23 – 28 ]. Despite these global trends, Nepal's ASMR and ASDR remained high in both 1990 and 2021, likely due to the persistent prevalence of smoking and the ongoing socioeconomic challenges experienced by the nation [ 29 , 30 ]. Nevertheless, absolute burden increased, particularly in Low-middle and Middle SDI strata, where rapid demographic expansion and ageing offset epidemiologic gains. These patterns align with the decomposition findings, in which population growth and ageing contributed positively to DALY changes, whereas epidemiologic change contributed negatively, suggesting real—but insufficient—progress against risk and disease severity. The results of the decomposition analysis further support this conclusion. Inadequate tobacco control measures, limited access to healthcare, and delays in COPD diagnosis and treatment may also contribute to the growing absolute burden in settings with limited resources [ 31 ]. Although East Asia and South Asia have made notable strides toward lowering the burden of COPD, they still account for the highest number of deaths and DALYs globally. This persistent burden can be attributed to high smoking prevalence, aging populations, and challenges in healthcare infrastructure and early COPD diagnosis [ 32 ]. While tobacco control efforts have been strengthened in some countries, continued attention to public health strategies and targeted measures is crucial to further alleviating the disease burden in these regions. It is important to note that China has experienced a decrease in the number of DALYs attributable to smoking-related COPD in recent years, largely due to stricter tobacco regulations and heightened public health awareness [ 33 ]. Nevertheless, China continues to report the highest number of deaths and DALYs, reflecting its large population and aging population. In contrast, India has experienced a significant surge in both deaths and DALYs. This contrast can be attributed to differences in smoking prevalence trends, healthcare access, and the execution of tobacco control policies. Despite India's noteworthy advancements in tobacco control, bidi smoking remains widespread in certain regions, accounting for about 85% of total smoked tobacco consumption [ 34 ]. Compared to conventional cigarettes, bidis contain elevated concentrations of nicotine and tar [ 35 , 36 ], and are more strongly associated with COPD risk [ 37 ]. Their predominant use among low-income and rural populations [ 38 ], coupled with limited healthcare access and high out-of-pocket costs [ 39 ], exacerbates delayed diagnosis and poor outcomes. It is imperative that concerted efforts are made in public health education, smoking cessation programs, and enhancing healthcare accessibility in order to address this growing problem. Sex- and age-specific patterns were stable and biologically plausible. Males bore higher burdens than females across outcomes, consistent with greater cumulative smoking exposure in many settings [ 40 ]. Burden increased with age, with DALYs peaking in the 70–74 year group and rates (ASMR/ASDR) peaking at older ages, reflecting the combined effects of cumulative exposure, age-related lung function decline, and comorbidity [ 41 , 42 ]. These findings reinforce the need to intensify cessation among men, while expanding early detection and comprehensive, life-course COPD care for ageing populations. The frontier analysis indicates that the burden of COPD attributable to smoking generally decreases with rising levels of socioeconomic development. However, certain countries exhibit a discernible discrepancy between their observed burden and the expected levels based on the SDI. A number of low-SDI countries, including Somalia, Burkina Faso, and Ethiopia, have demonstrated a prevalence of COPD burdens that approaches the theoretical minimum predicted for their respective development levels. Despite their limited resources, these nations have maintained relatively low ASDRs, and their strategies may offer valuable insights for COPD prevention in countries with analogous socioeconomic contexts. Conversely, numerous high-SDI countries, including the United States, the United Kingdom, and Denmark, persist in exhibiting disproportionately elevated COPD burdens. The available evidence suggests that this pattern is largely attributable to the combined effects of long-term smoking and population aging. In the United States, for instance, approximately 80% of COPD cases are attributed to smoking [ 12 ]. The demographic aging observed in both the United States and the United Kingdom are associated with an increased risk of developing COPD [ 41 , 43 , 44 ]. This phenomenon is further exacerbated by the prolongation of life expectancy, which amplifies the cumulative impact of smoking exposure over time. These findings underscore the necessity of sustained tobacco control efforts and age-specific preventive strategies, even in high-SDI settings, to mitigate the future burden of COPD. Decomposition analysis identified demographic transitions as the dominant drivers of smoking-attributable COPD burden: population ageing and growth continue to push deaths and DALYs upward, only partially offset by advances in prevention, diagnosis and treatment. The disproportionate impact in Middle SDI settings signals a critical inflection point, with demand from demographic change outpacing health-system capacity [ 45 ]. Priority responses include sustained tobacco control, expanded early detection (risk-based case finding and spirometry) and accessible long-term management integrated into primary care. Implemented at scale, these measures should temper the trajectory of tobacco-related COPD in these regions. Several limitations about this study should be considered. First, reliance on GBD data introduces potential biases due to the limitations of model-based estimates, especially in regions with insufficient primary data [ 46 ]. Second, the cross-sectional nature of some data limits our ability to establish causal relationships between smoking-related COPD burden and various demographic or regional factors. Third, although decomposition clarifies the direction and relative magnitude of demographic versus epidemiologic forces, unmeasured factors (e.g., air pollution trends, occupational exposures, or care quality) may also influence trajectories. In conclusion, our study reveals substantial progress in lowering ASMR and ASDR associated with smoking-induced COPD across numerous regions, especially in countries with a high SDI. However, the absolute burden remains substantia, with counts concentrated in Low-middle and Middle SDI regions where rapid population growth and ageing predominate. Accordingly, context-tailored public-health interventions are warranted in these settings. Targeting by sex and age is warranted, given consistently higher burdens in males and older adults. Further reductions will depend on strengthening tobacco-control policies, widening healthcare access, and closing implementation gaps where demographic pressures are greatest. Abbreviations ASDR: Age-standardized DALYs rate ASMR: Age-standardized mortality rate ASR: Age-standardized rate BAPC: Bayesian age-period-cohort CI: Confidence interval COPD: Chronic obstructive pulmonary disease CRDs: Chronic respiratory diseases DALYs: Disability-adjusted life years EAPC: Estimated annual percentage change ERS: European Respiratory Society FEV1: Forced expiratory volume in one second FVC: Forced vital capacity GBD: Global Burden of Diseases GHDx: Global Health Data Exchange GOLD: Global Initiative for Chronic Obstructive Lung Disease INLA: Integrated Nested Laplace Approximations LLN: Lower limit of normal SDI: Socio-Demographic Index UI: Uncertainty interval Declarations Funding This study received financial support from the Student Research Training Program of Shandong First Medical University & Shandong Academy of Medical Sciences (No. 2024104390508; No. 2024104391194), and College Student Research Training Program of Shandong Youth & Children of Academy Educational Sciences (No. 24SSR225; 24SSR310). Role of funding source The funding source had no involvement in the research's conceptualization, data gathering, analysis, interpretation, or manuscript preparation. Authors’ contributions Tianqi Ma : Formal analysis, Data curation, Writing – original draft. Xianfeng Yue : Formal analysis, Data curation, Writing – original draft. Shuyu Rong : Methodology, Writing – review & editing. Rongqian Sun : Formal analysis. Junyao Wang : Data curation. Xin Zheng : Formal analysis. Xueyu Chen : Methodology, Validation. Rongqin Sun : Methodology, Writing – review & editing. Data sharing statement The data utilized in this analysis are accessible to the public through the Global Health Data Exchange (GHDx) at http://ghdx.healthdata.org/gbd-results-tool. Declaration of interests All authors have declared no conflicts of interest. Compliance with Ethics Requirements Ethical approval was waived by the institutional review board, as the study was based entirely on publicly available data. Acknowledgements We would like to express our profound gratitude to the collaborators of the Global Burden of Disease, Injuries, and Risk Factors Study 2021 for their invaluable contributions, as well as to the Institute for Health Metrics and Evaluation (IHME) for providing access to the GBD data. We gratefully acknowledge the funding assistance provided by the Student Research Training Program of Shandong First Medical University & Shandong Academy of Medical Sciences (No. 2024104390508; No. 2024104391194), and the College Student Research Training Program of Shandong Youth & Children of Academy Educational Sciences (No. 24SSR225; 24SSR310). References Labaki WW, Rosenberg SR: Chronic Obstructive Pulmonary Disease . Annals of internal medicine 2020, 173 (3):Itc17-itc32. Lareau SC, Fahy B, Meek P, Wang A: Chronic Obstructive Pulmonary Disease (COPD) . American journal of respiratory and critical care medicine 2019, 199 (1):P1-p2. 