{"paper_id":"3550f2ef-35aa-4b8e-a3d8-239192eb843d","body_text":"Analysis and Projections of the Global Burden of Anxiety, Depression, Bipolar Disorder, and Schizophrenia Among Women of Reproductive Age (1990–2021) | 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 Analysis and Projections of the Global Burden of Anxiety, Depression, Bipolar Disorder, and Schizophrenia Among Women of Reproductive Age (1990–2021) Xiaohang Wang, Yaru Kong, Xingfa Long, Yi Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7970352/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Objective Mental disorders are a leading cause of years lived with disability (YLDs) and disability-adjusted life years (DALYs) worldwide, with high prevalence and severe consequences, including elevated suicide rates. These issues remain a priority for the World Health Organization. Depression, anxiety, bipolar disorder, and schizophrenia are among the most common and impactful mental disorders, and women of reproductive age experience unique physical and psychological stressors, increasing their vulnerability. This study aims to comprehensively analyze the global burden of these four mental disorders in this population, identify potential influencing factors, and project future trends. Methods Data from the Global Burden of Disease (GBD) 2021 database were used to assess the prevalence and DALYs of depression, anxiety, bipolar disorder, and schizophrenia among women of reproductive age. Trends were analyzed across different age groups, Socio-Demographic Index (SDI) regions, GBD regions, and countries. Correlations between disease burden indicators and SDI were examined, along with regional disparities. Decomposition analysis was conducted to assess potential factors contributing to the observed changes in disease burden. Future trends were projected using the Bayesian-Aperiodic-People-Cohort (BAPC) model. Results Between 1990 and 2021, the age-standardized incidence and DALY rates of anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age remained relatively stable. However, the absolute number of cases and DALYs increased, particularly for anxiety and depression. By 2021, the estimated number of cases and DALYs were 18,962,131 and 16,448,344 for anxiety, 133,248,593 and 21,042,424 for depression, 826,397 and 2,806,894 for bipolar disorder, and 513,255 and 4,836,703 for schizophrenia, respectively. The corresponding ASRs were 976.14/100,000 and 844.05/100,000 for anxiety, 6,808.01/100,000 and 1,073.5/100,000 for depression, 43.16/100,000 and 143.77/100,000 for bipolar disorder, and 26.71/100,000 and 243.46/100,000 for schizophrenia. Adolescents had the highest incidence and DALYs for anxiety, bipolar disorder, and schizophrenia, whereas depression incidence and DALYs increased with age. North America and Latin America exhibited the highest and fastest-growing burdens, while East Asia had the lowest burden, largely influenced by an aging population. Health disparities in mental illness burdens persisted over time. Projections indicate a substantial increase in anxiety and depression cases and DALYs among women of reproductive age in the coming decade. Conclusions The global burden of depression, anxiety, bipolar disorder, and schizophrenia among women of reproductive age continues to rise, particularly for anxiety and depression. Significant health disparities persist, necessitating urgent and targeted interventions. mental disorders burden of disease Deaths DALYs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction According to the latest Global Burden of Disease (GBD) studies, mental disorders are the second leading cause of years lived with disability (YLDs) worldwide and the seventh leading cause of disability-adjusted life years (DALYs) globally. They are also strongly associated with high suicide rates (Vigo et al., 2016 ; Bachmann, 2018 ; WHO, 2024). Depression, anxiety disorders, bipolar disorder, and schizophrenia are among the most prevalent and impactful mental illnesses. Of these, depression and anxiety disorders account for 37.3% and 22.9% of mental disorder-related DALYs, respectively. Both conditions frequently co-occur, and recent epidemiological data indicate a significant rise in their prevalence over the past decades (GBD 2019 Mental Disorders Collaborators, 2022 ). Although bipolar disorder and schizophrenia have relatively lower global prevalence rates, their substantial socioeconomic burden—due to prolonged duration, high treatment costs, and frequent relapses—cannot be overlooked (Grande et al., 2016 ; Jauhar et al., 2022 ). These disorders not only directly impair patients' quality of life, leading to significant losses in work capacity and daily functioning, but also negatively affect their families, workplaces, and social interactions. Recognizing mental health as a fundamental human right, the World Health Organization continues to prioritize this issue. While numerous epidemiological studies have assessed the global burden of mental disorders, most have focused on specific countries, regions, or individual diseases, lacking comprehensive cross-border, cross-cultural, and multi-disease comparisons (Tian et al., 2025 ; Safiri et al., 2024 ; Iran Subnational Mental Health GBD Collaborators, 2024 ; Zhang et al., 2025 ). Additionally, existing research often emphasizes disease prevalence and mortality, with less attention given to the socioeconomic impacts and disparities across age, gender, and social groups (Opio et al., 2022 ; Lauron et al., 2023 ; Pai et al., 2022 ). Furthermore, studies have frequently overlooked women of reproductive age, a population experiencing heightened physical and psychological stress due to childbirth and child-rearing. This critical life stage increases vulnerability to mental disorders, particularly during pregnancy, childbirth, and the postpartum period, when mental health issues are especially pronounced and may adversely affect both maternal and neonatal outcomes (Cantwell et al., 2021; Jones et al., 2014 ). The GBD 2021 dataset provides comprehensive data on incidence, prevalence, deaths, and disability-adjusted life years (DALYs) for 371 diseases and injuries, as well as 88 behavioral, environmental, occupational, and metabolic risk factors across 204 countries worldwide (GBD 2021 Diseases and Injuries Collaborators, 2024 ). A key strength of this dataset is its use of harmonized diagnostic criteria and methodologies, ensuring high-quality, comparable data. Additionally, it enables a systematic assessment of global and regional disease burdens by tracking health status across different countries and regions. Building on this dataset, the present study will conduct an in-depth analysis of the global burden of depression, anxiety disorders, bipolar disorder, and schizophrenia among women of reproductive age. It will evaluate regional, socioeconomic, and age-related differences in disease burden, examine trends over time, explore potential influencing factors, and project future trajectories. The findings aim to provide a scientific basis for global mental health policy development, supporting more effective and targeted interventions. Methods 1. Data Acquisition and Sources This study used the Global Burden of Disease (GBD) 2021 data to assess the global impact of anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age. The GBD 2021 dataset is accessible through the Global Health Data Exchange (GHDx) Results Tool( http://ghdx.healthdata.org/ ). It provides open access to detailed global and regional health metrics. The dataset estimates 371 diseases and injuries, covering incidence, prevalence, mortality, and Disability-Adjusted Life Years (DALYs). Additionally, it includes 88 risk factors pertaining to behavioral, environmental, occupational, and metabolic domains. This analysis focused on the mortality and DALYs associated with anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age in the worldwide. DALYs, which combine years lived with disability (YLDs) and years of life lost (YLLs) due to early death, serve as a measure for evaluating both fatal and nonfatal disease impacts. The Socio-population Index (SDI) is a composite measure of socioeconomic development that combines total fertility rates, mean years of education, and lagged per capita income for individuals aged 15 and older across various regions. The SDI ranges from 0 to 1, classifying countries into five tiers: low, low-middle, middle, high-middle, and high. The study analyzed data from 204 countries and territories from 1990 to 2021. It provided a comprehensive trend analysis and an in-depth evaluation of the global burden of anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age. Ethical approval and informed consent were not necessary since this study utilized publicly available data. The research adhered to guidelines for accurate and transparent reporting in health assessments. 2. Population Analysis and Global Burden Analysis We analyzed age-standardized incidence, DALYs, and their 95% uncertainty intervals for anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age using data from the GBD 2021 study, which included 204 countries, 21 GBD regions, and 5 Socio-Demographic Index (SDI) quintiles. Additionally, we stratified the prevalence, incidence, death and DALYs by sex into two groups. We then divided the population into 4 age groups (15–19 years, 20–24 years…45–49 years) based on a 5-year cycle. The study utilized high-resolution maps to visualize the global burden of tuberculosis, emphasizing disparities across socio-demographic and geographic contexts. his spatial representation provided valuable insights into the patterns of anxiety, depression, bipolar disorder, and schizophrenia among women ages 15 to 49 worldwide. 3. Decomposition Analysis We used decomposition analysis to measure how population growth, aging, and epidemiological changes contributed to trends in total incidence, prevalence, mortality and DALYs. This approach provided clearer insights into how demographic and health system factors influence the disease burden. This study uses a method consistent with the analytical framework of previous Global Burden of Disease (GBD) studies. These studies estimate how changes in population structure and risk factors affect shifts in disease outcomes. 4. Health Inequality Analysis Health inequality analysis examines disparities in disease burden across countries and regions, helping to shape public health policies. This analysis employs the slope index of inequality (SII) and the concentration index (CII) to measure health inequalities. The SII illustrates the relationship between health indicators and socioeconomic status. It utilizes the Sociodemographic Index (SDI) in a linear regression analysis. The CI ranges from − 1 to 1 and indicates the variation of health outcomes based on economic status. Values closer to 0 reflect less inequality, positive values favor the wealthy, and negative values favor the impoverished. This study calculated the SII and CI for the Mortality and DALYs of caries in anxiety, depression, bipolar disorder, and schizophrenia between 1990 and 2021. It emphasizes health inequalities in a global context, across various regions, and among 204 countries. 5. Prediction Analysis To better formulate public health policies and allocate medical resources, we divided the population into gender-based subgroups (females and males) and used the Bayesian-Aperiodic-People-Cohort (BAPC) model to predict trends in the incidence and prevalence of anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age over the next 15 years. By considering temporal variations and age-specific trends, these models provide a reliable and comprehensive outlook on the future burden of tooth decay. Studies have shown that combining the Integrated Nested Laplace Approximation (INLA) with the BAPC model effectively approximates marginal posterior distributions, thereby avoiding the mixing and convergence issues typically associated with the Markov Chain Monte Carlo (MCMC) sampling technique used in traditional methods. 6. Statistical Software All statistical analyses were conducted using R (version 4.3.3) and Stata 18 (StataCorp, College Station, TX). We created custom scripts to perform decomposition and sensitivity analyses. We performed Bayesian analysis using WinBUGS (version 1.4) as part of our analytical process. Geographic and spatial analyses were conducted with ArcGIS Pro and QGIS (version 3.16), allowing for the creation of high-resolution maps that visualize the TBL burden and disparities. We generated data visualizations, including bi-lateral and two-axis plots, using the 'ggplot2' and 'Benchmarking' packages in R. 7. Statistical Significance In this study, we set the p < 0.05 threshold for all analyses to determine statistical significance. This approach aligns with standard practices in epidemiological and public health research, especially concerning studies on the Global Burden of Disease. Results 1. Analysis of the Current Status and Trends in the Burden of Psychiatric Disorders Among Women of Reproductive Age Globally (1990–2021) From 1990 to 2021, there was no significant global increase in the incidence of anxiety, depression, bipolar disorder, or schizophrenia, nor in the age-standardized rates (ASRs) of DALYs among women of reproductive age. However, the absolute number of cases continued to rise, particularly for anxiety and depression, which saw substantial increases in 2020. Specifically, the incidence of anxiety disorders and the number of associated DALYs increased by 18.3% and 17.5%, respectively, while depression saw increases of 22.8% and 19.1%. The corresponding ASRs for these disorders increased nearly 1.2-fold.By 2021, the incidence and DALY counts for anxiety disorders were 18,962,131 (95% uncertainty interval [UI]: 12,660,029–26,295,396) and 16,448,344 (95% UI: 10,383,333–24,009,992), respectively. For depression, these numbers were 133,248,593 (95% UI: 99,032,450–177,876,463) and 21,042,424 (95% UI: 13,468,194–30,593,481). For bipolar disorder, they were 826,397 (95% UI: 484,408–1,270,443) and 2,806,894 (95% UI: 1,740,129–4,226,051), while schizophrenia had corresponding values of 513,255 (95% UI: 302,798–767,916) and 4,836,703 (95% UI: 3,318,473–6,567,286). The respective ASRs were 976.14 per 100,000 (95% UI: 650.09–1,355.11) and 844.05 per 100,000 (95% UI: 532.79–1,232.57) for anxiety disorders, 6,808.01 per 100,000 (95% UI: 5,049.99–9,106.66) and 1,073.5 per 100,000 (95% UI: 686.73–1,562.48) for depression, 43.16 per 100,000 (95% UI: 25.43–66.23) and 143.77 per 100,000 (95% UI: 89.11–216.63) for bipolar disorder, and 26.71 per 100,000 (95% UI: 15.76–39.94) and 243.46 per 100,000 (95% UI: 166.79–331.29) for schizophrenia. Compared to 1990, these figures represent increases of 76.7% and 76.3% for anxiety disorders, 71.4% and 69.1% for depression, 43.6% and 48.5% for bipolar disorder, and 35.7% and 55.3% for schizophrenia in terms of incidence and DALYs, respectively. The estimated annual percentage changes (EAPCs) of ASRs were 0.24 (95% confidence interval [CI]: 0.08–0.39) and 0.16 (95% CI: 0.01–0.32) for anxiety disorders, -0.18 (95% CI: -0.39–0.04) and − 0.14 (95% CI: -0.32–0.04) for depression, 0.09 (95% CI: 0.07–0.11) and 0.03 (95% CI: 0.02–0.04) for bipolar disorder, and − 0.02 (95% CI: -0.03– -0.01) and 0 (95% CI: 0–0.01) for schizophrenia (Fig. 1 , Tables 1 – 8 ). Table 1 The incidence cases and age-standardized incidence rate of anxiety disorders disease in 1990 and 2021, along with their temporal trend. Rate per 100 000(95%UI) 2021 1990–2021 1990 Incidence cases The age-standardized incidence rate Incidence cases The age-standardized incidence rate EAPC Global 10728548 (7058385 to 14938651) 797.81 (527.95 to 1108.31) 18962131 (12660029 to 26295396) 976.14 (650.09 to 1355.11) 0.24 (0.08 to 0.39) SDI region High SDI 2135047 (1432423 to 2971475) 948.44 (635.2 to 1320.95) 2853074 (1899052 to 3973078) 1218.56 (806.6 to 1696.8) 0.28 (0.09 to 0.46) High-middle SDI 2129500 (1403378 to 2983302) 763.79 (505.13 to 1068.56) 2737964 (1803315 to 3843299) 924.49 (600.29 to 1305.46) 0.08 (-0.09 to 0.25) Middle SDI 3529827 (2308097 to 4896476) 786.68 (520.36 to 1086.36) 6204189 (4143079 to 8539181) 1010.08 (670.08 to 1393.73) 0.34 (0.18 to 0.51) Low-middle SDI 2100995 (1382205 to 2930806) 769.57 (510.29 to 1068.67) 4814788 (3223652 to 6659907) 949.69 (637.84 to 1312.17) 0.41 (0.26 to 0.56) Low SDI 822937 (530429 to 1170280) 728.7 (475.17 to 1031.36) 2336119 (1518389 to 3297914) 841.34 (553.46 to 1182.79) 0.19 (0.07 to 0.32) GBD region Andean Latin America 104609 (64381 to 154429) 1088.67 (676.77 to 1599.44) 269514 (161062 to 409142) 1543.39 (921.02 to 2343.89) 0.44 (0.14 to 0.74) Australasia 62989 (39165 to 91645) 1178.99 (730.54 to 1716.7) 91094 (55171 to 135833) 1308.88 (788.4 to 1954.57) 0.25 (0.13 to 0.38) Caribbean 88536 (55517 to 128306) 947.17 (598.11 to 1366.17) 140334 (88145 to 208576) 1169.28 (732.93 to 1739.96) 0.24 (0.09 to 0.4) Central Asia 83455 (51961 to 120817) 494.98 (308.72 to 714.6) 148239 (92314 to 217621) 612.93 (379.18 to 904.01) 0.19 (0.02 to 0.35) Central Europe 225279 (146931 to 321264) 733.12 (476.79 to 1047.11) 252984 (165661 to 358878) 1001.24 (647.65 to 1429.92) 0.31 (0.08 to 0.53) Central Latin America 334381 (214670 to 472080) 793.53 (515.47 to 1114.68) 776238 (505479 to 1088452) 1141.19 (742.81 to 1600.72) 0.74 (0.53 to 0.95) Central Sub-Saharan Africa 92717 (56706 to 137694) 739.56 (455.95 to 1090.15) 273421 (163102 to 417667) 824.55 (493.33 to 1255.53) 0.14 (0.03 to 0.25) East Asia 2295505 (1494840 to 3206710) 687.33 (453.65 to 954.73) 2312917 (1556700 to 3211205) 704.81 (465.63 to 985.62) -0.53 (-0.69 to -0.37) Eastern Europe 396215 (264350 to 547754) 721.42 (477.82 to 999.85) 462974 (310102 to 634514) 1003.54 (664.25 to 1383.18) 0.32 (0.08 to 0.55) Eastern Sub-Saharan Africa 363103 (231266 to 514198) 820.93 (529.06 to 1157.75) 1057053 (674589 to 1500615) 963.86 (623.05 to 1361.54) 0.11 (-0.03 to 0.24) High-income Asia Pacific 296798 (193791 to 412287) 652.89 (426.19 to 907.51) 285336 (187416 to 399116) 775.34 (505.43 to 1085.75) -0.16 (-0.33 to 0) High-income North America 874484 (603133 to 1194303) 1181.68 (811.78 to 1615.36) 1296755 (870228 to 1790275) 1576.93 (1055.1 to 2175.1) 0.29 (0.02 to 0.56) North Africa and Middle East 770067 (493743 to 1115136) 954.55 (618.48 to 1377) 1820024 (1135627 to 2695730) 1146.18 (714.54 to 1698.15) 0.45 (0.3 to 0.6) Oceania 12020 (7446 to 17683) 767.11 (479.13 to 1121.36) 29960 (18001 to 45819) 858.74 (517.15 to 1311.02) 0.12 (0.04 to 0.2) South Asia 1848991 (1230669 to 2539891) 729.75 (489.22 to 999.65) 4459968 (3040346 to 6039337) 903.5 (616.96 to 1222.77) 0.44 (0.26 to 0.62) Southeast Asia 948880 (619385 to 1323116) 789.29 (519.05 to 1096.93) 1849098 (1232332 to 2566238) 1012.28 (672.67 to 1406.43) 0.28 (0.1 to 0.45) Southern Latin America 132381 (86387 to 189926) 1061.65 (693.63 to 1522.73) 228331 (136197 to 344501) 1329.58 (792.3 to 2007.36) 0.16 (-0.02 to 0.34) Southern Sub-Saharan Africa 102575 (66222 to 143736) 768.44 (502.15 to 1070.42) 221230 (144908 to 309387) 1015.64 (664.21 to 1421.63) 0.26 (0.06 to 0.46) Tropical Latin America 482618 (315467 to 668967) 1207.11 (793.75 to 1670.2) 1111559 (737246 to 1565120) 1851.05 (1223.06 to 2604.6) 1.08 (0.76 to 1.4) Western Europe 957600 (612529 to 1390417) 1016.1 (650.54 to 1473.56) 1115696 (715206 to 1601914) 1249.18 (795.33 to 1795.92) 0.35 (0.21 to 0.5) Western Sub-Saharan Africa 255344 (163413 to 361679) 573.1 (371.64 to 808.27) 759407 (488449 to 1073860) 621.15 (403.37 to 876.82) 0.18 (0.04 to 0.33) Abbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. “ EAPC is expressed as 95% CIs. Table 2 The DALYs cases and age-standardized DALYs rate of anxiety disorders disease in 1990 and 2021, along with their temporal trend. Rate per 100 000(95%UI) 2021 1990–2021 1990 DALYs cases The age-standardized DALYs rate DALYs cases The age-standardized DALYs rate EAPC Global 9328956 (5917676 to 13620685) 698.3 (442.6 to 1018.65) 16448344 (10383333 to 24009992) 844.05 (532.79 to 1232.57) 0.16 (0.01 to 0.32) SDI region High SDI 2043455 (1301043 to 2990142) 900.03 (572.56 to 1317.41) 2745188 (1743682 to 4004979) 1140.64 (724.95 to 1666.29) 0.14 (-0.08 to 0.35) High-middle SDI 1927404 (1227564 to 2806442) 692.52 (440.41 to 1008.29) 2487171 (1551501 to 3657646) 835.89 (521.28 to 1232.17) 0.07 (-0.1 to 0.23) Middle SDI 3024602 (1926600 to 4410929) 675.54 (429.85 to 983.78) 5417644 (3456898 to 7877801) 877.44 (559.52 to 1276.98) 0.36 (0.19 to 0.52) Low-middle SDI 1664857 (1044787 to 2443621) 617.24 (387.16 to 904.27) 3896043 (2458016 to 5672689) 771.79 (486.96 to 1122.66) 0.46 (0.32 to 0.6) Low SDI 659781 (411510 to 983257) 591.7 (369.02 to 880.6) 1888211 (1162549 to 2819269) 686.86 (422.94 to 1024.05) 0.22 (0.1 to 0.34) GBD region Andean Latin America 96613 (57712 to 147281) 1028.17 (614.34 to 1567) 253659 (149262 to 395132) 1449.82 (853.42 to 2256.86) 0.45 (0.16 to 0.74) Australasia 65972 (40727 to 99527) 1226.54 (756.38 to 1851.6) 96214 (57071 to 148232) 1331.41 (790.19 to 2050) 0.24 (0.14 to 0.33) Caribbean 76856 (45799 to 118118) 829.26 (493.92 to 1271.04) 123221 (73950 to 188783) 1023.85 (614.13 to 1569) 0.25 (0.1 to 0.4) Central Asia 64797 (38189 to 99708) 384.94 (226.32 to 592.5) 115505 (67532 to 179181) 477.48 (278.81 to 741.09) 0.19 (0.02 to 0.36) Central Europe 187798 (115483 to 281746) 610.93 (375.26 to 918.47) 211608 (130502 to 317266) 829.55 (511.7 to 1244.04) 0.3 (0.08 to 0.52) Central Latin America 278259 (172888 to 411442) 664.85 (412.96 to 981.57) 658936 (407957 to 984002) 967.11 (599.01 to 1444.37) 0.72 (0.48 to 0.97) Central Sub-Saharan Africa 76381 (45808 to 117479) 614.13 (368.37 to 940.98) 225913 (131912 to 360784) 685.83 (400.83 to 1093.44) 0.17 (0.06 to 0.27) East Asia 2011893 (1291935 to 2941224) 596.53 (382.41 to 871.85) 1992595 (1262971 to 2950597) 616.8 (390.72 to 913.98) -0.53 (-0.7 to -0.37) Eastern Europe 331368 (214027 to 485124) 600.47 (386.95 to 879.93) 386537 (246701 to 563962) 829.05 (528.46 to 1214.66) 0.32 (0.09 to 0.55) Eastern Sub-Saharan Africa 291169 (181267 to 433062) 677.93 (421.77 to 1006.24) 847625 (525221 to 1283511) 790.95 (489.83 to 1193.93) 0.12 (-0.02 to 0.25) High-income Asia Pacific 249111 (157076 to 367725) 547.21 (345.53 to 808.62) 241960 (150489 to 363236) 649.35 (405.91 to 973.4) -0.11 (-0.28 to 0.05) High-income North America 802494 (522068 to 1154699) 1060.44 (688.74 to 1526.44) 1196413 (775406 to 1736433) 1414.22 (916.57 to 2054.75) 0.15 (-0.2 to 0.51) North Africa and Middle East 773967 (485447 to 1138638) 966.97 (607.2 to 1420.87) 1882833 (1142662 to 2817198) 1183.83 (718.06 to 1772.49) 0.37 (0.24 to 0.5) Oceania 10349 (6231 to 15969) 658.88 (395.98 to 1014.17) 25991 (14566 to 41937) 744.76 (417.71 to 1200.08) 0.13 (0.05 to 0.2) South Asia 1357075 (861459 to 1974664) 544.66 (345.95 to 790.85) 3273256 (2109051 to 4705742) 665.93 (429.4 to 956.5) 0.45 (0.26 to 0.64) Southeast Asia 746044 (470314 to 1100271) 626.6 (394.12 to 922.7) 1485464 (934317 to 2202813) 810.68 (510.29 to 1202.37) 0.29 (0.12 to 0.46) Southern Latin America 139173 (88869 to 204582) 1123.82 (717.37 to 1651.88) 240552 (139628 to 365818) 1381.49 (802.14 to 2101.38) 0.03 (-0.18 to 0.24) Southern Sub-Saharan Africa 84034 (53387 to 123078) 633.35 (401.81 to 925.12) 181302 (116407 to 263722) 833.78 (534.76 to 1213.06) 0.24 (0.04 to 0.44) Tropical Latin America 436030 (282337 to 624903) 1105.86 (716.24 to 1584.31) 1146277 (734480 to 1642912) 1874.23 (1199.81 to 2688.52) 1.4 (0.88 to 1.93) Western Europe 1031704 (638828 to 1533835) 1087.27 (673.49 to 1616.79) 1209541 (749486 to 1785519) 1333.41 (827.15 to 1967.63) 0.27 (0.11 to 0.43) Western Sub-Saharan Africa 217869 (136174 to 325874) 484.48 (302.7 to 723.7) 652943 (405725 to 977339) 529.25 (328.86 to 791.21) 0.2 (0.09 to 0.31) Abbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. “ EAPC is expressed as 95% CIs. Table 3 The incidence cases and age-standardized incidence rate of depressive disorders disease in 1990 and 2021, along with their temporal trend. Rate per 100 000(95%UI) 2021 1990–2021 1990 Incidence cases The