High Sodium Intake: A Silent Killer Driving Global Gastric Cancer Burden

High Sodium Intake: A Silent Killer Driving Global Gastric Cancer Burden | 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 High Sodium Intake: A Silent Killer Driving Global Gastric Cancer Burden Xiqiang Zhang, Kexin Wang, Zhaoyi Jing, Ao Yu, Xinzhen Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5037516/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Oct, 2025 Read the published version in BMC Cancer → Version 1 posted 16 You are reading this latest preprint version Abstract Background High sodium intake is a recognized risk factor for increased gastric cancer mortality. This study examines the trends and distribution of stomach cancer burden associated with high sodium intake from 1990 to 2021, with a focus on its relationship with age, period, and birth cohort. Methods Utilizing data from the 2021 Global Burden of Disease study, we applied an age-period-cohort model to conduct statistical analysis. We calculated age, period, and cohort effects, as well as net drift (overall annual percentage change), for gastric cancer deaths and disability-adjusted life years (DALYs) associated with high sodium intake across 204 countries and regions. Results In 2021, 7.93% of global gastric cancer deaths and 7.92% of DALYs were linked to high sodium intake. Populations in East Asia and those with a high-middle Sociodemographic Index (SDI) bore the heaviest burden. Over the 32-year period, the global age-standardized mortality rate[Net drift= -2.33(95%CI:-2.37 to -2.28)] and age-standardized DALYs rate[Net drift = -2.56(95%CI:-2.65 to -2.47)] generally demonstrated a declining trend, particularly in high SDI regions [Net drift = − 2.91 (95%CI: -3.02 to -2.81)]. China, as a representative country, exhibited unfavorable age, period, and cohort effects. Future projections suggest further declines in mortality and DALYs numbers, along with corresponding age-standardized rates. Conclusion Despite ongoing global efforts to reduce sodium intake, gastric cancer remains a significant public health challenge, especially in East Asia. The findings underscore the necessity of developing targeted prevention strategies for high-risk groups, such as the elderly and males, to mitigate the global burden of gastric cancer. global burden of disease gastric cancer mortality disability-adjusted life years net drift Age-period- cohort Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Gastric cancer ranks among the most prevalent malignant neoplasms in the digestive system globally[ 1 ], with around 1.08 million new cases and 769,000 deaths reported in 2020.Dietary sodium, a key component in home cooking and food preservation[ 2 ], plays a crucial role in maintaining the body's internal homeostasis. The primary source of sodium for the human body is through dietary intake. The World Health Organization (WHO) advises adults to restrict their daily sodium intake to 2 grams (equivalent to about 5 grams of salt). However, the global average sodium intake is 3.95 grams per day[ 3 ]. Numerous studies and epidemiological surveys have confirmed a significant link between high sodium consumption and a heightened gastric cancer risk[ 4 – 6 ]. In East Asia, the average sodium intake per capita exceeds 4.2 grams per day, potentially contributing to the high incidence of gastric cancer in this region, warranting particular attention[ 3 , 7 ]. The primary mechanisms linking high sodium intake to stomach cancer include damage to the gastric mucosal barrier, chronic inflammation, and the enhancement of Helicobacter pylori infection[ 8 , 9 ]. Despite improvements in early detection rates, optimized treatment methods, and enhanced systemic therapy, which have effectively reduced the global incidence and mortality rates of gastric cancer, the disease remains highly invasive and specific, with a 5-year overall survival rate of only 20%-30%[ 10 ]. Therefore, gastric cancer continues to pose a significant challenge to global health. A 2010 study of the global burden of stomach cancer found that in high Human Development Index (HDI) countries, the median proportion of gastric cancer risk linked to sodium intake was 22.5% among men and 7.2%-16.6% among women, both higher than in countries with a lower HDI (10.1%)[ 11 ]. This suggests that understanding the distributional differences in high sodium intake can aid in developing targeted prevention and control policies to further reduce the incidence of gastric cancer. However, existing literature primarily focuses on individual-level risks[ 12 , 13 ], leaving gaps in understanding the population-level disease burden related to sodium intake, especially across different regions, genders, and age groups. Additionally, while high sodium intake has been consistently linked to gastric cancer risk, the geographic and temporal heterogeneity in this relationship remains underexplored. Furthermore, global demographic changes such as population aging, declining birth rates, and rapid technological and economic development are altering dietary habits, including sodium consumption patterns, and shaping future disease burdens. Therefore, there is an urgent need to better understand the current levels of sodium exposure across various populations and how these levels are linked to gastric cancer incidence. The Global Burden of Disease (GBD) study is the largest and most comprehensive disease burden research project to date[ 14 ]. The GBD 2021 study employs advanced statistical modeling methods to provide new opportunities for studying and predicting global trends in high sodium consumption and the associated risk of gastric malignancy deaths through 2035. Unlike previous prospective studies that primarily assessed individual-level risk[ 15 ], this study focuses on revealing population-level disparities in disease burden across countries, regions, sexes, and age groups. By applying the age–period–cohort (APC) model to GBD 2021 data, we analyze and project the temporal patterns and demographic heterogeneity of gastric cancer deaths and disability-adjusted life years (DALYs) associated with high sodium intake. The goal is to identify high-burden subpopulations and regions, thereby providing new scientific evidence to support the development of targeted prevention and intervention strategies by governments and public health agencies. 2. Methods 2.1 Overview and Data Collection Data on gastric cancer-related deaths and DALYs associated with high sodium intake from 1990 to 2021 were retrieved from the GBD database, covering global, 204 countries, 21 regions, and five Sociodemographic Index (SDI) levels. We used the Global Health Data Exchange (GHDx) query tool for data analysis. As no individual-specific information was involved, the study was exempted from informed consent by Qilu Hospital of Shandong University. In the 2021 GBD study, the epidemiological data for gastric cancer (ICD-10 code C16) and diets high in sodium (defined as an average 24-hour urinary sodium excretion exceeding 1–5 grams per day) were systematically recorded. In some countries or regions—particularly those with underdeveloped health surveillance systems—estimates may be subject to uncertainty due to underreporting, small population size, or missing data. These regional disparities in data completeness and accuracy are addressed through the statistical modeling framework of the GBD 2021 study, such as imputation and Bayesian modeling to handle missing or incomplete data, which has been extensively documented in previous publications[ 16 , 17 ]. Further details are provided in Supplementary Appendix 1. We analyzed spatial and temporal differences in mortality and DALYs rates across age, gender, and SDI levels. The SDI index, which combines economic, educational, and fertility levels, was divided into five levels to quantify the socioeconomic development of different countries or regions[ 18 ]. Detailed information about the GBD protocol is available online. 2.2 Statistical Analysis We calculated the number of deaths, DALYs, and their corresponding age-standardized mortality rate (ASMR) and age-standardized DALYs rate (ASDR), reported with 95% uncertainty intervals (UI). In cases where the lower limit of the 95% UI approached '0.00' due to small sample sizes, we marked it with an asterisk (*), indicating that while the lower bound is very small, the actual value is not zero. Population Attributable Fraction (PAF) was calculated using exposure levels, associated risk estimates, and the theoretical minimum risk level, estimating the number of deaths and DALYs linked to high sodium consumption in relation to gastric cancer. Age standardization of rates (ASR) was performed to eliminate differences in age structure. Estimated annual percentage change (EAPC) was used to explore patterns in high sodium intake- connected gastric cancer ASMR and ASDR over the period 1990–2021. Additionally, Join-point regression analysis was used to determine key years of change in global gastric cancer ASR. In this study, Spearman correlation coefficients and Pearson correlation analysis were employed to assess the associations between SDI and ASR, and between EAPC and ASR, in relation to high sodium intake. Considering the current and future demographic changes, we used the Bayesian Age-Period-Cohort (BAPC) model to project the number of gastric cancer deaths, DALYs, and corresponding ASRs associated with high sodium diets by 2035. All statistical analyses were conducted using R Studio version 4.4.2, with p-values being two-sided and p < 0.05 considered statistically significant. 2.3 Age-Period-Cohort (APC) Model Analysis The APC model independently assesses potential burden trends across different age groups, periods, and birth cohorts[ 19 , 20 ]. Net drift and local drift are used to evaluate the overall trend of increase or decrease in gastric cancer burden across the population over time, and to identify specific changes in risk trends within certain age groups. Age effects are illustrated by age deviation curves and longitudinal age curves. Period effects are represented by fitted time trends and period deviation curves, tracking relative risk ratios over time. Cohort effects capture specific risk characteristics of populations born in the same time period. In the APC model, the population data studied adopts equal intervals of age and time periods, using 5-year age groups and 5-year period intervals. For example, age intervals are divided into 50–54 years, 55–59 years, etc., and period intervals are divided into [1994]1992–1996, [1999]1997–2001, etc., representing specific time frames. Wald's chi-square test was employed to analyze the impact of the annual percentage trend. 3. Results 3.1 Excess Sodium Intake as a Risk Factor for Gastric Cancer Burden In 2021, 7.93% of global gastric cancer deaths and 7.92% of DALYs were linked to high sodium intake. This proportion peaked in regions with medium SDI and Central Europe, whereas in North Africa and the Middle East, it accounted for only 4.79% and 5.08%, respectively (Fig. 1 ). From 1990 to 2021, trends in deaths and DALYs associated with high sodium consumption among stomach malignancy patients were nearly identical, showing an increasing trend in higher SDI regions, while decreasing across areas with middle and lower SDI. Recently, after a decline, the global attributable percentage seems poised for a rebound(Fig. 1 e, 1 f). 3.2 Global Burden of Gastric Cancer In relation to High Sodium Diets In 2021, high sodium diets were associated with 75,661 stomach cancer deaths and 1,804,592 DALYs. Between 1990 and 2021, stomach cancer deaths linked to high sodium diets increased by 11.52%, while DALYs decreased by 2.22%, indicating an upward trend in mortality but a decline in disease burden compared to 1990. However, in 2021, the ASMR was 0.89 per 100,000 people, with the rate for males (1.29 per 100,000) being twice that of females. Over the 32-year period, the EAPC was − 2.26 (95% CI: -2.35 to -2.18) (Tables 1 and 2 ). Similarly, the ASDR in 2021 was significantly lower compared to 1990 (Table 2 ). The net drift of ASMR and ASDR from 1990 to 2021 was − 2.33 (95% CI: -2.37 to -2.28) and − 2.56 (95% CI: -2.65 to -2.47), respectively (Tables 1 and 2 ). Moreover, the AAPC analysis identified three key years of change in disease burden (1998, 2003, and 2014), with ASMR [APC = -2.971 (95% CI: -3.105 to -2.836), p < 0.001] and ASDR [APC = -3.305 (95% CI: -3.427 to -3.184), p < 0.001] decreasing most rapidly between 2003 and 2014 (Fig. 2 a, 2 b). Table 1 Trends in stomach cancer mortality related to high sodium intake across SDI regions and 21 GBD regions from 1990 to 2021. Deaths ASMR Trend Characteristics 1990 (95%UI) 2021 (95%UI) Changes of number (%) 1990 (95%UI) 2021 (95%UI) Percent of change (%) EAPC (95%CI) Net drift (95%CI) Global 67844.54 (-0.00*-339512.73) 75661.15 (-0.00*-372194.01) 11.52 (-48.17-27.57) 1.74 (-0.00*-8.74) 0.89 (-0.00*-4.37) -49.15 (-75.12–41.55) -2.26 (-2.35–2.18) -2.33 (-2.37–2.28) Sex Male 43642.27 (-0.00*-220825.45) 50373.79 (-0.00*-247168.29) 15.42 (-52.93-43.03) 2.46 (-0.00*-12.43) 1.29 (-0.00*-6.34) -47.44 (-86.35–35.47) -2.12 (-2.22–2.02) -2.21 (-2.27–2.16) Female 24202.27 (0.00*-123377.92) 25287.36 (0.00*-129117.91) 4.48 (-75.40-18.90) 1.15 (0.00*-5.86) 0.55 (0.00*-2.79) -52.63 (-87.13–45.76) -2.57 (-2.67–2.48) -2.59 (-2.65–2.54) SDI Low SDI 1904.18 (0.00*-9916.48) 2893.77 (0.00*-15443.34) 51.97 (-44.99-71.04) 0.86 (0.00*-4.48) 0.60 (0.00*-3.18) -30.17 (-73.37–21.51) -1.14 (-1.20–1.08) -1.21 (-1.46–0.97) Low-middle SDI 4880.69 (0.00*-24751.39) 8255.39 (0.00*-41815.52) 69.14 (-38.62-93.80) 0.82 (0.00*-4.11) 0.59 (0.00*-3.00) -27.47 (-72.91–16.22) -0.95 (-1.00–0.90) -1.00 (-1.13–0.87) Middle SDI 23250.29 (-0.00*-115302.52) 28816.28 (-0.00*-141923.38) 23.94 (-84.20-44.27) 2.30 (-0.00*-11.48) 1.11 (-0.00*-5.43) -51.91 (-93.66–44.26) -2.47 (-2.60–2.34) -2.52 (-2.58–2.45) High-middle SDI 24047.50 (-0.00*-122047.46) 23438.81 (-0.00*-114552.68) -2.53 (-51.25-30.73) 2.44 (-0.00*-12.41) 1.18 (-0.00*-5.79) -51.52 (-69.69–33.89) -2.43 (-2.57–2.30) -2.53 (-2.6–2.45) High SDI 13705.92 (0.00*-69402.89) 12208.66 (0.00*-61721.58) -10.92 (-75.10–3.51) 1.24 (0.00*-6.27) 0.54 (0.00*-2.73) -56.11 (-86.55–52.63) -2.72 (-2.75–2.70) -2.91 (-3.02–2.81) Region Oceania 35.84 (0.00*-195.58) 72.09 (0.00*-380.84) 101.13 (-75.86-179.47) 1.36 (0.00*-7.12) 1.06 (0.00*-5.52) -22.14 (-89.73-3.14) -0.83 (-0.89–0.78) -0.77 (-2.24-0.72) Southeast Asia 2263.81 (0.00*-11384.92) 3571.60 (0.00*-18178.29) 57.77 (-38.20-100.35) 0.91 (0.00*-4.61) 0.56 (0.00*-2.89) -37.76 (-73.17–21.51) -1.71 (-1.78–1.64) -1.66 (-1.84–1.48) East Asia 31815.60 (-0.00*-155223.57) 37861.80 (-0.00*-188112.18) 19.00 (-73.97-48.15) 3.77 (-0.00*-18.42) 1.76 (-0.00*-8.69) -53.31 (-88.29–41.62) -2.54 (-2.75–2.33) -2.59 (-2.69–2.5) Central Asia 998.02 (0.00*-4972.32) 722.78 (0.00*-3724.68) -27.58 (-86.55–21.03) 2.13 (0.00*-10.58) 0.90 (0.00*-4.60) -57.75 (-90.80–54.04) -2.54 (-2.64–2.43) -2.68 (-3–2.35) High-income Asia Pacific 6059.35 (0.00*-29936.84) 5863.70 (0.00*-29532.38) -3.23 (-77.05-3.96) 3.09 (0.00*-15.26) 1.09 (0.00*-5.43) -64.74 (-88.86–62.25) -3.44 (-3.48–3.39) -3.83 (-4–3.65) Central Europe 2259.36 (-0.00*-11339.28) 1600.00* (0.00*-7857.90) -29.18 (-86.41–23.00) 1.54 (-0.00*-7.69) 0.71 (0.00*-3.49) -53.66 (-80.91–49.62) -2.58 (-2.67–2.49) -2.55 (-2.84–2.25) Eastern Europe 6322.64 (-0.00*-32995.76) 3240.56 (0.00*-16644.57) -48.75 (-106.61–38.98) 2.25 (-0.00*-11.72) 0.92 (0.00*-4.74) -58.94 (-106.54–47.68) -3.11 (-3.21–3.01) -2.99 (-3.16–2.82) Australasia 113.13 (0.00*-630.21) 138.14 (0.00*-787.55) 22.11 (-84.93-85.13) 0.49 (0.00*-2.70) 0.25 (0.00*-1.41) -48.29 (-95.44–2.40) -2.10 (-2.23–1.98) -2.05 (-3.03–1.07) Western Europe 6132.03 (0.00*-32392.14) 4049.66 (0.00*-21330.73) -33.96 (-89.19–22.77) 1.04 (0.00*-5.47) 0.41 (0.00*-2.10) -60.90 (-88.10–47.62) -3.00 (-3.10–2.89) -2.90 (-3.1–2.71) Southern Latin America 685.50 (0.00*-3454.82) 751.84 (0.00*-3766.66) 9.68 (-54.96-77.48) 1.52 (0.00*-7.61) 0.85 (0.00*-4.26) -43.97 (-75.67–8.38) -1.69 (-1.81–1.58) -1.61 (-2–1.21) High-income North America 1413.91 (0.00*-7465.54) 1481.69 (0.00*-7675.10) 4.79 (-3.82-567.15) 0.40 (0.00*-2.10) 0.23 (0.00*-1.17) -42.96 (-47.06-301.08) -1.88 (-1.92–1.83) -1.55 (-1.82–1.28) Caribbean 236.21 (0.00*-1222.59) 311.09 (0.00*-1670.65) 31.70 (-79.82-53.63) 0.94 (0.00*-4.83) 0.58 (0.00*-3.10) -38.38 (-87.19–27.45) -1.48 (-1.56–1.40) -1.32 (-1.88–0.75) Andean Latin America 522.25 (0.00*-2644.79) 988.32 (0.00*-4998.75) 89.24 (-40.68-139.55) 2.67 (0.00*-13.48) 1.71 (0.00*-8.65) -36.04 (-78.38–17.97) -1.64 (-1.80–1.49) -1.57 (-1.9–1.24) Central Latin America 1316.45 (0.00*-6624.97) 2295.17 (0.00*-11894.87) 74.35 (-37.46-106.66) 1.70 (0.00*-8.53) 0.93 (0.00*-4.83) -45.01 (-79.55–35.36) -2.18 (-2.26–2.10) -1.86 (-2.06–1.65) Tropical Latin America 1355.85 (-0.00*-6802.21) 1988.04 (-0.00*-10206.04) 46.63 (-65.55-68.09) 1.58 (-0.00*-7.95) 0.78 (-0.00*-4.01) -50.57 (-88.06–42.62) -2.36 (-2.42–2.30) -2.18 (-2.41–1.95) North