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Global, regional, and national burden of neck pain, 1990-2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021 . The Lancet Rheumatology 2024, 6 (3):e142-e155. Additional Declarations No competing interests reported. Supplementary Files SupplementaryAppendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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17:45:21\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":3879566,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe COPD burden attributable to smoking by SDI regions from 1990 to 2021. (A) deaths; (B) DALYs; (C) ASMR; (D) ASDR. COPD, chronic obstructive pulmonary disease; SDI, Socio-demographic Index; ASMR, age-standardized mortality rate; EAPC, estimated annual percentage change; DALYs, disability-adjusted life years; ASDR, age-standardized DALYs rate\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7797060/v1/33ecf8060e78e33e5085aab5.png\"},{\"id\":95574910,\"identity\":\"71900050-c7b7-4bfe-9718-38175440ed99\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 17:45:22\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":36527949,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe burden of COPD attributable to smoking for both sexes in 204 countries and territories. (A) ASMR in 2021; (B) ASDR in 2021; (C) The EAPC of ASMR between 1990 and 2021; (D) The EAPC of ASDR between 1990 and 2021. COPD, chronic obstructive pulmonary disease; ASMR, age-standardized mortality rate; EAPC, estimated annual percentage change; ASDR, age-standardized disability-adjusted life years rate\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7797060/v1/76dc2929b356903e7fbc9082.png\"},{\"id\":95574901,\"identity\":\"e94d85a6-42ec-417f-8451-a0ddd359dae8\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 17:45:21\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":21599955,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTemporal trends and age structure of the burden of smoking-related COPD by sex. (A) The number of deaths and ASMR from 1990 to 2021; (B) The number of DALYs and ASDR from 1990 to 2021; (C) The number of deaths and deaths rate in 2021; (D) The number of DALYs and DALYs rate in 2021. COPD, chronic obstructive pulmonary disease; ASMR, age-standardized mortality rate; DALYs, disability-adjusted life years; ASDR, age-standardized DALYs rate\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7797060/v1/81fdc8de8ab6bff583f14570.png\"},{\"id\":95655288,\"identity\":\"7326c140-ad09-417c-bf43-04371db0e837\",\"added_by\":\"auto\",\"created_at\":\"2025-11-11 16:15:08\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":5082678,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFrontier analysis of the relationship between the SDI and ASDR for smoking-related COPD. (A) Temporal shift from 1990 to 2021, with color gradients (light to dark purple) indicating changes over time; (B) Cross-sectional analysis in 2021, where each point represents a country or region. The black line denotes the efficiency frontier. Country names in black highlight the 15 countries with the largest deviations from the frontier. Names in blue denote low-SDI countries with minimal deviation, while names in red represent high-SDI countries with substantial deviation. The color of each point reflects the direction of ASDR change from 1990 to 2021: purple indicates a decrease, and red indicates an increase. SDI, Socio-demographic Index; ASDR, age-standardized disability-adjusted life years rate; COPD, chronic obstructive pulmonary disease\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7797060/v1/ab876f13acd89e7eeb9bba1a.png\"},{\"id\":95574882,\"identity\":\"5c561e6c-7b17-417c-903b-d50d5fb0df11\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 17:45:21\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":789526,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDecomposition analysis of changes in smoking-attributable COPD DALYs from 1990 to 2021. The decomposition includes contributions from population aging, population growth, and epidemiological changes. Positive values indicate an increase in DALYs due to the respective component, while negative values represent a reduction. Black dots represent the net overall change contributed by all three components combined. COPD, chronic obstructive pulmonary disease; DALYs, disability-adjusted life years; SDI, Socio-demographic Index\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7797060/v1/68761ee7b932b08b78b5e9b9.png\"},{\"id\":95656455,\"identity\":\"02becd1c-7b5b-4618-a7c9-b21f9316f7ce\",\"added_by\":\"auto\",\"created_at\":\"2025-11-11 16:18:43\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":4769284,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFuture forecasts of global burden of COPD attributable to smoking from 2022 to 2040. (A)ASMR; (B) ASDR. COPD, chronic obstructive pulmonary disease; ASMR, age-standardized mortality rate; ASDR, age-standardized DALYs rate; DALYs, disability-adjusted life years\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7797060/v1/8564ef9cca0d0900ad72030b.png\"},{\"id\":96248846,\"identity\":\"401718c7-10e5-4cc7-b775-03b5b41aa2fa\",\"added_by\":\"auto\",\"created_at\":\"2025-11-19 07:29:29\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":73870493,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7797060/v1/bf77cec5-654f-4e63-ab41-e5f8505a0fa1.pdf\"},{\"id\":95655719,\"identity\":\"2279a3b3-239e-4723-af65-570c57ff8858\",\"added_by\":\"auto\",\"created_at\":\"2025-11-11 16:16:46\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":175367,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryAppendix.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7797060/v1/635e8e8b8044fe647b1bd81e.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Smoking-attributable burden of chronic obstructive pulmonary disease from 1990 to 2021: Temporal trends and evidence from the global burden of disease study\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eChronic obstructive pulmonary disease (COPD) is a preventable and treatable lung disorder characterized by persistent respiratory symptoms and spirometry-verified progressive airflow limitation [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Development of COPD has been associated with an aberrant pulmonary inflammatory response to inhaled noxious particles and gases [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Recent global estimates attribute more than three million deaths per year to COPD, underscoring its substantial contribution to global mortality [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. As prevalence continues to climb, COPD has moved from the fourth to the third leading cause of death worldwide and, by 2020, has already surpassed 400\\u0026nbsp;million cases\\u0026mdash;ten years earlier than the World Health Organization\\u0026rsquo;s original projection for 2030 [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Numerous factors contribute to COPD development, including air pollution, occupational exposures, genetic predisposition, recurrent respiratory infections, and socioeconomic factors, all of which play critical roles in disease onset and progression [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eMajor risk factors for COPD remain widespread worldwide. An estimated 2\\u0026nbsp;billion people are exposed to emissions from biomass-fuel combustion, 1\\u0026nbsp;billion to ambient (outdoor) air pollution, and about 1\\u0026nbsp;billion are current smokers\\u0026mdash;thereby exposing a comparable number of nonsmokers to secondhand smoke [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Environmental and occupational hazards together with tobacco use are the predominant risk factors for chronic respiratory diseases (CRDs), with smoking constituting the single most important modifiable driver of COPD. The distribution of tobacco smoking exposures varies by geography, culture, age, and sex [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Tobacco smoke is a complex aerosol of particulate tar and reactive gases that deposits throughout the respiratory tract depending on particle size, with smaller particles reaching peripheral bronchioles and alveoli and initiating chronic injury of the airway and parenchyma [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. The chemical constituents drive oxidative stress, persistent airway inflammation, mucociliary dysfunction, and defective epithelial repair, fostering infection, small airway remodeling, and the progressive airflow limitation that typifies COPD [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eAlthough the effects of tobacco smoking on COPD have been widely studied, notable gaps persist\\u0026mdash;particularly the scarcity of long-term spatiotemporal analyses across diverse geographies and Socio-demographic Index (SDI) strata, as well as forward-looking burden projections [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Much of the literature relies on localized or short-duration data and lacks the comprehensive global and regional assessments needed to guide targeted public-health action. Leveraging data from the Global Burden of Disease (GBD) 2021 study, we quantify temporal and spatial trends in smoking-attributable COPD burden from 1990 to 2021, delineate heterogeneity by sex, age, region, and SDI level, and project future burden. The results of this study would provide policy-relevant evidence to inform the design and targeting of tobacco-control and respiratory-health interventions worldwide.\\u003c/p\\u003e\"},{\"header\":\"2. Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1 Overview\\u003c/h2\\u003e\\u003cp\\u003eData were obtained from the GBD 2021 database, a comprehensive, systematic resource covering 371 diseases and injuries and 88 risk factors[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Estimates with 95% uncertainty intervals (\\u003cem\\u003eUIs\\u003c/em\\u003e) for smoking-attributable deaths, disability-adjusted life years (DALYs), and age-standardized rates (ASRs) for COPD were extracted. ASRs include the age-standardized mortality rates (ASMR) and age-standardized disability-adjusted life years (ASDR). Analyses were performed for 5-year age groups from 30 to 95 years old, with an additional category for \\u0026ge;\\u0026thinsp;95 years.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2 Definition of smoking and COPD\\u003c/h2\\u003e\\u003cp\\u003eAccording to GBD 2021, smoking is defined as current using any kind of combustible tobacco product, whether daily or occasional.\\u003c/p\\u003e\\u003cp\\u003eCOPD is defined according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria, with a forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) ratio of less than 0.7 following a spirometry test. The International Classification of Diseases (ICD) codes employed by the GBD study include J41, J42, J43, J44, and J47 (ICD-10 codes), as well as 491, 492, and 496 (ICD-9 codes), for the identification of cases related to COPD. In addition to the GOLD criteria, the GBD also takes into account a variety of alternative diagnostic standards. These include pre-bronchiectasis GOLD criteria, lower limit of normal (LLN), and European Respiratory Society (ERS) guidelines.