age-standardized incidence rate Incidence cases The age-standardized incidence rate EAPC Global 77722841 (58860491 to 102539344) 5898.5 (4486.52 to 7740.66) 133248593 (99032450 to 177876463) 6808.01 (5049.99 to 9106.66) -0.18 (-0.39 to 0.04) SDI region High SDI 14095700 (11172406 to 17813539) 6219.44 (4915.78 to 7869.11) 21662488 (16621406 to 28113424) 9099.46 (6930.43 to 11859.63) 0.54 (0.31 to 0.77) High-middle SDI 14966527 (11469344 to 19395257) 5424.7 (4166.97 to 7008.76) 17857233 (13115937 to 23816969) 5778.86 (4201.28 to 7786.22) -0.33 (-0.57 to -0.09) Middle SDI 22649073 (16997951 to 29922163) 5183.69 (3915.67 to 6798.65) 36081478 (26891514 to 47657463) 5767.56 (4281.65 to 7652.92) -0.25 (-0.48 to -0.01) Low-middle SDI 18391509 (13457053 to 25013203) 7035.36 (5182.88 to 9485.43) 37747940 (27469874 to 51290417) 7551.4 (5510.78 to 10222.82) -0.6 (-0.86 to -0.34) Low SDI 7553850 (5354318 to 10472429) 7097.66 (5079.82 to 9744.65) 19801185 (13890117 to 27443214) 7509.35 (5325.8 to 10306.14) -0.36 (-0.53 to -0.19) GBD region Andean Latin America 438667 (303751 to 617425) 4737 (3296.37 to 6625.44) 1105337 (751097 to 1589967) 6320.57 (4292.05 to 9087.82) 0.06 (-0.33 to 0.45) Australasia 480002 (369325 to 616432) 8977.64 (6898.68 to 11536.06) 723160 (498995 to 1010531) 10269.64 (7057.12 to 14369.31) 0.24 (0.08 to 0.41) Caribbean 708019 (507841 to 969849) 7697.77 (5552.01 to 10488.7) 969573 (658741 to 1384728) 8029.7 (5444.45 to 11483.95) -0.5 (-0.76 to -0.24) Central Asia 762529 (544489 to 1049479) 4676.28 (3367.02 to 6381.95) 1367571 (948058 to 1922301) 5623.36 (3891.23 to 7918.02) 0.1 (-0.09 to 0.3) Central Europe 1272969 (942663 to 1698354) 4115.36 (3043.59 to 5505.79) 1273327 (914036 to 1725195) 4795.85 (3393.82 to 6608.56) -0.47 (-0.8 to -0.15) Central Latin America 2056464 (1474404 to 2831110) 5119.69 (3689.12 to 6999.46) 5395961 (3919153 to 7296866) 7882.55 (5720.17 to 10666.28) 1.02 (0.8 to 1.24) Central Sub-Saharan Africa 1276623 (879908 to 1821332) 10640.71 (7408.07 to 15062.94) 3584294 (2373076 to 5212807) 11270.02 (7523.17 to 16274.56) -0.02 (-0.13 to 0.1) East Asia 14573702 (11063941 to 19051300) 4412.52 (3367.36 to 5732.14) 10395449 (7909206 to 13367181) 2956.12 (2227.57 to 3840.52) -1.4 (-1.65 to -1.16) Eastern Europe 3022733 (2188449 to 4091174) 5392.25 (3903.27 to 7297.88) 3496268 (2499383 to 4757666) 6990.81 (4956.63 to 9586.01) -0.13 (-0.41 to 0.15) Eastern Sub-Saharan Africa 2992873 (2132522 to 4133293) 7375.7 (5319.95 to 10067.85) 8187853 (5707046 to 11439404) 7985.77 (5630.58 to 11024.96) -0.27 (-0.43 to -0.12) High-income Asia Pacific 1741956 (1355004 to 2240796) 3838.31 (2978.91 to 4945.44) 1832829 (1393439 to 2364638) 5010.7 (3763.15 to 6511.04) 0.47 (0.27 to 0.66) High-income North America 5275313 (4101575 to 6757929) 7145.82 (5538.1 to 9178.7) 10479475 (8142164 to 13299662) 12689.95 (9828.58 to 16139.08) 0.82 (0.47 to 1.16) North Africa and Middle East 6585654 (4704056 to 9116662) 8629.46 (6216.24 to 11853.62) 16021220 (10983016 to 22831950) 10032.79 (6876.71 to 14305.05) 0.2 (0.04 to 0.36) Oceania 62234 (43091 to 88312) 3968.59 (2766.94 to 5579.09) 143677 (95168 to 209653) 4125.32 (2738.85 to 6006.85) -0.13 (-0.19 to -0.06) South Asia 17346564 (12898192 to 23184706) 7116.76 (5321.38 to 9440.27) 35575994 (26421845 to 47285175) 7283.76 (5421.29 to 9654.43) -0.99 (-1.31 to -0.66) Southeast Asia 3802109 (2781508 to 5120365) 3196.08 (2353.81 to 4271.57) 7017745 (5014067 to 9561434) 3829.96 (2729.29 to 5227.5) -0.04 (-0.26 to 0.17) Southern Latin America 868789 (655862 to 1148697) 6983.96 (5277.99 to 9228.68) 1401345 (1012961 to 1899048) 8133.97 (5867.86 to 11029.02) -0.17 (-0.43 to 0.08) Southern Sub-Saharan Africa 851742 (638669 to 1125686) 6761.59 (5111.67 to 8866.45) 1889699 (1403730 to 2506386) 8722.61 (6487.11 to 11549.81) 0.37 (0.09 to 0.64) Tropical Latin America 3243572 (2443732 to 4270770) 8304.3 (6295.63 to 10868.09) 6004237 (4494351 to 7859870) 9756.89 (7276.88 to 12821.9) -0.44 (-0.82 to -0.07) Western Europe 7776728 (6187704 to 9786383) 8121.24 (6445.97 to 10229.83) 9415888 (6929830 to 12706630) 10094.71 (7338.27 to 13758.87) 0.2 (-0.01 to 0.42) Western Sub-Saharan Africa 2583599 (1839829 to 3578085) 6321.17 (4550.59 to 8648.31) 6967690 (4926014 to 9745602) 6114.54 (4369.47 to 8446.05) -0.33 (-0.44 to -0.21) Abbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. “ EAPC is expressed as 95% CIs. Table 4 The DALYs cases and age-standardized DALYs rate of depressive disorders disease in 1990 and 2021, along with their temporal trend. Rate per 100 000(95%UI) 2021 1990–2021 1990 DALYs cases The age-standardized DALYs rate DALYs cases The age-standardized DALYs rate EAPC Global 12445336 (8084219 to 18023437) 948.86 (617.06 to 1369.93) 21042424 (13468194 to 30593481) 1073.5 (686.73 to 1562.48) -0.14 (-0.32 to 0.04) SDI region High SDI 2291807 (1525885 to 3251697) 1007.25 (670.08 to 1431.55) 3350602 (2211216 to 4819019) 1399.31 (921.2 to 2022.31) 0.44 (0.25 to 0.64) High-middle SDI 2449324 (1601463 to 3518044) 889.89 (582.12 to 1275.93) 2939216 (1878568 to 4276996) 939.53 (596.4 to 1375.97) -0.28 (-0.48 to -0.08) Middle SDI 3677230 (2385351 to 5345046) 850.96 (552.69 to 1231.79) 5819959 (3723678 to 8429630) 926.08 (591.77 to 1344.73) -0.22 (-0.42 to -0.03) Low-middle SDI 2832462 (1799426 to 4175678) 1088.11 (691.58 to 1595.85) 5817166 (3674561 to 8529565) 1164.37 (735.95 to 1704.52) -0.49 (-0.71 to -0.27) Low SDI 1183844 (747044 to 1751936) 1116.33 (706.66 to 1643.03) 3099941 (1938028 to 4603046) 1179.58 (740.73 to 1739.54) -0.27 (-0.41 to -0.13) GBD region Andean Latin America 69125 (42310 to 105173) 751.79 (460.41 to 1140.63) 169290 (101535 to 261073) 966.63 (579.65 to 1490.12) 0.06 (-0.28 to 0.39) Australasia 73943 (48093 to 106574) 1380.17 (896.99 to 1991.52) 110902 (68365 to 167971) 1563.09 (962.3 to 2373.22) 0.24 (0.09 to 0.38) Caribbean 106489 (66718 to 161464) 1161.78 (729.68 to 1756.61) 144581 (87039 to 222582) 1195.95 (719.21 to 1842.41) -0.48 (-0.72 to -0.25) Central Asia 125704 (79388 to 185289) 772.01 (488.55 to 1132.2) 220831 (137416 to 332913) 903.45 (561.45 to 1363.66) 0.1 (-0.06 to 0.26) Central Europe 215905 (137301 to 313409) 695.87 (442.07 to 1011.41) 211070 (132809 to 308756) 788.38 (492.87 to 1163.38) -0.38 (-0.64 to -0.12) Central Latin America 316373 (199590 to 471536) 791.2 (499.51 to 1175.67) 800325 (496086 to 1189258) 1168.22 (724.13 to 1736.5) 0.91 (0.71 to 1.11) Central Sub-Saharan Africa 190214 (117316 to 291338) 1597.25 (989.08 to 2430.71) 535682 (313866 to 827727) 1695.2 (994.94 to 2610.73) 0.02 (-0.07 to 0.12) East Asia 2490860 (1624451 to 3611963) 766.37 (500.81 to 1106.86) 2009848 (1320287 to 2848265) 560.51 (365.8 to 799.17) -1.15 (-1.34 to -0.95) Eastern Europe 486387 (309306 to 712816) 864.27 (549.26 to 1266.81) 546448 (346405 to 807155) 1083.11 (684.77 to 1608.23) -0.09 (-0.33 to 0.15) Eastern Sub-Saharan Africa 481183 (303359 to 708083) 1188.39 (751.52 to 1736.98) 1302601 (802604 to 1945053) 1273.15 (789.45 to 1889.91) -0.21 (-0.33 to -0.08) High-income Asia Pacific 280947 (183633 to 402273) 618.37 (403.62 to 885.93) 290039 (188979 to 417635) 783.79 (508.07 to 1134.18) 0.38 (0.2 to 0.56) High-income North America 886754 (586930 to 1271514) 1193.29 (788.61 to 1716.28) 1597172 (1063927 to 2290899) 1929.21 (1284.02 to 2772.95) 0.64 (0.36 to 0.92) North Africa and Middle East 1005848 (626768 to 1502447) 1323.9 (828.06 to 1967.04) 2420065 (1465303 to 3673290) 1515.15 (916.91 to 2301.13) 0.19 (0.05 to 0.34) Oceania 10693 (6680 to 16226) 698.73 (437.04 to 1054.93) 24892 (14825 to 38303) 720.59 (430.31 to 1107.37) -0.09 (-0.14 to -0.04) South Asia 2645823 (1692290 to 3895331) 1088.15 (696.41 to 1593.93) 5462958 (3482818 to 7949804) 1118.19 (713.21 to 1625.21) -0.82 (-1.1 to -0.54) Southeast Asia 692004 (446214 to 1012842) 593.89 (383.57 to 865.93) 1260592 (802918 to 1855937) 683.77 (434.95 to 1007.98) -0.01 (-0.17 to 0.15) Southern Latin America 132096 (85584 to 194500) 1063.1 (688.88 to 1563.55) 210975 (131446 to 313050) 1221.46 (760.65 to 1814.09) -0.2 (-0.44 to 0.05) Southern Sub-Saharan Africa 135540 (88225 to 197114) 1080.34 (703.43 to 1562.11) 288124 (185614 to 421681) 1329.3 (856.5 to 1943.43) 0.28 (0.04 to 0.52) Tropical Latin America 477478 (309004 to 697269) 1225.46 (794.24 to 1783.06) 876601 (555830 to 1277429) 1419.51 (900.1 to 2073.83) -0.41 (-0.75 to -0.06) Western Europe 1207940 (802909 to 1707060) 1258.72 (836.26 to 1781.26) 1429185 (913491 to 2114351) 1527.48 (969.93 to 2272.7) 0.18 (-0.02 to 0.37) Western Sub-Saharan Africa 414027 (258812 to 611944) 1017.39 (639.5 to 1494.49) 1130242 (707465 to 1676792) 996.09 (626.15 to 1467.43) -0.24 (-0.33 to -0.14) Abbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. “ EAPC is expressed as 95% CIs. Table 5 The incidence cases and age-standardized incidence rate of Bipolar disorder disease in 1990 and 2021, along with their temporal trend. Rate per 100 000(95%UI) 2021 1990–2021 1990 Incidence cases The age-standardized incidence rate Incidence cases The age-standardized incidence rate EAPC Global 575625 (344082 to 875035) 41.64 (24.69 to 63.45) 826397 (484408 to 1270443) 43.16 (25.43 to 66.23) 0.09 (0.07 to 0.11) SDI region High SDI 122166 (77076 to 179264) 56.43 (36.24 to 82.11) 127979 (81132 to 187291) 57.29 (37.47 to 82.78) 0.07 (0.05 to 0.09) High-middle SDI 103636 (59717 to 161399) 37.22 (21.48 to 57.83) 107786 (60502 to 169743) 38.88 (22.39 to 60.79) 0.1 (0.03 to 0.18) Middle SDI 177098 (106839 to 269544) 37.17 (22.04 to 56.91) 243395 (142322 to 374670) 41.13 (24.37 to 63) 0.26 (0.22 to 0.3) Low-middle SDI 117716 (69184 to 181683) 40.54 (23.38 to 62.94) 210928 (121371 to 328059) 41.06 (23.54 to 63.88) 0.02 (0.01 to 0.03) Low SDI 54319 (31049 to 85052) 45.4 (25.48 to 71.53) 135497 (77503 to 212314) 46.12 (25.89 to 72.74) 0.05 (0.04 to 0.05) GBD region Andean Latin America 8032 (4472 to 13025) 76.94 (42.13 to 125.6) 13139 (7136 to 21571) 76.95 (42.16 to 125.71) 0 (0 to 0) Australasia 4879 (2998 to 7306) 93.63 (58.07 to 139.58) 6130 (3700 to 9269) 93.74 (58.34 to 139.78) 0.01 (0 to 0.01) Caribbean 7684 (4270 to 12426) 77.3 (42.53 to 125.32) 9079 (4933 to 14791) 77.31 (42.25 to 125.57) 0 (0 to 0) Central Asia 7855 (4201 to 12921) 45.76 (24.38 to 75.05) 10676 (5628 to 17580) 45.8 (24.36 to 75.01) 0 (0 to 0) Central Europe 14989 (8411 to 23835) 49.65 (28.05 to 78.81) 11866 (6544 to 18856) 49.53 (28.01 to 78.4) -0.01 (-0.01 to -0.01) Central Latin America 36259 (22289 to 54247) 77.85 (46.66 to 117.56) 52209 (31016 to 79187) 77.78 (46.49 to 117.68) -0.01 (-0.01 to 0) Central Sub-Saharan Africa 6429 (3480 to 10541) 48.59 (25.94 to 80) 16936 (9173 to 27741) 48.59 (25.96 to 79.95) 0 (0 to 0) East Asia 57465 (33907 to 87569) 16.65 (9.75 to 25.4) 50533 (28838 to 77652) 16.6 (9.76 to 25.27) -0.02 (-0.03 to -0.01) Eastern Europe 24875 (14324 to 38020) 46.97 (27.45 to 71.23) 20779 (11800 to 31808) 46.95 (27.47 to 71.11) 0 (0 to 0) Eastern Sub-Saharan Africa 25498 (14677 to 39661) 54.16 (30.42 to 84.94) 62648 (35945 to 97534) 54.17 (30.44 to 84.94) 0 (0 to 0) High-income Asia Pacific 21879 (12697 to 33653) 48.22 (28.07 to 74.23) 17162 (9667 to 26691) 48.43 (28.08 to 74.82) 0.01 (0 to 0.01) High-income North America 37251 (26563 to 48660) 53.44 (39.13 to 68.87) 42434 (30962 to 55088) 52.9 (39.24 to 68.07) 0.06 (0.04 to 0.08) North Africa and Middle East 58208 (33895 to 92517) 67.28 (38.18 to 107.74) 105578 (59400 to 169849) 67.09 (37.91 to 107.78) -0.01 (-0.02 to 0) Oceania 423 (222 to 699) 26.04 (13.53 to 43.06) 915 (475 to 1517) 26.08 (13.52 to 43.16) 0 (0 to 0) South Asia 79452 (46713 to 121118) 30.24 (17.55 to 46.33) 150377 (87408 to 230646) 30.32 (17.62 to 46.49) 0.01 (0.01 to 0.01) Southeast Asia 38736 (21965 to 60194) 31.1 (17.43 to 48.49) 56258 (31469 to 87671) 31.17 (17.54 to 48.46) 0 (0 to 0) Southern Latin America 8774 (4734 to 14305) 69.5 (37.32 to 113.45) 11710 (6169 to 19309) 69.73 (37.15 to 114.59) 0.01 (0.01 to 0.01) Southern Sub-Saharan Africa 6952 (4101 to 10536) 49.08 (28.36 to 74.9) 10565 (6064 to 16176) 49.03 (28.2 to 74.9) 0 (0 to 0) Tropical Latin America 41003 (25915 to 60082) 95.8 (59.56 to 141.5) 53544 (32338 to 80380) 95.7 (59.41 to 141.64) 0 (0 to 0) Western Europe 67452 (39187 to 105259) 74.11 (43.92 to 114.65) 64927 (37445 to 101246) 75.3 (44.57 to 116.45) 0.07 (0.06 to 0.08) Western Sub-Saharan Africa 21531 (12381 to 33344) 45.89 (25.77 to 71.67) 58930 (33877 to 91119) 45.9 (25.8 to 71.59) 0 (0 to 0) Abbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. “ EAPC is expressed as 95% CIs. Table 6 The DALYs cases and age-standardized DALYs rate of Bipolar disorder disease in 1990 and 2021, along with their temporal trend. Rate per 100 000(95%UI) 2021 1990–2021 1990 DALYs cases The age-standardized DALYs rate DALYs cases The age-standardized DALYs rate EAPC Global 1890347 (1181247 to 2850106) 142.3 (88.88 to 214.07) 2806894 (1740129 to 4226051) 143.77 (89.11 to 216.63) 0.03 (0.02 to 0.04) SDI region High SDI 476243 (304074 to 701694) 209.57 (133.75 to 309.08) 508105 (324844 to 747828) 209.74 (134.33 to 309.17) -0.01 (-0.03 to 0.01) High-middle SDI 352148 (215010 to 536387) 127.35 (77.68 to 193.81) 397619 (241487 to 605343) 130.1 (78.73 to 199.51) 0.05 (0 to 0.1) Middle SDI 563348 (350870 to 850162) 126.54 (78.75 to 190.39) 856066 (531423 to 1286754) 138.24 (85.78 to 208.03) 0.26 (0.24 to 0.28) Low-middle SDI 344110 (211221 to 528752) 127.42 (78.29 to 194.83) 657159 (401179 to 1006754) 130.21 (79.49 to 199.18) 0.09 (0.07 to 0.1) Low SDI 152185 (90539 to 237235) 138.56 (82.7 to 214.57) 385068 (229419 to 600144) 142.77 (85.35 to 221.17) 0.1 (0.1 to 0.11) GBD region Andean Latin America 26888 (15171 to 43335) 282.14 (160.13 to 451.63) 49363 (28351 to 78745) 281.66 (161.72 to 449.59) 0.01 (0 to 0.02) Australasia 19999 (12448 to 30672) 372.74 (231.69 to 572.69) 26654 (16555 to 40578) 371.1 (229.26 to 568.49) -0.03 (-0.04 to -0.02) Caribbean 26513 (15282 to 41812) 282.86 (163.6 to 443.88) 33738 (19623 to 53188) 280.14 (162.76 to 442.11) -0.02 (-0.03 to -0.01) Central Asia 23978 (13317 to 38414) 144.43 (80.74 to 229.72) 35341 (19631 to 56570) 144.18 (79.93 to 231.49) 0 (0 to 0.01) Central Europe 49820 (29752 to 76785) 161.57 (96.34 to 249.34) 42410 (25359 to 65013) 160.96 (96.05 to 248.21) 0 (-0.01 to 0) Central Latin America 120167 (74864 to 181326) 284.61 (177.36 to 427.92) 193702 (119606 to 290723) 283.94 (175.27 to 426.36) -0.01 (-0.01 to 0) Central Sub-Saharan Africa 17957 (9757 to 29430) 148.37 (81.66 to 241.19) 48153 (26542 to 78759) 150.49 (83.54 to 244.45) 0.04 (0.04 to 0.05) East Asia 182018 (112814 to 279614) 55.07 (34.16 to 84.45) 185766 (114795 to 284101) 55.04 (33.98 to 84.44) 0.01 (0 to 0.01) Eastern Europe 81167 (50296 to 123134) 144.92 (89.63 to 220.32) 71637 (44433 to 107751) 144.71 (89.54 to 218.93) 0.01 (0.01 to 0.01) Eastern Sub-Saharan Africa 71630 (42805 to 111553) 169.77 (101.61 to 262.27) 179442 (106912 to 279283) 170.81 (102.12 to 264.19) 0.04 (0.03 to 0.04) High-income Asia Pacific 74724 (46027 to 114661) 162.78 (100.3 to 250.32) 62715 (38368 to 95593) 160.61 (98.63 to 246.17) -0.05 (-0.06 to -0.05) High-income North America 148732 (96947 to 214524) 200.98 (130.89 to 290.01) 164702 (107235 to 236941) 197.04 (128.19 to 283.58) -0.04 (-0.05 to -0.03) North Africa and Middle East 190541 (113362 to 295985) 242.54 (144.49 to 374.22) 386391 (230734 to 597119) 242.63 (144.72 to 375.36) 0.01 (0 to 0.01) Oceania 1153 (601 to 1960) 76.21 (40.18 to 128.37) 2637 (1369 to 4481) 76.56 (39.96 to 129.61) 0.01 (0.01 to 0.02) South Asia 212741 (131193 to 325527) 85.58 (52.78 to 130.56) 423922 (257549 to 645982) 86.22 (52.37 to 131.31) 0.04 (0.03 to 0.04) Southeast Asia 109381 (64957 to 170364) 93.27 (55.41 to 144.49) 172630 (102960 to 265692) 93.38 (55.66 to 143.96) 0.02 (0.01 to 0.02) Southern Latin America 30554 (17234 to 48638) 247.09 (139.54 to 392.83) 42591 (23866 to 67882) 242.96 (135.85 to 388.05) -0.06 (-0.08 to -0.05) Southern Sub-Saharan Africa 19462 (11936 to 29746) 149.77 (92.06 to 227.76) 32154 (19577 to 48949) 147.94 (90.01 to 225.09) -0.03 (-0.04 to -0.02) Tropical Latin America 149882 (94845 to 226256) 370.63 (234.61 to 558.14) 222218 (140784 to 331760) 368.9 (233.33 to 551.85) 0 (0 to 0.01) Western Europe 273939 (168310 to 415187) 285.55 (175.17 to 433.66) 266773 (163831 to 403802) 286.38 (175.86 to 435.47) 0.02 (0.02 to 0.02) Western Sub-Saharan Africa 59101 (35479 to 91899) 139.36 (83.84 to 214.98) 163955 (98081 to 254881) 140.34 (84.21 to 216.98) 0.03 (0.03 to 0.04) Abbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. “ EAPC is expressed as 95% CIs. Table 7 The incidence cases and age-standardized incidence rate of schizophreniadisease in 1990 and 2021, along with their temporal trend. Rate per 100 000(95%UI) 2021 1990–2021 1990 Incidence cases The age-standardized incidence rate Incidence cases The age-standardized incidence rate EAPC Global 378287 (227553 to 559592) 26.98 (16.19 to 40.01) 513255 (302798 to 767916) 26.71 (15.76 to 39.94) -0.02 (-0.03 to -0.01) SDI region High SDI 62087 (37803 to 92044) 27.76 (16.9 to 41.13) 64248 (39289 to 94639) 28.21 (17.26 to 41.5) 0.09 (0.06 to 0.11) High-middle SDI 79777 (50348 to 114017) 27.77 (17.48 to 39.78) 80697 (49608 to 117006) 28.81 (17.78 to 41.62) 0.11 (0.1 to 0.13) Middle SDI 132302 (79545 to 194526) 27.55 (16.46 to 40.73) 159547 (93310 to 239328) 26.7 (15.65 to 39.99) -0.09 (-0.1 to -0.08) Low-middle SDI 72609 (41822 to 110172) 25.01 (14.39 to 38.08) 132003 (76447 to 200514) 25.38 (14.69 to 38.6) 0.05 (0.03 to 0.07) Low SDI 31214 (17710 to 47752) 26.45 (15.05 to 40.45) 76403 (43471 to 117297) 26.55 (15.15 to 40.74) 0.02 (0.01 to 0.02) GBD region Andean Latin America 2157 (1140 to 3438) 21.44 (11.36 to 34.14) 3791 (2011 to 6134) 21.46 (11.38 to 34.77) 0 (-0.01 to 0.01) Australasia 1692 (1087 to 2404) 31.83 (20.54 to 45.08) 2205 (1433 to 3157) 31.91 (20.96 to 45.3) 0.01 (0 to 0.02) Caribbean 1912 (1025 to 3088) 19.51 (10.5 to 31.48) 2325 (1226 to 3765) 19.42 (10.25 to 31.45) -0.03 (-0.04 to -0.01) Central Asia 4120 (2199 to 6572) 22.71 (12.17 to 36.21) 5467 (2917 to 8794) 22.65 (12.1 to 36.51) -0.01 (-0.01 to 0) Central Europe 6823 (3820 to 10607) 22.89 (12.81 to 35.61) 5361 (3019 to 8235) 23.1 (12.98 to 35.59) 0.03 (0.02 to 0.04) Central Latin America 9963 (5654 to 15312) 22.22 (12.6 to 34.14) 15055 (8472 to 23176) 22.16 (12.46 to 34.13) -0.02 (-0.03 to -0.01) Central Sub-Saharan Africa 3400 (1838 to 5380) 26.1 (14.2 to 41.1) 8751 (4748 to 14001) 25.8 (14.12 to 41.09) -0.02 (-0.03 to -0.01) East Asia 112065 (71228 to 158701) 31.41 (19.75 to 44.84) 95909 (58924 to 138302) 32.29 (20.09 to 46.12) 0.02 (-0.01 to 0.04) Eastern Europe 11466 (6746 to 16992) 20.91 (12.33 to 31.02) 9358 (5426 to 14023) 21.79 (12.71 to 32.45) 0.19 (0.15 to 0.22) Eastern Sub-Saharan Africa 12167 (6952 to 18630) 26.75 (15.39 to 40.85) 29744 (16919 to 45778) 26.51 (15.17 to 40.69) -0.01 (-0.02 to 0) High-income Asia Pacific 12039 (6972 to 18348) 27.05 (15.64 to 41.21) 9786 (5679 to 14672) 27.86 (16.08 to 41.91) 0.22 (0.15 to 0.29) High-income North America 22705 (14153 to 32884) 31.54 (19.77 to 45.46) 24839 (15409 to 35946) 31.34 (19.52 to 45.19) -0.03 (-0.05 to -0.02) North Africa and Middle East 20939 (11550 to 32462) 24.93 (13.78 to 38.58) 39211 (21714 to 61431) 24.77 (13.71 to 38.85) -0.03 (-0.04 to -0.03) Oceania 469 (247 to 755) 28.25 (14.99 to 45.37) 1017 (538 to 1605) 28.41 (15.09 to 44.84) 0.01 (-0.01 to 0.03) South Asia 65796 (38493 to 98839) 24.3 (14.14 to 36.73) 125191 (73100 to 188502) 24.72 (14.4 to 37.31) 0.06 (0.03 to 0.09) Southeast Asia 36922 (20997 to 55906) 28.61 (16.28 to 43.42) 51865 (29701 to 78697) 28.81 (16.5 to 43.71) 0.1 (0.06 to 0.13) Southern Latin America 3406 (1829 to 5470) 27.2 (14.64 to 43.63) 4710 (2502 to 7544) 27.39 (14.5 to 43.97) 0 (-0.02 to 0.02) Southern Sub-Saharan Africa 3688 (2162 to 5493) 26.18 (15.38 to 39.01) 5717 (3374 to 8542) 25.98 (15.34 to 38.86) 0 (-0.01 to 0.01) Tropical Latin America 9377 (5515 to 13870) 22.33 (13.11 to 33.09) 13202 (7745 to 19618) 22.38 (13.12 to 33.26) 0 (-0.01 to 0.01) Western Europe 22937 (14067 to 34147) 24.01 (14.7 to 35.79) 21280 (12952 to 31716) 23.66 (14.26 to 35.47) -0.04 (-0.06 to -0.02) Western Sub-Saharan Africa 14244 (8203 to 21537) 30.76 (17.85 to 46.4) 38471 (22228 to 58100) 30.63 (17.78 to 46.2) -0.02 (-0.02 to -0.01) Abbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. “ EAPC is expressed as 95% CIs. Table 8 The DALYs cases and age-standardized DALYs rate of schizophreniadisease in 1990 and 2021, along with their temporal trend. Rate per 100 000(95%UI) 2021 1990–2021 1990 DALYs cases The age-standardized DALYs rate DALYs cases The age-standardized DALYs rate EAPC Global 3114984 (2138675 to 4238277) 244.12 (168.33 to 329.91) 4836703 (3318473 to 6567286) 243.46 (166.79 to 331.29) 0 (0 to 0.01) SDI region High SDI 625687 (434444 to 846991) 265.73 (184.12 to 360.7) 696195 (485441 to 928011) 264.42 (183.43 to 355.41) 0.01 (-0.01 to 0.04) High-middle SDI 672326 (467381 to 898636) 245.4 (170.92 to 327.05) 874087 (614730 to 1154235) 262.16 (183.06 to 349.75) 0.19 (0.18 to 0.21) Middle SDI 1029028 (703067 to 1410514) 247.12 (170.17 to 335.27) 1576391 (1085696 to 2141396) 244.76 (168.02 to 333.99) -0.02 (-0.03 to -0.01) Low-middle SDI 564224 (380198 to 782537) 225.05 (152.9 to 308.25) 1134533 (765367 to 1583760) 229.78 (155.49 to 319.34) 0.09 (0.07 to 0.12) Low SDI 221196 (145125 to 312452) 219.69 (146.04 to 306.07) 552163 (364040 to 780207) 222.41 (148.23 to 310.27) 0.07 (0.06 to 0.08) GBD region Andean Latin America 16860 (10165 to 25596) 194.82 (119.27 to 290.55) 34512 (20991 to 51594) 196.14 (119.39 to 293.12) 0.04 (0.03 to 0.05) Australasia 16151 (11008 to 21890) 294.74 (200.5 to 400.09) 22786 (15761 to 30509) 293.96 (202.28 to 395.85) 0 (-0.01 to 0.01) Caribbean 15675 (9835 to 23367) 179.06 (113.58 to 263.89) 21475 (13708 to 31547) 175.9 (112.05 to 258.71) -0.04 (-0.05 to -0.03) Central Asia 31813 (20101 to 47603) 200.93 (128.58 to 296.31) 50917 (32148 to 74598) 200.69 (126.23 to 295.3) 0.02 (0.01 to 0.04) Central Europe 65027 (42702 to 91369) 205.08 (134.2 to 289.62) 59564 (39572 to 82440) 208.33 (136.66 to 292.93) 0.06 (0.05 to 0.07) Central Latin America 76792 (50135 to 109813) 202.02 (133.3 to 284.96) 138687 (93391 to 195251) 201.3 (135.46 to 283.64) -0.01 (-0.02 to 0) Central Sub-Saharan Africa 22743 (13574 to 34385) 205.9 (125.6 to 306.16) 60440 (36906 to 90571) 204.75 (127.02 to 301.9) 0.02 (0 to 0.05) East Asia 893224 (621394 to 1197732) 281.46 (196.96 to 374.74) 1069773 (755348 to 1409687) 295.9 (207.28 to 393.75) 0.11 (0.08 to 0.13) Eastern Europe 107986 (73516 to 147464) 186.03 (126.15 to 255.05) 105114 (71644 to 141678) 193.92 (130.8 to 265.36) 0.21 (0.17 to 0.26) Eastern Sub-Saharan Africa 78930 (51044 to 113501) 208.61 (137.18 to 295.17) 202311 (131136 to 289608) 211.12 (138.53 to 297.67) 0.09 (0.07 to 0.1) High-income Asia Pacific 114729 (77400 to 158883) 244.9 (164.58 to 341.03) 106485 (72599 to 146142) 249.5 (168.01 to 347.54) 0.15 (0.1 to 0.2) High-income North America 256201 (181673 to 339040) 327.38 (231.52 to 434.21) 279996 (198177 to 368294) 318.29 (224.62 to 420.46) -0.07 (-0.1 to -0.05) North Africa and Middle East 155320 (101217 to 222271) 219.24 (144.88 to 309.25) 354136 (234634 to 505572) 219.52 (145.26 to 313.92) -0.01 (-0.01 to 0) Oceania 3418 (2084 to 5090) 238.01 (147.47 to 349.35) 8096 (4945 to 12207) 238.94 (146.82 to 358.18) 0.01 (-0.01 to 0.03) South Asia 541119 (367787 to 739711) 228.33 (156.11 to 309.09) 1143907 (779553 to 1568013) 235.38 (160.72 to 321.62) 0.13 (0.1 to 0.16) Southeast Asia 282665 (185712 to 401691) 249.91 (165.53 to 351) 480916 (322313 to 669290) 257.01 (171.91 to 358.59) 0.18 (0.14 to 0.21) Southern Latin America 30027 (18483 to 44134) 247.47 (152.84 to 362.62) 44689 (27665 to 65283) 246.78 (152.04 to 362.14) 0.01 (-0.01 to 0.03) Southern Sub-Saharan Africa 25785 (17078 to 36218) 215.04 (143.91 to 298.09) 45975 (30907 to 63777) 210.8 (141.82 to 291.98) -0.04 (-0.05 to -0.02) Tropical Latin America 74646 (50293 to 103442) 198.43 (134.47 to 272.8) 126685 (86015 to 172103) 199.05 (134.82 to 271.53) 0.02 (0 to 0.04) Western Europe 211485 (144166 to 292825) 213.85 (145.53 to 297) 213924 (145945 to 291357) 208.66 (141.2 to 