Africa and Middle East 1257.34 (0.00*-7340.69) 1976.55 (0.00*-11965.72) 57.20 (-25.35-255.55) 0.75 (0.00*-4.40) 0.45 (0.00*-2.75) -40.01 (-75.61-39.47) -1.58 (-1.64–1.51) -1.66 (-1.91–1.41) South Asia 3630.87 (0.00*-18682.73) 6498.89 (0.00*-32699.54) 78.99 (45.98-197.59) 0.62 (0.00*-3.21) 0.45 (0.00*-2.25) -28.11 (-40.12-26.68) -0.95 (-1.04–0.86) -0.99 (-1.16–0.82) Eastern Sub-Saharan Africa 657.50 (0.00*-3325.25) 852.33 (0.00*-4457.26) 29.63 (-63.47-51.72) 0.90 (0.00*-4.50) 0.54 (0.00*-2.79) -39.89 (-84.11–32.28) -1.90 (-2.00–1.81) -1.94 (-2.38–1.5) Southern Sub-Saharan Africa 148.19 (0.00*-796.35) 254.56 (0.00*-1387.35) 71.77 (-65.11-104.27) 0.55 (0.00*-3.00) 0.45 (0.00*-2.48) -18.42 (-81.70–4.44) -0.69 (-1.03–0.35) -0.67 (-1.3–0.03) Western Sub-Saharan Africa 478.73 (0.00*-2558.73) 880.31 (0.00*-4609.45) 83.89 (40.82-313.43) 0.57 (0.00*-3.06) 0.49 (0.00*-2.54) -15.00 (-33.71-93.43) -0.28 (-0.37–0.20) -0.47 (-0.88–0.07) Central Sub-Saharan Africa 141.97 (0.00*-838.20) 262.04 (0.00*-1543.94) 84.58 (21.52-731.66) 0.68 (0.00*-4.03) 0.51 (0.00*-2.99) -24.61 (-46.75-341.36) -0.96 (-1.00–0.92) -0.94 (-1.82–0.05) ASMR, Age-standardized mortality rate; EAPC, estimated annual percentage changes; Net drift, overall annual percentage change; SDI, Socio-demographic Index; UI, uncertainty interval; CI, confidence interval. When the sample size was small, the lower limit of the 95% uncertainty interval (UI) appeared as '0.00'. We marked this with an asterisk '*', indicating that the lower limit is very small, but the actual value is not zero. Table 2 Trends in stomach cancer Disability-Adjusted Life Years related to high sodium intake across SDI regions and 21 GBD regions from 1990 to 2021. DALYS ASDR Trend Characteristics 1990 (95%UI) 2021 (95%UI) Changes of number (%) 1990 (95%UI) 2021 (95%UI) Percent of change (%) EAPC (95%CI) Net drift (95%CI) Global 1845616.80 (-0.03-9206157.73) 1804591.52 (-0.00*-8884379.02) -2.22 (-65.17-12.11) 44.53 (-0.00*-222.31) 20.78 (-0.00*-102.38) -53.33 (-82.78–46.23) -2.56 (-2.65–2.47) -2.32 (-2.38–2.26) Sex Male 1221058.35 (-0.04-6151893.29) 1231290.32 (-0.02-6026424.24) 0.84 (-56.99-22.50) 62.20 (-0.00*-314.71) 29.90 (-0.00*-146.65) -51.93 (-82.84–41.44) -2.43 (-2.52–2.33) -2.21 (-2.29–2.13) Female 624558.45 (-0.00*-3176862.29) 573301.21 (0.00*-2940974.24) -8.21 (-56.66-5.57) 28.71 (-0.00*-146.18) 12.61 (0.00*-64.62) -56.10 (-79.11–49.49) -2.84 (-2.94–2.74) -2.59 (-2.64–2.54) SDI Low SDI 56793.80 (0.00*-295505.22) 82603.09 (0.00*-441280.65) 45.44 (-43.09-66.13) 22.47 (0.00*-116.97) 14.71 (0.00*-78.41) -34.52 (-75.54–25.60) -1.41 (-1.47–1.36) -1.22 (-1.36–1.08) Low-middle SDI 145052.73 (0.00*-737564.41) 226298.29 (0.00*-1148264.79) 56.01 (-54.59-78.87) 21.39 (0.00*-108.46) 14.78 (0.00*-75.09) -30.89 (-79.13–21.19) -1.13 (-1.16–1.09) -1.00 (-1.07–0.93) Middle SDI 667339.27 (-0.02-3270306.50) 712581.58 (-0.00*-3527047.08) 6.78 (-77.70-25.15) 59.02 (-0.00*-290.91) 25.82 (-0.00*-127.58) -56.26 (-94.25–48.84) -2.79 (-2.91–2.67) -2.51 (-2.61–2.41) High-middle SDI 653552.16 (-0.02-3320830.23) 551396.41 (-0.00*-2681101.33) -15.63 (-75.64-14.03) 63.60 (-0.00*-322.96) 28.14 (-0.00*-136.92) -55.76 (-78.94–39.56) -2.76 (-2.90–2.63) -2.52 (-2.61–2.43) High SDI 321495.69 (0.00*-1625677.68) 230573.68 (0.00*-1154959.78) -28.28 (-75.13–22.92) 29.89 (0.00*-151.09) 11.60 (0.00*-58.09) -61.18 (-84.55–58.62) -3.11 (-3.14–3.08) -2.90 (-2.96–2.85) Region Oceania 1064.57 (0.00*-5908.38) 2143.71 (0.00*-11558.10) 101.37 (-90.42-196.10) 32.71 (0.00*-178.43) 25.52 (0.00*-134.98) -21.97 (-89.55-8.29) -0.82 (-0.89–0.76) -0.77 (-1.18–0.35) Southeast Asia 66675.66 (0.00*-336540.35) 97670.36 (0.00*-497996.69) 46.49 (-55.32-83.59) 23.49 (0.00*-118.14) 13.98 (0.00*-71.25) -40.48 (-77.92–24.37) -1.85 (-1.92–1.78) -1.66 (-1.72–1.6) East Asia 910165.99 (-0.04-4421005.46) 906420.39 (-0.01-4574157.79) -0.41 (-68.64-27.17) 96.58 (-0.00*-469.07) 41.09 (-0.00*-206.63) -57.45 (-84.22–45.66) -2.88 (-3.07–2.69) -2.58 (-2.73–2.44) Central Asia 28789.80 (0.00*-143224.49) 20435.50 (0.00*-105882.27) -29.02 (-91.01–22.39) 57.97 (0.00*-288.43) 23.08 (0.00*-119.44) -60.19 (-92.70–56.69) -2.80 (-2.88–2.73) -2.70 (-2.8–2.59) High-income Asia Pacific 151937.98 (0.00*-743669.54) 99016.59 (0.00*-495285.28) -34.83 (-82.65–29.38) 74.62 (0.00*-365.66) 22.57 (0.00*-112.13) -69.75 (-91.26–67.20) -3.92 (-3.97–3.87) -3.81 (-3.88–3.75) Central Europe 55098.53 (-0.00*-277908.93) 34860.24 (0.00*-170908.90) -36.73 (-89.53–31.12) 36.72 (-0.00*-185.31) 16.71 (0.00*-81.88) -54.50 (-88.39–50.46) -2.62 (-2.71–2.53) -2.55 (-2.6–2.49) Eastern Europe 173732.78 (-0.00*-903204.03) 79239.65 (0.00*-402533.45) -54.39 (-105.18–45.58) 61.63 (-0.00*-320.72) 23.54 (0.00*-119.38) -61.80 (-99.77–50.88) -3.40 (-3.52–3.28) -3.01 (-3.14–2.88) Australasia 2602.89 (0.00*-14210.09) 2808.90 (0.00*-15482.52) 7.91 (-84.77-99.46) 11.25 (0.00*-61.37) 5.72 (0.00*-31.14) -49.21 (-95.37-5.00) -2.16 (-2.27–2.05) -2.02 (-2.2–1.85) Western Europe 127536.05 (0.00*-667811.00) 76170.59 (0.00*-392854.74) -40.28 (-87.44–25.01) 22.85 (0.00*-119.24) 8.93 (0.00*-45.98) -60.90 (-88.96–43.96) -2.96 (-3.05–2.87) -2.89 (-2.95–2.83) Southern Latin America 16277.62 (0.00*-82265.41) 16598.73 (0.00*-82996.48) 1.97 (-49.89-63.74) 35.00 (0.00*-176.91) 19.43 (0.00*-97.04) -44.50 (-71.44–10.94) -1.73 (-1.85–1.61) -1.60 (-1.68–1.53) High-income North America 31635.14 (0.00*-165388.25) 32630.83 (0.00*-166471.04) 3.15 (-3.79-601.26) 9.34 (0.00*-48.84) 5.52 (0.00*-28.10) -40.91 (-44.67-341.77) -1.73 (-1.77–1.68) -1.53 (-1.58–1.48) Caribbean 5804.55 (0.00*-30337.97) 7607.75 (0.00*-40990.47) 31.07 (-76.76-65.27) 21.93 (0.00*-114.63) 14.24 (0.00*-76.67) -35.07 (-89.41–20.11) -1.31 (-1.41–1.20) -1.32 (-1.44–1.21) Andean Latin America 13409.97 (0.00*-68083.61) 23127.69 (0.00*-116683.73) 72.47 (-39.13-120.43) 62.37 (0.00*-316.56) 38.45 (0.00*-193.86) -38.35 (-78.90–21.38) -1.79 (-1.93–1.64) -1.57 (-1.64–1.5) Central Latin America 33796.36 (0.00*-170527.82) 57296.69 (0.00*-297890.07) 69.54 (-62.34-97.93) 38.56 (0.00*-194.24) 22.41 (0.00*-116.52) -41.88 (-85.38–31.56) -2.00 (-2.09–1.92) -1.86 (-1.9–1.81) Tropical Latin America 35939.59 (-0.00*-179651.92) 49223.00 (-0.00*-252502.16) 36.96 (-46.99-69.31) 37.33 (-0.00*-186.56) 18.88 (-0.00*-96.83) -49.42 (-81.41–39.42) -2.33 (-2.39–2.27) -2.18 (-2.23–2.13) North Africa and Middle East 37024.39 (0.00*-214620.55) 54759.40 (0.00*-325675.88) 47.90 (-26.50-210.16) 19.66 (0.00*-114.77) 11.00 (0.00*-65.92) -44.02 (-69.62-22.29) -1.83 (-1.90–1.76) -1.66 (-1.74–1.58) South Asia 112571.19 (0.00*-573787.74) 180806.07 (0.00*-908078.08) 60.61 (39.66-186.64) 17.02 (0.00*-87.29) 11.45 (0.00*-57.60) -32.71 (-43.16-22.12) -1.19 (-1.27–1.11) -1.00 (-1.17–0.83) Eastern Sub-Saharan Africa 19716.43 (0.00*-99792.75) 24470.02 (0.00*-129514.73) 24.11 (-68.25-45.93) 23.37 (0.00*-118.12) 12.94 (0.00*-67.73) -44.61 (-84.09–35.28) -2.21 (-2.32–2.10) -1.95 (-2.08–1.82) Southern Sub-Saharan Africa 4431.39 (0.00*-23681.44) 7339.70 (0.00*-39115.11) 65.63 (-103.65-88.45) 14.62 (0.00*-78.11) 11.52 (0.00*-62.18) -21.18 (-84.47–7.90) -0.75 (-1.10–0.41) -0.66 (-0.86–0.46) Western Sub-Saharan Africa 13169.32 (0.00*-70651.20) 24102.24 (0.00*-126886.42) 83.02 (41.95-301.39) 14.02 (0.00*-75.09) 11.32 (0.00*-59.28) -19.30 (-37.49-80.85) -0.48 (-0.56–0.39) -0.47 (-0.58–0.37) Central Sub-Saharan Africa 4236.60 (0.00*-24980.08) 7863.51 (0.00*-46176.26) 85.61 (-55.98-487.40) 16.97 (0.00*-100.02) 12.54 (0.00*-73.89) -26.11 (-48.27-212.99) -1.02 (-1.06–0.98) -0.94 (-1.2–0.68) ASDR, Age-standardized Disability-Adjusted Life Years rate; EAPC, estimated annual percentage changes; Net drift, overall annual percentage change; SDI, Socio-demographic Index; UI, uncertainty interval; CI, confidence interval. When the sample size was small, the lower limit of the 95% uncertainty interval (UI) appeared as '0.00'. We marked this with an asterisk '*', indicating that the lower limit is very small, but the actual value is not zero. 3.3 Stomach Cancer Burden In relation to High Sodium Intake by SDI and GBD Regions Despite the passage of 32 years, the disease burden in high-middle SDI areas remained the highest. Notably, both mortality and DALY rates decreased across all SDI regions (Fig, 2c, 2d), but the decline in high SDI regions was much faster than in other regions (Table 1 ,Table 2 , Fig. 2 ), making them the regions with the lowest disease burden in 2021. The net drift of ASMR ranged from − 2.91% per year (95% CI: -3.02 to -2.81) in high SDI regions to -1.21% per year (95% CI: -1.46 to -0.97) in low SDI regions, while the ASDR net drift varied between − 2.90% (95% CI: -2.96 to -2.85) to -1.22% (95% CI: -1.36 to -1.08) (Table 1 , Table 2 ). Detailed EAPC changes by region are shown in Table 1 . Table 1 presents the number of deaths, ASMR, EAPC, percentage change, and net drift of mortality for each SDI region and 21 geographical regions in 1990 and 2021, while Table 2 provides the corresponding information for DALYs. In 2021, seven regions had mortality and DALY rates exceeding the global average, with the heaviest burden observed in East Asia, accounting for nearly half of the world's deaths. Naturally, East Asia also had the highest ASMR and ASDR (Fig. S1 ). In contrast, the ASMR and ASDR in High-income North America remained the lowest in both 1990 and 2021, with average annual decreases of -1.88% (95% CI: -1.92 to -1.83) and − 1.73% (95% CI: -1.77 to -1.68), respectively (Tables 1 and 2 ). From 1990 to 2021, the most significant decreases in mortality net drift were observed in High-income Asia Pacific [-3.83 (95% CI: -4.00 to -3.65)], followed by Eastern Europe and Western Europe, with similar declines in ASDR net drift (Tables 1 and 2 ). Table 1 shows EAPC changes across GBD regions, with Western Sub-Saharan Africa exhibiting the smallest changes, where the net drift trend aligns with the EAPC trend. 3.4 National Trends in Stomach Cancer Burden As indicated in Supplementary Tables S1 and S2, in 2021, the countries with the highest ASMR linked to high sodium intake were Mongolia [2.96 (95% UI: 0.00*-15.68)], Bolivia (Plurinational State of) [2.58 (95% UI: 0.00*-13.23)], and Guatemala [1.98 (95% UI: 0.00*-10.25)], with 46 countries having an ASMR exceeding 0.89/100,000 and with 52 countries having an ASDR exceeding 20.78/100,000. Among the 204 countries, China's situation caught our attention, with the number of deaths increasing from 31,208 in 1990 to 36,958 in 2021, with an average annual net drift decrease of 2.62% (95% CI: -2.72 to -2.52). The average annual net drift in DALYs also decreased by 2.62% (95% CI: -2.77 to -2.47). Nevertheless, China's death and DALY numbers accounted for nearly half of the global total. Over the 32-year period, the burden increased in nine countries, including Egypt, Lesotho, and Zimbabwe, with Egypt having the largest net drift, with ASMR[1.94 (95% CI: 0.70–3.19)] and ASDR[1.93 (95% CI: 1.56–2.31)]. For the remaining countries, the net drift was < 0, predominantly found in high and high-middle SDI areas, represented by Singapore, Japan, and Switzerland (net drift ≤ -3.0%).The fastest reductions in ASMR and ASDR were observed in the Republic of Korea. The distribution of stomach carcinoma deaths and DALYs linked to high sodium diets and EAPC by country is shown in Figure S1 . Overall, the trends in disease burden varied across countries. Moreover, the EAPC burden trends do not necessarily align with net drift, highlighting the need to further distinguish period effects from cohort effects. 3.5 Correlation Between SDI and Disease Burden and Influencing Factors of EAPC There was a notable positive correlation between SDI and ASMR in 21 regions (R=-0.005, p < 0.001) and 204 countries (R = -0.328, p < 0.001). Similarly, SDI was significantly negatively correlated with ASDR in 21 regions (R = -0.029, p < 0.001) and 204 countries (R = -0.357, p < 0.001) (Fig. 3 ). We observed an upward relationship between EAPC and ASMR (R = 0.25, p < 0.001) and ASDR (R = 0.26, p < 0.001). Additionally, the study found a strong positive association between SDI and EAPC for both ASMR (-0.62, p < 0.001) and ASDR (-0.59, p < 0.001) (Fig. 3 e, 3 f). Countries with higher SDI scores experienced faster declines in ASMR and ASDR. 3.6 Age and Gender Patterns and Overall Temporal Trends In 2021, gastric neoplasm deaths and DALYs in relation to high dietary sodium were primarily concentrated in individuals aged 70–74 years. However, ASMR and ASDR generally rose with age, with a decline noted in men only after 94 years (Fig. 4 a, 4 b).It is noteworthy that the disease burden among men far exceeded that of women across almost all age groups (Fig. 4 a, 4 b). We divided all age groups into six categories, and globally, the mortality and DALY rates in the 85 + age group accounted for the largest proportions at 34.5% and 22.6%, respectively, with even more pronounced proportions observed in High-income Asia Pacific and Australia (Fig. 4 c, 4 d). Figures 4 e and 4 f detail the gender differences in ASMR and ASDR across GBD regions, with East Asia showing the most striking differences [ASMR (male 2.67 vs female 0.99), ASDR (male 61.57 vs female 22.03)].From 1990 to 2021, ASMR showed a similar overall downward trend across all age groups except for the 15–49 age group, despite experiencing minor fluctuations. For ASDR, the 70–74 and 75–79 age groups experienced a rapid decline and are no longer the groups with the highest DALY rates (Fig. 4 g, 4 h). Detailed time trends for age groups in different SDI regions are shown in Figure S2 . 3.7 Projections of Stomach Cancer Burden In relation to High Sodium Diets Figure S3 shows global projections of gastric neoplasm deaths and DALYs linked to high dietary sodium, predicting further declines in the count of deaths, DALYs, and corresponding ASMR and ASDR from 2022 to 2035. By 2035, ASMR and ASDR related to high sodium diets are projected to be 1.06 (95% UI: 0.97–1.16) and 24.77 (95% UI: 22.54–27.01), respectively (Table S3 , Table S4 ). Additionally, global deaths are projected to reach 60,070 and DALYs to 1,398,848 by 2035 (Table S5 , Table S6 ). 3.8 Age-Period-Cohort Effects We further explored the independent contributions and interactions of age, period, and birth cohort factors on gastric cancer burden, revealing significant heterogeneity across global and different SDI regions (Fig. 5 and Fig. S4 ). The age effect indicated that mortality and DALYs rates consistently increased with age, peaking around 85 years, while DALY rates peaked between 50 to 60 years (Panel D),this trend was further supported by the age deviation plot (Panel A). This trend was almost consistent across SDI regions but was particularly pronounced in low SDI regions. The period effect (Panel B) reflected the risk changes observed during the study period (1990–2021), with larger fluctuations indicating instability in medical and public health interventions. Nevertheless, the fitted time trends (Panel G) and risk ratios (RR) showed an overall decline in ASMR and ASDR, particularly in recent decades. Although fluctuations in low-middle SDI regions were smaller than in other regions, a consistent period effect was observed across all five SDI areas, with a rapid decline in middle and high-middle SDI regions. The cohort effect (Panel C) further revealed that those born in the 1920s and 1930s may have been exposed to higher sodium levels early in life, leading to a heightened risk of gastric cancer later in life. In contrast, those born in more recent cohorts (after the 1970s) showed a significant downward trend. Notably, this difference was more pronounced in higher SDI regions, as reflected in the cohort risk ratio (Panel I). Finally, local drift and net drift analysis showed that the decline in stomach cancer burden related to high dietary sodium was fastest between the ages of 30 and 50, slowing down after 50, with almost no change by around 90 years old (Panel J). Furthermore, we focused on a representative country-China-where the temporal trends and age-gender differences in stomach cancer burden closely mirrored the global patterns (Fig. S5 ). The disease burden declined most rapidly between 2003 and 2015 (Fig. S5 ). The age-period-cohort effects are detailed in Figure S6 . Overall, the trends of these effects were similar to the global trends, but China exhibited more pronounced changes in certain aspects, particularly in the magnitude of the age effect (a rapid increase in DALY rates between 60 and 70 years), the volatility of the period effect (with significant up and down trends between 2000 and 2010), and the significance of the cohort effect. 