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3 Socio-demographic Index (SDI)\\u003c/h2\\u003e\\u003cp\\u003eSDI is a composite measure of overall socioeconomic development, scaled from 0 to 1, with higher values indicating greater development [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. The present study employed SDI to examine the relationship between the burden of smoking- attributable COPD and the degree of socioeconomic development. The dataset includes 204 countries and regions, stratified into five distinct regions based on SDI levels: Low (SDI\\u0026thinsp;\\u0026le;\\u0026thinsp;0.47), Low-middle (0.47\\u0026thinsp;\\u0026lt;\\u0026thinsp;SDI\\u0026thinsp;\\u0026le;\\u0026thinsp;0.62), Middle (0.62\\u0026thinsp;\\u0026lt;\\u0026thinsp;SDI\\u0026thinsp;\\u0026le;\\u0026thinsp;0.71), High-middle (0.71\\u0026thinsp;\\u0026lt;\\u0026thinsp;SDI\\u0026thinsp;\\u0026le;\\u0026thinsp;0.81), and High (SDI\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.81) [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. In addition, the classification system is segmented into 21 GBD regions, which are distinguished for geographic comparisons.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.4 Time-trend analysis\\u003c/h2\\u003e\\u003cp\\u003eTemporal trends in ASMR and ASDR were quantified using the estimated annual percentage change (EAPC) [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. For each location, a linear model was fitted to the natural logarithm of the ASR over calendar year, The following equation was employed to estimate the EAPC:\\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:ln\\\\left(ASR\\\\right)=\\\\alpha\\\\:+\\\\beta\\\\:\\\\times\\\\:Year+ϵ$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Equb\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equb\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:EAPC=100\\\\times\\\\:\\\\left({e}^{\\\\beta\\\\:}-1\\\\right)$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe 95% confidence interval (\\u003cem\\u003eCI\\u003c/em\\u003e) for the EAPC was derived from the standard error of \\u003cem\\u003eβ\\u003c/em\\u003e. Specifically, if the lower limit entirely above or below 0 indicated a significant increase or decrease; otherwise, the trend was considered stable.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.5 Frontier analysis\\u003c/h2\\u003e\\u003cp\\u003eFrontier analysis was conducted in order to examine the non-linear association between the smoking-attributable COPD burden, measured by ASDR, and the SDI. Unlike conventional regression, this approach constructs an efficiency frontier representing the lowest theoretically attainable ASDR at each SDI level. For each country or territory, the distance to the frontier quantifies the gap between the observed burden and the potential minimum, indicating the scope for further reduction.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.6 Decomposition analysis\\u003c/h2\\u003e\\u003cp\\u003eDas Gupta decomposition method was employed to disaggregate the changes in smoking-related COPD burden between 1990 and 2021 into the effects of aging, population growth, and epidemiologic changes [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. This methodological approach enabled the disentangling of the overall variation in disease burden and the quantification of the independent effect of each factor. A thorough examination of these trends has enabled us to develop a more profound understanding of the potential factors that may lead to changes in the global burden of smoking-induced COPD.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.7 Bayesian Age-Period-Cohort (BAPC)\\u003c/h2\\u003e\\u003cp\\u003eTo estimate and forecast the future burden of smoking-attributable COPD, the BAPC model was applied. Parameter was estimated via the Integrated Nested Laplace Approximations (INLA) method in R, incorporating the effects of age, period, and birth cohort. A comparison of the BAPC approach with traditional models reveals that it offers enhanced stability and improved long-term predictive performance, particularly in scenarios involving sparse or complex data [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. The ASMR and ASDR projections extend through the year 2040, with the results reported as medians accompanied by their respective 95% \\u003cem\\u003eCI.\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.8 Statistical analysis\\u003c/h2\\u003e\\u003cp\\u003eAnalyses were stratified by sex, age group, SDI, country, and GBD region. The objective was to elucidate variation in the burden of smoking-induced COPD among different regions and demographics. Unless otherwise specified, point estimates were presented with 95% \\u003cem\\u003eUIs\\u003c/em\\u003e (GBD outputs) or 95% \\u003cem\\u003eCIs\\u003c/em\\u003e (model-based statistics such as EAPC). All analyses and graphical presentations were performed using the R software program (Version 4.4.2).\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.1 Global burden and trends of smoking-related COPD\\u003c/h2\\u003e\\u003cp\\u003eFrom 1990 to 2021, the global deaths and DALYs attributable to smoking-related COPD exhibited a gradual upward trajectory, increased by 27% and 18%, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA and B). Deaths rose from 10,538 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 8,724\\u0026thinsp;\\u0026minus;\\u0026thinsp;12,339) hundred in 1990 to 13,350 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 10,533\\u0026thinsp;\\u0026minus;\\u0026thinsp;15,966) hundred in 2021, while DALYs increased from 23,601 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 19,648\\u0026thinsp;\\u0026minus;\\u0026thinsp;27,495) thousand to 27,795 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 22,234\\u0026thinsp;\\u0026minus;\\u0026thinsp;32,884) thousand (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Despite the rising counts, both ASMR and ASDR decline over time (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC and D). In 2021, the global ASMR and ASDR were 16.12 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 12.68\\u0026ndash;19.29) and 325.52 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 260.32-385.52) per 100,000 people, respectively. EAPC was \\u0026minus;\\u0026thinsp;2.26 (95% \\u003cem\\u003eCI\\u003c/em\\u003e: -3.37 to -2.16) for ASMR and \\u0026minus;\\u0026thinsp;2.31 (95% \\u003cem\\u003eCI\\u003c/em\\u003e: -2.39 to -2.23) for ASDR, indicating sustained downward trajectory (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eThe global burden of COPD attributable to smoking in 1990 and 2021 and the temporal trends during 1990\\u0026ndash;2021\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"13\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\"\\u0026minus;\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\"\\u0026minus;\\\" class=\\\"colspec\\\" colname=\\\"c13\\\" colnum=\\\"13\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003elocation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c5\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003e1990\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c10\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e2021\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c13\\\" namest=\\\"c12\\\"\\u003e\\u003cp\\u003eEAPC\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eDeath cases\\u003c/p\\u003e\\u003cp\\u003eNo.\\u0026times;10\\u003csup\\u003e2\\u003c/sup\\u003e (95% \\u003cem\\u003eUI\\u003c/em\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eASMR per 10\\u003csup\\u003e5\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eNo. (95% \\u003cem\\u003eUI\\u003c/em\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eDALYs\\u003c/p\\u003e\\u003cp\\u003eNo.\\u0026times;10\\u003csup\\u003e3\\u003c/sup\\u003e(95% \\u003cem\\u003eUI\\u003c/em\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eASDR per 10\\u003csup\\u003e5\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eNo. (95% \\u003cem\\u003eUI\\u003c/em\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eDeath cases\\u003c/p\\u003e\\u003cp\\u003eNo.\\u0026times;10\\u003csup\\u003e2\\u003c/sup\\u003e (95% \\u003cem\\u003eUI\\u003c/em\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eASMR per 10\\u003csup\\u003e5\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eNo. (95% \\u003cem\\u003eUI\\u003c/em\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003eDALYs No.\\u0026times;10\\u003csup\\u003e3\\u003c/sup\\u003e(95% \\u003cem\\u003eUI\\u003c/em\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eASDR per 10\\u003csup\\u003e5\\u003c/sup\\u003e No. (95% \\u003cem\\u003eUI\\u003c/em\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003eASMR\\u003c/p\\u003e\\u003cp\\u003eNo. (95% \\u003cem\\u003eCI\\u003c/em\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003eASDR\\u003c/p\\u003e\\u003cp\\u003eNo. (95% \\u003cem\\u003eCI\\u003c/em\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eGlobal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10538\\u003c/p\\u003e\\u003cp\\u003e(8724,12339)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e29.87\\u003c/p\\u003e\\u003cp\\u003e(24.55,35.06)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e23601\\u003c/p\\u003e\\u003cp\\u003e(19648,27495)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e619.26\\u003c/p\\u003e\\u003cp\\u003e(513.93,721.20)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e13350\\u003c/p\\u003e\\u003cp\\u003e(10533,15966)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e16.12\\u003c/p\\u003e\\u003cp\\u003e(12.68,19.29)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e27795\\u003c/p\\u003e\\u003cp\\u003e(22234,32884)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e325.52\\u003c/p\\u003e\\u003cp\\u003e(260.32,385.52)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-2.26\\u003c/p\\u003e\\u003cp\\u003e(-2.37,-2.16)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-2.31\\u003c/p\\u003e\\u003cp\\u003e(-2.39,-2.23)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSex\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e8570\\u003c/p\\u003e\\u003cp\\u003e(7184,10049)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e58.50\\u003c/p\\u003e\\u003cp\\u003e(48.95,68.45)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e19347\\u003c/p\\u003e\\u003cp\\u003e(16359,22615)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1153.6\\u003c/p\\u003e\\u003cp\\u003e(974.89,1350.