287.71) -0.07 (-0.1 to -0.04) Western Sub-Saharan Africa 94388 (62049 to 134465) 245.42 (163.91 to 343.73) 266315 (174938 to 375887) 248.2 (165.12 to 345.52) 0.06 (0.05 to 0.06) Abbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. “ EAPC is expressed as 95% CIs. 2. Global Burden of Disease and Trends in Psychiatric Disorders Among Women of Reproductive Age Across Subgroups (Age, SDI, Region, and Country) Globally, the incidence of psychiatric disorders and DALYs increased significantly across all disease types and age groups from 1990 to 2021. However, the overall trends in disease burden remained consistent across age groups and aligned with broader age-related patterns (Fig. 2 , Fig. S1).The 15–19 age group exhibited the highest number of anxiety and bipolar disorder cases, as well as the highest ASRs, with bipolar disorder being particularly prevalent in this group. However, this age group had the lowest incidence and ASRs of depression and the smallest DALY burden among the four psychiatric disorders. In contrast, the 45–49 age group had the highest incidence and DALY burden of depression, with the fastest-growing ASRs. The 20–24 age group had the highest incidence and ASRs of schizophrenia, although its DALY burden remained relatively limited, only slightly exceeding that of the 15–19 age group (Fig. S1). Across all Socio-Demographic Index (SDI) regions, the burden of psychiatric disorders increased significantly from 1990. Depression and anxiety disorders were the most prevalent, while bipolar disorder and schizophrenia exhibited relatively stable incidence rates across SDI regions. However, schizophrenia contributed to a considerably greater disease burden (Fig. 3 ).With the exception of high-SDI regions, which exhibited higher ASRs, the ASRs of psychiatric disorders varied only slightly across SDI regions. However, middle- and low-middle-SDI regions had the highest absolute number of cases and DALYs (Fig. 3 ). Over time, the trends in psychiatric disorder burden remained consistent across all SDI regions (Fig. S2). Across the 21 GBD regions, from 1990 to 2021, the incidence and DALY burden of all four psychiatric disorders increased most rapidly in South Asia, Southeast Asia, North Africa and the Middle East, Western Sub-Saharan Africa, Eastern Sub-Saharan Africa, Tropical Latin America, and Central Latin America. These regions accounted for a growing proportion of the global burden. In contrast, densely populated East Asia experienced a decline in the incidence and DALYs of most psychiatric disorders, except for bipolar disorder and schizophrenia, where DALYs continued to rise. Regarding ASRs, the age-standardized incidence rate (ASIR) and age-standardized disability-adjusted life year rate (ASDR) for bipolar disorder and schizophrenia remained relatively stable across all 21 regions. However, Tropical Latin America and Australasia recorded the highest ASIRs and ASDRs for bipolar disorder, while East Asia had the lowest. Schizophrenia ASIRs and ASDRs were highest in East Asia, Australasia, and High-income North America. For depression, Central Latin America, High-income North America, and Central Sub-Saharan Africa had the highest and increasing ASIRs and ASDRs. The fastest-growing ASDRs were observed in Tropical Latin America, Central Latin America, Andean Latin America, and High-income North America, while East Asia experienced the largest and fastest-growing ASIRs and ASDRs for depression. The estimated annual percentage change (EAPC) for depression was − 1.4 (95% CI: -1.65 to -1.16) for ASIRs and − 1.15 (95% CI: -1.34 to -0.95) for ASDRs, making East Asia the region with the most rapid decline. A more modest decline in ASIRs and ASDRs for depression was also observed in Oceania (Fig. 4 ). Figure 5 – 6 illustrates the global trend of psychiatric disorder burden over time in relation to SDI across countries. The prevalence of anxiety disorders increased worldwide, with DALYs rising in nearly all countries, except for a few. The most rapid increases occurred in countries with SDI > 0.6, particularly Brazil, Portugal, and Paraguay. A similar trend was observed for depression, with incidence and DALYs increasing globally, peaking in Greenland. Compared to anxiety disorders, depression showed a weaker correlation between disease burden and SDI.For bipolar disorder and schizophrenia, ASRs remained largely unchanged across countries. Bipolar disorder prevalence increased, while DALY rates declined, particularly in high-SDI countries, with New Zealand exhibiting the fastest rise in both prevalence and DALYs. The burden of schizophrenia varied across countries, with high-SDI nations such as the Netherlands, the United States, and Denmark experiencing the most significant changes. 3. SDI Correlation Analysis Overall, the incidence and DALYs of the four aforementioned mental disorders were positively correlated with the SDI, increasing with higher SDI levels, particularly for anxiety disorders. At the regional level, the relationship between the incidence and DALYs of bipolar disorder and SDI followed a \"W\" pattern, with a significant decline at SDI < 0.4, followed by a notable increase as SDI continued to rise. In contrast, schizophrenia exhibited a \"U\"-shaped relationship with SDI, with the lowest prevalence and DALYs observed at an SDI of 0.6. The disease burden of anxiety disorders steadily increased with SDI, whereas the burden of depression remained relatively stable at SDI < 0.6 before rising sharply thereafter. At the national level, the incidence and DALY rates of all four disorders increased with SDI growth, with more pronounced increases observed at SDI > 0.75(Fig. 7 ). 4. Health Inequities From 1990 to 2021, the slope indices for all four psychiatric disorders showed an upward trend, while the concentration indices declined (Fig. 8 A/B). Specifically, the slope index for schizophrenia incidence increased from − 2.90 (95% CI: -4.26 to -1.55) in 1990 to -2.16 (95% CI: -3.55 to -0.78) in 2021, while the corresponding slope index for DALYs rose from 3.14 (95% CI: -7.60 to 13.88) to 5.84 (95% CI: -5.34 to 17.01). Similarly, the slope indices for the incidence of bipolar disorder, depression, and anxiety increased from 16.85 (95% CI: 9.00 to 24.69), -842.74 (95% CI: -1866.14 to 180.66), and 196.96 (95% CI: 131.14 to 262.78) to 17.97 (95% CI: 10.44 to 25.49), 465.39 (95% CI: -736.83 to 1667.60), and 330.90 (95% CI: 247.18 to 414.61), respectively. The corresponding slope indices for DALYs increased from 87.25 (95% CI: 54.75 to 119.75), -138.47 (95% CI: -276.91 to -0.03), and − 138.47 (95% CI: -276.91 to -0.03) to 88.51 (95% CI: 57.70 to 119.31), 39.84 (95% CI: -122.73 to 202.41), and 432.21 (95% CI: 329.22 to 535.21), respectively.Meanwhile, the concentration indices for the incidence of these four disorders decreased from − 0.08 (95% CI: -0.23 to 0.16), 0.02 (95% CI: -0.15 to 0.21), 0.15 (95% CI: -0.15 to 0.21), and 0.21 (95% CI: -0.15 to 0.21) to -0.10 (95% CI: -0.28 to 0.06), -0.09 (95% CI: -0.22 to 0.05), -0.06 (95% CI: -0.25 to 0.12), and − 0.08 (95% CI: -0.22 to 0.12), respectively. Likewise, the concentration index for DALYs also showed a downward trend, with schizophrenia decreasing from − 0.03 (95% CI: -0.21 to 0.26) to -0.04 (95% CI: -0.24 to 0.13), bipolar disorder from 0.09 (95% CI: -0.09 to 0.27) to -0.03 (95% CI: -0.16 to 0.11), depression from − 0.05 (95% CI: -0.23 to 0.20) to -0.08 (95% CI: -0.25 to 0.11), and anxiety disorders from 0.02 (95% CI: -0.16 to 0.26) to -0.02 (95% CI: -0.19 to 0.14).These findings highlight the complex changes in the global burden of mental disorders in the context of socioeconomic development, indicating that global health inequities have worsened over time and underscoring the urgent need to strengthen mental health services in low-SDI regions. 5. Decomposition Analysis Globally, and particularly in low-SDI regions such as Africa, Latin America, South Asia, and Southeast Asia, population growth is the primary driver of the increasing burden of psychiatric disorders, excluding anxiety disorders. In East Asia, however, aging is the key factor, especially for schizophrenia and bipolar disorder. Aging has a negative impact on morbidity, contributing 112.57% and 91.72%, respectively, while positively influencing DALYs, with contributions of 76.3% and 139.99%.Regarding anxiety disorders, the global increase in both incidence and DALYs is largely attributed to population growth, which contributes 66% and 62.23%, respectively. However, epidemiological changes also play a significant role, contributing 35.85% and 33.75%, respectively. The impact of these changes becomes more pronounced as SDI increases, particularly in high-SDI areas, where epidemiological changes contribute 83.15% and 78.14%, respectively, and in high-middle-SDI regions, contributing 74.18% and 72.4%. In these regions, the contribution of population growth and aging is far less significant (Fig. 9 ). 6. 2050 Projections Figure 9 illustrates that the incidence and DALYs of anxiety disorders and depression are projected to increase significantly over the next decade, with a particularly sharp rise expected from 2030 onward. This trend suggests that anxiety disorders and depression will become increasingly prominent public health challenges. In contrast, although bipolar disorder and schizophrenia are also projected to rise, their growth will be more moderate, indicating that the burden from these disorders will increase at a slower pace. Overall, the burden of mental illness, particularly among women, is expected to continue rising, with anxiety and depression being of particular concern. This could have important implications for public health policy and resource allocation (Fig. 10 ). Discussion To the best of our knowledge, our study is the first to analyze and compare the four most common or impactful psychiatric disorders in women of reproductive age globally. Our findings show that while the incidence and DALYs for anxiety, depression, bipolar disorder, and schizophrenia in women of childbearing age have all increased modestly at the global level from 1990 to 2021, and the corresponding ASRs have generally trended downward, anxiety disorders have shown rising rates of episodes and DALYs in nearly all countries, and depression has followed a similar pattern in most countries, particularly in higher-SDI regions. Furthermore, there are significant differences in the burden of these four mental disorders across age groups, regions, and countries, with varying influencing factors in each region. In 2021, our study found that anxiety, depression, bipolar disorder, and schizophrenia together accounted for 45,134,365 DALYs among women of childbearing age globally, with the respective proportions being 36.4%, 46.6%, 6.2%, and 10.7%. These findings suggest that depression and anxiety disorders continue to be the most prevalent psychiatric disorders among women of childbearing age, consistent with previous population-wide studies. Additionally, both depression and anxiety disorders showed a significant increase in 2020, a trend confirmed by two recent studies. The COVID-19 pandemic in 2020, combined with increased uncertainty about health risks, rising unemployment, economic downturns, geographic lockdowns, and shifts in lifestyles, all contributed to heightened feelings of despair, loneliness, anxiety, and even depression. Our study revealed distinct age-dependent patterns in psychiatric disorder prevalence: depression predominated in older adults, anxiety and bipolar disorders were more prevalent among adolescents (15–19 years), whereas schizophrenia incidence peaked in the 20–24 age cohort. These distributions likely stem from multifactorial interactions involving neurotransmitter imbalances, genetic predispositions, regional brain dysfunction, and environmental stressors (Sullivan et al., 2003 ; Alexopoulos, 2005 ; Casey et al., 2008 ; Stein and Stein, 2008 ; Katon, 2011 ; Diniz et al., 2013 ; American Psychological Association, 2018 ; Cale et al., 2024 ). Mechanistically, age-related central nervous system decline in older adults reduces serotonin (5-HT), dopamine (DA), and norepinephrine (NE) levels, impairing mood regulation and elevating depression risk (Alexopoulos, 2005 ). In adolescents, neurodevelopmental immaturity—particularly in frontal/temporal lobes and hippocampal volumes—coupled with dysregulated 5-HT, γ-aminobutyric acid (GABA), and cortisol levels, compromises emotional stability, predisposing to anxiety and bipolar disorders (Casey et al., 2008 ; Stein and Stein, 2008 ). Concurrent psychosocial stressors differentially affect age groups: older adults face elevated depression risks due to social isolation (post-retirement, widowhood, or \"empty nest\" syndrome) and comorbidities with neurodegenerative (e.g., Alzheimer’s, Parkinson’s) and chronic metabolic diseases (e.g., hypertension, diabetes) (Katon, 2011 ; Diniz et al., 2013 ). Conversely, younger populations exhibit heightened anxiety susceptibility attributable to academic pressure, occupational demands, and interpersonal conflicts characteristic of modern fast-paced lifestyles (American Psychological Association, 2018 ). Our study highlights significant regional and global disparities in psychiatric disorders among women of childbearing age. Notably, high-income North America, Latin America, and Australasia exhibit the highest and most rapidly increasing burden of psychiatric disorders, a trend identified and prioritized in public health agendas two decades ago (Kohn et al., 2005 ). Conversely, East Asia reports the lowest rates of anxiety, depression, and bipolar disorder, as well as the lowest DALYs. Specifically, depression prevalence in East Asia has declined significantly since 1990, driven largely by a > 12% reduction in major depressive disorder cases in China (Tian et al., 2025 ). While direct evidence for these regional differences is limited, contextual factors provide plausible explanations. For instance, Japan’s mental health policies—such as capping out-of-pocket costs for psychiatric care at 10% of total expenses and including transcranial magnetic stimulation therapy in public healthcare—have significantly improved access to treatment. Consequently, according to Kawakami ( 2014 ) and the Japan Local Life Support Association (JLSA, 2018), consultation rates for major depression have more than doubled in Japan. Moreover, depression is increasingly recognized as a societal indicator in Japanese media, fostering greater public awareness and resilience. Similarly, China and South Korea demonstrate elevated psychological resilience, potentially contributing to lower mental health burdens (Qin et al., 2025 ). In contrast, North America’s economic prosperity masks stark social inequalities, where mental health issues are exacerbated by high living costs, healthcare expenses, and inadequate social support for low-income populations (Sareen et al., 2011 ). Latin America faces distinct challenges, including economic instability, poverty, and unemployment, which elevate anxiety and depression rates (Lund et al., 2010 ). Cultural norms, such as “machismo,” further hinder timely mental health interventions, particularly among men. Compounding these issues are pervasive social stressors, including political unrest, over-urbanization, gang violence, high crime rates, drug trade, and child abuse, which collectively sustain high psychiatric disorder prevalence in the region (Sapag et al., 2018 ; Mascayano et al., 2016 ; Peen et al., 2010 ; Libby et al., 2005 ). The global burden of anxiety and depression among women of childbearing age is rising rapidly, particularly in high-income regions of North America, Latin America, and parts of Africa. To address this growing issue, effective public health policies must be developed urgently. East Asian countries offer potential models, providing valuable insights through substantial governmental funding for healthcare, well-established primary mental health care systems, and a supportive social environment.Additionally, special attention is needed for adolescents and older adults, who are particularly vulnerable to psychiatric disorders due to neurodevelopmental changes, cognitive decline, and multidimensional social disadvantages. For these populations, fostering a supportive and engaging environment, along with adequate social support, is crucial in mitigating exposure to harmful external stressors. While our study highlights the importance of psychosomatic health in women of reproductive age, it has several limitations. First, our analysis primarily accounts for health-related losses, while broader welfare losses associated with mental illness are not included due to the complexity of quantification. Second, diagnosing and categorizing mental disorders remains a clinical challenge, particularly in low-SDI regions, where data quality may be inconsistent. Additionally, economic and cultural barriers often prevent individuals in these areas from seeking medical treatment, further complicating accurate assessment. In conclusion, our study underscores the persistent and increasing burden of psychiatric disorders among women of reproductive age worldwide, with notable regional disparities, especially in anxiety and depression. The lack of significant improvement over time suggests that current prevention and treatment strategies have yet to yield substantial real-world benefits. However, by leveraging big data, our study offers a comprehensive analysis of the factors contributing to these regional variations. Identifying these key determinants can serve as a foundation for targeted interventions aimed at reducing the global impact of psychiatric disorders in this vulnerable population. Declarations Funding information: No funding was received for this study. Author Contribution X.W. conducted formal analysis, developed the methodology, and implemented the software; X.W. and Y.K. performed data curation; Y.K. and Y.J. handled visualization; X.W., Y.K., and Y.J. wrote the original draft; X.L. provided project administration, supervision, and validation; all authors (X.W., Y.K., X.L., and Y.J.) contributed to conceptualization and writing—review and editing; X.L. is the corresponding author. Acknowledgement AcknowledgmentsWe thank the works by the GBD 2021 Diseases and Injuries Collaborators. Data availability The data used in this study can be derived from the GBD 2021 (Available at: https://vizhub.healthdata.org/gbd-results/ ). Clinical trial number: not applicable. References Alexopoulos GS (2005) Depression in the elderly. Lancet 365(9475):1961–1970. https://doi.org/10.1016/S0140-6736(05)66665-2 American Psychological Association (2018) Stress in America: Generation Z . Washington, DC: American Psychological Association. Retrieved from https://www.apa.org/news/press/releases/stress/2018/stress-gen-z.pdf Bachmann S (2018) Epidemiology of suicide and the psychiatric perspective. Int Journal Environ Res Public Health 15(7):1425. https://doi.org/10.3390/ijerph15071425 Cale JA, Chauhan EJ, Cleaver JJ, Fusciardi AR, McCann S, Waters HC, Žavbi J, King MV (2024) GABAergic and inflammatory changes in the frontal cortex following neonatal PCP plus isolation rearing, as a dual-hit neurodevelopmental model for schizophrenia. Mol Neurobiol 61(9):6968–6983. https://doi.org/10.1007/s12035-024-03987-y Cantwell R (2021) Mental disorder in pregnancy and the early postpartum. Anaesthesia 76(4):76–83. https://doi.org/10.1111/anae.15424 Casey BJ, Jones RM, Hare TA (2008) The adolescent brain. Ann N Y Acad Sci 1124(1):111–126. https://doi.org/10.1196/annals.1440.010 Diniz BS, Butters MA, Albert SM, Dew MA, Reynolds CF III (2013) Late-life depression and risk of vascular dementia and Alzheimer's disease: Systematic review and meta-analysis of community-based cohort studies. Br J Psychiatry 202(5):329–335. https://doi.org/10.1192/bjp.bp.112.118307 GBD 2019 Mental Disorders Collaborators (2022) Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry 9(2):137–150. https://doi.org/10.1016/S2215-0366(21)00395-3 GBD 2021 Diseases and Injuries Collaborators (2024) Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: A systematic analysis for the Global Burden of Disease Study 2021. Lancet 403(10440):2133–2161. https://doi.org/10.1016/S0140-6736(24)00757-8 Grande I, Berk M, Birmaher B, Vieta E (2016) Bipolar disorder. Lancet 387(10027):1561–1572. https://doi.org/10.1016/S0140-6736(15)00241-X Iran Subnational Mental Health GBD Collaborators (2024) National and subnational burden of mental disorders in Iran (1990–2019): Findings of the Global Burden of Disease 2019 study. Lancet Global Health 12 (12), e1984-e1992. https://doi.org/10.1016/S2214-109X(24)00342-5 Japan Local Life Support Association (2018) Japan's medical system that supports individuals with mental disorders. Available at: https://jlsa-net.jp/sei/seihin-iryou/?utm_source Jauhar S, Johnstone M, McKenna PJ (2022) Schizophrenia. Lance t, 399 (10323), 473–486. https://doi.org/10.1016/S0140-6736(21)01730-X Jones I, Chandra PS, Dazzan P, Howard LM (2014) Bipolar disorder, affective psychosis, and schizophrenia in pregnancy and the post-partum period. Lancet 384(9956):1789–1799. https://doi.org/10.1016/S0140-6736(14)61278-2 Katon WJ (2011) Epidemiology and treatment of depression in patients with chronic medical illness. Dialog Clin Neurosci 13(1):7–23. https://doi.org/10.31887/DCNS.2011.13.1/wkaton Kawakami N (2014) A large-scale epidemiological study on the prevalence of mental disorders in Japan: World Mental Health Japan Survey Second. Available at: https://mhlw-grants.niph.go.jp/project/22776?utm_source Kohn R, Levav I, de Almeida JM, Vicente B, Andrade L, Caraveo-Anduaga JJ, Saxena S, Saraceno B (2005) Los trastornos mentales en América Latina y el Caribe: Asunto prioritario para la salud pública [Mental disorders in Latin America and the Caribbean: A public health priority]. Revista Panam de Salud Pública 18(4–5):229–240. https://doi.org/10.1590/s1020-49892005000900002 Lauron S, Plasse C, Vaysset M, Pereira B, D'Incan M, Rondepierre F, Jalenques I (2023) Prevalence and odds of depressive and anxiety disorders and symptoms in children and adults with alopecia areata: A systematic review and meta-analysis. JAMA Dermatology 159(3):281–288. https://doi.org/10.1001/jamadermatol.2022.6085 Libby AM, Orton HD, Novins DK, Beals J, Manson SM, AI-SUPERPFP Team (2005) Childhood physical and sexual abuse and subsequent depressive and anxiety disorders for two American Indian tribes. Psychol Med 35(3):329–340. https://doi.org/10.1017/s0033291704003599 Lund C, Breen A, Flisher AJ, Kakuma R, Corrigall J, Joska JA, Swartz L, Patel V (2010) Poverty and common mental disorders in low and middle income countries: A systematic review. Soc Sci Med 71(3):517–528. https://doi.org/10.1016/j.socscimed.2010.04.027 Mascayano F, Tapia T, Schilling S, Alvarado R, Tapia E, Lips W, Yang LH (2016) Stigma toward mental illness in Latin America and the Caribbean: A systematic review. Braz J Psychiatry 38(1):73–85. https://doi.org/10.1590/1516-4446-2015-1652 Opio JN, Munn Z, Aromataris E (2022) Prevalence of mental disorders in Uganda: A systematic review and meta-analysis. Psychiatr Q 93(1):199–226. https://doi.org/10.1007/s11126-021-09941-8 Pai N, Vella SL, Castle D (2022) A comparative review of the epidemiology of mental disorders in Australia and India. Asia-Pacific Psychiatry 14(4):e12517. https://doi.org/10.1111/appy.12517 Peen J, Schoevers RA, Beekman AT, Dekker J (2010) The current status of urban-rural differences in psychiatric disorders. Acta Psychiatr Scand 121(2):84–93. https://doi.org/10.1111/j.1600-0447.2009.01438.x Qin C, Lee M, Deng J, Lee Y, You M, Liu J (2025) Mental health and psychological resilience amid the spread of the Omicron variant: A comparison between China and Korea. Front Public Health 12:1451318. https://doi.org/10.3389/fpubh.2024.1451318 Safiri S, Noori M, Nejadghaderi SA, Shamekh A, Sullman MJM, Collins GS, Kolahi AA (2024) The burden of schizophrenia in the Middle East and North Africa region, 1990–2019. Sci Rep 14(1):9720. https://doi.org/10.1038/s41598-024-59905-8 Sapag JC, Sena BF, Bustamante IV, Bobbili SJ, Velasco PR, Mascayano F, Alvarado R, Khenti A (2018) Stigma towards mental illness and substance use issues in primary health care: Challenges and opportunities for Latin America. Glob Public Health 13(10):1468–1480. https://doi.org/10.1080/17441692.2017.1356347 Sareen J, Afifi TO, McMillan KA, Asmundson GJ (2011) Relationship between household income and mental disorders: Findings from a population-based longitudinal study. Arch Gen Psychiatry 68(4):419–427. https://doi.org/10.1001/archgenpsychiatry.2011.15 Stein MB, Stein DJ (2008) Social anxiety disorder. Lancet 371(9618):1115–1125. https://doi.org/10.1016/S0140-6736(08)60488-2 Sullivan PF, Kendler KS, Neale MC (2003) Schizophrenia as a complex trait: Evidence from a meta-analysis of twin studies. Arch Gen Psychiatry 60(12):1187–1192. https://doi.org/10.1001/archpsyc.60.12.1187 Tian W, Yan G, Xiong S, Zhang J, Peng J, Zhang X, Zhou Y, Liu T, Zhang Y, Ye P, Zhao W, Tian M (2025) Burden of depressive and anxiety disorders in China and its provinces, 1990–2021: Findings from the Global Burden of Disease Study 2021. Br J Psychiatry 1–11. https://doi.org/10.1192/bjp.2024.267 Vigo D, Thornicroft G, Atun R (2016) Estimating the true global burden of mental illness. Lancet Psychiatry 3(2):171–178. https://doi.org/10.1016/S2215-0366(15)00505-2 World Health Organization (2024), August 29 Suicide . https://www.who.int/news-room/fact-sheets/detail/suicide Worrall S, Pike O, Christiansen P, Jackson L, De Pascalis L, Harrold JA, Fallon V, Silverio SA (2025) Psychosocial experiences of pregnant women during the COVID-19 pandemic: A UK-wide study of prevalence rates and risk factors for clinically relevant depression and anxiety. J Psychosom Obstet Gynecol 46(1):2459619. https://doi.org/10.1080/0167482X.2025.2459619 Zhang J, Liu Y, Zhang X (2025) The burden of mental disorders, substance use disorders and self-harm among young people in Asia, 2019–2021: Findings from the global burden of disease study 2021. Psychiatry Res 345:116370. https://doi.org/10.1016/j.psychres.2025.116370 Additional Declarations No competing interests reported. Supplementary Files Tables.18.docx Fig.S1.jpg Fig.S1 The changes in the number and age-standardized rates of disease burden for anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age globally in different age groups from 1990 to 2021 (A. Number of incidents; B. Age-standardized incidence rate; C. Number of DALYs; D. Age-standardized DALY rate). Fig.S2.jpg Fig.S2 The changes in the number and age-standardized rates of disease burden for anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age globally in different SDI regions from 1990 to 2021 (A. Number of incidents; B. Age-standardized incidence rate; C. Number of DALYs; D. Age-standardized DALY rate). 