4. Discussion In 2021, 7.93% of global gastric cancer deaths and 7.92% of DALYs were estimated to be associated with high sodium diets. Despite the declining trend in ASMR and ASDR over the last 32 years globally, there were 75,661 deaths from gastric cancer linked to high sodium intake in 2021, marking an 11.52% increase since 1990, a figure partially explained by population aging and structural changes[ 21 , 22 ]. Despite the World Health Organization’s global strategies to reduce sodium intake, including food labeling regulations, the disease burden in some countries, including China, remains above the global average and is even increasing in certain regions and countries. This study utilized the GBD 2021 database to characterize the epidemiological characteristics of stomach cancer associated with high sodium diets, capturing temporal trends and demographic heterogeneity in disease burden. It is the first to apply an APC model to examine the effects of age, period, and cohort factors. The study also projects future trends in disease burden, offering valuable scientific evidence and novel insights for the primary prevention of stomach cancer. High sodium intake is documented as the second leading risk factor for gastric cancer after smoking[ 23 ]. Studies have explored the potential mechanisms by which high concentrations of salt elevate gastric cancer risk[ 6 ], however, the biological link between high sodium intake and gastric cancer remains worth exploring. Excessive salt may activate the Wnt/β-catenin signaling pathway, thereby activating the expression of oncogenes[ 24 ]. Additionally, a high sodium environment may lead to methylation changes, making certain oncogenes, such as c-Myc , more prone to activation[ 25 ]. It should be acknowledged that other metabolic or lifestyle-related factors, such as obesity, alcohol use, or Helicobacter pylori infection, may also influence gastric cancer risk and potentially co-vary with sodium intake[ 26 ]. Although not included in the GBD 2021 list of risk-outcome pairs for gastric cancer, these variables may interact biologically or behaviorally with high sodium diets, complicating direct causal interpretations. Finally, individuals under significant stress or with emotional instability are more likely to choose high-salt, heavily flavored foods to relieve stress[ 27 ]. Chronic sleep deprivation[ 28 ] and irregular eating habits[ 29 ], including increased consumption of high-salt fast foods and snacks, exacerbate gastric damage[ 30 ]. The disease burden varies significantly across countries and regions[ 11 ]. We observed a consistently high burden in East Asia, especially in China, this may be related to traditional dietary habits where high-salt foods, such as soy sauce and pickled products, play a significant role, and these habits are difficult to change quickly[ 31 ]. In high-SDI regions such as Singapore and Luxembourg, ASMR and ASDR have declined rapidly. In 2019, Singapore’s Health Promotion Board launched the "Healthier Choice" program, labeling low-sodium foods and encouraging manufacturers to reduce sodium content in products. In contrast, in countries like Egypt and Lesotho, limited access to treatment due to economic development constraints and a lack of public awareness about the health risks of high salt diets. Overall, despite many countries enacting sodium intake regulations, the lack of enforcement and effective global cooperation has limited the promotion and implementation of these measures. Globally and regionally, gastric cancer mortality and DALYs rates correlated with high sodium intake increase with age, exposing the high risk among middle-aged and older populations. This is related to physiological aging and cumulative exposure risks. Meanwhile, in high-SDI regions, the disease burden among younger individuals is declining rapidly. Mortality and DALYs do not show a trend toward younger ages, likely due to public health policies in high-SDI regions focusing more on the promotion of healthy diets and other preventive measures, with young people being the primary targets for education and intervention. Moreover, younger individuals may be more likely to undergo regular health checks and early screening programs. We found that the disease burden in males was notably greater than in females, aligning with previous studies[ 32 ]. Smoking and alcohol consumption are established risk factors for gastric malignancy[ 33 ], the former can exacerbate H. pylori infection, while the latter may increase the irritating damage of salt to the gastric mucosa, making men more susceptible to these unhealthy habits[ 34 ]. Additionally, in East Asian culture, men more frequently participate in social activities, where men typically consume large amounts of sodium-rich snacks. Some studies suggest that the male gastric mucosa may respond more strongly to sodium-induced oxidative stress. Certain hormone levels (e.g., testosterone) may also make men more vulnerable to the negative effects of high sodium diets[ 35 , 36 ]. Regarding period effects, the relative risk curve showed a downward trend, possibly due to advances in medical technology and increased health awareness. The faster decline in gastric cancer burden in middle and high-middle SDI regions reflects significant progress in public health policies and medical interventions. For instance, China has revised food labeling regulations since 2007 and promoted health education, reducing the average daily sodium intake to 5.2g by 2012. We also explored factors such as the reduction in H. pylori infection rates[ 37 ], advances in food preservation technology[ 38 ], dietary habit changes, smoking rate reductions, and socioeconomic improvements, which collectively support the observation that later birth cohorts have lower gastric cancer mortality and disease burden (DALYs) risks compared to those born in the 1920s and 1930s. This study has some limitations: (1) Data collection in low-income countries is limited, and the quality of data may obscure epidemiological characteristics; (2) The analysis based on the APC model still requires future cohort studies to verify specific spatiotemporal risk differences; (3) The differences in implementation and regulation of salt control policies across countries make it difficult to link our study with policy changes; (4) In the 2021 GBD comparative risk assessment framework, only smoking and high sodium intake were included as established risk factors for gastric cancer. Therefore, the current analysis may not fully capture the potential confounding or interactive effects of other variables such as obesity, alcohol consumption, and broader dietary patterns. Further research is warranted to investigate the complex interplay of multiple coexisting risk factors across diverse populations. Future research should further elucidate the biological mechanisms linking high sodium intake to gastric cancer, particularly through longitudinal and experimental studies. It is also essential to integrate more comprehensive dietary data and apply multi-omics approaches or large-scale cohort analyses to disentangle the independent and interactive effects of confounders such as Helicobacter pylori infection and smoking. Moreover, targeted investigations into the cultural and behavioral factors underlying the high disease burden among older adults, males, and populations in East Asia are critical for context-specific interventions. From a policy perspective, future studies should assess the effectiveness of interventions such as restrictions on high-sodium food advertising, sales, and taxation across varying socioeconomic contexts. Priority should also be given to developing predictive models to improve early screening and risk stratification. Efforts must be made to enhance data collection in low-income countries and improve the precision of national-level estimates to better support evidence-based health policy planning and resource allocation. 5. Conclusion Despite the implementation of salt reduction policies, the burden of gastric cancer related to high sodium intake remains a significant public health challenge, particularly in East Asia and countries with rising mortality rates (e.g., Egypt), highlighting the importance of government strategies. Early screening and intervention for high-risk populations such as males and the elderly, and actively managing sodium levels in cancer survivors, can effectively reduce the burden of gastric cancer. Our study provides important evidence for health departments in resource allocation and offers practical public health strategies. Abbreviations GBD, global burden of disease; ASR, age-standardized rates; ASMR, Age-standardized mortality rate ASDR, Age-standardized disability-adjusted life years rate; EAPC, estimated annual percentage changes; SDI, Socio-demographic Index; AMI, acute mesenteric ischemia; APC, age‑period cohort; AAPC, average annual percentage change; BAPC, Bayesian Age-Period-Cohort UI, uncertainty intervals; CI, confidence intervals. Declarations Ethics approval and consent to participate ： This study utilized the Global Health Data Exchange (GHDx) query tool for data analysis and did not involve any specific personal information. As a result, it was granted an exemption from informed consent by Qilu Hospital of Shandong University. Consent for publication: Not applicable Availability of data and materials: The GBD 2021 database is an open-access resource, with unrestricted access and usage. The datasets provided in this study can be accessed through the Institute for Health Metrics and Evaluation (IHME) website at http://www.healthdata.org/. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding: This research was supported by National Natural Science Foundation of China (grant numbers No. 82070852, grant numbers No. 82270901). Acknowledgements: Not applicable Research involving Human Participants and/or Animals : Not applicable Informed consent : Not applicable Author Contributions ： X.Z. drafted the initial manuscript, designed the study, and performed data collection, analysis, interpretation, and visualization. Z.J contributed to the methodology and supervision, participated in the literature review, and provided support for the theoretical framework. 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Aging disease. 2024;15(2):640–97. Machlowska J, Baj J, Sitarz M, Maciejewski R, Sitarz R. Gastric Cancer: Epidemiology, Risk Factors, Classification, Genomic Characteristics and Treatment Strategies. Int J Mol Sci 2020, 21(11). Henry JP. Stress, salt and hypertension. Social science & medicine (1982) 1988, 26(3):293–302. Shen XZ, Koo MW, Cho CH. Sleep deprivation increase the expression of inducible heat shock protein 70 in rat gastric mucosa. World J Gastroenterol. 2001;7(4):496–9. Cai L, Zheng ZL, Zhang ZF. Risk factors for the gastric cardia cancer: a case-control study in Fujian Province. World J Gastroenterol. 2003;9(2):214–8. Wu X, Zhang Q, Guo H, Wang N, Fan X, Zhang B, Zhang W, Wang W, Fang Z, Wu J. Dietary patterns and risk for gastric cancer: A case-control study in residents of the Huaihe River Basin, China. Front Nutr. 2023;10:1118113. Peleteiro B, Lopes C, Figueiredo C, Lunet N. Salt intake and gastric cancer risk according to Helicobacter pylori infection, smoking, tumour site and histological type. Br J Cancer. 2011;104(1):198–207. Luo G, Zhang Y, Guo P, Wang L, Huang Y, Li K. Global patterns and trends in stomach cancer incidence: Age, period and birth cohort analysis. Int J Cancer. 2017;141(7):1333–44. Jiang L, Wang A, Yang S, Fang H, Wang Q, Li H, Liu S, Liu A. The Burden of Gastric Cancer Attributable to High Sodium Intake: A Longitudinal Study from 1990 to 2019 in China. Nutrients 2023, 15(24). Jeong Y, Kim ES, Lee J, Kim Y. Trends in sodium intake and major contributing food groups and dishes in Korea: the Korea National Health and Nutrition Examination Survey 2013–2017. Nutr Res Pract. 2021;15(3):382–95. Saugandhika S, Sapra L, Kumari K, Srivastava RK. High Salt Diet Impairs Male Fertility in Mice via Modulating the Skeletal Homeostasis. Reproductive Sci (Thousand Oaks Calif). 2023;30(11):3339–52. Bello II, Omigbodun A, Morhason-Bello I. Common salt aggravated pathology of testosterone-induced benign prostatic hyperplasia in adult male Wistar rat. BMC Urol. 2023;23(1):207. Hooi JKY, Lai WY, Ng WK, Suen MMY, Underwood FE, Tanyingoh D, Malfertheiner P, Graham DY, Wong VWS, Wu JCY, et al. Global Prevalence of Helicobacter pylori Infection: Systematic Review and Meta-Analysis. Gastroenterology. 2017;153(2):420–9. Moodie R, Stuckler D, Monteiro C, Sheron N, Neal B, Thamarangsi T, Lincoln P, Casswell S. Profits and pandemics: prevention of harmful effects of tobacco, alcohol, and ultra-processed food and drink industries. Lancet (London England). 2013;381(9867):670–9. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx TableS2.docx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx FigureS1.pdf FigureS2.pdf FigureS3.pdf FigureS4.pdf FigureS5.pdf FigureS6.pdf Appendix1.pdf Cite Share Download PDF Status: Published Journal Publication published 06 Oct, 2025 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 01 Aug, 2025 Reviews received at journal 01 Aug, 2025 Reviews received at journal 30 Jul, 2025 Reviews received at journal 27 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviews received at journal 21 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers agreed at journal 18 Jul, 2025 Reviewers agreed at journal 17 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers agreed at journal 15 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers invited by journal 14 Jul, 2025 Submission checks completed at journal 14 Jul, 2025 First submitted to journal 13 Jul, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5037516","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485326566,"identity":"282641de-7492-4e1a-ae5f-46e76c3f108d","order_by":0,"name":"Xiqiang Zhang","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xiqiang","middleName":"","lastName":"Zhang","suffix":""},{"id":485326568,"identity":"50cfee7c-f7af-415f-a41a-86c47f79bcf6","order_by":1,"name":"Kexin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYDACHgbmx38qbOTY2NsPEK2FzYDnTJoxH8+ZBKK1MEjwth1KnCfhYECcDoMzZwwMJNgOpLdJMCQw/KjYRoSWs20JDwx47uS2STceYOw5c5uwFrPzzAcMEiSe5bbJHEhgZmwjSgtjg8QBg8PpbBIJBkRqOdt8QLIh4XAC8VrszxxLM2Y4kGbYBgzkg0T5RbInx/gx4z8befn29oMPflQQoQUFHCBR/SgYBaNgFIwCXAAArhM99EcQ72IAAAAASUVORK5CYII=","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Kexin","middleName":"","lastName":"Wang","suffix":""},{"id":485326569,"identity":"ed667d11-450b-4ac1-90e2-15dd9effb390","order_by":2,"name":"Zhaoyi Jing","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhaoyi","middleName":"","lastName":"Jing","suffix":""},{"id":485326570,"identity":"a998db44-2190-4844-a8dc-1863b1946c90","order_by":3,"name":"Ao Yu","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Ao","middleName":"","lastName":"Yu","suffix":""},{"id":485326571,"identity":"8d08b8bf-441d-44e2-a703-2ee56eb74f04","order_by":4,"name":"Xinzhen Xu","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xinzhen","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-09-05 10:41:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5037516/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5037516/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-025-14891-6","type":"published","date":"2025-10-06T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86767454,"identity":"53752a6d-1143-4477-87d8-31ed2536e14c","added_by":"auto","created_at":"2025-07-15 11:12:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":393216,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProportion of stomach cancer burden associated with high sodium intake across global and regional levels (1990-2021).\u003c/strong\u003e(a, b) The proportion of stomach cancer deaths and DALYs associated withhigh sodium intake in 2021, segmented by SDI regions; (c, d) further detail these proportions across various GBD regions; (e, f) The trends from 1990 to 2021 in the proportion of DALYs and deaths attributable to high sodium intake.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5037516/v1/ab19347e4c70ef26f49f93a1.jpg"},{"id":86765931,"identity":"be86257e-90d5-43a7-83e0-46fb2798180e","added_by":"auto","created_at":"2025-07-15 11:04:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":238859,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal trends in stomach cancer burden associated with high sodium intake.\u003c/strong\u003e (a, b) Identify key years of change in deaths and DALYs attributable to high sodium intake over 32 years using a join-point regression model; (c, d) The temporal trends of high sodium-related stomach cancer ASMR and ASDR globally and across SDI regions.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5037516/v1/86724ab74990cc829296b71d.jpg"},{"id":86764981,"identity":"cc546c0a-ae81-4e1a-94c1-dc365870f340","added_by":"auto","created_at":"2025-07-15 10:56:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":489044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between SDI and disease burden, and factors influencing EAPC\u003c/strong\u003e (a, b) The correlation between ASMR attributable to high sodium intake and the SDI at the country and regional levels; (c, d) The correlation between ASDR and SDI; (e) The correlation between the EAPC and ASMR as well as SDI; (f) The correlation between EAPC and ASDR as well as SDI.