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e11029\\u003c/p\\u003e\\u003cp\\u003e(8710,13251)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e31.20\\u003c/p\\u003e\\u003cp\\u003e(24.43,37.48)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e22843\\u003c/p\\u003e\\u003cp\\u003e(18383,26947)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e595.27\\u003c/p\\u003e\\u003cp\\u003e(478.16,703.87)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-2.28\\u003c/p\\u003e\\u003cp\\u003e(-2.37,-2.18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-2.36\\u003c/p\\u003e\\u003cp\\u003e(-2.44,-2.29)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1967\\u003c/p\\u003e\\u003cp\\u003e(1377,2586)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e9.98\\u003c/p\\u003e\\u003cp\\u003e(7.00,13.12)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4253\\u003c/p\\u003e\\u003cp\\u003e(3087,5495)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e205.55\\u003c/p\\u003e\\u003cp\\u003e(149.09,265.86)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2320\\u003c/p\\u003e\\u003cp\\u003e(1636,3161)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e4.94\\u003c/p\\u003e\\u003cp\\u003e(3.49,6.73)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e4952\\u003c/p\\u003e\\u003cp\\u003e(3630,6491)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e106.2\\u003c/p\\u003e\\u003cp\\u003e(77.92,138.98)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-2.62\\u003c/p\\u003e\\u003cp\\u003e(-2.80,-2.44)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-2.39\\u003c/p\\u003e\\u003cp\\u003e(-2.52,-2.26)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSDI Regions\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHigh SDI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1405\\u003c/p\\u003e\\u003cp\\u003e(1148,1657)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e12.31\\u003c/p\\u003e\\u003cp\\u003e(10.07,14.54)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3321\\u003c/p\\u003e\\u003cp\\u003e(2725,3899)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e295.46\\u003c/p\\u003e\\u003cp\\u003e(242.74,346.62)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1736\\u003c/p\\u003e\\u003cp\\u003e(1323,2166)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e7.43\\u003c/p\\u003e\\u003cp\\u003e(5.73,9.18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e4040\\u003c/p\\u003e\\u003cp\\u003e(3160,4936)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e190.05\\u003c/p\\u003e\\u003cp\\u003e(149.49,230.91)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-1.72\\u003c/p\\u003e\\u003cp\\u003e(-1.79,-1.66)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-1.44\\u003c/p\\u003e\\u003cp\\u003e(-1.48,-1.40)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHigh-middle SDI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2983\\u003c/p\\u003e\\u003cp\\u003e(2449,3514)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e34.18\\u003c/p\\u003e\\u003cp\\u003e(27.66,40.46)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6302\\u003c/p\\u003e\\u003cp\\u003e(5250,7368)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e662.96\\u003c/p\\u003e\\u003cp\\u003e(551.54,779.67)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2863\\u003c/p\\u003e\\u003cp\\u003e(2188,3522)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e14.74\\u003c/p\\u003e\\u003cp\\u003e(11.25,18.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e5562\\u003c/p\\u003e\\u003cp\\u003e(4429,6757)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e281.31\\u003c/p\\u003e\\u003cp\\u003e(223.53,341.61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-3.30\\u003c/p\\u003e\\u003cp\\u003e(-3.53,-3.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-3.30\\u003c/p\\u003e\\u003cp\\u003e(-3.48,-3.11)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMiddle SDI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3989\\u003c/p\\u003e\\u003cp\\u003e(3296,4726)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e50.26\\u003c/p\\u003e\\u003cp\\u003e(41.30,59.30)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8779\\u003c/p\\u003e\\u003cp\\u003e(7306,10388)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e953.07\\u003c/p\\u003e\\u003cp\\u003e(788.04,1129.47)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e4816\\u003c/p\\u003e\\u003cp\\u003e(3679,5924)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e21.02\\u003c/p\\u003e\\u003cp\\u003e(16.13,25.85)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e9510\\u003c/p\\u003e\\u003cp\\u003e(7477,11518)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e379.97\\u003c/p\\u003e\\u003cp\\u003e(297.64,460.97)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-3.20\\u003c/p\\u003e\\u003cp\\u003e(-3.34,-3.06)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-3.31\\u003c/p\\u003e\\u003cp\\u003e(-3.42,-3.19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLow-middle SDI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1764\\u003c/p\\u003e\\u003cp\\u003e(1333,2188)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e36.26\\u003c/p\\u003e\\u003cp\\u003e(27.54,45.56)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4225\\u003c/p\\u003e\\u003cp\\u003e(3206,5215)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e757.26\\u003c/p\\u003e\\u003cp\\u003e(579.41,938.32)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e3290\\u003c/p\\u003e\\u003cp\\u003e(2615,4005)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e27.47\\u003c/p\\u003e\\u003cp\\u003e(21.81,33.52)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e7193\\u003c/p\\u003e\\u003cp\\u003e(5755,8715)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e542.25\\u003c/p\\u003e\\u003cp\\u003e(432.86,659.20)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-0.75\\u003c/p\\u003e\\u003cp\\u003e(-0.87,-0.63)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-0.99\\u003c/p\\u003e\\u003cp\\u003e(-1.08,-0.91)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLow SDI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e392\\u003c/p\\u003e\\u003cp\\u003e(282,498)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e23.09\\u003c/p\\u003e\\u003cp\\u003e(16.64,29.70)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e960\\u003c/p\\u003e\\u003cp\\u003e(702,1211)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e475.7\\u003c/p\\u003e\\u003cp\\u003e(351.51,602.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e639\\u003c/p\\u003e\\u003cp\\u003e(479,803)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e16.97\\u003c/p\\u003e\\u003cp\\u003e(12.71,21.25)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e1475\\u003c/p\\u003e\\u003cp\\u003e(1136,1842)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e335.21\\u003c/p\\u003e\\u003cp\\u003e(255.29,419.43)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-0.79\\u003c/p\\u003e\\u003cp\\u003e(-0.97,-0.61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-1.07\\u003c/p\\u003e\\u003cp\\u003e(-1.18,-0.95)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eGBD Regions\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAndean Latin America\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e7\\u003c/p\\u003e\\u003cp\\u003e(5,9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.95\\u003c/p\\u003e\\u003cp\\u003e(2.99,5.01)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e14\\u003c/p\\u003e\\u003cp\\u003e(11,18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e75.99\\u003c/p\\u003e\\u003cp\\u003e(57.88,95.34)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e12\\u003c/p\\u003e\\u003cp\\u003e(8,16)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e2.16\\u003c/p\\u003e\\u003cp\\u003e(1.53,2.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e25\\u003c/p\\u003e\\u003cp\\u003e(18,32)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e42.91\\u003c/p\\u003e\\u003cp\\u003e(31.06,56.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-1.56\\u003c/p\\u003e\\u003cp\\u003e(-1.68,-1.43)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-1.55\\u003c/p\\u003e\\u003cp\\u003e(-1.64,-1.46)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAustralasia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e28\\u003c/p\\u003e\\u003cp\\u003e(22,35)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11.59\\u003c/p\\u003e\\u003cp\\u003e(9.12,14.30)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e64\\u003c/p\\u003e\\u003cp\\u003e(51,77)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e264.05\\u003c/p\\u003e\\u003cp\\u003e(208.46,319.57)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e27\\u003c/p\\u003e\\u003cp\\u003e(20,36)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e4.6\\u003c/p\\u003e\\u003cp\\u003e(3.35,6.03)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e57\\u003c/p\\u003e\\u003cp\\u003e(43,73)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e104.24\\u003c/p\\u003e\\u003cp\\u003e(78.53,132.94)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-3.17\\u003c/p\\u003e\\u003cp\\u003e(-3.39,-2.95)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-3.16\\u003c/p\\u003e\\u003cp\\u003e(-3.36,-2.97)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCaribbean\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14\\u003c/p\\u003e\\u003cp\\u003e(11,18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.01\\u003c/p\\u003e\\u003cp\\u003e(4.68,7.44)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e34\\u003c/p\\u003e\\u003cp\\u003e(27,41)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e132.83\\u003c/p\\u003e\\u003cp\\u003e(105.02,161.49)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e31\\u003c/p\\u003e\\u003cp\\u003e(23,39)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e5.69\\u003c/p\\u003e\\u003cp\\u003e(4.25,7.17)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e70\\u003c/p\\u003e\\u003cp\\u003e(53,87)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e128.60\\u003c/p\\u003e\\u003cp\\u003e(98.71,160.48)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-0.37\\u003c/p\\u003e\\u003cp\\u003e(-0.61,-0.13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-0.29\\u003c/p\\u003e\\u003cp\\u003e(-0.52,-0.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCentral Asia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e49\\u003c/p\\u003e\\u003cp\\u003e(40,57)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11.11\\u003c/p\\u003e\\u003cp\\u003e(9.11,13.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e125\\u003c/p\\u003e\\u003cp\\u003e(104,145)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e264.51\\u003c/p\\u003e\\u003cp\\u003e(219.27,307.91)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e48\\u003c/p\\u003e\\u003cp\\u003e(38,58)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e6.61\\u003c/p\\u003e\\u003cp\\u003e(5.28,8.04)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e128\\u003c/p\\u003e\\u003cp\\u003e(103,153)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e157.67\\u003c/p\\u003e\\u003cp\\u003e(126.36,190.87)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-1.93\\u003c/p\\u003e\\u003cp\\u003e(-2.17,-1.68)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-2.10\\u003c/p\\u003e\\u003cp\\u003e(-2.37,-1.83)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCentral Europe\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e190\\u003c/p\\u003e\\u003cp\\u003e(156,225)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e13.06\\u003c/p\\u003e\\u003cp\\u003e(10.65,15.60)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e481\\u003c/p\\u003e\\u003cp\\u003e(402,560)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e319.05\\u003c/p\\u003e\\u003cp\\u003e(265.76,371.73)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e145\\u003c/p\\u003e\\u003cp\\u003e(112,177)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e6.23\\u003c/p\\u003e\\u003cp\\u003e(4.83,7.59)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e380\\u003c/p\\u003e\\u003cp\\u003e(298,456)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e174.27\\u003c/p\\u003e\\u003cp\\u003e(137.45,208.