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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-7970352\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":570486713,\"identity\":\"260e0112-ec40-4a2f-a49a-4b46e11f4e8a\",\"order_by\":0,\"name\":\"Xiaohang Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universiti Putra Malaysia\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiaohang\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":570486715,\"identity\":\"2998b33f-58cd-4e4e-a0b6-0465e1e62256\",\"order_by\":1,\"name\":\"Yaru 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Incidence; B. DALYs).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7970352/v1/e4d0fd195f242488cd2eb903.jpg\"},{\"id\":100356874,\"identity\":\"a28cec5b-459f-4ecc-9262-4be6dc5ee723\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:17:53\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":4688633,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eA comparison of the number and age-standardized rates of disease burden for anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age globally in different age groups in 1990 and 2021 (A. Incidence number of ; B. Age-standardized incidence rate; C. DALYs number; D. Age-standardized DALY rate).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7970352/v1/4b8e786a41ab9d5f66b29714.jpg\"},{\"id\":100356972,\"identity\":\"6118c8d3-5375-4467-882c-ded4ca08a807\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:18:10\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":3426606,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eA comparison of the number and age-standardized rates of disease burden for anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age globally in different SDI regions in 1990 and 2021 (A. Incidence number of ; B. Age-standardized incidence rate; C. DALYs number; D. Age-standardized DALY rate).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7970352/v1/82b69d9056d38d4a118e27a0.jpg\"},{\"id\":100356431,\"identity\":\"63230755-ff5f-4a29-857e-cfe6dd3da51e\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:09:09\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":7841136,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eA comparison of the number and age-standardized rates of disease burden for anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age globally in different GBD regions in 1990 and 2021 (A. Incidence number of ; B. Age-standardized incidence rate; C. DALYs number; D. Age-standardized DALY rate).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7970352/v1/7080b553572962265fefe855.jpg\"},{\"id\":100356520,\"identity\":\"5a66115f-c8c8-45a2-86ce-86280872b8f4\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:14:51\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":10131316,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe geographical distribution of the number and age-standardized rates of disease burden for anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age globally across 204 countries in 2021.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7970352/v1/5fb9640fe12bcf7c6af09bb0.png\"},{\"id\":100357253,\"identity\":\"fedd856e-4076-4aa7-8f5c-1d53c359d170\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:19:31\",\"extension\":\"jpg\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":8023964,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eA frontier visualization analysis of the disease burden for anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age globally from 1990 to 2021 (A. Anxiety; B. Bipolar; C. Depression; D. Schizophrenia).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.6.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7970352/v1/c0c30f2a8aa69c4ed80bb419.jpg\"},{\"id\":100357110,\"identity\":\"97500526-c408-4992-9943-c575d17ae483\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:18:49\",\"extension\":\"jpg\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":25701232,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe correlation between the disease burden of anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age globally and SDI (A/B. Incidence; C/D. DALYs; A/C. Regional level; B/D. Country level).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.7.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7970352/v1/9759500f1961b39faece6a9f.jpg\"},{\"id\":100357252,\"identity\":\"e43bf8c7-d130-4fe1-bddd-069a97595d9b\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:19:31\",\"extension\":\"jpg\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":5289678,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe changes in health inequities of the disease burden for anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age globally from 1990 to 2021 (A. Anxiety; B. Bipolar; C. Depression; D. Schizophrenia).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.8A.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7970352/v1/3ad0e45730082be8c129c4a3.jpg\"},{\"id\":99833573,\"identity\":\"8be2ccce-40a1-4de9-a657-031981e8c451\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 18:09:15\",\"extension\":\"jpg\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":4540607,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eA decomposition analysis of the disease burden for anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age globally and across different SDI and GBD regions, in terms of aging, population growth, and epidemiological changes(A.Incidence;B.DALYs).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.9.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7970352/v1/bb54c24f5d63714d3932375a.jpg\"},{\"id\":100356843,\"identity\":\"93ae3bd3-dfb2-4089-9bc8-c829130a4b73\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:17:45\",\"extension\":\"jpg\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":5622585,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe projection of the number and age-standardized rates of disease burden for anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age globally until 2035.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.10.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7970352/v1/77b1bf6bf8784acb0173373f.jpg\"},{\"id\":99833545,\"identity\":\"48e9d284-ef3c-485a-9222-9b48a50fa991\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 18:09:14\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":87256,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Tables.18.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7970352/v1/1e593eb6b82c1836f34a70c7.docx\"},{\"id\":99833550,\"identity\":\"f3ffdca4-b80d-4095-a03a-ede5fe51d448\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 18:09:14\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":14460070,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFig.S1 \\u003c/strong\\u003eThe changes in the number and age-standardized rates of disease burden for anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age globally in different age groups from 1990 to 2021 (A. Number of incidents; B. Age-standardized incidence rate; C. Number of DALYs; D. Age-standardized DALY rate).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.S1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7970352/v1/27e4ce684cf5c3ec313aad17.jpg\"},{\"id\":99833555,\"identity\":\"f1e6555b-3cf2-4a1b-8e4d-e8374c0a962b\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 18:09:15\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":15397350,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFig.S2 \\u003c/strong\\u003eThe changes in the number and age-standardized rates of disease burden for anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age globally in different SDI regions from 1990 to 2021 (A. Number of incidents; B. Age-standardized incidence rate; C. Number of DALYs; D. Age-standardized DALY rate).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.S2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7970352/v1/aa5668224f267af0c78adb34.jpg\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Analysis and Projections of the Global Burden of Anxiety, Depression, Bipolar Disorder, and Schizophrenia Among Women of Reproductive Age (1990–2021)\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eAccording to the latest Global Burden of Disease (GBD) studies, mental disorders are the second leading cause of years lived with disability (YLDs) worldwide and the seventh leading cause of disability-adjusted life years (DALYs) globally. They are also strongly associated with high suicide rates (Vigo et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Bachmann, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; WHO, 2024). Depression, anxiety disorders, bipolar disorder, and schizophrenia are among the most prevalent and impactful mental illnesses. Of these, depression and anxiety disorders account for 37.3% and 22.9% of mental disorder-related DALYs, respectively. Both conditions frequently co-occur, and recent epidemiological data indicate a significant rise in their prevalence over the past decades (GBD 2019 Mental Disorders Collaborators, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Although bipolar disorder and schizophrenia have relatively lower global prevalence rates, their substantial socioeconomic burden\\u0026mdash;due to prolonged duration, high treatment costs, and frequent relapses\\u0026mdash;cannot be overlooked (Grande et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Jauhar et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). These disorders not only directly impair patients' quality of life, leading to significant losses in work capacity and daily functioning, but also negatively affect their families, workplaces, and social interactions. Recognizing mental health as a fundamental human right, the World Health Organization continues to prioritize this issue.\\u003c/p\\u003e \\u003cp\\u003eWhile numerous epidemiological studies have assessed the global burden of mental disorders, most have focused on specific countries, regions, or individual diseases, lacking comprehensive cross-border, cross-cultural, and multi-disease comparisons (Tian et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Safiri et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Iran Subnational Mental Health GBD Collaborators, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Zhang et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Additionally, existing research often emphasizes disease prevalence and mortality, with less attention given to the socioeconomic impacts and disparities across age, gender, and social groups (Opio et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Lauron et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Pai et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Furthermore, studies have frequently overlooked women of reproductive age, a population experiencing heightened physical and psychological stress due to childbirth and child-rearing. This critical life stage increases vulnerability to mental disorders, particularly during pregnancy, childbirth, and the postpartum period, when mental health issues are especially pronounced and may adversely affect both maternal and neonatal outcomes (Cantwell et al., 2021; Jones et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe GBD 2021 dataset provides comprehensive data on incidence, prevalence, deaths, and disability-adjusted life years (DALYs) for 371 diseases and injuries, as well as 88 behavioral, environmental, occupational, and metabolic risk factors across 204 countries worldwide (GBD 2021 Diseases and Injuries Collaborators, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). A key strength of this dataset is its use of harmonized diagnostic criteria and methodologies, ensuring high-quality, comparable data. Additionally, it enables a systematic assessment of global and regional disease burdens by tracking health status across different countries and regions. Building on this dataset, the present study will conduct an in-depth analysis of the global burden of depression, anxiety disorders, bipolar disorder, and schizophrenia among women of reproductive age. It will evaluate regional, socioeconomic, and age-related differences in disease burden, examine trends over time, explore potential influencing factors, and project future trajectories. The findings aim to provide a scientific basis for global mental health policy development, supporting more effective and targeted interventions.\\u003c/p\\u003e \"},{\"header\":\"Methods\",\"content\":\"\\n\\u003ch3\\u003e1. Data Acquisition and Sources\\u003c/h3\\u003e\\n\\u003cp\\u003eThis study used the Global Burden of Disease (GBD) 2021 data to assess the global impact of anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age. The GBD 2021 dataset is accessible through the Global Health Data Exchange (GHDx) Results Tool(\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://ghdx.healthdata.org/\\u003c/span\\u003e\\u003cspan address=\\\"http://ghdx.healthdata.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). It provides open access to detailed global and regional health metrics. The dataset estimates 371 diseases and injuries, covering incidence, prevalence, mortality, and Disability-Adjusted Life Years (DALYs). Additionally, it includes 88 risk factors pertaining to behavioral, environmental, occupational, and metabolic domains. This analysis focused on the mortality and DALYs associated with anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age in the worldwide. DALYs, which combine years lived with disability (YLDs) and years of life lost (YLLs) due to early death, serve as a measure for evaluating both fatal and nonfatal disease impacts. The Socio-population Index (SDI) is a composite measure of socioeconomic development that combines total fertility rates, mean years of education, and lagged per capita income for individuals aged 15 and older across various regions. The SDI ranges from 0 to 1, classifying countries into five tiers: low, low-middle, middle, high-middle, and high. The study analyzed data from 204 countries and territories from 1990 to 2021. It provided a comprehensive trend analysis and an in-depth evaluation of the global burden of anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age. Ethical approval and informed consent were not necessary since this study utilized publicly available data. The research adhered to guidelines for accurate and transparent reporting in health assessments.\\u003c/p\\u003e\\n\\u003ch3\\u003e2. Population Analysis and Global Burden Analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eWe analyzed age-standardized incidence, DALYs, and their 95% uncertainty intervals for anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age using data from the GBD 2021 study, which included 204 countries, 21 GBD regions, and 5 Socio-Demographic Index (SDI) quintiles. Additionally, we stratified the prevalence, incidence, death and DALYs by sex into two groups. We then divided the population into 4 age groups (15\\u0026ndash;19 years, 20\\u0026ndash;24 years\\u0026hellip;45\\u0026ndash;49 years) based on a 5-year cycle. The study utilized high-resolution maps to visualize the global burden of tuberculosis, emphasizing disparities across socio-demographic and geographic contexts. his spatial representation provided valuable insights into the patterns of anxiety, depression, bipolar disorder, and schizophrenia among women ages 15 to 49 worldwide.\\u003c/p\\u003e\\n\\u003ch3\\u003e3. Decomposition Analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eWe used decomposition analysis to measure how population growth, aging, and epidemiological changes contributed to trends in total incidence, prevalence, mortality and DALYs. This approach provided clearer insights into how demographic and health system factors influence the disease burden. This study uses a method consistent with the analytical framework of previous Global Burden of Disease (GBD) studies. These studies estimate how changes in population structure and risk factors affect shifts in disease outcomes.\\u003c/p\\u003e\\n\\u003ch3\\u003e4. Health Inequality Analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eHealth inequality analysis examines disparities in disease burden across countries and regions, helping to shape public health policies. This analysis employs the slope index of inequality (SII) and the concentration index (CII) to measure health inequalities. The SII illustrates the relationship between health indicators and socioeconomic status. It utilizes the Sociodemographic Index (SDI) in a linear regression analysis. The CI ranges from \\u0026minus;\\u0026thinsp;1 to 1 and indicates the variation of health outcomes based on economic status. Values closer to 0 reflect less inequality, positive values favor the wealthy, and negative values favor the impoverished. This study calculated the SII and CI for the Mortality and DALYs of caries in anxiety, depression, bipolar disorder, and schizophrenia between 1990 and 2021. It emphasizes health inequalities in a global context, across various regions, and among 204 countries.\\u003c/p\\u003e\\n\\u003ch3\\u003e5. Prediction Analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eTo better formulate public health policies and allocate medical resources, we divided the population into gender-based subgroups (females and males) and used the Bayesian-Aperiodic-People-Cohort (BAPC) model to predict trends in the incidence and prevalence of anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age over the next 15 years. By considering temporal variations and age-specific trends, these models provide a reliable and comprehensive outlook on the future burden of tooth decay. Studies have shown that combining the Integrated Nested Laplace Approximation (INLA) with the BAPC model effectively approximates marginal posterior distributions, thereby avoiding the mixing and convergence issues typically associated with the Markov Chain Monte Carlo (MCMC) sampling technique used in traditional methods.\\u003c/p\\u003e\\n\\u003ch3\\u003e6. Statistical Software\\u003c/h3\\u003e\\n\\u003cp\\u003eAll statistical analyses were conducted using R (version 4.3.3) and Stata 18 (StataCorp, College Station, TX). We created custom scripts to perform decomposition and sensitivity analyses. We performed Bayesian analysis using WinBUGS (version 1.4) as part of our analytical process. Geographic and spatial analyses were conducted with ArcGIS Pro and QGIS (version 3.16), allowing for the creation of high-resolution maps that visualize the TBL burden and disparities. We generated data visualizations, including bi-lateral and two-axis plots, using the 'ggplot2' and 'Benchmarking' packages in R.\\u003c/p\\u003e\\n\\u003ch3\\u003e7. Statistical Significance\\u003c/h3\\u003e\\n\\u003cp\\u003eIn this study, we set the p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 threshold for all analyses to determine statistical significance. This approach aligns with standard practices in epidemiological and public health research, especially concerning studies on the Global Burden of Disease.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003e1. Analysis of the Current Status and Trends in the Burden of Psychiatric Disorders Among Women of Reproductive Age Globally (1990\\u0026ndash;2021)\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eFrom 1990 to 2021, there was no significant global increase in the incidence of anxiety, depression, bipolar disorder, or schizophrenia, nor in the age-standardized rates (ASRs) of DALYs among women of reproductive age. However, the absolute number of cases continued to rise, particularly for anxiety and depression, which saw substantial increases in 2020. Specifically, the incidence of anxiety disorders and the number of associated DALYs increased by 18.3% and 17.5%, respectively, while depression saw increases of 22.8% and 19.1%. The corresponding ASRs for these disorders increased nearly 1.2-fold.By 2021, the incidence and DALY counts for anxiety disorders were 18,962,131 (95% uncertainty interval [UI]: 12,660,029\\u0026ndash;26,295,396) and 16,448,344 (95% UI: 10,383,333\\u0026ndash;24,009,992), respectively. For depression, these numbers were 133,248,593 (95% UI: 99,032,450\\u0026ndash;177,876,463) and 21,042,424 (95% UI: 13,468,194\\u0026ndash;30,593,481). For bipolar disorder, they were 826,397 (95% UI: 484,408\\u0026ndash;1,270,443) and 2,806,894 (95% UI: 1,740,129\\u0026ndash;4,226,051), while schizophrenia had corresponding values of 513,255 (95% UI: 302,798\\u0026ndash;767,916) and 4,836,703 (95% UI: 3,318,473\\u0026ndash;6,567,286). The respective ASRs were 976.14 per 100,000 (95% UI: 650.09\\u0026ndash;1,355.11) and 844.05 per 100,000 (95% UI: 532.79\\u0026ndash;1,232.57) for anxiety disorders, 6,808.01 per 100,000 (95% UI: 5,049.99\\u0026ndash;9,106.66) and 1,073.5 per 100,000 (95% UI: 686.73\\u0026ndash;1,562.48) for depression, 43.16 per 100,000 (95% UI: 25.43\\u0026ndash;66.23) and 143.77 per 100,000 (95% UI: 89.11\\u0026ndash;216.63) for bipolar disorder, and 26.71 per 100,000 (95% UI: 15.76\\u0026ndash;39.94) and 243.46 per 100,000 (95% UI: 166.79\\u0026ndash;331.29) for schizophrenia. Compared to 1990, these figures represent increases of 76.7% and 76.3% for anxiety disorders, 71.4% and 69.1% for depression, 43.6% and 48.5% for bipolar disorder, and 35.7% and 55.3% for schizophrenia in terms of incidence and DALYs, respectively. The estimated annual percentage changes (EAPCs) of ASRs were 0.24 (95% confidence interval [CI]: 0.08\\u0026ndash;0.39) and 0.16 (95% CI: 0.01\\u0026ndash;0.32) for anxiety disorders, -0.18 (95% CI: -0.39\\u0026ndash;0.04) and \\u0026minus;\\u0026thinsp;0.14 (95% CI: -0.32\\u0026ndash;0.04) for depression, 0.09 (95% CI: 0.07\\u0026ndash;0.11) and 0.03 (95% CI: 0.02\\u0026ndash;0.04) for bipolar disorder, and \\u0026minus;\\u0026thinsp;0.02 (95% CI: -0.03\\u0026ndash; -0.01) and 0 (95% CI: 0\\u0026ndash;0.01) for schizophrenia (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan refid=\\\"Tab8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThe incidence cases and age-standardized incidence rate of anxiety disorders disease in 1990 and 2021, along with their temporal trend.