\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5037516/v1/80857cd7bba6d3f96f8d7e96.jpg"},{"id":86765927,"identity":"1e6d4c89-8234-40eb-946c-5a39a311d018","added_by":"auto","created_at":"2025-07-15 11:04:12","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":552957,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge and sex patterns of stomach cancer burden associated withhigh sodium intake (2021) and temporal trends (1990-2021).\u003c/strong\u003e (a, b) age and gender dual coordinate curves of deaths and DALYs; (c, d) Proportion of deaths and DALYs among different age groups in 21 regions; (e, f)sex distribution of ASMR and ASDR in 21 regions; (g, h)Time trends in ASMR and ASDR in different age groups.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5037516/v1/38350bcd48619b09083852a9.jpg"},{"id":86764992,"identity":"5417ad40-7447-4269-9f57-ddb1c5158cd1","added_by":"auto","created_at":"2025-07-15 10:56:12","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":309641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge-Period-Cohort Analysis of stomach cancer burden associated with high sodium intake across global. \u003c/strong\u003e(A-C) Show age, period, and cohort deviations, respectively; (D-F)The longitudinal age curve, cross-sectional age curve, and the comparison of rate ratios (RR) between longitudinal and cross-sectional analyses; (G-I) The fitted temporal trends, period-specific RR, and cohort-specific RR; (J) The local drifts and net drift across age, indicating changes in the stomach cancer burden over time within different age groups.\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5037516/v1/501fc9c4b4392063a3549205.jpg"},{"id":93420021,"identity":"d6ff3ff7-8147-45bf-80a0-e1c96e6ef5df","added_by":"auto","created_at":"2025-10-13 16:09:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3495120,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5037516/v1/04e1e4e8-97b1-403b-a6a7-67495de018d1.pdf"},{"id":86764984,"identity":"44fd5aaf-3427-480d-a9aa-80fa1b3e5fce","added_by":"auto","created_at":"2025-07-15 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10:56:12","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":438322,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5037516/v1/29fbeacfd669ca079d06e051.pdf"},{"id":86765935,"identity":"ce25a757-3ef7-44cc-a344-4dd9c3b69226","added_by":"auto","created_at":"2025-07-15 11:04:13","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":484886,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5037516/v1/b90a8aa16d8e42f7644039aa.pdf"},{"id":86765936,"identity":"ed7cc87c-0023-4fe5-bfc7-9d0977d4b0b8","added_by":"auto","created_at":"2025-07-15 11:04:13","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":1347433,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5037516/v1/92846c70476677a3bd7aff7d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eHigh Sodium Intake: A Silent Killer Driving Global Gastric Cancer Burden\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGastric cancer ranks among the most prevalent malignant neoplasms in the digestive system globally[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], with around 1.08\u0026nbsp;million new cases and 769,000 deaths reported in 2020.Dietary sodium, a key component in home cooking and food preservation[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], plays a crucial role in maintaining the body's internal homeostasis. The primary source of sodium for the human body is through dietary intake. The World Health Organization (WHO) advises adults to restrict their daily sodium intake to 2 grams (equivalent to about 5 grams of salt). However, the global average sodium intake is 3.95 grams per day[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Numerous studies and epidemiological surveys have confirmed a significant link between high sodium consumption and a heightened gastric cancer risk[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In East Asia, the average sodium intake per capita exceeds 4.2 grams per day, potentially contributing to the high incidence of gastric cancer in this region, warranting particular attention[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The primary mechanisms linking high sodium intake to stomach cancer include damage to the gastric mucosal barrier, chronic inflammation, and the enhancement of Helicobacter pylori infection[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite improvements in early detection rates, optimized treatment methods, and enhanced systemic therapy, which have effectively reduced the global incidence and mortality rates of gastric cancer, the disease remains highly invasive and specific, with a 5-year overall survival rate of only 20%-30%[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, gastric cancer continues to pose a significant challenge to global health. A 2010 study of the global burden of stomach cancer found that in high Human Development Index (HDI) countries, the median proportion of gastric cancer risk linked to sodium intake was 22.5% among men and 7.2%-16.6% among women, both higher than in countries with a lower HDI (10.1%)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This suggests that understanding the distributional differences in high sodium intake can aid in developing targeted prevention and control policies to further reduce the incidence of gastric cancer. However, existing literature primarily focuses on individual-level risks[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], leaving gaps in understanding the population-level disease burden related to sodium intake, especially across different regions, genders, and age groups. Additionally, while high sodium intake has been consistently linked to gastric cancer risk, the geographic and temporal heterogeneity in this relationship remains underexplored. Furthermore, global demographic changes such as population aging, declining birth rates, and rapid technological and economic development are altering dietary habits, including sodium consumption patterns, and shaping future disease burdens. Therefore, there is an urgent need to better understand the current levels of sodium exposure across various populations and how these levels are linked to gastric cancer incidence.\u003c/p\u003e\u003cp\u003eThe Global Burden of Disease (GBD) study is the largest and most comprehensive disease burden research project to date[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The GBD 2021 study employs advanced statistical modeling methods to provide new opportunities for studying and predicting global trends in high sodium consumption and the associated risk of gastric malignancy deaths through 2035. Unlike previous prospective studies that primarily assessed individual-level risk[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], this study focuses on revealing population-level disparities in disease burden across countries, regions, sexes, and age groups. By applying the age\u0026ndash;period\u0026ndash;cohort (APC) model to GBD 2021 data, we analyze and project the temporal patterns and demographic heterogeneity of gastric cancer deaths and disability-adjusted life years (DALYs) associated with high sodium intake. The goal is to identify high-burden subpopulations and regions, thereby providing new scientific evidence to support the development of targeted prevention and intervention strategies by governments and public health agencies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Overview and Data Collection\u003c/h2\u003e\u003cp\u003eData on gastric cancer-related deaths and DALYs associated with high sodium intake from 1990 to 2021 were retrieved from the GBD database, covering global, 204 countries, 21 regions, and five Sociodemographic Index (SDI) levels. We used the Global Health Data Exchange (GHDx) query tool for data analysis. As no individual-specific information was involved, the study was exempted from informed consent by Qilu Hospital of Shandong University. In the 2021 GBD study, the epidemiological data for gastric cancer (ICD-10 code C16) and diets high in sodium (defined as an average 24-hour urinary sodium excretion exceeding 1\u0026ndash;5 grams per day) were systematically recorded. In some countries or regions\u0026mdash;particularly those with underdeveloped health surveillance systems\u0026mdash;estimates may be subject to uncertainty due to underreporting, small population size, or missing data. These regional disparities in data completeness and accuracy are addressed through the statistical modeling framework of the GBD 2021 study, such as imputation and Bayesian modeling to handle missing or incomplete data, which has been extensively documented in previous publications[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Further details are provided in Supplementary Appendix 1. We analyzed spatial and temporal differences in mortality and DALYs rates across age, gender, and SDI levels. The SDI index, which combines economic, educational, and fertility levels, was divided into five levels to quantify the socioeconomic development of different countries or regions[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Detailed information about the GBD protocol is available online.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Statistical Analysis\u003c/h2\u003e\u003cp\u003eWe calculated the number of deaths, DALYs, and their corresponding age-standardized mortality rate (ASMR) and age-standardized DALYs rate (ASDR), reported with 95% uncertainty intervals (UI). In cases where the lower limit of the 95% UI approached '0.00' due to small sample sizes, we marked it with an asterisk (*), indicating that while the lower bound is very small, the actual value is not zero. Population Attributable Fraction (PAF) was calculated using exposure levels, associated risk estimates, and the theoretical minimum risk level, estimating the number of deaths and DALYs linked to high sodium consumption in relation to gastric cancer. Age standardization of rates (ASR) was performed to eliminate differences in age structure. Estimated annual percentage change (EAPC) was used to explore patterns in high sodium intake- connected gastric cancer ASMR and ASDR over the period 1990\u0026ndash;2021. Additionally, Join-point regression analysis was used to determine key years of change in global gastric cancer ASR. In this study, Spearman correlation coefficients and Pearson correlation analysis were employed to assess the associations between SDI and ASR, and between EAPC and ASR, in relation to high sodium intake. Considering the current and future demographic changes, we used the Bayesian Age-Period-Cohort (BAPC) model to project the number of gastric cancer deaths, DALYs, and corresponding ASRs associated with high sodium diets by 2035. All statistical analyses were conducted using R Studio version 4.4.2, with p-values being two-sided and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Age-Period-Cohort (APC) Model Analysis\u003c/h2\u003e\u003cp\u003eThe APC model independently assesses potential burden trends across different age groups, periods, and birth cohorts[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Net drift and local drift are used to evaluate the overall trend of increase or decrease in gastric cancer burden across the population over time, and to identify specific changes in risk trends within certain age groups. Age effects are illustrated by age deviation curves and longitudinal age curves. Period effects are represented by fitted time trends and period deviation curves, tracking relative risk ratios over time. Cohort effects capture specific risk characteristics of populations born in the same time period. In the APC model, the population data studied adopts equal intervals of age and time periods, using 5-year age groups and 5-year period intervals. For example, age intervals are divided into 50\u0026ndash;54 years, 55\u0026ndash;59 years, etc., and period intervals are divided into [1994]1992\u0026ndash;1996, [1999]1997\u0026ndash;2001, etc., representing specific time frames. Wald's chi-square test was employed to analyze the impact of the annual percentage trend.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Excess Sodium Intake as a Risk Factor for Gastric Cancer Burden\u003c/h2\u003e\u003cp\u003eIn 2021, 7.93% of global gastric cancer deaths and 7.92% of DALYs were linked to high sodium intake. This proportion peaked in regions with medium SDI and Central Europe, whereas in North Africa and the Middle East, it accounted for only 4.79% and 5.08%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). From 1990 to 2021, trends in deaths and DALYs associated with high sodium consumption among stomach malignancy patients were nearly identical, showing an increasing trend in higher SDI regions, while decreasing across areas with middle and lower SDI. Recently, after a decline, the global attributable percentage seems poised for a rebound(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Global Burden of Gastric Cancer In relation to High Sodium Diets\u003c/h2\u003e\u003cp\u003eIn 2021, high sodium diets were associated with 75,661 stomach cancer deaths and 1,804,592 DALYs. Between 1990 and 2021, stomach cancer deaths linked to high sodium diets increased by 11.52%, while DALYs decreased by 2.22%, indicating an upward trend in mortality but a decline in disease burden compared to 1990. However, in 2021, the ASMR was 0.89 per 100,000 people, with the rate for males (1.29 per 100,000) being twice that of females. Over the 32-year period, the EAPC was \u0026minus;\u0026thinsp;2.26 (95% CI: -2.35 to -2.18) (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, the ASDR in 2021 was significantly lower compared to 1990 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The net drift of ASMR and ASDR from 1990 to 2021 was \u0026minus;\u0026thinsp;2.33 (95% CI: -2.37 to -2.28) and \u0026minus;\u0026thinsp;2.56 (95% CI: -2.65 to -2.47), respectively (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, the AAPC analysis identified three key years of change in disease burden (1998, 2003, and 2014), with ASMR [APC = -2.971 (95% CI: -3.105 to -2.836), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] and ASDR [APC = -3.305 (95% CI: -3.427 to -3.184), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] decreasing most rapidly between 2003 and 2014 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\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\u003eTrends in stomach cancer mortality related to high sodium intake across SDI regions and 21 GBD regions from 1990 to 2021.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"21\"\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=\"left\" 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=\"left\" 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=\"left\" 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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c17\" namest=\"c12\"\u003e\u003cp\u003eASMR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c21\" namest=\"c19\"\u003e\u003cp\u003eTrend\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1990 (95%UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2021 (95%UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eChanges of number (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e1990 (95%UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e2021 (95%UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003ePercent of change (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003eEAPC (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003eNet drift (95%CI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGlobal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e67844.54 (-0.00*-339512.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e75661.15 (-0.00*-372194.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e11.52 (-48.17-27.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e1.74 (-0.00*-8.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.89 (-0.00*-4.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-49.15 (-75.12\u0026ndash;41.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-2.26\u003c/p\u003e\u003cp\u003e(-2.35\u0026ndash;2.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-2.33\u003c/p\u003e\u003cp\u003e(-2.37\u0026ndash;2.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e43642.27 (-0.00*-220825.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e50373.79 (-0.00*-247168.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e15.42 (-52.93-43.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e2.46 (-0.00*-12.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e1.29 (-0.00*-6.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-47.44 (-86.35\u0026ndash;35.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-2.12\u003c/p\u003e\u003cp\u003e(-2.22\u0026ndash;2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-2.21\u003c/p\u003e\u003cp\u003e(-2.27\u0026ndash;2.