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-2.17\\u003c/p\\u003e\\u003cp\\u003e(-2.29,-2.05)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-1.76\\u003c/p\\u003e\\u003cp\\u003e(-1.87,-1.65)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCentral Latin America\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e77\\u003c/p\\u003e\\u003cp\\u003e(62,94)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11.71\\u003c/p\\u003e\\u003cp\\u003e(9.20,14.19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e160\\u003c/p\\u003e\\u003cp\\u003e(129,192)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e215.18\\u003c/p\\u003e\\u003cp\\u003e(172.68,258.59)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e119\\u003c/p\\u003e\\u003cp\\u003e(89,151)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e5.08\\u003c/p\\u003e\\u003cp\\u003e(3.80,6.45)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e240\\u003c/p\\u003e\\u003cp\\u003e(185,302)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e99.13\\u003c/p\\u003e\\u003cp\\u003e(76.45,125.12)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-2.97\\u003c/p\\u003e\\u003cp\\u003e(-3.19,-2.75)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-2.82\\u003c/p\\u003e\\u003cp\\u003e(-3.03,-2.61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCentral Sub-Saharan Africa\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e13\\u003c/p\\u003e\\u003cp\\u003e(9,18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e7.12\\u003c/p\\u003e\\u003cp\\u003e(4.72,9.84)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e37\\u003c/p\\u003e\\u003cp\\u003e(25,51)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e169.46\\u003c/p\\u003e\\u003cp\\u003e(114.78,231.53)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e21\\u003c/p\\u003e\\u003cp\\u003e(14,29)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e4.57\\u003c/p\\u003e\\u003cp\\u003e(3.13,6.37)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e64\\u003c/p\\u003e\\u003cp\\u003e(45,87)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e116.40\\u003c/p\\u003e\\u003cp\\u003e(83.45,158.43)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-1.43\\u003c/p\\u003e\\u003cp\\u003e(-1.61,-1.25)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-1.20\\u003c/p\\u003e\\u003cp\\u003e(-1.37,-1.03)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEast Asia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5457\\u003c/p\\u003e\\u003cp\\u003e(4464,6542)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e93.29\\u003c/p\\u003e\\u003cp\\u003e(74.65,111.22)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e11404\\u003c/p\\u003e\\u003cp\\u003e(9353,13624)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1583.70\\u003c/p\\u003e\\u003cp\\u003e(1290.64,1885.68)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e5757\\u003c/p\\u003e\\u003cp\\u003e(4272,7285)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e30.56\\u003c/p\\u003e\\u003cp\\u003e(22.68,38.48)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e10446\\u003c/p\\u003e\\u003cp\\u003e(8000,12958)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e508.68\\u003c/p\\u003e\\u003cp\\u003e(387.70,633.16)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-4.07\\u003c/p\\u003e\\u003cp\\u003e(-4.27,-3.88)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-4.09\\u003c/p\\u003e\\u003cp\\u003e(-4.25,-3.93)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEastern Europe\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e360\\u003c/p\\u003e\\u003cp\\u003e(307,410)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e13.08\\u003c/p\\u003e\\u003cp\\u003e(11.09,14.98)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e893\\u003c/p\\u003e\\u003cp\\u003e(769,1016)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e313.29\\u003c/p\\u003e\\u003cp\\u003e(269.19,356.86)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e171\\u003c/p\\u003e\\u003cp\\u003e(139,203)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e4.74\\u003c/p\\u003e\\u003cp\\u003e(3.87,5.63)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e452\\u003c/p\\u003e\\u003cp\\u003e(372,532)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e129.52\\u003c/p\\u003e\\u003cp\\u003e(106.65,152.50)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-4.20\\u003c/p\\u003e\\u003cp\\u003e(-4.59,-3.80)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-3.82\\u003c/p\\u003e\\u003cp\\u003e(-4.23,-3.42)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEastern Sub-Saharan Africa\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e51\\u003c/p\\u003e\\u003cp\\u003e(38,65)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e9.12\\u003c/p\\u003e\\u003cp\\u003e(6.67,11.64)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e131\\u003c/p\\u003e\\u003cp\\u003e(99,164)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e194.05\\u003c/p\\u003e\\u003cp\\u003e(146.05,243.35)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e64\\u003c/p\\u003e\\u003cp\\u003e(47,82)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e4.88\\u003c/p\\u003e\\u003cp\\u003e(3.57,6.36)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e180\\u003c/p\\u003e\\u003cp\\u003e(135,229)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e113.96\\u003c/p\\u003e\\u003cp\\u003e(85.12,145.17)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-2.25\\u003c/p\\u003e\\u003cp\\u003e(-2.34,-2.16)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-1.93\\u003c/p\\u003e\\u003cp\\u003e(-2.02,-1.85)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHigh-income Asia Pacific\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e125\\u003c/p\\u003e\\u003cp\\u003e(102,145)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.92\\u003c/p\\u003e\\u003cp\\u003e(5.65,8.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e300\\u003c/p\\u003e\\u003cp\\u003e(248,351)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e154.94\\u003c/p\\u003e\\u003cp\\u003e(128.12,181.40)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e170\\u003c/p\\u003e\\u003cp\\u003e(127,217)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e2.68\\u003c/p\\u003e\\u003cp\\u003e(2.05,3.38)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e375\\u003c/p\\u003e\\u003cp\\u003e(292,469)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e73.13\\u003c/p\\u003e\\u003cp\\u003e(57.66,90.80)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-3.27\\u003c/p\\u003e\\u003cp\\u003e(-3.45,-3.08)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-2.44\\u003c/p\\u003e\\u003cp\\u003e(-2.52,-2.36)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHigh-income North America\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e478\\u003c/p\\u003e\\u003cp\\u003e(384,576)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e12.92\\u003c/p\\u003e\\u003cp\\u003e(10.39,15.54)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1291\\u003c/p\\u003e\\u003cp\\u003e(1039,1537)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e363.95\\u003c/p\\u003e\\u003cp\\u003e(293.81,431.59)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e796\\u003c/p\\u003e\\u003cp\\u003e(593,1007)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e11.32\\u003c/p\\u003e\\u003cp\\u003e(8.50,14.27)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e2024\\u003c/p\\u003e\\u003cp\\u003e(1565,2509)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e303.03\\u003c/p\\u003e\\u003cp\\u003e(235.42,374.47)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-0.50\\u003c/p\\u003e\\u003cp\\u003e(-0.68,-0.32)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-0.61\\u003c/p\\u003e\\u003cp\\u003e(-0.76,-0.45)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNorth Africa and Middle East\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e180\\u003c/p\\u003e\\u003cp\\u003e(141,219)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e13.22\\u003c/p\\u003e\\u003cp\\u003e(10.27,16.18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e469\\u003c/p\\u003e\\u003cp\\u003e(373,562)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e294.16\\u003c/p\\u003e\\u003cp\\u003e(232.55,356.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e320\\u003c/p\\u003e\\u003cp\\u003e(247,391)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e8.79\\u003c/p\\u003e\\u003cp\\u003e(6.71,10.72)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e851\\u003c/p\\u003e\\u003cp\\u003e(668,1033)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e199.59\\u003c/p\\u003e\\u003cp\\u003e(156.74,242.97)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-1.25\\u003c/p\\u003e\\u003cp\\u003e(-1.38,-1.12)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-1.24\\u003c/p\\u003e\\u003cp\\u003e(-1.32,-1.16)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOceania\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e9\\u003c/p\\u003e\\u003cp\\u003e(6,12)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e36.84\\u003c/p\\u003e\\u003cp\\u003e(25.39,50.63)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e23\\u003c/p\\u003e\\u003cp\\u003e(16,32)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e818.89\\u003c/p\\u003e\\u003cp\\u003e(570.83,1114.45)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e15\\u003c/p\\u003e\\u003cp\\u003e(11,20)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e24.23\\u003c/p\\u003e\\u003cp\\u003e(17.43,32.73)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e40\\u003c/p\\u003e\\u003cp\\u003e(30,54)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e549.31\\u003c/p\\u003e\\u003cp\\u003e(402.65,734.15)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-1.53\\u003c/p\\u003e\\u003cp\\u003e(-1.62,-1.44)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-1.45\\u003c/p\\u003e\\u003cp\\u003e(-1.54,-1.36)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSouth Asia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1941\\u003c/p\\u003e\\u003cp\\u003e(1408,2440)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e44.25\\u003c/p\\u003e\\u003cp\\u003e(32.42,55.84)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4671\\u003c/p\\u003e\\u003cp\\u003e(3458,5793)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e911.90\\u003c/p\\u003e\\u003cp\\u003e(672.62,1136.61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e3703\\u003c/p\\u003e\\u003cp\\u003e(2875,4588)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e30.26\\u003c/p\\u003e\\u003cp\\u003e(23.48,37.23)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e8051\\u003c/p\\u003e\\u003cp\\u003e(6283,9895)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e591.70\\u003c/p\\u003e\\u003cp\\u003e(464.04,727.28)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-1.06\\u003c/p\\u003e\\u003cp\\u003e(-1.21,-0.91)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-1.31\\u003c/p\\u003e\\u003cp\\u003e(-1.41,-1.21)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSoutheast Asia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e543\\u003c/p\\u003e\\u003cp\\u003e(428,655)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e27.30\\u003c/p\\u003e\\u003cp\\u003e(21.39,33.00)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1275\\u003c/p\\u003e\\u003cp\\u003e(1015,1522)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e555.44\\u003c/p\\u003e\\u003cp\\u003e(439.24,666.67)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e922\\u003c/p\\u003e\\u003cp\\u003e(743,1109)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e17.12\\u003c/p\\u003e\\u003cp\\u003e(13.84,20.63)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e2159\\u003c/p\\u003e\\u003cp\\u003e(1768,2588)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e353.74\\u003c/p\\u003e\\u003cp\\u003e(288.47,423.88)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-1.68\\u003c/p\\u003e\\u003cp\\u003e(-1.82,-1.53)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-1.62\\u003c/p\\u003e\\u003cp\\u003e(-1.73,-1.50)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSouthern Latin America\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e40\\u003c/p\\u003e\\u003cp\\u003e(31,48)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e8.72\\u003c/p\\u003e\\u003cp\\u003e(6.82,10.75)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e100\\u003c/p\\u003e\\u003cp\\u003e(80,119)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e213.15\\u003c/p\\u003e\\u003cp\\u003e(170.12,254.80)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e53\\u003c/p\\u003e\\u003cp\\u003e(39,68)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e5.91\\u003c/p\\u003e\\u003cp\\u003e(4.37,7.50)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e121\\u003c/p\\u003e\\u003cp\\u003e(92,149)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e138.28\\u003c/p\\u003e\\u003cp\\u003e(105.51,170.25)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-1.12\\u003c/p\\u003e\\u003cp\\u003e(-1.41,-0.83)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-1.31\\u003c/p\\u003e\\u003cp\\u003e(-1.54,-1.08)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSouthern Sub-Saharan Africa\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e35\\u003c/p\\u003e\\u003cp\\u003e(28,42)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e15.41\\u003c/p\\u003e\\u003cp\\u003e(12.30,19.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e91\\u003c/p\\u003e\\u003cp\\u003e(73,110)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e355.30\\u003c/p\\u003e\\u003cp\\u003e(285.70,428.86)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e45\\u003c/p\\u003e\\u003cp\\u003e(36,56)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e9.06\\u003c/p\\u003e\\u003cp\\u003e(7.02,11.40)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e129\\u003c/p\\u003e\\u003cp\\u003e(103,158)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e228.28\\u003c/p\\u003e\\u003cp\\u003e(180.35,280.24)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-2.03\\u003c/p\\u003e\\u003cp\\u003e(-2.40,-1.66)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-1.67\\u003c/p\\u003e\\u003cp\\u003e(-1.98,-1.36)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTropical Latin America\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e167\\u003c/p\\u003e\\u003cp\\u003e(136,198)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e22.20\\u003c/p\\u003e\\u003cp\\u003e(17.91,26.79)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e383\\u003c/p\\u003e\\u003cp\\u003e(316,444)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e449.05\\u003c/p\\u003e\\u003cp\\u003e(367.72,524.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e202\\u003c/p\\u003e\\u003cp\\u003e(152,257)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e8.12\\u003c/p\\u003e\\u003cp\\u003e(6.08,10.37)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e450\\u003c/p\\u003e\\u003cp\\u003e(343,564)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e176.20\\u003c/p\\u003e\\u003cp\\u003e(133.86,221.14)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-3.79\\u003c/p\\u003e\\u003cp\\u003e(-4.08,-3.49)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-3.60\\u003c/p\\u003e\\u003cp\\u003e(-3.89,-3.32)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWestern Europe\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e747\\u003c/p\\u003e\\u003cp\\u003e(609,877)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e12.18\\u003c/p\\u003e\\u003cp\\u003e(9.92,14.31)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1579\\u003c/p\\u003e\\u003cp\\u003e(1294,1840)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e266.31\\u003c/p\\u003e\\u003cp\\u003e(218.94,309.97)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e695\\u003c/p\\u003e\\u003cp\\u003e(520,870)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e6.39\\u003c/p\\u003e\\u003cp\\u003e(4.88,7.90)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e1440\\u003c/p\\u003e\\u003cp\\u003e(1115,1756)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e151.56\\u003c/p\\u003e\\u003cp\\u003e(118.88,183.30)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-2.07\\u003c/p\\u003e\\u003cp\\u003e(-2.15,-1.99)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-1.78\\u003c/p\\u003e\\u003cp\\u003e(-1.85,-1.71)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWestern Sub-Saharan Africa\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e27\\u003c/p\\u003e\\u003cp\\u003e(19,35)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.71\\u003c/p\\u003e\\u003cp\\u003e(2.60,4.88)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e75\\u003c/p\\u003e\\u003cp\\u003e(54,98)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e88.25\\u003c/p\\u003e\\u003cp\\u003e(63.65,115.68)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e35\\u003c/p\\u003e\\u003cp\\u003e(26,45)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e2.21\\u003c/p\\u003e\\u003cp\\u003e(1.58,2.85)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e113\\u003c/p\\u003e\\u003cp\\u003e(83,142)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e57.95\\u003c/p\\u003e\\u003cp\\u003e(42.45,73.13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e-1.52\\u003c/p\\u003e\\u003cp\\u003e(-1.60,-1.44)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e-1.26\\u003c/p\\u003e\\u003cp\\u003e(-1.32,-1.20)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"13\\\"\\u003eASMR, age-standardized mortality rate; ASDR, age-standardized DALYs rate; DALYs, disability-adjusted life years; EAPC, estimated annual percentage change; UI, Uncertainty interval; SDI, Socio-demographic Index; CI, Confidence interval\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.2 Regional burden and trends of smoking-related COPD\\u003c/h2\\u003e\\u003cp\\u003eIn 1990, the burden of smoking-related COPD was primarily concentrated in Middle and High-middle SDI region (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). By 2021, deaths and DALYs increased in all SDI regions except for the High-middle SDI region. The most substantial increase was observed in Low-middle SDI region, which rose to rank second, following Middle SDI region (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA and B). The highest ASMR and ASDR in 2021 were observed in Low-middle SDI region, with values of 27.47 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 21.81\\u0026ndash;33.52) and 542.25 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 432.86\\u0026ndash;659.20) per 100,000 population, respectively (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC and D). From 1990 to 2021, the EAPCs of ASMR were negative in all SDI regions. The most pronounced decline was observed in High-middle SDI region, with EAPC of -3.30 (95% \\u003cem\\u003eCI\\u003c/em\\u003e: -3.53 to -3.07) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eAmong the 21 GBD regions, both in 1990 and 2021, East Asia and South Asia exhibited a substantially higher burden of COPD attributable to smoking compared to other regions (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA and B). In 2021, East Asia reported the highest deaths (5,757 hundred; 95% \\u003cem\\u003eUI\\u003c/em\\u003e: 4,272-7,285) and DALYs (10,446 thousand; 95% \\u003cem\\u003eUI\\u003c/em\\u003e: 8,000\\u0026ndash;12,958), followed by South Asia with 3,703 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 2,875-4,588) hundred deaths and 8,051 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 6,283-9,895) thousand DALYs. With respect to ASR, East Asia exhibited the highest ASMR and ASDR in 1990 (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). By 2021, East Asia maintained its position as the region with the highest ASMR, while South Asia surpassed East Asia in terms of ASDR. Conversely, Andean Latin America consistently exhibited the lowest burden in both 1990 and 2021, across all indicators (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The EAPCs for both ASMR and ASDR were negative in all GBD regions. East Asia demonstrated the most significant decrease in ASDR, exhibiting an EAPC of -4.09 (95% \\u003cem\\u003eCI\\u003c/em\\u003e: -4.25 to -3.93). This was followed by Eastern Europe (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.3 National burden and trends of smoking-related COPD\\u003c/h2\\u003e\\u003cp\\u003eWith respect to national burden, China and India had the highest smoking-related COPD deaths and DALYs in 1990 and remained consistent in 2021. In 2021, DALYs reached 101,874 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 77,472\\u0026thinsp;\\u0026minus;\\u0026thinsp;126,914) thousand in China and 66,816 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 51,993\\u0026thinsp;\\u0026minus;\\u0026thinsp;83,099) thousand in India (Table S2). Relative to 1990, China experienced a 9.4% decrease in DALYs, whereas India showed an 82.7% increase. For ASMR, China had the highest ASMR in 1990 (96.37 per 100,000; 95% \\u003cem\\u003eUI\\u003c/em\\u003e: 77.06-115.04), followed by Nepal (88.39 per 100,000; 95% \\u003cem\\u003eUI\\u003c/em\\u003e: 61.21-118.16) and Myanmar (73.92 per 100,000; 95% \\u003cem\\u003eUI\\u003c/em\\u003e: 53.54\\u0026ndash;97.36). By 2021, Nepal ranked first in ASMR, followed by Myanmar and Kiribati (Table S6 and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). Most countries exhibited declines in ASMR and ASDR over time (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC and D). The largest reductions were observed in Singapore, Ukraine, and Belarus, whereas Georgia experienced the largest increase, indicating substantial heterogeneity in global progress (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e0 and S11).