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eRate per 100 000(95%UI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1990\\u0026ndash;2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e1990\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIncidence cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe age-standardized incidence rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eIncidence cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eThe age-standardized incidence rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eEAPC\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGlobal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10728548 (7058385 to 14938651)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e797.81 (527.95 to 1108.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18962131 (12660029 to 26295396)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e976.14 (650.09 to 1355.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.24 (0.08 to 0.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSDI region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2135047 (1432423 to 2971475)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e948.44 (635.2 to 1320.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2853074 (1899052 to 3973078)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1218.56 (806.6 to 1696.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.28 (0.09 to 0.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2129500 (1403378 to 2983302)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e763.79 (505.13 to 1068.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2737964 (1803315 to 3843299)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e924.49 (600.29 to 1305.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.08 (-0.09 to 0.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMiddle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3529827 (2308097 to 4896476)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e786.68 (520.36 to 1086.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6204189 (4143079 to 8539181)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1010.08 (670.08 to 1393.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.34 (0.18 to 0.51)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2100995 (1382205 to 2930806)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e769.57 (510.29 to 1068.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4814788 (3223652 to 6659907)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e949.69 (637.84 to 1312.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.41 (0.26 to 0.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e822937 (530429 to 1170280)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e728.7 (475.17 to 1031.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2336119 (1518389 to 3297914)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e841.34 (553.46 to 1182.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.19 (0.07 to 0.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGBD region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAndean Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e104609 (64381 to 154429)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1088.67 (676.77 to 1599.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e269514 (161062 to 409142)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1543.39 (921.02 to 2343.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.44 (0.14 to 0.74)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAustralasia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e62989 (39165 to 91645)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1178.99 (730.54 to 1716.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e91094 (55171 to 135833)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1308.88 (788.4 to 1954.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.25 (0.13 to 0.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCaribbean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e88536 (55517 to 128306)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e947.17 (598.11 to 1366.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e140334 (88145 to 208576)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1169.28 (732.93 to 1739.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.24 (0.09 to 0.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e83455 (51961 to 120817)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e494.98 (308.72 to 714.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e148239 (92314 to 217621)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e612.93 (379.18 to 904.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.19 (0.02 to 0.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e225279 (146931 to 321264)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e733.12 (476.79 to 1047.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e252984 (165661 to 358878)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1001.24 (647.65 to 1429.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.31 (0.08 to 0.53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e334381 (214670 to 472080)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e793.53 (515.47 to 1114.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e776238 (505479 to 1088452)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1141.19 (742.81 to 1600.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.74 (0.53 to 0.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e92717 (56706 to 137694)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e739.56 (455.95 to 1090.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e273421 (163102 to 417667)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e824.55 (493.33 to 1255.53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.14 (0.03 to 0.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2295505 (1494840 to 3206710)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e687.33 (453.65 to 954.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2312917 (1556700 to 3211205)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e704.81 (465.63 to 985.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.53 (-0.69 to -0.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e396215 (264350 to 547754)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e721.42 (477.82 to 999.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e462974 (310102 to 634514)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1003.54 (664.25 to 1383.18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.32 (0.08 to 0.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e363103 (231266 to 514198)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e820.93 (529.06 to 1157.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1057053 (674589 to 1500615)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e963.86 (623.05 to 1361.54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.11 (-0.03 to 0.24)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income Asia Pacific\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e296798 (193791 to 412287)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e652.89 (426.19 to 907.51)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e285336 (187416 to 399116)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e775.34 (505.43 to 1085.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.16 (-0.33 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income North America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e874484 (603133 to 1194303)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1181.68 (811.78 to 1615.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1296755 (870228 to 1790275)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1576.93 (1055.1 to 2175.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.29 (0.02 to 0.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNorth Africa and Middle East\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e770067 (493743 to 1115136)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e954.55 (618.48 to 1377)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1820024 (1135627 to 2695730)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1146.18 (714.54 to 1698.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.45 (0.3 to 0.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOceania\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12020 (7446 to 17683)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e767.11 (479.13 to 1121.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e29960 (18001 to 45819)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e858.74 (517.15 to 1311.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.12 (0.04 to 0.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouth Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1848991 (1230669 to 2539891)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e729.75 (489.22 to 999.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4459968 (3040346 to 6039337)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e903.5 (616.96 to 1222.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.44 (0.26 to 0.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoutheast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e948880 (619385 to 1323116)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e789.29 (519.05 to 1096.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1849098 (1232332 to 2566238)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1012.28 (672.67 to 1406.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.28 (0.1 to 0.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e132381 (86387 to 189926)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1061.65 (693.63 to 1522.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e228331 (136197 to 344501)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1329.58 (792.3 to 2007.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.16 (-0.02 to 0.34)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102575 (66222 to 143736)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e768.44 (502.15 to 1070.42)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e221230 (144908 to 309387)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1015.64 (664.21 to 1421.63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.26 (0.06 to 0.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTropical Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e482618 (315467 to 668967)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1207.11 (793.75 to 1670.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1111559 (737246 to 1565120)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1851.05 (1223.06 to 2604.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.08 (0.76 to 1.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e957600 (612529 to 1390417)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1016.1 (650.54 to 1473.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1115696 (715206 to 1601914)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1249.18 (795.33 to 1795.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.35 (0.21 to 0.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e255344 (163413 to 361679)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e573.1 (371.64 to 808.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e759407 (488449 to 1073860)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e621.15 (403.37 to 876.82)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.18 (0.04 to 0.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eAbbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. \\u0026ldquo; EAPC is expressed as 95% CIs.\\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\\u003eThe DALYs cases and age-standardized DALYs rate of anxiety disorders disease in 1990 and 2021, along with their temporal trend.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eRate per 100 000(95%UI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1990\\u0026ndash;2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e1990\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDALYs cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe age-standardized DALYs rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDALYs cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eThe age-standardized DALYs rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eEAPC\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGlobal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9328956 (5917676 to 13620685)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e698.3 (442.6 to 1018.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16448344 (10383333 to 24009992)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e844.05 (532.79 to 1232.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.16 (0.01 to 0.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSDI region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2043455 (1301043 to 2990142)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e900.03 (572.56 to 1317.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2745188 (1743682 to 4004979)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1140.64 (724.95 to 1666.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.14 (-0.08 to 0.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1927404 (1227564 to 2806442)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e692.52 (440.41 to 1008.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2487171 (1551501 to 3657646)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e835.89 (521.28 to 1232.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.07 (-0.1 to 0.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMiddle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3024602 (1926600 to 4410929)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e675.54 (429.85 to 983.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5417644 (3456898 to 7877801)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e877.44 (559.52 to 1276.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.36 (0.19 to 0.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1664857 (1044787 to 2443621)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e617.24 (387.16 to 904.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3896043 (2458016 to 5672689)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e771.79 (486.96 to 1122.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.46 (0.32 to 0.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e659781 (411510 to 983257)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e591.7 (369.02 to 880.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1888211 (1162549 to 2819269)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e686.86 (422.94 to 1024.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.22 (0.1 to 0.34)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGBD region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAndean Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e96613 (57712 to 147281)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1028.17 (614.34 to 1567)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e253659 (149262 to 395132)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1449.82 (853.42 to 2256.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.45 (0.16 to 0.74)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAustralasia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e65972 (40727 to 99527)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1226.54 (756.38 to 1851.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e96214 (57071 to 148232)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1331.41 (790.19 to 2050)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.24 (0.14 to 0.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCaribbean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e76856 (45799 to 118118)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e829.26 (493.92 to 1271.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e123221 (73950 to 188783)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1023.85 (614.13 to 1569)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.25 (0.1 to 0.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e64797 (38189 to 99708)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e384.94 (226.32 to 592.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e115505 (67532 to 179181)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e477.48 (278.81 to 741.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.19 (0.02 to 0.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e187798 (115483 to 281746)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e610.93 (375.26 to 918.47)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e211608 (130502 to 317266)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e829.55 (511.7 to 1244.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.3 (0.08 to 0.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e278259 (172888 to 411442)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e664.85 (412.96 to 981.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e658936 (407957 to 984002)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e967.11 (599.01 to 1444.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.72 (0.48 to 0.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e76381 (45808 to 117479)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e614.13 (368.37 to 940.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e225913 (131912 to 360784)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e685.83 (400.83 to 1093.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.17 (0.06 to 0.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2011893 (1291935 to 2941224)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e596.53 (382.41 to 871.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1992595 (1262971 to 2950597)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e616.8 (390.72 to 913.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.53 (-0.7 to -0.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e331368 (214027 to 485124)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e600.47 (386.95 to 879.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e386537 (246701 to 563962)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e829.05 (528.46 to 1214.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.32 (0.09 to 0.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e291169 (181267 to 433062)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e677.93 (421.77 to 1006.24)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e847625 (525221 to 1283511)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e790.95 (489.83 to 1193.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.12 (-0.02 to 0.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income Asia Pacific\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e249111 (157076 to 367725)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e547.21 (345.53 to 808.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e241960 (150489 to 363236)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e649.35 (405.91 to 973.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.11 (-0.28 to 0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income North America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e802494 (522068 to 1154699)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1060.44 (688.74 to 1526.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1196413 (775406 to 1736433)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1414.22 (916.57 to 2054.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.15 (-0.2 to 0.51)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNorth Africa and Middle East\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e773967 (485447 to 1138638)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e966.97 (607.2 to 1420.87)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1882833 (1142662 to 2817198)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1183.83 (718.06 to 1772.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.37 (0.24 to 0.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOceania\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10349 (6231 to 15969)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e658.88 (395.98 to 1014.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e25991 (14566 to 41937)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e744.76 (417.71 to 1200.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.13 (0.05 to 0.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouth Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1357075 (861459 to 1974664)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e544.66 (345.95 to 790.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3273256 (2109051 to 4705742)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e665.93 (429.4 to 956.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.45 (0.26 to 0.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoutheast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e746044 (470314 to 1100271)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e626.6 (394.12 to 922.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1485464 (934317 to 2202813)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e810.68 (510.29 to 1202.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.29 (0.12 to 0.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e139173 (88869 to 204582)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1123.82 (717.37 to 1651.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e240552 (139628 to 365818)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1381.49 (802.14 to 2101.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.03 (-0.18 to 0.24)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e84034 (53387 to 123078)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e633.35 (401.81 to 925.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e181302 (116407 to 263722)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e833.78 (534.76 to 1213.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.24 (0.04 to 0.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTropical Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e436030 (282337 to 624903)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1105.86 (716.24 to 1584.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1146277 (734480 to 1642912)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1874.23 (1199.81 to 2688.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.4 (0.88 to 1.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1031704 (638828 to 1533835)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1087.27 (673.49 to 1616.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1209541 (749486 to 1785519)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1333.41 (827.15 to 1967.63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.27 (0.11 to 0.