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e24202.27 (0.00*-123377.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e25287.36 (0.00*-129117.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e4.48 (-75.40-18.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e1.15 (0.00*-5.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.55 (0.00*-2.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-52.63 (-87.13\u0026ndash;45.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-2.57\u003c/p\u003e\u003cp\u003e(-2.67\u0026ndash;2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-2.59\u003c/p\u003e\u003cp\u003e(-2.65\u0026ndash;2.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLow SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1904.18 (0.00*-9916.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2893.77 (0.00*-15443.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e51.97 (-44.99-71.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.86 (0.00*-4.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.60 (0.00*-3.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-30.17 (-73.37\u0026ndash;21.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-1.14\u003c/p\u003e\u003cp\u003e(-1.20\u0026ndash;1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-1.21\u003c/p\u003e\u003cp\u003e(-1.46\u0026ndash;0.97)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLow-middle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4880.69 (0.00*-24751.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e8255.39 (0.00*-41815.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e69.14 (-38.62-93.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.82 (0.00*-4.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.59 (0.00*-3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-27.47 (-72.91\u0026ndash;16.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-0.95\u003c/p\u003e\u003cp\u003e(-1.00\u0026ndash;0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-1.00\u003c/p\u003e\u003cp\u003e(-1.13\u0026ndash;0.87)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMiddle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e23250.29 (-0.00*-115302.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e28816.28 (-0.00*-141923.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e23.94 (-84.20-44.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e2.30 (-0.00*-11.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e1.11 (-0.00*-5.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-51.91 (-93.66\u0026ndash;44.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-2.47\u003c/p\u003e\u003cp\u003e(-2.60\u0026ndash;2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-2.52\u003c/p\u003e\u003cp\u003e(-2.58\u0026ndash;2.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHigh-middle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e24047.50 (-0.00*-122047.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e23438.81 (-0.00*-114552.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e-2.53 (-51.25-30.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e2.44 (-0.00*-12.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e1.18 (-0.00*-5.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-51.52 (-69.69\u0026ndash;33.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-2.43\u003c/p\u003e\u003cp\u003e(-2.57\u0026ndash;2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-2.53\u003c/p\u003e\u003cp\u003e(-2.6\u0026ndash;2.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHigh SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e13705.92 (0.00*-69402.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e12208.66 (0.00*-61721.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e-10.92 (-75.10\u0026ndash;3.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e1.24 (0.00*-6.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.54 (0.00*-2.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-56.11 (-86.55\u0026ndash;52.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-2.72\u003c/p\u003e\u003cp\u003e(-2.75\u0026ndash;2.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-2.91\u003c/p\u003e\u003cp\u003e(-3.02\u0026ndash;2.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOceania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e35.84 (0.00*-195.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e72.09 (0.00*-380.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e101.13 (-75.86-179.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e1.36 (0.00*-7.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e1.06 (0.00*-5.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-22.14 (-89.73-3.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-0.83\u003c/p\u003e\u003cp\u003e(-0.89\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-0.77\u003c/p\u003e\u003cp\u003e(-2.24-0.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2263.81 (0.00*-11384.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e3571.60 (0.00*-18178.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e57.77 (-38.20-100.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.91 (0.00*-4.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.56 (0.00*-2.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-37.76 (-73.17\u0026ndash;21.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-1.71\u003c/p\u003e\u003cp\u003e(-1.78\u0026ndash;1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-1.66\u003c/p\u003e\u003cp\u003e(-1.84\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e31815.60 (-0.00*-155223.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e37861.80 (-0.00*-188112.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e19.00 (-73.97-48.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e3.77 (-0.00*-18.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e1.76 (-0.00*-8.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-53.31 (-88.29\u0026ndash;41.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-2.54\u003c/p\u003e\u003cp\u003e(-2.75\u0026ndash;2.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-2.59\u003c/p\u003e\u003cp\u003e(-2.69\u0026ndash;2.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCentral Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e998.02 (0.00*-4972.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e722.78 (0.00*-3724.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e-27.58 (-86.55\u0026ndash;21.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e2.13 (0.00*-10.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.90 (0.00*-4.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-57.75 (-90.80\u0026ndash;54.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-2.54\u003c/p\u003e\u003cp\u003e(-2.64\u0026ndash;2.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-2.68\u003c/p\u003e\u003cp\u003e(-3\u0026ndash;2.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e6059.35 (0.00*-29936.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e5863.70 (0.00*-29532.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e-3.23 (-77.05-3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e3.09 (0.00*-15.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e1.09 (0.00*-5.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-64.74 (-88.86\u0026ndash;62.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-3.44\u003c/p\u003e\u003cp\u003e(-3.48\u0026ndash;3.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-3.83\u003c/p\u003e\u003cp\u003e(-4\u0026ndash;3.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCentral Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2259.36\u003c/p\u003e\u003cp\u003e(-0.00*-11339.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1600.00* (0.00*-7857.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e-29.18 (-86.41\u0026ndash;23.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e1.54 (-0.00*-7.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.71 (0.00*-3.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-53.66 (-80.91\u0026ndash;49.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-2.58\u003c/p\u003e\u003cp\u003e(-2.67\u0026ndash;2.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-2.55\u003c/p\u003e\u003cp\u003e(-2.84\u0026ndash;2.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eEastern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e6322.64\u003c/p\u003e\u003cp\u003e(-0.00*-32995.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e3240.56 (0.00*-16644.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e-48.75 (-106.61\u0026ndash;38.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e2.25 (-0.00*-11.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.92 (0.00*-4.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-58.94 (-106.54\u0026ndash;47.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-3.11\u003c/p\u003e\u003cp\u003e(-3.21\u0026ndash;3.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-2.99\u003c/p\u003e\u003cp\u003e(-3.16\u0026ndash;2.82)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAustralasia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e113.13 (0.00*-630.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e138.14 (0.00*-787.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e22.11 (-84.93-85.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.49 (0.00*-2.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.25 (0.00*-1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-48.29 (-95.44\u0026ndash;2.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-2.10\u003c/p\u003e\u003cp\u003e(-2.23\u0026ndash;1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-2.05\u003c/p\u003e\u003cp\u003e(-3.03\u0026ndash;1.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWestern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e6132.03 (0.00*-32392.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4049.66 (0.00*-21330.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e-33.96 (-89.19\u0026ndash;22.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e1.04 (0.00*-5.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.41 (0.00*-2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-60.90 (-88.10\u0026ndash;47.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-3.00\u003c/p\u003e\u003cp\u003e(-3.10\u0026ndash;2.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-2.90\u003c/p\u003e\u003cp\u003e(-3.1\u0026ndash;2.71)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSouthern Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e685.50 (0.00*-3454.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e751.84 (0.00*-3766.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e9.68 (-54.96-77.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e1.52 (0.00*-7.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.85 (0.00*-4.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-43.97 (-75.67\u0026ndash;8.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-1.69\u003c/p\u003e\u003cp\u003e(-1.81\u0026ndash;1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-1.61\u003c/p\u003e\u003cp\u003e(-2\u0026ndash;1.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHigh-income North America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1413.91 (0.00*-7465.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1481.69 (0.00*-7675.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e4.79 (-3.82-567.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.40 (0.00*-2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.23 (0.00*-1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-42.96 (-47.06-301.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-1.88\u003c/p\u003e\u003cp\u003e(-1.92\u0026ndash;1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-1.55\u003c/p\u003e\u003cp\u003e(-1.82\u0026ndash;1.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCaribbean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e236.21 (0.00*-1222.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e311.09 (0.00*-1670.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e31.70 (-79.82-53.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.94 (0.00*-4.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.58 (0.00*-3.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-38.38 (-87.19\u0026ndash;27.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-1.48\u003c/p\u003e\u003cp\u003e(-1.56\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-1.32\u003c/p\u003e\u003cp\u003e(-1.88\u0026ndash;0.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAndean Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e522.25 (0.00*-2644.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e988.32 (0.00*-4998.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e89.24 (-40.68-139.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e2.67 (0.00*-13.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e1.71 (0.00*-8.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-36.04 (-78.38\u0026ndash;17.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-1.64\u003c/p\u003e\u003cp\u003e(-1.80\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-1.57\u003c/p\u003e\u003cp\u003e(-1.9\u0026ndash;1.24)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCentral Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1316.45 (0.00*-6624.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2295.17 (0.00*-11894.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e74.35 (-37.46-106.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e1.70 (0.00*-8.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.93 (0.00*-4.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-45.01 (-79.55\u0026ndash;35.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-2.18\u003c/p\u003e\u003cp\u003e(-2.26\u0026ndash;2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-1.86\u003c/p\u003e\u003cp\u003e(-2.06\u0026ndash;1.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTropical Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1355.85\u003c/p\u003e\u003cp\u003e(-0.00*-6802.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1988.04 (-0.00*-10206.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e46.63 (-65.55-68.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e1.58 (-0.00*-7.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.78 (-0.00*-4.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-50.57 (-88.06\u0026ndash;42.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-2.36\u003c/p\u003e\u003cp\u003e(-2.42\u0026ndash;2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-2.18\u003c/p\u003e\u003cp\u003e(-2.41\u0026ndash;1.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1257.34 (0.00*-7340.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1976.55 (0.00*-11965.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e57.20 (-25.35-255.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.75 (0.00*-4.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.45 (0.00*-2.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-40.01 (-75.61-39.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-1.58\u003c/p\u003e\u003cp\u003e(-1.64\u0026ndash;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-1.66\u003c/p\u003e\u003cp\u003e(-1.91\u0026ndash;1.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3630.87 (0.00*-18682.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e6498.89 (0.00*-32699.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e78.99 (45.98-197.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.62 (0.00*-3.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.45 (0.00*-2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-28.11 (-40.12-26.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-0.95\u003c/p\u003e\u003cp\u003e(-1.04\u0026ndash;0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-0.99\u003c/p\u003e\u003cp\u003e(-1.16\u0026ndash;0.82)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e657.50 (0.00*-3325.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e852.33 (0.00*-4457.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e29.63 (-63.47-51.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.90 (0.00*-4.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.54 (0.00*-2.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-39.89 (-84.11\u0026ndash;32.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-1.90\u003c/p\u003e\u003cp\u003e(-2.00\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-1.94\u003c/p\u003e\u003cp\u003e(-2.38\u0026ndash;1.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e148.19 (0.00*-796.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e254.56 (0.00*-1387.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e71.77 (-65.11-104.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.55 (0.00*-3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.45 (0.00*-2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-18.42 (-81.70\u0026ndash;4.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-0.69\u003c/p\u003e\u003cp\u003e(-1.03\u0026ndash;0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-0.67\u003c/p\u003e\u003cp\u003e(-1.3\u0026ndash;0.