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.4 Patterns of smoking-related COPD by sex and age\\u003c/h2\\u003e\\u003cp\\u003eBurden differed by sex throughout 1990 to 2021, with males consistently exceeding females in deaths and DALYs (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA and B). Males deaths increased from 8,570 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 7,184\\u0026thinsp;\\u0026minus;\\u0026thinsp;10,049) hundred to 11,029 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 8,710\\u0026thinsp;\\u0026minus;\\u0026thinsp;13,251) hundred. Females deaths rose modestly from 1,967 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 1,377-2,586) hundred to 2,320 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 1,636-3,161) hundred. ASMR and ASDR decreased in both sexes, with steeper declines in females, as reflected by larger absolute EAPCs (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Age patterns showed that DALYs increased gradually after the age of 40, peaking at 70\\u0026ndash;74 years and the declining. Similarly, both ASMR and ASDR demonstrated an increase with age, reaching a peak in the 90\\u0026ndash;94 age group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC and D).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.5 Frontier analysis and decomposition analysis\\u003c/h2\\u003e\\u003cp\\u003eFrontier analysis illustrated each location\\u0026rsquo;s distance to the efficiency frontier for ASDR given its SDI from 1990 to 2021. The distance generally decreased with higher SDI, implying that low SDI countries still have more potential to improvement (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). In 2021, among lower-SDI countries, Somalia, Burkina Faso, Niger, Ethiopia, and Benin were closest to the frontier, indicating relatively strong performance at their development level. In contrast, among the countries with higher SDI, Denmark, the United States, the United Kingdom, the Netherlands, and Belgium are the furthest from the frontier, suggesting more room for reduction than expected for their SDI. The 15 most distant locations included Nepal, Kiribati, Myanmar, Papua New Guinea, North Korea, and others (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eDecomposition analysis revealed that aging and population growth contributed to global DALYs increases by 118.70% and 417.74%, respectively (Table S12). Aging had a positive impact in all SDI regions, except Low SDI region. Population growth was positive in all five SDI regions. Conversely, epidemiological changes exerted a deleterious effect on global DALYs, contributing to a -436.43% globally, a pattern consistent across strata. All three components had the largest impacts in Middle SDI, underscoring this stratum as a priority for targeted interventions (Table S12 and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.6 Future projections of smoking-related COPD burden\\u003c/h2\\u003e\\u003cp\\u003eThe BAPC model projected continued decline in smoking-related COPD burden over the next 19 years (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). By 2040, ASMR is predicted to decline to 11.77 per 100,000 population and ASDR to 242.11 per 100,000 population (Table S13 and S14). By sex, ASDR in males is estimated to decrease by 24.3%, while females are projected to experience a 36.44% decline, indicating a greater relative improvement in women.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eCOPD is one of the leading causes of morbidity and mortality worldwide. Smoking is the primary modifiable risk factor for COPD. Despite sustained declines in ASMR and the ASDR for smoking-attributable COPD since 1990, absolute deaths and DALYs increased. Decomposition analysis attributed this divergence primarily to demographic change, population growth and population ageing. Marked heterogeneity by development level, geography, sex, and age underscores the need for context-specific prevention and control strategies.\\u003c/p\\u003e\\u003cp\\u003eA previous GBD study assessed the burden of smoking-related COPD and reported declining ASDR in nearly all SDI regions. Although the authors did not explicitly state the global trend, the figures in their report indicate a downward trajectory [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Our findings are consistent with this pattern, showing a continued decline in the global ASDR of smoking-related COPD between 1990 and 2021. This study also noted that the burden of smoking-related COPD was greater in males and increased with age, peaking at 85\\u0026ndash;89 years. Similarly, our study found that males experienced a substantially higher burden. However, we observed that the global ASDR peaked earlier, at 70\\u0026ndash;74 years. This variation may be due to differences in the study period, data updates, and methodological approaches. Compared with earlier studies, our analysis incorporates the most recent data and provides a more nuanced assessment by incorporating advanced analytical approaches, including BAPC modeling and decomposition analysis, providing a more up-to-date and comprehensive view of global and regional trends.\\u003c/p\\u003e\\u003cp\\u003eAt the global level, declining ASMR and ASDR are consistent with strengthened tobacco-control policies, earlier detection, and improvements in COPD management in many settings [\\u003cspan additionalcitationids=\\\"CR24 CR25 CR26 CR27\\\" citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. Despite these global trends, Nepal's ASMR and ASDR remained high in both 1990 and 2021, likely due to the persistent prevalence of smoking and the ongoing socioeconomic challenges experienced by the nation [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eNevertheless, absolute burden increased, particularly in Low-middle and Middle SDI strata, where rapid demographic expansion and ageing offset epidemiologic gains. These patterns align with the decomposition findings, in which population growth and ageing contributed positively to DALY changes, whereas epidemiologic change contributed negatively, suggesting real\\u0026mdash;but insufficient\\u0026mdash;progress against risk and disease severity. The results of the decomposition analysis further support this conclusion. Inadequate tobacco control measures, limited access to healthcare, and delays in COPD diagnosis and treatment may also contribute to the growing absolute burden in settings with limited resources [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eAlthough East Asia and South Asia have made notable strides toward lowering the burden of COPD, they still account for the highest number of deaths and DALYs globally. This persistent burden can be attributed to high smoking prevalence, aging populations, and challenges in healthcare infrastructure and early COPD diagnosis [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. While tobacco control efforts have been strengthened in some countries, continued attention to public health strategies and targeted measures is crucial to further alleviating the disease burden in these regions. It is important to note that China has experienced a decrease in the number of DALYs attributable to smoking-related COPD in recent years, largely due to stricter tobacco regulations and heightened public health awareness [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. Nevertheless, China continues to report the highest number of deaths and DALYs, reflecting its large population and aging population. In contrast, India has experienced a significant surge in both deaths and DALYs. This contrast can be attributed to differences in smoking prevalence trends, healthcare access, and the execution of tobacco control policies. Despite India's noteworthy advancements in tobacco control, bidi smoking remains widespread in certain regions, accounting for about 85% of total smoked tobacco consumption [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. Compared to conventional cigarettes, bidis contain elevated concentrations of nicotine and tar [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e], and are more strongly associated with COPD risk [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. Their predominant use among low-income and rural populations [\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e], coupled with limited healthcare access and high out-of-pocket costs [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e], exacerbates delayed diagnosis and poor outcomes. It is imperative that concerted efforts are made in public health education, smoking cessation programs, and enhancing healthcare accessibility in order to address this growing problem.\\u003c/p\\u003e\\u003cp\\u003eSex- and age-specific patterns were stable and biologically plausible. Males bore higher burdens than females across outcomes, consistent with greater cumulative smoking exposure in many settings [\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. Burden increased with age, with DALYs peaking in the 70\\u0026ndash;74 year group and rates (ASMR/ASDR) peaking at older ages, reflecting the combined effects of cumulative exposure, age-related lung function decline, and comorbidity [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. These findings reinforce the need to intensify cessation among men, while expanding early detection and comprehensive, life-course COPD care for ageing populations.\\u003c/p\\u003e\\u003cp\\u003eThe frontier analysis indicates that the burden of COPD attributable to smoking generally decreases with rising levels of socioeconomic development. However, certain countries exhibit a discernible discrepancy between their observed burden and the expected levels based on the SDI. A number of low-SDI countries, including Somalia, Burkina Faso, and Ethiopia, have demonstrated a prevalence of COPD burdens that approaches the theoretical minimum predicted for their respective development levels. Despite their limited resources, these nations have maintained relatively low ASDRs, and their strategies may offer valuable insights for COPD prevention in countries with analogous socioeconomic contexts. Conversely, numerous high-SDI countries, including the United States, the United Kingdom, and Denmark, persist in exhibiting disproportionately elevated COPD burdens. The available evidence suggests that this pattern is largely attributable to the combined effects of long-term smoking and population aging. In the United States, for instance, approximately 80% of COPD cases are attributed to smoking [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. The demographic aging observed in both the United States and the United Kingdom are associated with an increased risk of developing COPD [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. This phenomenon is further exacerbated by the prolongation of life expectancy, which amplifies the cumulative impact of smoking exposure over time. These findings underscore the necessity of sustained tobacco control efforts and age-specific preventive strategies, even in high-SDI settings, to mitigate the future burden of COPD.