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e217869 (136174 to 325874)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e484.48 (302.7 to 723.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e652943 (405725 to 977339)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e529.25 (328.86 to 791.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.2 (0.09 to 0.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eAbbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. \\u0026ldquo; EAPC is expressed as 95% CIs.\\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=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThe incidence cases and age-standardized incidence rate of depressive disorders disease in 1990 and 2021, along with their temporal trend.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eRate per 100 000(95%UI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1990\\u0026ndash;2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e1990\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIncidence cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe age-standardized incidence rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eIncidence cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eThe age-standardized incidence rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eEAPC\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGlobal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e77722841 (58860491 to 102539344)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5898.5 (4486.52 to 7740.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e133248593 (99032450 to 177876463)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6808.01 (5049.99 to 9106.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.18 (-0.39 to 0.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSDI region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14095700 (11172406 to 17813539)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6219.44 (4915.78 to 7869.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21662488 (16621406 to 28113424)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e 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-0.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMiddle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22649073 (16997951 to 29922163)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5183.69 (3915.67 to 6798.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e36081478 (26891514 to 47657463)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5767.56 (4281.65 to 7652.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.25 (-0.48 to -0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18391509 (13457053 to 25013203)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7035.36 (5182.88 to 9485.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e37747940 (27469874 to 51290417)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7551.4 (5510.78 to 10222.82)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.6 (-0.86 to -0.34)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7553850 (5354318 to 10472429)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7097.66 (5079.82 to 9744.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e19801185 (13890117 to 27443214)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7509.35 (5325.8 to 10306.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.36 (-0.53 to -0.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGBD region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAndean Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e438667 (303751 to 617425)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4737 (3296.37 to 6625.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1105337 (751097 to 1589967)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6320.57 (4292.05 to 9087.82)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.06 (-0.33 to 0.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAustralasia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e480002 (369325 to 616432)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8977.64 (6898.68 to 11536.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e723160 (498995 to 1010531)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10269.64 (7057.12 to 14369.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.24 (0.08 to 0.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCaribbean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e708019 (507841 to 969849)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7697.77 (5552.01 to 10488.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e969573 (658741 to 1384728)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8029.7 (5444.45 to 11483.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.5 (-0.76 to -0.24)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e762529 (544489 to 1049479)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4676.28 (3367.02 to 6381.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1367571 (948058 to 1922301)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5623.36 (3891.23 to 7918.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.1 (-0.09 to 0.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1272969 (942663 to 1698354)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4115.36 (3043.59 to 5505.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1273327 (914036 to 1725195)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4795.85 (3393.82 to 6608.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.47 (-0.8 to -0.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2056464 (1474404 to 2831110)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5119.69 (3689.12 to 6999.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5395961 (3919153 to 7296866)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7882.55 (5720.17 to 10666.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.02 (0.8 to 1.24)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1276623 (879908 to 1821332)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10640.71 (7408.07 to 15062.94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3584294 (2373076 to 5212807)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e11270.02 (7523.17 to 16274.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.02 (-0.13 to 0.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14573702 (11063941 to 19051300)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4412.52 (3367.36 to 5732.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10395449 (7909206 to 13367181)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2956.12 (2227.57 to 3840.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.4 (-1.65 to -1.16)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3022733 (2188449 to 4091174)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5392.25 (3903.27 to 7297.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3496268 (2499383 to 4757666)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6990.81 (4956.63 to 9586.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.13 (-0.41 to 0.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2992873 (2132522 to 4133293)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7375.7 (5319.95 to 10067.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8187853 (5707046 to 11439404)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7985.77 (5630.58 to 11024.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.27 (-0.43 to -0.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income Asia Pacific\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1741956 (1355004 to 2240796)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3838.31 (2978.91 to 4945.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1832829 (1393439 to 2364638)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5010.7 (3763.15 to 6511.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.47 (0.27 to 0.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income North America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5275313 (4101575 to 6757929)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7145.82 (5538.1 to 9178.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10479475 (8142164 to 13299662)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e12689.95 (9828.58 to 16139.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.82 (0.47 to 1.16)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNorth Africa and Middle East\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6585654 (4704056 to 9116662)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8629.46 (6216.24 to 11853.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16021220 (10983016 to 22831950)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10032.79 (6876.71 to 14305.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.2 (0.04 to 0.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOceania\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e62234 (43091 to 88312)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3968.59 (2766.94 to 5579.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e143677 (95168 to 209653)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4125.32 (2738.85 to 6006.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.13 (-0.19 to -0.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouth Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17346564 (12898192 to 23184706)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7116.76 (5321.38 to 9440.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35575994 (26421845 to 47285175)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7283.76 (5421.29 to 9654.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.99 (-1.31 to -0.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoutheast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3802109 (2781508 to 5120365)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3196.08 (2353.81 to 4271.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7017745 (5014067 to 9561434)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3829.96 (2729.29 to 5227.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.04 (-0.26 to 0.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e868789 (655862 to 1148697)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6983.96 (5277.99 to 9228.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1401345 (1012961 to 1899048)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8133.97 (5867.86 to 11029.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.17 (-0.43 to 0.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e851742 (638669 to 1125686)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6761.59 (5111.67 to 8866.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1889699 (1403730 to 2506386)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8722.61 (6487.11 to 11549.81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.37 (0.09 to 0.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTropical Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3243572 (2443732 to 4270770)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8304.3 (6295.63 to 10868.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6004237 (4494351 to 7859870)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9756.89 (7276.88 to 12821.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.44 (-0.82 to -0.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7776728 (6187704 to 9786383)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8121.24 (6445.97 to 10229.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9415888 (6929830 to 12706630)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10094.71 (7338.27 to 13758.87)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.2 (-0.01 to 0.42)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2583599 (1839829 to 3578085)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6321.17 (4550.59 to 8648.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6967690 (4926014 to 9745602)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6114.54 (4369.47 to 8446.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.33 (-0.44 to -0.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eAbbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. \\u0026ldquo; EAPC is expressed as 95% CIs.\\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=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThe DALYs cases and age-standardized DALYs rate of depressive disorders disease in 1990 and 2021, along with their temporal trend.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eRate per 100 000(95%UI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1990\\u0026ndash;2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e1990\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDALYs cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe age-standardized DALYs rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDALYs cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eThe age-standardized DALYs rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eEAPC\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGlobal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12445336 (8084219 to 18023437)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e948.86 (617.06 to 1369.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21042424 (13468194 to 30593481)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1073.5 (686.73 to 1562.48)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.14 (-0.32 to 0.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSDI region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2291807 (1525885 to 3251697)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1007.25 (670.08 to 1431.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3350602 (2211216 to 4819019)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1399.31 (921.2 to 2022.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.44 (0.25 to 0.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2449324 (1601463 to 3518044)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e889.89 (582.12 to 1275.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2939216 (1878568 to 4276996)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e939.53 (596.4 to 1375.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.28 (-0.48 to -0.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMiddle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3677230 (2385351 to 5345046)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e850.96 (552.69 to 1231.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5819959 (3723678 to 8429630)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e926.08 (591.77 to 1344.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.22 (-0.42 to -0.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2832462 (1799426 to 4175678)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1088.11 (691.58 to 1595.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5817166 (3674561 to 8529565)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1164.37 (735.95 to 1704.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.49 (-0.71 to -0.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1183844 (747044 to 1751936)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1116.33 (706.66 to 1643.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3099941 (1938028 to 4603046)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1179.58 (740.73 to 1739.54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.27 (-0.41 to -0.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGBD region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAndean Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e69125 (42310 to 105173)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e751.79 (460.41 to 1140.63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e169290 (101535 to 261073)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e966.63 (579.65 to 1490.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.06 (-0.28 to 0.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAustralasia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e73943 (48093 to 106574)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1380.17 (896.99 to 1991.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e110902 (68365 to 167971)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1563.09 (962.3 to 2373.22)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.24 (0.09 to 0.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCaribbean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e106489 (66718 to 161464)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1161.78 (729.68 to 1756.61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e144581 (87039 to 222582)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1195.95 (719.21 to 1842.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.48 (-0.72 to -0.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e125704 (79388 to 185289)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e772.01 (488.55 to 1132.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e220831 (137416 to 332913)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e903.45 (561.45 to 1363.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.1 (-0.06 to 0.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e215905 (137301 to 313409)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e695.87 (442.07 to 1011.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e211070 (132809 to 308756)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e788.38 (492.87 to 1163.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.38 (-0.64 to -0.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e316373 (199590 to 471536)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e791.2 (499.51 to 1175.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e800325 (496086 to 1189258)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1168.22 (724.13 to 1736.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.91 (0.71 to 1.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e190214 (117316 to 291338)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1597.25 (989.08 to 2430.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e535682 (313866 to 827727)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1695.2 (994.94 to 2610.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.02 (-0.07 to 0.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2490860 (1624451 to 3611963)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e766.37 (500.81 to 1106.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2009848 (1320287 to 2848265)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e560.51 (365.8 to 799.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.15 (-1.34 to -0.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e486387 (309306 to 712816)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e864.27 (549.26 to 1266.81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e546448 (346405 to 807155)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1083.11 (684.77 to 1608.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.09 (-0.33 to 0.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e481183 (303359 to 708083)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1188.39 (751.52 to 1736.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1302601 (802604 to 1945053)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1273.15 (789.45 to 1889.91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.21 (-0.33 to -0.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income Asia Pacific\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e280947 (183633 to 402273)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e618.37 (403.62 to 885.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e290039 (188979 to 417635)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e783.79 (508.07 to 1134.18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.38 (0.2 to 0.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income North America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e886754 (586930 to 1271514)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1193.29 (788.61 to 1716.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1597172 (1063927 to 2290899)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1929.21 (1284.02 to 2772.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.64 (0.36 to 0.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNorth Africa and Middle East\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1005848 (626768 to 1502447)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1323.9 (828.06 to 1967.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2420065 (1465303 to 3673290)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1515.15 (916.91 to 2301.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.19 (0.05 to 0.34)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOceania\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10693 (6680 to 16226)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e698.73 (437.04 to 1054.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e24892 (14825 to 38303)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e720.59 (430.31 to 1107.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.09 (-0.14 to -0.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouth Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2645823 (1692290 to 3895331)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1088.15 (696.41 to 1593.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5462958 (3482818 to 7949804)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1118.19 (713.21 to 1625.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.82 (-1.1 to -0.54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoutheast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e692004 (446214 to 1012842)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e593.89 (383.57 to 865.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1260592 (802918 to 1855937)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e683.77 (434.95 to 1007.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.01 (-0.17 to 0.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e132096 (85584 to 194500)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1063.1 (688.88 to 1563.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e210975 (131446 to 313050)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1221.46 (760.65 to 1814.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.2 (-0.44 to 0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e135540 (88225 to 197114)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1080.34 (703.43 to 1562.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e288124 (185614 to 421681)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1329.3 (856.5 to 1943.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.28 (0.04 to 0.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTropical Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e477478 (309004 to 697269)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1225.46 (794.24 to 1783.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e876601 (555830 to 1277429)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1419.51 (900.1 to 2073.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.41 (-0.75 to -0.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1207940 (802909 to 1707060)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1258.72 (836.26 to 1781.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1429185 (913491 to 2114351)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1527.48 (969.93 to 2272.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.18 (-0.02 to 0.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e414027 (258812 to 611944)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1017.39 (639.5 to 1494.