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e478.73 (0.00*-2558.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e880.31 (0.00*-4609.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e83.89 (40.82-313.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.57 (0.00*-3.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.49 (0.00*-2.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-15.00 (-33.71-93.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-0.28\u003c/p\u003e\u003cp\u003e(-0.37\u0026ndash;0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-0.47\u003c/p\u003e\u003cp\u003e(-0.88\u0026ndash;0.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e141.97 (0.00*-838.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e262.04 (0.00*-1543.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e84.58 (21.52-731.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e0.68 (0.00*-4.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e0.51 (0.00*-2.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e-24.61 (-46.75-341.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e\u003cp\u003e-0.96\u003c/p\u003e\u003cp\u003e(-1.00\u0026ndash;0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e-0.94\u003c/p\u003e\u003cp\u003e(-1.82\u0026ndash;0.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"21\"\u003eASMR, Age-standardized mortality rate; EAPC, estimated annual percentage changes; Net drift, overall annual percentage change; SDI, Socio-demographic Index; UI, uncertainty interval; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"21\"\u003eWhen the sample size was small, the lower limit of the 95% uncertainty interval (UI) appeared as '0.00'. We marked this with an asterisk '*', indicating that the lower limit is very small, but the actual value is not zero.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTrends in stomach cancer Disability-Adjusted Life Years related to high sodium intake across SDI regions and 21 GBD regions from 1990 to 2021.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"15\"\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=\"left\" 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=\"left\" 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=\"left\" 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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e\u003cp\u003eDALYS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eASDR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003eTrend\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e1990 (95%UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2021 (95%UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eChanges of number (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1990 (95%UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2021 (95%UI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003ePercent of change (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003eEAPC (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003eNet drift (95%CI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGlobal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e1845616.80 (-0.03-9206157.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1804591.52 (-0.00*-8884379.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.22 (-65.17-12.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e44.53 (-0.00*-222.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e20.78 (-0.00*-102.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-53.33 (-82.78\u0026ndash;46.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.56 (-2.65\u0026ndash;2.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-2.32 (-2.38\u0026ndash;2.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\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\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e1221058.35 (-0.04-6151893.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1231290.32 (-0.02-6026424.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.84 (-56.99-22.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e62.20 (-0.00*-314.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e29.90 (-0.00*-146.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-51.93 (-82.84\u0026ndash;41.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.43 (-2.52\u0026ndash;2.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-2.21 (-2.29\u0026ndash;2.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e624558.45 (-0.00*-3176862.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e573301.21 (0.00*-2940974.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-8.21 (-56.66-5.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e28.71 (-0.00*-146.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e12.61 (0.00*-64.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-56.10 (-79.11\u0026ndash;49.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.84 (-2.94\u0026ndash;2.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-2.59 (-2.64\u0026ndash;2.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\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\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLow SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e56793.80 (0.00*-295505.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e82603.09 (0.00*-441280.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e45.44 (-43.09-66.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e22.47 (0.00*-116.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e14.71 (0.00*-78.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-34.52 (-75.54\u0026ndash;25.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.41 (-1.47\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-1.22 (-1.36\u0026ndash;1.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLow-middle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e145052.73 (0.00*-737564.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e226298.29 (0.00*-1148264.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e56.01 (-54.59-78.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21.39 (0.00*-108.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e14.78 (0.00*-75.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-30.89 (-79.13\u0026ndash;21.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.13 (-1.16\u0026ndash;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-1.00 (-1.07\u0026ndash;0.93)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMiddle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e667339.27 (-0.02-3270306.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e712581.58 (-0.00*-3527047.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.78 (-77.70-25.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e59.02 (-0.00*-290.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e25.82 (-0.00*-127.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-56.26 (-94.25\u0026ndash;48.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.79 (-2.91\u0026ndash;2.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-2.51 (-2.61\u0026ndash;2.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eHigh-middle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e653552.16 (-0.02-3320830.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e551396.41 (-0.00*-2681101.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-15.63 (-75.64-14.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e63.60 (-0.00*-322.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e28.14 (-0.00*-136.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-55.76 (-78.94\u0026ndash;39.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.76 (-2.90\u0026ndash;2.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-2.52 (-2.61\u0026ndash;2.43)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eHigh SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e321495.69 (0.00*-1625677.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e230573.68 (0.00*-1154959.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-28.28 (-75.13\u0026ndash;22.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e29.89 (0.00*-151.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11.60 (0.00*-58.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-61.18 (-84.55\u0026ndash;58.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-3.11 (-3.14\u0026ndash;3.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-2.90 (-2.96\u0026ndash;2.85)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\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\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eOceania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1064.57 (0.00*-5908.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2143.71 (0.00*-11558.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e101.37 (-90.42-196.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e32.71 (0.00*-178.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e25.52 (0.00*-134.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-21.97 (-89.55-8.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-0.82 (-0.89\u0026ndash;0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.77 (-1.18\u0026ndash;0.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66675.66 (0.00*-336540.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e97670.36 (0.00*-497996.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e46.49 (-55.32-83.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e23.49 (0.00*-118.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e13.98 (0.00*-71.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-40.48 (-77.92\u0026ndash;24.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.85 (-1.92\u0026ndash;1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-1.66 (-1.72\u0026ndash;1.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e910165.99 (-0.04-4421005.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e906420.39 (-0.01-4574157.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.41 (-68.64-27.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e96.58 (-0.00*-469.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e41.09 (-0.00*-206.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-57.45 (-84.22\u0026ndash;45.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.88 (-3.07\u0026ndash;2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-2.58 (-2.73\u0026ndash;2.44)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eCentral Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28789.80 (0.00*-143224.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20435.50 (0.00*-105882.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-29.02 (-91.01\u0026ndash;22.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e57.97 (0.00*-288.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e23.08 (0.00*-119.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-60.19 (-92.70\u0026ndash;56.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.80 (-2.88\u0026ndash;2.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-2.70 (-2.8\u0026ndash;2.59)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e151937.98 (0.00*-743669.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e99016.59 (0.00*-495285.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-34.83 (-82.65\u0026ndash;29.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e74.62 (0.00*-365.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e22.57 (0.00*-112.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-69.75 (-91.26\u0026ndash;67.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-3.92 (-3.97\u0026ndash;3.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-3.81 (-3.88\u0026ndash;3.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eCentral Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55098.53 (-0.00*-277908.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e34860.24 (0.00*-170908.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-36.73 (-89.53\u0026ndash;31.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e36.72 (-0.00*-185.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e16.71 (0.00*-81.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-54.50 (-88.39\u0026ndash;50.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.62 (-2.71\u0026ndash;2.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-2.55 (-2.6\u0026ndash;2.49)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eEastern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e173732.78 (-0.00*-903204.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e79239.65 (0.00*-402533.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-54.39 (-105.18\u0026ndash;45.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e61.63 (-0.00*-320.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e23.54 (0.00*-119.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-61.80 (-99.77\u0026ndash;50.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-3.40 (-3.52\u0026ndash;3.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-3.01 (-3.14\u0026ndash;2.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAustralasia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2602.89 (0.00*-14210.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2808.90 (0.00*-15482.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.91 (-84.77-99.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e11.25 (0.00*-61.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.72 (0.00*-31.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-49.21 (-95.37-5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.16 (-2.27\u0026ndash;2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-2.02 (-2.2\u0026ndash;1.85)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eWestern Europe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e127536.05 (0.00*-667811.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e76170.59 (0.00*-392854.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-40.28 (-87.44\u0026ndash;25.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e22.85 (0.00*-119.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.93 (0.00*-45.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-60.90 (-88.96\u0026ndash;43.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.96 (-3.05\u0026ndash;2.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-2.89 (-2.95\u0026ndash;2.83)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eSouthern Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16277.62 (0.00*-82265.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16598.73 (0.00*-82996.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.97 (-49.89-63.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e35.00 (0.00*-176.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e19.43 (0.00*-97.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-44.50 (-71.44\u0026ndash;10.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.73 (-1.85\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-1.60 (-1.68\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eHigh-income North America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31635.14 (0.00*-165388.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32630.83 (0.00*-166471.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.15 (-3.79-601.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.34 (0.00*-48.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.52 (0.00*-28.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-40.91 (-44.67-341.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.73 (-1.77\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-1.53 (-1.58\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eCaribbean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5804.55 (0.00*-30337.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7607.75 (0.00*-40990.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e31.07 (-76.76-65.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21.93 (0.00*-114.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e14.24 (0.00*-76.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-35.07 (-89.41\u0026ndash;20.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.31 (-1.41\u0026ndash;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-1.32 (-1.44\u0026ndash;1.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAndean Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13409.97 (0.00*-68083.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23127.69 (0.00*-116683.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e72.47 (-39.13-120.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e62.37 (0.00*-316.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e38.45 (0.00*-193.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-38.35 (-78.90\u0026ndash;21.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.79 (-1.93\u0026ndash;1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-1.57 (-1.64\u0026ndash;1.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eCentral Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33796.36 (0.00*-170527.