\\u003c/p\\u003e\\u003cp\\u003eDecomposition analysis identified demographic transitions as the dominant drivers of smoking-attributable COPD burden: population ageing and growth continue to push deaths and DALYs upward, only partially offset by advances in prevention, diagnosis and treatment. The disproportionate impact in Middle SDI settings signals a critical inflection point, with demand from demographic change outpacing health-system capacity [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. Priority responses include sustained tobacco control, expanded early detection (risk-based case finding and spirometry) and accessible long-term management integrated into primary care. Implemented at scale, these measures should temper the trajectory of tobacco-related COPD in these regions.\\u003c/p\\u003e\\u003cp\\u003eSeveral limitations about this study should be considered. First, reliance on GBD data introduces potential biases due to the limitations of model-based estimates, especially in regions with insufficient primary data [\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e]. Second, the cross-sectional nature of some data limits our ability to establish causal relationships between smoking-related COPD burden and various demographic or regional factors. Third, although decomposition clarifies the direction and relative magnitude of demographic versus epidemiologic forces, unmeasured factors (e.g., air pollution trends, occupational exposures, or care quality) may also influence trajectories.\\u003c/p\\u003e\\u003cp\\u003eIn conclusion, our study reveals substantial progress in lowering ASMR and ASDR associated with smoking-induced COPD across numerous regions, especially in countries with a high SDI. However, the absolute burden remains substantia, with counts concentrated in Low-middle and Middle SDI regions where rapid population growth and ageing predominate. Accordingly, context-tailored public-health interventions are warranted in these settings. Targeting by sex and age is warranted, given consistently higher burdens in males and older adults. Further reductions will depend on strengthening tobacco-control policies, widening healthcare access, and closing implementation gaps where demographic pressures are greatest.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eASDR: Age-standardized DALYs rate\\u003c/p\\u003e\\n\\u003cp\\u003eASMR: Age-standardized mortality rate\\u003c/p\\u003e\\n\\u003cp\\u003eASR: Age-standardized rate\\u003c/p\\u003e\\n\\u003cp\\u003eBAPC: Bayesian age-period-cohort\\u003c/p\\u003e\\n\\u003cp\\u003eCI: Confidence interval\\u003c/p\\u003e\\n\\u003cp\\u003eCOPD: Chronic obstructive pulmonary disease\\u003c/p\\u003e\\n\\u003cp\\u003eCRDs: Chronic respiratory diseases\\u003c/p\\u003e\\n\\u003cp\\u003eDALYs: Disability-adjusted life years\\u003c/p\\u003e\\n\\u003cp\\u003eEAPC: Estimated annual percentage change\\u003c/p\\u003e\\n\\u003cp\\u003eERS: European Respiratory Society\\u003c/p\\u003e\\n\\u003cp\\u003eFEV1: Forced expiratory volume in one second\\u003c/p\\u003e\\n\\u003cp\\u003eFVC: Forced vital capacity\\u003c/p\\u003e\\n\\u003cp\\u003eGBD: Global Burden of Diseases\\u003c/p\\u003e\\n\\u003cp\\u003eGHDx: Global Health Data Exchange\\u003c/p\\u003e\\n\\u003cp\\u003eGOLD: Global Initiative for Chronic Obstructive Lung Disease\\u003c/p\\u003e\\n\\u003cp\\u003eINLA: Integrated Nested Laplace Approximations\\u003c/p\\u003e\\n\\u003cp\\u003eLLN: Lower limit of normal\\u003c/p\\u003e\\n\\u003cp\\u003eSDI: Socio-Demographic Index\\u003c/p\\u003e\\n\\u003cp\\u003eUI: Uncertainty interval\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study received financial support from the Student Research Training Program of Shandong First Medical University \\u0026amp; Shandong Academy of Medical Sciences (No. 2024104390508; No. 2024104391194), and College Student Research Training Program of Shandong Youth \\u0026amp; Children of Academy Educational Sciences (No. 24SSR225; 24SSR310).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRole of funding source\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe funding source had no involvement in the research's conceptualization, data gathering, analysis, interpretation, or manuscript preparation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors’ contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTianqi Ma\\u003c/strong\\u003e: Formal analysis, Data curation, Writing\\u0026nbsp;–\\u0026nbsp;original draft. \\u003cstrong\\u003eXianfeng Yue\\u003c/strong\\u003e: Formal analysis, Data curation, Writing\\u0026nbsp;–\\u0026nbsp;original draft. \\u003cstrong\\u003eShuyu Rong\\u003c/strong\\u003e: Methodology, Writing\\u0026nbsp;–\\u0026nbsp;review \\u0026amp; editing. \\u003cstrong\\u003eRongqian Sun\\u003c/strong\\u003e:\\u0026nbsp;Formal analysis. \\u003cstrong\\u003eJunyao Wang\\u003c/strong\\u003e: Data curation. \\u003cstrong\\u003eXin Zheng\\u003c/strong\\u003e: Formal analysis. \\u003cstrong\\u003eXueyu Chen\\u003c/strong\\u003e: Methodology, Validation. \\u003cstrong\\u003eRongqin Sun\\u003c/strong\\u003e: Methodology, Writing\\u0026nbsp;–\\u0026nbsp;review \\u0026amp; editing.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData sharing statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data utilized in this analysis are accessible to the public through the Global Health Data Exchange (GHDx) at http://ghdx.healthdata.org/gbd-results-tool.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDeclaration of interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors have declared no conflicts of interest.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompliance with Ethics Requirements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eEthical approval was waived by the institutional review board, as the study was based entirely on publicly available data.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe would like to express our profound gratitude to the collaborators of the Global Burden of Disease, Injuries, and Risk Factors Study 2021 for their invaluable contributions, as well as to the Institute for Health Metrics and Evaluation (IHME) for providing access to the GBD data. 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\\u003cstrong\\u003e6\\u003c/strong\\u003e(3):e142-e155.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Chronic obstructive pulmonary disease, smoking, Global Burden of Disease, socio-demographic index, age-standardized rates\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7797060/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7797060/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cb\\u003eBackground\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eChronic obstructive pulmonary disease (COPD) is a leading cause of death and disability worldwide, with smoking being the primary contributor. This study aims to assess the temporal and spatial trends in the burden of smoking-related COPD from 1990 to 2021 and project future trajectories. The results aim to provide insights and methodological references for COPD prevention and control strategies.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eMethods\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe data were sourced from the Global Burden of Disease (GBD) 2021 database, incorporating estimates and uncertainty intervals (\\u003cem\\u003eUI\\u003c/em\\u003e) for deaths, disability-adjusted life years (DALYs), and age-standardized rates (ASRs) of smoking-related COPD across 204 countries and regions worldwide. The present study employed a variety of statistical methodologies, including estimated annual percentage change (EAPC) to measure trend shifts, frontier analysis to assess the association between the Socio-demographic Index (SDI) and COPD disease burden, decomposition analysis to clarify the impacts of population aging, population growth, and epidemiological changes, and Bayesian age-period-cohort (BAPC) modeling for future disease burden projections.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eResults\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eFrom 1990 to 2021, global smoking-related COPD deaths increased from 10,538 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 8,724\\u0026thinsp;\\u0026minus;\\u0026thinsp;12,339) hundred to 13,350 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 10,533\\u0026thinsp;\\u0026minus;\\u0026thinsp;15,966) hundred, and DALYs rose from 23,601 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 19,648\\u0026thinsp;\\u0026minus;\\u0026thinsp;27,495) thousand to 27,795 (95% \\u003cem\\u003eUI\\u003c/em\\u003e: 22,234\\u0026thinsp;\\u0026minus;\\u0026thinsp;32,884) thousand. However, both age-standardized mortality rates (ASMR) and age-standardized disability-adjusted life years rates (ASDR) demonstrated a decline across most regions. The largest decreases were observed in High-middle SDI regions. Males consistently bore a higher burden than females. The burden increased with age, peaking at 70\\u0026ndash;74 years. Aging and population growth were the main contributors to the rise in DALYs, while epidemiological changes had a negative effect. Projections indicate a continued decline in ASMR and ASDR through 2040.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eConclusions\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eDespite global progress in reducing the ASRs burden of smoking-related COPD, the absolute burden continues to rise. The findings underscore the necessity for targeted public health interventions in these regions, with a focus on enhancing tobacco control policies, improving healthcare access, and addressing age- and gender-specific risk factors.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Smoking-attributable burden of chronic obstructive pulmonary disease from 1990 to 2021: Temporal trends and evidence from the global burden of disease study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-10 17:45:16\",\"doi\":\"10.21203/rs.3.rs-7797060/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"649010f2-1793-4662-863e-b392210d7bc4\",\"owner\":[],\"postedDate\":\"November 10th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-11-17T17:53:43+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-10 17:45:16\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7797060\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7797060\",\"identity\":\"rs-7797060\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}