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1130242 (707465 to 1676792)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e996.09 (626.15 to 1467.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.24 (-0.33 to -0.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eAbbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. \\u0026ldquo; EAPC is expressed as 95% CIs.\\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=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThe incidence cases and age-standardized incidence rate of Bipolar disorder disease in 1990 and 2021, along with their temporal trend.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eRate per 100 000(95%UI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1990\\u0026ndash;2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e1990\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIncidence cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe age-standardized incidence rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eIncidence cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eThe age-standardized incidence rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eEAPC\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGlobal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e575625 (344082 to 875035)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e41.64 (24.69 to 63.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e826397 (484408 to 1270443)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e43.16 (25.43 to 66.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.09 (0.07 to 0.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSDI region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e122166 (77076 to 179264)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e56.43 (36.24 to 82.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e127979 (81132 to 187291)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e57.29 (37.47 to 82.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.07 (0.05 to 0.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e103636 (59717 to 161399)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e37.22 (21.48 to 57.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e107786 (60502 to 169743)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e38.88 (22.39 to 60.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.1 (0.03 to 0.18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMiddle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e177098 (106839 to 269544)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e37.17 (22.04 to 56.91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e243395 (142322 to 374670)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e41.13 (24.37 to 63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.26 (0.22 to 0.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e117716 (69184 to 181683)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e40.54 (23.38 to 62.94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e210928 (121371 to 328059)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e41.06 (23.54 to 63.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.02 (0.01 to 0.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e54319 (31049 to 85052)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e45.4 (25.48 to 71.53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e135497 (77503 to 212314)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e46.12 (25.89 to 72.74)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.05 (0.04 to 0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGBD region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAndean Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8032 (4472 to 13025)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e76.94 (42.13 to 125.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13139 (7136 to 21571)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e76.95 (42.16 to 125.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAustralasia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4879 (2998 to 7306)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e93.63 (58.07 to 139.58)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6130 (3700 to 9269)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e93.74 (58.34 to 139.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01 (0 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCaribbean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7684 (4270 to 12426)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e77.3 (42.53 to 125.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9079 (4933 to 14791)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e77.31 (42.25 to 125.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7855 (4201 to 12921)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e45.76 (24.38 to 75.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10676 (5628 to 17580)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e45.8 (24.36 to 75.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14989 (8411 to 23835)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e49.65 (28.05 to 78.81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11866 (6544 to 18856)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e49.53 (28.01 to 78.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.01 (-0.01 to -0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e36259 (22289 to 54247)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e77.85 (46.66 to 117.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e52209 (31016 to 79187)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e77.78 (46.49 to 117.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.01 (-0.01 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6429 (3480 to 10541)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e48.59 (25.94 to 80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16936 (9173 to 27741)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e48.59 (25.96 to 79.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e57465 (33907 to 87569)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16.65 (9.75 to 25.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e50533 (28838 to 77652)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e16.6 (9.76 to 25.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.02 (-0.03 to -0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24875 (14324 to 38020)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e46.97 (27.45 to 71.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20779 (11800 to 31808)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e46.95 (27.47 to 71.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e25498 (14677 to 39661)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e54.16 (30.42 to 84.94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e62648 (35945 to 97534)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e54.17 (30.44 to 84.94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income Asia Pacific\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e21879 (12697 to 33653)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e48.22 (28.07 to 74.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17162 (9667 to 26691)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e48.43 (28.08 to 74.82)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01 (0 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income North America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e37251 (26563 to 48660)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e53.44 (39.13 to 68.87)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e42434 (30962 to 55088)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e52.9 (39.24 to 68.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.06 (0.04 to 0.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNorth Africa and Middle East\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e58208 (33895 to 92517)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e67.28 (38.18 to 107.74)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e105578 (59400 to 169849)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e67.09 (37.91 to 107.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.01 (-0.02 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOceania\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e423 (222 to 699)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26.04 (13.53 to 43.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e915 (475 to 1517)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e26.08 (13.52 to 43.16)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouth Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e79452 (46713 to 121118)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30.24 (17.55 to 46.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e150377 (87408 to 230646)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30.32 (17.62 to 46.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01 (0.01 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoutheast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e38736 (21965 to 60194)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e31.1 (17.43 to 48.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e56258 (31469 to 87671)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e31.17 (17.54 to 48.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8774 (4734 to 14305)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69.5 (37.32 to 113.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11710 (6169 to 19309)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e69.73 (37.15 to 114.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01 (0.01 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6952 (4101 to 10536)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e49.08 (28.36 to 74.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10565 (6064 to 16176)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e49.03 (28.2 to 74.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTropical Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e41003 (25915 to 60082)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e95.8 (59.56 to 141.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e53544 (32338 to 80380)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e95.7 (59.41 to 141.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e67452 (39187 to 105259)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e74.11 (43.92 to 114.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e64927 (37445 to 101246)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e75.3 (44.57 to 116.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.07 (0.06 to 0.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e21531 (12381 to 33344)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e45.89 (25.77 to 71.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e58930 (33877 to 91119)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e45.9 (25.8 to 71.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eAbbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. \\u0026ldquo; EAPC is expressed as 95% CIs.\\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=\\\"Tab6\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThe DALYs cases and age-standardized DALYs rate of Bipolar disorder disease in 1990 and 2021, along with their temporal trend.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eRate per 100 000(95%UI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1990\\u0026ndash;2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e1990\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDALYs cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe age-standardized DALYs rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDALYs cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eThe age-standardized DALYs rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eEAPC\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGlobal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1890347 (1181247 to 2850106)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e142.3 (88.88 to 214.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2806894 (1740129 to 4226051)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e143.77 (89.11 to 216.63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.03 (0.02 to 0.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSDI region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e476243 (304074 to 701694)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e209.57 (133.75 to 309.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e508105 (324844 to 747828)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e209.74 (134.33 to 309.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.01 (-0.03 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e352148 (215010 to 536387)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e127.35 (77.68 to 193.81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e397619 (241487 to 605343)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e130.1 (78.73 to 199.51)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.05 (0 to 0.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMiddle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e563348 (350870 to 850162)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e126.54 (78.75 to 190.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e856066 (531423 to 1286754)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e138.24 (85.78 to 208.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.26 (0.24 to 0.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e344110 (211221 to 528752)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e127.42 (78.29 to 194.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e657159 (401179 to 1006754)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e130.21 (79.49 to 199.18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.09 (0.07 to 0.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e152185 (90539 to 237235)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e138.56 (82.7 to 214.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e385068 (229419 to 600144)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e142.77 (85.35 to 221.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.1 (0.1 to 0.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGBD region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAndean Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26888 (15171 to 43335)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e282.14 (160.13 to 451.63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e49363 (28351 to 78745)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e281.66 (161.72 to 449.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01 (0 to 0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAustralasia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e19999 (12448 to 30672)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e372.74 (231.69 to 572.69)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e26654 (16555 to 40578)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e371.1 (229.26 to 568.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.03 (-0.04 to -0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCaribbean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26513 (15282 to 41812)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e282.86 (163.6 to 443.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33738 (19623 to 53188)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e280.14 (162.76 to 442.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.02 (-0.03 to -0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23978 (13317 to 38414)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e144.43 (80.74 to 229.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35341 (19631 to 56570)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e144.18 (79.93 to 231.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e49820 (29752 to 76785)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e161.57 (96.34 to 249.34)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e42410 (25359 to 65013)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e160.96 (96.05 to 248.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (-0.01 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e120167 (74864 to 181326)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e284.61 (177.36 to 427.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e193702 (119606 to 290723)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e283.94 (175.27 to 426.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.01 (-0.01 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17957 (9757 to 29430)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e148.37 (81.66 to 241.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e48153 (26542 to 78759)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e150.49 (83.54 to 244.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.04 (0.04 to 0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e182018 (112814 to 279614)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e55.07 (34.16 to 84.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e185766 (114795 to 284101)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e55.04 (33.98 to 84.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01 (0 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e81167 (50296 to 123134)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e144.92 (89.63 to 220.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e71637 (44433 to 107751)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e144.71 (89.54 to 218.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01 (0.01 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e71630 (42805 to 111553)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e169.77 (101.61 to 262.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e179442 (106912 to 279283)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e170.81 (102.12 to 264.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.04 (0.03 to 0.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income Asia Pacific\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e74724 (46027 to 114661)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e162.78 (100.3 to 250.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e62715 (38368 to 95593)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e160.61 (98.63 to 246.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.05 (-0.06 to -0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income North America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e148732 (96947 to 214524)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e200.98 (130.89 to 290.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e164702 (107235 to 236941)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e197.04 (128.19 to 283.58)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.04 (-0.05 to -0.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNorth Africa and Middle East\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e190541 (113362 to 295985)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e242.54 (144.49 to 374.22)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e386391 (230734 to 597119)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e242.63 (144.72 to 375.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01 (0 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOceania\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1153 (601 to 1960)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e76.21 (40.18 to 128.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2637 (1369 to 4481)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e76.56 (39.96 to 129.61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01 (0.01 to 0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouth Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e212741 (131193 to 325527)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e85.58 (52.78 to 130.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e423922 (257549 to 645982)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e86.22 (52.37 to 131.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.04 (0.03 to 0.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoutheast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e109381 (64957 to 170364)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e93.27 (55.41 to 144.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e172630 (102960 to 265692)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e93.38 (55.66 to 143.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.02 (0.01 to 0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e30554 (17234 to 48638)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e247.09 (139.54 to 392.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e42591 (23866 to 67882)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e242.96 (135.85 to 388.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.06 (-0.08 to -0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e19462 (11936 to 29746)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e149.77 (92.06 to 227.76)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e32154 (19577 to 48949)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e147.94 (90.01 to 225.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.03 (-0.04 to -0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTropical Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e149882 (94845 to 226256)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e370.63 (234.61 to 558.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e222218 (140784 to 331760)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e368.9 (233.33 to 551.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e273939 (168310 to 415187)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e285.55 (175.17 to 433.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e266773 (163831 to 403802)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e286.38 (175.86 to 435.47)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.02 (0.02 to 0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e59101 (35479 to 91899)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e139.36 (83.84 to 214.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e163955 (98081 to 254881)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e140.34 (84.21 to 216.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.03 (0.03 to 0.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eAbbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. \\u0026ldquo; EAPC is expressed as 95% CIs.\\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=\\\"Tab7\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 7\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThe incidence cases and age-standardized incidence rate of schizophreniadisease in 1990 and 2021, along with their temporal trend.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eRate per 100 000(95%UI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1990\\u0026ndash;2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e1990\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIncidence cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe age-standardized incidence rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eIncidence cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eThe age-standardized incidence rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eEAPC\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGlobal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e378287 (227553 to 559592)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26.98 (16.19 to 40.