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57296.69 (0.00*-297890.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e69.54 (-62.34-97.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e38.56 (0.00*-194.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e22.41 (0.00*-116.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-41.88 (-85.38\u0026ndash;31.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.00 (-2.09\u0026ndash;1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-1.86 (-1.9\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eTropical Latin America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35939.59 (-0.00*-179651.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e49223.00 (-0.00*-252502.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e36.96 (-46.99-69.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e37.33 (-0.00*-186.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e18.88 (-0.00*-96.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-49.42 (-81.41\u0026ndash;39.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.33 (-2.39\u0026ndash;2.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-2.18 (-2.23\u0026ndash;2.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37024.39 (0.00*-214620.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54759.40 (0.00*-325675.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e47.90 (-26.50-210.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e19.66 (0.00*-114.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11.00 (0.00*-65.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-44.02 (-69.62-22.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.83 (-1.90\u0026ndash;1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-1.66 (-1.74\u0026ndash;1.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e112571.19 (0.00*-573787.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e180806.07 (0.00*-908078.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e60.61 (39.66-186.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17.02 (0.00*-87.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11.45 (0.00*-57.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-32.71 (-43.16-22.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.19 (-1.27\u0026ndash;1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-1.00 (-1.17\u0026ndash;0.83)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19716.43 (0.00*-99792.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24470.02 (0.00*-129514.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24.11 (-68.25-45.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e23.37 (0.00*-118.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e12.94 (0.00*-67.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-44.61 (-84.09\u0026ndash;35.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.21 (-2.32\u0026ndash;2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-1.95 (-2.08\u0026ndash;1.82)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4431.39 (0.00*-23681.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7339.70 (0.00*-39115.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e65.63 (-103.65-88.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14.62 (0.00*-78.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11.52 (0.00*-62.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-21.18 (-84.47\u0026ndash;7.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-0.75 (-1.10\u0026ndash;0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.66 (-0.86\u0026ndash;0.46)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13169.32 (0.00*-70651.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24102.24 (0.00*-126886.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e83.02 (41.95-301.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14.02 (0.00*-75.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11.32 (0.00*-59.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-19.30 (-37.49-80.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-0.48 (-0.56\u0026ndash;0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.47 (-0.58\u0026ndash;0.37)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4236.60 (0.00*-24980.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7863.51 (0.00*-46176.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e85.61 (-55.98-487.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16.97 (0.00*-100.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e12.54 (0.00*-73.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-26.11 (-48.27-212.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-1.02 (-1.06\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.94 (-1.2\u0026ndash;0.68)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"15\"\u003eASDR, Age-standardized Disability-Adjusted Life Years rate; EAPC, estimated annual percentage changes; Net drift, overall annual percentage change; SDI, Socio-demographic Index; UI, uncertainty interval; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"15\"\u003eWhen the sample size was small, the lower limit of the 95% uncertainty interval (UI) appeared as '0.00'. We marked this with an asterisk '*', indicating that the lower limit is very small, but the actual value is not zero.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Stomach Cancer Burden In relation to High Sodium Intake by SDI and GBD Regions\u003c/h2\u003e\u003cp\u003eDespite the passage of 32 years, the disease burden in high-middle SDI areas remained the highest. Notably, both mortality and DALY rates decreased across all SDI regions (Fig, 2c, 2d), but the decline in high SDI regions was much faster than in other regions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e,Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), making them the regions with the lowest disease burden in 2021. The net drift of ASMR ranged from \u0026minus;\u0026thinsp;2.91% per year (95% CI: -3.02 to -2.81) in high SDI regions to -1.21% per year (95% CI: -1.46 to -0.97) in low SDI regions, while the ASDR net drift varied between \u0026minus;\u0026thinsp;2.90% (95% CI: -2.96 to -2.85) to -1.22% (95% CI: -1.36 to -1.08) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Detailed EAPC changes by region are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the number of deaths, ASMR, EAPC, percentage change, and net drift of mortality for each SDI region and 21 geographical regions in 1990 and 2021, while Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides the corresponding information for DALYs. In 2021, seven regions had mortality and DALY rates exceeding the global average, with the heaviest burden observed in East Asia, accounting for nearly half of the world's deaths. Naturally, East Asia also had the highest ASMR and ASDR (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In contrast, the ASMR and ASDR in High-income North America remained the lowest in both 1990 and 2021, with average annual decreases of -1.88% (95% CI: -1.92 to -1.83) and \u0026minus;\u0026thinsp;1.73% (95% CI: -1.77 to -1.68), respectively (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). From 1990 to 2021, the most significant decreases in mortality net drift were observed in High-income Asia Pacific [-3.83 (95% CI: -4.00 to -3.65)], followed by Eastern Europe and Western Europe, with similar declines in ASDR net drift (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows EAPC changes across GBD regions, with Western Sub-Saharan Africa exhibiting the smallest changes, where the net drift trend aligns with the EAPC trend.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4 National Trends in Stomach Cancer Burden\u003c/h2\u003e\u003cp\u003eAs indicated in Supplementary Tables S1 and S2, in 2021, the countries with the highest ASMR linked to high sodium intake were Mongolia [2.96 (95% UI: 0.00*-15.68)], Bolivia (Plurinational State of) [2.58 (95% UI: 0.00*-13.23)], and Guatemala [1.98 (95% UI: 0.00*-10.25)], with 46 countries having an ASMR exceeding 0.89/100,000 and with 52 countries having an ASDR exceeding 20.78/100,000. Among the 204 countries, China's situation caught our attention, with the number of deaths increasing from 31,208 in 1990 to 36,958 in 2021, with an average annual net drift decrease of 2.62% (95% CI: -2.72 to -2.52). The average annual net drift in DALYs also decreased by 2.62% (95% CI: -2.77 to -2.47). Nevertheless, China's death and DALY numbers accounted for nearly half of the global total. Over the 32-year period, the burden increased in nine countries, including Egypt, Lesotho, and Zimbabwe, with Egypt having the largest net drift, with ASMR[1.94 (95% CI: 0.70\u0026ndash;3.19)] and ASDR[1.93 (95% CI: 1.56\u0026ndash;2.31)]. For the remaining countries, the net drift was \u0026lt;\u0026thinsp;0, predominantly found in high and high-middle SDI areas, represented by Singapore, Japan, and Switzerland (net drift \u0026le; -3.0%).The fastest reductions in ASMR and ASDR were observed in the Republic of Korea. The distribution of stomach carcinoma deaths and DALYs linked to high sodium diets and EAPC by country is shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Overall, the trends in disease burden varied across countries. Moreover, the EAPC burden trends do not necessarily align with net drift, highlighting the need to further distinguish period effects from cohort effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Correlation Between SDI and Disease Burden and Influencing Factors of EAPC\u003c/h2\u003e\u003cp\u003eThere was a notable positive correlation between SDI and ASMR in 21 regions (R=-0.005, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 204 countries (R = -0.328, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, SDI was significantly negatively correlated with ASDR in 21 regions (R = -0.029, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 204 countries (R = -0.357, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We observed an upward relationship between EAPC and ASMR (R\u0026thinsp;=\u0026thinsp;0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ASDR (R\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, the study found a strong positive association between SDI and EAPC for both ASMR (-0.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ASDR (-0.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Countries with higher SDI scores experienced faster declines in ASMR and ASDR.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Age and Gender Patterns and Overall Temporal Trends\u003c/h2\u003e\u003cp\u003eIn 2021, gastric neoplasm deaths and DALYs in relation to high dietary sodium were primarily concentrated in individuals aged 70\u0026ndash;74 years. However, ASMR and ASDR generally rose with age, with a decline noted in men only after 94 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).It is noteworthy that the disease burden among men far exceeded that of women across almost all age groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). We divided all age groups into six categories, and globally, the mortality and DALY rates in the 85\u0026thinsp;+\u0026thinsp;age group accounted for the largest proportions at 34.5% and 22.6%, respectively, with even more pronounced proportions observed in High-income Asia Pacific and Australia (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Figures\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ee and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ef detail the gender differences in ASMR and ASDR across GBD regions, with East Asia showing the most striking differences [ASMR (male 2.67 vs female 0.99), ASDR (male 61.57 vs female 22.03)].From 1990 to 2021, ASMR showed a similar overall downward trend across all age groups except for the 15\u0026ndash;49 age group, despite experiencing minor fluctuations. For ASDR, the 70\u0026ndash;74 and 75\u0026ndash;79 age groups experienced a rapid decline and are no longer the groups with the highest DALY rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eg, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eh). Detailed time trends for age groups in different SDI regions are shown in Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Projections of Stomach Cancer Burden In relation to High Sodium Diets\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e shows global projections of gastric neoplasm deaths and DALYs linked to high dietary sodium, predicting further declines in the count of deaths, DALYs, and corresponding ASMR and ASDR from 2022 to 2035. By 2035, ASMR and ASDR related to high sodium diets are projected to be 1.06 (95% UI: 0.97\u0026ndash;1.16) and 24.77 (95% UI: 22.54\u0026ndash;27.01), respectively (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e, Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Additionally, global deaths are projected to reach 60,070 and DALYs to 1,398,848 by 2035 (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e, Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Age-Period-Cohort Effects\u003c/h2\u003e\u003cp\u003eWe further explored the independent contributions and interactions of age, period, and birth cohort factors on gastric cancer burden, revealing significant heterogeneity across global and different SDI regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe age effect indicated that mortality and DALYs rates consistently increased with age, peaking around 85 years, while DALY rates peaked between 50 to 60 years (Panel D),this trend was further supported by the age deviation plot (Panel A). This trend was almost consistent across SDI regions but was particularly pronounced in low SDI regions. The period effect (Panel B) reflected the risk changes observed during the study period (1990\u0026ndash;2021), with larger fluctuations indicating instability in medical and public health interventions. Nevertheless, the fitted time trends (Panel G) and risk ratios (RR) showed an overall decline in ASMR and ASDR, particularly in recent decades. Although fluctuations in low-middle SDI regions were smaller than in other regions, a consistent period effect was observed across all five SDI areas, with a rapid decline in middle and high-middle SDI regions. The cohort effect (Panel C) further revealed that those born in the 1920s and 1930s may have been exposed to higher sodium levels early in life, leading to a heightened risk of gastric cancer later in life. In contrast, those born in more recent cohorts (after the 1970s) showed a significant downward trend. Notably, this difference was more pronounced in higher SDI regions, as reflected in the cohort risk ratio (Panel I). Finally, local drift and net drift analysis showed that the decline in stomach cancer burden related to high dietary sodium was fastest between the ages of 30 and 50, slowing down after 50, with almost no change by around 90 years old (Panel J).\u003c/p\u003e\u003cp\u003eFurthermore, we focused on a representative country-China-where the temporal trends and age-gender differences in stomach cancer burden closely mirrored the global patterns (Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). The disease burden declined most rapidly between 2003 and 2015 (Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). The age-period-cohort effects are detailed in Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e. Overall, the trends of these effects were similar to the global trends, but China exhibited more pronounced changes in certain aspects, particularly in the magnitude of the age effect (a rapid increase in DALY rates between 60 and 70 years), the volatility of the period effect (with significant up and down trends between 2000 and 2010), and the significance of the cohort effect.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn 2021, 7.93% of global gastric cancer deaths and 7.92% of DALYs were estimated to be associated with high sodium diets. Despite the declining trend in ASMR and ASDR over the last 32 years globally, there were 75,661 deaths from gastric cancer linked to high sodium intake in 2021, marking an 11.52% increase since 1990, a figure partially explained by population aging and structural changes[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Despite the World Health Organization\u0026rsquo;s global strategies to reduce sodium intake, including food labeling regulations, the disease burden in some countries, including China, remains above the global average and is even increasing in certain regions and countries. This study utilized the GBD 2021 database to characterize the epidemiological characteristics of stomach cancer associated with high sodium diets, capturing temporal trends and demographic heterogeneity in disease burden. It is the first to apply an APC model to examine the effects of age, period, and cohort factors. The study also projects future trends in disease burden, offering valuable scientific evidence and novel insights for the primary prevention of stomach cancer.\u003c/p\u003e\u003cp\u003eHigh sodium intake is documented as the second leading risk factor for gastric cancer after smoking[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Studies have explored the potential mechanisms by which high concentrations of salt elevate gastric cancer risk[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], however, the biological link between high sodium intake and gastric cancer remains worth exploring. Excessive salt may activate the Wnt/β-catenin signaling pathway, thereby activating the expression of oncogenes[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, a high sodium environment may lead to methylation changes, making certain oncogenes, such as \u003cem\u003ec-Myc\u003c/em\u003e, more prone to activation[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It should be acknowledged that other metabolic or lifestyle-related factors, such as obesity, alcohol use, or Helicobacter pylori infection, may also influence gastric cancer risk and potentially co-vary with sodium intake[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Although not included in the GBD 2021 list of risk-outcome pairs for gastric cancer, these variables may interact biologically or behaviorally with high sodium diets, complicating direct causal interpretations. Finally, individuals under significant stress or with emotional instability are more likely to choose high-salt, heavily flavored foods to relieve stress[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Chronic sleep deprivation[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and irregular eating habits[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], including increased consumption of high-salt fast foods and snacks, exacerbate gastric damage[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The disease burden varies significantly across countries and regions[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. We observed a consistently high burden in East Asia, especially in China, this may be related to traditional dietary habits where high-salt foods, such as soy sauce and pickled products, play a significant role, and these habits are difficult to change quickly[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In high-SDI regions such as Singapore and Luxembourg, ASMR and ASDR have declined rapidly. In 2019, Singapore\u0026rsquo;s Health Promotion Board launched the \"Healthier Choice\" program, labeling low-sodium foods and encouraging manufacturers to reduce sodium content in products. In contrast, in countries like Egypt and Lesotho, limited access to treatment due to economic development constraints and a lack of public awareness about the health risks of high salt diets. Overall, despite many countries enacting sodium intake regulations, the lack of enforcement and effective global cooperation has limited the promotion and implementation of these measures.\u003c/p\u003e\u003cp\u003eGlobally and regionally, gastric cancer mortality and DALYs rates correlated with high sodium intake increase with age, exposing the high risk among middle-aged and older populations. This is related to physiological aging and cumulative exposure risks. Meanwhile, in high-SDI regions, the disease burden among younger individuals is declining rapidly. Mortality and DALYs do not show a trend toward younger ages, likely due to public health policies in high-SDI regions focusing more on the promotion of healthy diets and other preventive measures, with young people being the primary targets for education and intervention. Moreover, younger individuals may be more likely to undergo regular health checks and early screening programs. We found that the disease burden in males was notably greater than in females, aligning with previous studies[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Smoking and alcohol consumption are established risk factors for gastric malignancy[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], the former can exacerbate \u003cem\u003eH. pylori\u003c/em\u003e infection, while the latter may increase the irritating damage of salt to the gastric mucosa, making men more susceptible to these unhealthy habits[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, in East Asian culture, men more frequently participate in social activities, where men typically consume large amounts of sodium-rich snacks. Some studies suggest that the male gastric mucosa may respond more strongly to sodium-induced oxidative stress. Certain hormone levels (e.g., testosterone) may also make men more vulnerable to the negative effects of high sodium diets[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Regarding period effects, the relative risk curve showed a downward trend, possibly due to advances in medical technology and increased health awareness. The faster decline in gastric cancer burden in middle and high-middle SDI regions reflects significant progress in public health policies and medical interventions. For instance, China has revised food labeling regulations since 2007 and promoted health education, reducing the average daily sodium intake to 5.2g by 2012. We also explored factors such as the reduction in \u003cem\u003eH. pylori\u003c/em\u003e infection rates[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], advances in food preservation technology[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], dietary habit changes, smoking rate reductions, and socioeconomic improvements, which collectively support the observation that later birth cohorts have lower gastric cancer mortality and disease burden (DALYs) risks compared to those born in the 1920s and 1930s.\u003c/p\u003e\u003cp\u003eThis study has some limitations: (1) Data collection in low-income countries is limited, and the quality of data may obscure epidemiological characteristics; (2) The analysis based on the APC model still requires future cohort studies to verify specific spatiotemporal risk differences; (3) The differences in implementation and regulation of salt control policies across countries make it difficult to link our study with policy changes; (4) In the 2021 GBD comparative risk assessment framework, only smoking and high sodium intake were included as established risk factors for gastric cancer. Therefore, the current analysis may not fully capture the potential confounding or interactive effects of other variables such as obesity, alcohol consumption, and broader dietary patterns. Further research is warranted to investigate the complex interplay of multiple coexisting risk factors across diverse populations.\u003c/p\u003e\u003cp\u003eFuture research should further elucidate the biological mechanisms linking high sodium intake to gastric cancer, particularly through longitudinal and experimental studies. It is also essential to integrate more comprehensive dietary data and apply multi-omics approaches or large-scale cohort analyses to disentangle the independent and interactive effects of confounders such as Helicobacter pylori infection and smoking. Moreover, targeted investigations into the cultural and behavioral factors underlying the high disease burden among older adults, males, and populations in East Asia are critical for context-specific interventions. From a policy perspective, future studies should assess the effectiveness of interventions such as restrictions on high-sodium food advertising, sales, and taxation across varying socioeconomic contexts. Priority should also be given to developing predictive models to improve early screening and risk stratification. Efforts must be made to enhance data collection in low-income countries and improve the precision of national-level estimates to better support evidence-based health policy planning and resource allocation.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eDespite the implementation of salt reduction policies, the burden of gastric cancer related to high sodium intake remains a significant public health challenge, particularly in East Asia and countries with rising mortality rates (e.g., Egypt), highlighting the importance of government strategies. Early screening and intervention for high-risk populations such as males and the elderly, and actively managing sodium levels in cancer survivors, can effectively reduce the burden of gastric cancer. Our study provides important evidence for health departments in resource allocation and offers practical public health strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGBD, global burden of disease;\u003c/p\u003e\n\u003cp\u003eASR, age-standardized rates;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eASMR, Age-standardized mortality rate\u003c/p\u003e\n\u003cp\u003eASDR, Age-standardized disability-adjusted life years rate;\u003c/p\u003e\n\u003cp\u003eEAPC, estimated annual percentage changes;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSDI, Socio-demographic Index;\u003c/p\u003e\n\u003cp\u003eAMI, acute mesenteric ischemia;\u003c/p\u003e\n\u003cp\u003eAPC, age‑period cohort;\u003c/p\u003e\n\u003cp\u003eAAPC, average annual percentage change;\u003c/p\u003e\n\u003cp\u003eBAPC, Bayesian Age-Period-Cohort\u003c/p\u003e\n\u003cp\u003eUI, uncertainty intervals;\u003c/p\u003e\n\u003cp\u003eCI, confidence intervals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis study utilized the Global Health Data Exchange (GHDx) query tool for data analysis and did not involve any specific personal information. As a result, it was granted an exemption from informed consent by Qilu Hospital of Shandong University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe GBD 2021 database is an open-access resource, with unrestricted access and usage. The datasets provided in this study can be accessed through the Institute for Health Metrics and Evaluation (IHME) website at http://www.healthdata.org/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was supported by National Natural Science Foundation of China (grant numbers No. 82070852, grant numbers No. 82270901).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch involving Human Participants and/or Animals\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eX.Z. drafted the initial manuscript, designed the study, and performed data collection, analysis, interpretation, and visualization. Z.J contributed to the methodology and supervision, participated in the literature review, and provided support for the theoretical framework. A.Y. was responsible for part of the data visualization and the logical adjustments of the manuscript. X.X.conducted the formal analysis and validation. K.W.* contributed to the conceptualization and administrative support, determined the overall research methodology, supervised the entire study process, and performed the final review and approval of the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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The Burden of Gastric Cancer Attributable to High Sodium Intake: A Longitudinal Study from 1990 to 2019 in China. \u003cem\u003eNutrients\u003c/em\u003e 2023, 15(24).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJeong Y, Kim ES, Lee J, Kim Y. Trends in sodium intake and major contributing food groups and dishes in Korea: the Korea National Health and Nutrition Examination Survey 2013\u0026ndash;2017. Nutr Res Pract. 2021;15(3):382\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaugandhika S, Sapra L, Kumari K, Srivastava RK. High Salt Diet Impairs Male Fertility in Mice via Modulating the Skeletal Homeostasis. Reproductive Sci (Thousand Oaks Calif). 2023;30(11):3339\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBello II, Omigbodun A, Morhason-Bello I. Common salt aggravated pathology of testosterone-induced benign prostatic hyperplasia in adult male Wistar rat. BMC Urol. 2023;23(1):207.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHooi JKY, Lai WY, Ng WK, Suen MMY, Underwood FE, Tanyingoh D, Malfertheiner P, Graham DY, Wong VWS, Wu JCY, et al. Global Prevalence of Helicobacter pylori Infection: Systematic Review and Meta-Analysis. Gastroenterology. 2017;153(2):420\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoodie R, Stuckler D, Monteiro C, Sheron N, Neal B, Thamarangsi T, Lincoln P, Casswell S. Profits and pandemics: prevention of harmful effects of tobacco, alcohol, and ultra-processed food and drink industries. Lancet (London England). 2013;381(9867):670\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"global burden of disease, gastric cancer, mortality, disability-adjusted life years, net drift, Age-period- cohort","lastPublishedDoi":"10.21203/rs.3.rs-5037516/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5037516/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHigh sodium intake is a recognized risk factor for increased gastric cancer mortality. This study examines the trends and distribution of stomach cancer burden associated with high sodium intake from 1990 to 2021, with a focus on its relationship with age, period, and birth cohort.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUtilizing data from the 2021 Global Burden of Disease study, we applied an age-period-cohort model to conduct statistical analysis. We calculated age, period, and cohort effects, as well as net drift (overall annual percentage change), for gastric cancer deaths and disability-adjusted life years (DALYs) associated with high sodium intake across 204 countries and regions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn 2021, 7.93% of global gastric cancer deaths and 7.92% of DALYs were linked to high sodium intake. Populations in East Asia and those with a high-middle Sociodemographic Index (SDI) bore the heaviest burden. Over the 32-year period, the global age-standardized mortality rate[Net drift= -2.33(95%CI:-2.37 to -2.28)] and age-standardized DALYs rate[Net drift = -2.56(95%CI:-2.65 to -2.47)] generally demonstrated a declining trend, particularly in high SDI regions [Net drift\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.91 (95%CI: -3.02 to -2.81)]. China, as a representative country, exhibited unfavorable age, period, and cohort effects. Future projections suggest further declines in mortality and DALYs numbers, along with corresponding age-standardized rates.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eDespite ongoing global efforts to reduce sodium intake, gastric cancer remains a significant public health challenge, especially in East Asia. The findings underscore the necessity of developing targeted prevention strategies for high-risk groups, such as the elderly and males, to mitigate the global burden of gastric cancer.\u003c/p\u003e","manuscriptTitle":"High Sodium Intake: A Silent Killer Driving Global Gastric Cancer Burden","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 10:56:07","doi":"10.21203/rs.3.rs-5037516/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-01T17:20:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-01T14:11:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-30T09:20:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T09:43:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334027818515059285295013139075744487380","date":"2025-07-21T14:22:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-21T08:26:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116594821092786540862249412649073035800","date":"2025-07-21T06:09:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70792230528739286156136302011926825097","date":"2025-07-18T08:44:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179546397949805210291916586873934081824","date":"2025-07-17T04:44:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217322986166666197636070792081274433676","date":"2025-07-16T22:55:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329950545733602383883487784992267262791","date":"2025-07-16T16:38:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253958303177967354070545307591870111152","date":"2025-07-15T08:17:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62082880791607118669826586829060615181","date":"2025-07-14T16:35:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-14T16:30:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-14T06:41:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-07-13T08:46:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aaca06be-f47f-43cb-aec3-788773f9d7d9","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:06:41+00:00","versionOfRecord":{"articleIdentity":"rs-5037516","link":"https://doi.org/10.1186/s12885-025-14891-6","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2025-10-06 15:57:44","publishedOnDateReadable":"October 6th, 2025"},"versionCreatedAt":"2025-07-15 10:56:07","video":"","vorDoi":"10.1186/s12885-025-14891-6","vorDoiUrl":"https://doi.org/10.1186/s12885-025-14891-6","workflowStages":[]},"version":"v1","identity":"rs-5037516","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5037516","identity":"rs-5037516","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}