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e513255 (302798 to 767916)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e26.71 (15.76 to 39.94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.02 (-0.03 to -0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSDI region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e62087 (37803 to 92044)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.76 (16.9 to 41.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e64248 (39289 to 94639)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e28.21 (17.26 to 41.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.09 (0.06 to 0.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e79777 (50348 to 114017)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.77 (17.48 to 39.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e80697 (49608 to 117006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e28.81 (17.78 to 41.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.11 (0.1 to 0.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMiddle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e132302 (79545 to 194526)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.55 (16.46 to 40.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e159547 (93310 to 239328)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e26.7 (15.65 to 39.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.09 (-0.1 to -0.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e72609 (41822 to 110172)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25.01 (14.39 to 38.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e132003 (76447 to 200514)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e25.38 (14.69 to 38.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.05 (0.03 to 0.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e31214 (17710 to 47752)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26.45 (15.05 to 40.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e76403 (43471 to 117297)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e26.55 (15.15 to 40.74)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.02 (0.01 to 0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGBD region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAndean Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2157 (1140 to 3438)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21.44 (11.36 to 34.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3791 (2011 to 6134)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e21.46 (11.38 to 34.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (-0.01 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAustralasia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1692 (1087 to 2404)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e31.83 (20.54 to 45.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2205 (1433 to 3157)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e31.91 (20.96 to 45.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01 (0 to 0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCaribbean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1912 (1025 to 3088)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19.51 (10.5 to 31.48)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2325 (1226 to 3765)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e19.42 (10.25 to 31.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.03 (-0.04 to -0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4120 (2199 to 6572)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.71 (12.17 to 36.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5467 (2917 to 8794)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e22.65 (12.1 to 36.51)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.01 (-0.01 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6823 (3820 to 10607)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.89 (12.81 to 35.61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5361 (3019 to 8235)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e23.1 (12.98 to 35.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.03 (0.02 to 0.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9963 (5654 to 15312)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.22 (12.6 to 34.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15055 (8472 to 23176)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e22.16 (12.46 to 34.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.02 (-0.03 to -0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3400 (1838 to 5380)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26.1 (14.2 to 41.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8751 (4748 to 14001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e25.8 (14.12 to 41.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.02 (-0.03 to -0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e112065 (71228 to 158701)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e31.41 (19.75 to 44.84)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e95909 (58924 to 138302)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e32.29 (20.09 to 46.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.02 (-0.01 to 0.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e11466 (6746 to 16992)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20.91 (12.33 to 31.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9358 (5426 to 14023)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e21.79 (12.71 to 32.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.19 (0.15 to 0.22)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12167 (6952 to 18630)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26.75 (15.39 to 40.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e29744 (16919 to 45778)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e26.51 (15.17 to 40.69)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.01 (-0.02 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income Asia Pacific\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12039 (6972 to 18348)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.05 (15.64 to 41.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9786 (5679 to 14672)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e27.86 (16.08 to 41.91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.22 (0.15 to 0.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income North America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22705 (14153 to 32884)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e31.54 (19.77 to 45.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e24839 (15409 to 35946)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e31.34 (19.52 to 45.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.03 (-0.05 to -0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNorth Africa and Middle East\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e20939 (11550 to 32462)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.93 (13.78 to 38.58)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e39211 (21714 to 61431)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e24.77 (13.71 to 38.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.03 (-0.04 to -0.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOceania\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e469 (247 to 755)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28.25 (14.99 to 45.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1017 (538 to 1605)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e28.41 (15.09 to 44.84)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01 (-0.01 to 0.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouth Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e65796 (38493 to 98839)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.3 (14.14 to 36.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e125191 (73100 to 188502)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e24.72 (14.4 to 37.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.06 (0.03 to 0.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoutheast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e36922 (20997 to 55906)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28.61 (16.28 to 43.42)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e51865 (29701 to 78697)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e28.81 (16.5 to 43.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.1 (0.06 to 0.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3406 (1829 to 5470)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.2 (14.64 to 43.63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4710 (2502 to 7544)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e27.39 (14.5 to 43.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (-0.02 to 0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3688 (2162 to 5493)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26.18 (15.38 to 39.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5717 (3374 to 8542)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e25.98 (15.34 to 38.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (-0.01 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTropical Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9377 (5515 to 13870)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.33 (13.11 to 33.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13202 (7745 to 19618)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e22.38 (13.12 to 33.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (-0.01 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22937 (14067 to 34147)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.01 (14.7 to 35.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21280 (12952 to 31716)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e23.66 (14.26 to 35.47)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.04 (-0.06 to -0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14244 (8203 to 21537)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30.76 (17.85 to 46.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e38471 (22228 to 58100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30.63 (17.78 to 46.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.02 (-0.02 to -0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eAbbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. \\u0026ldquo; EAPC is expressed as 95% CIs.\\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=\\\"Tab8\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 8\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThe DALYs cases and age-standardized DALYs rate of schizophreniadisease in 1990 and 2021, along with their temporal trend.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eRate per 100 000(95%UI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1990\\u0026ndash;2021\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e1990\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDALYs cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe age-standardized DALYs rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDALYs cases\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eThe age-standardized DALYs rate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eEAPC\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGlobal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3114984 (2138675 to 4238277)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e244.12 (168.33 to 329.91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4836703 (3318473 to 6567286)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e243.46 (166.79 to 331.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSDI region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e625687 (434444 to 846991)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e265.73 (184.12 to 360.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e696195 (485441 to 928011)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e264.42 (183.43 to 355.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01 (-0.01 to 0.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e672326 (467381 to 898636)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e245.4 (170.92 to 327.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e874087 (614730 to 1154235)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e262.16 (183.06 to 349.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.19 (0.18 to 0.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMiddle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1029028 (703067 to 1410514)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e247.12 (170.17 to 335.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1576391 (1085696 to 2141396)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e244.76 (168.02 to 333.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.02 (-0.03 to -0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow-middle SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e564224 (380198 to 782537)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e225.05 (152.9 to 308.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1134533 (765367 to 1583760)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e229.78 (155.49 to 319.34)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.09 (0.07 to 0.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow SDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e221196 (145125 to 312452)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e219.69 (146.04 to 306.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e552163 (364040 to 780207)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e222.41 (148.23 to 310.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.07 (0.06 to 0.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGBD region\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAndean Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16860 (10165 to 25596)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e194.82 (119.27 to 290.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e34512 (20991 to 51594)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e196.14 (119.39 to 293.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.04 (0.03 to 0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAustralasia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16151 (11008 to 21890)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e294.74 (200.5 to 400.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e22786 (15761 to 30509)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e293.96 (202.28 to 395.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (-0.01 to 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCaribbean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15675 (9835 to 23367)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e179.06 (113.58 to 263.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21475 (13708 to 31547)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e175.9 (112.05 to 258.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.04 (-0.05 to -0.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e31813 (20101 to 47603)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e200.93 (128.58 to 296.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e50917 (32148 to 74598)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e200.69 (126.23 to 295.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.02 (0.01 to 0.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e65027 (42702 to 91369)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e205.08 (134.2 to 289.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e59564 (39572 to 82440)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e208.33 (136.66 to 292.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.06 (0.05 to 0.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e76792 (50135 to 109813)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e202.02 (133.3 to 284.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e138687 (93391 to 195251)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e201.3 (135.46 to 283.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.01 (-0.02 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCentral Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22743 (13574 to 34385)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e205.9 (125.6 to 306.16)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60440 (36906 to 90571)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e204.75 (127.02 to 301.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.02 (0 to 0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e893224 (621394 to 1197732)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e281.46 (196.96 to 374.74)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1069773 (755348 to 1409687)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e295.9 (207.28 to 393.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.11 (0.08 to 0.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e107986 (73516 to 147464)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e186.03 (126.15 to 255.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e105114 (71644 to 141678)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e193.92 (130.8 to 265.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.21 (0.17 to 0.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e78930 (51044 to 113501)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e208.61 (137.18 to 295.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e202311 (131136 to 289608)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e211.12 (138.53 to 297.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.09 (0.07 to 0.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income Asia Pacific\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e114729 (77400 to 158883)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e244.9 (164.58 to 341.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e106485 (72599 to 146142)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e249.5 (168.01 to 347.54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.15 (0.1 to 0.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh-income North America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e256201 (181673 to 339040)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e327.38 (231.52 to 434.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e279996 (198177 to 368294)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e318.29 (224.62 to 420.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.07 (-0.1 to -0.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNorth Africa and Middle East\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e155320 (101217 to 222271)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e219.24 (144.88 to 309.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e354136 (234634 to 505572)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e219.52 (145.26 to 313.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.01 (-0.01 to 0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOceania\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3418 (2084 to 5090)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e238.01 (147.47 to 349.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8096 (4945 to 12207)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e238.94 (146.82 to 358.18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01 (-0.01 to 0.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouth Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e541119 (367787 to 739711)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e228.33 (156.11 to 309.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1143907 (779553 to 1568013)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e235.38 (160.72 to 321.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.13 (0.1 to 0.16)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoutheast Asia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e282665 (185712 to 401691)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e249.91 (165.53 to 351)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e480916 (322313 to 669290)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e257.01 (171.91 to 358.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.18 (0.14 to 0.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e30027 (18483 to 44134)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e247.47 (152.84 to 362.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e44689 (27665 to 65283)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e246.78 (152.04 to 362.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01 (-0.01 to 0.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouthern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e25785 (17078 to 36218)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e215.04 (143.91 to 298.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e45975 (30907 to 63777)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e210.8 (141.82 to 291.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.04 (-0.05 to -0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTropical Latin America\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e74646 (50293 to 103442)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e198.43 (134.47 to 272.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e126685 (86015 to 172103)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e199.05 (134.82 to 271.53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.02 (0 to 0.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Europe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e211485 (144166 to 292825)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e213.85 (145.53 to 297)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e213924 (145945 to 291357)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e208.66 (141.2 to 287.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.07 (-0.1 to -0.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e94388 (62049 to 134465)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e245.42 (163.91 to 343.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e266315 (174938 to 375887)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e248.2 (165.12 to 345.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.06 (0.05 to 0.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eAbbreviations: EAPC, estimated annual percentage change, SDl, Sociodemographic Index; Ul,uncertainty interval. \\u0026ldquo; EAPC is expressed as 95% CIs.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003e2. Global Burden of Disease and Trends in Psychiatric Disorders Among Women of Reproductive Age Across Subgroups (Age, SDI, Region, and Country)\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eGlobally, the incidence of psychiatric disorders and DALYs increased significantly across all disease types and age groups from 1990 to 2021. However, the overall trends in disease burden remained consistent across age groups and aligned with broader age-related patterns (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, Fig. S1).The 15\\u0026ndash;19 age group exhibited the highest number of anxiety and bipolar disorder cases, as well as the highest ASRs, with bipolar disorder being particularly prevalent in this group. However, this age group had the lowest incidence and ASRs of depression and the smallest DALY burden among the four psychiatric disorders. In contrast, the 45\\u0026ndash;49 age group had the highest incidence and DALY burden of depression, with the fastest-growing ASRs. The 20\\u0026ndash;24 age group had the highest incidence and ASRs of schizophrenia, although its DALY burden remained relatively limited, only slightly exceeding that of the 15\\u0026ndash;19 age group (Fig. S1).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAcross all Socio-Demographic Index (SDI) regions, the burden of psychiatric disorders increased significantly from 1990. Depression and anxiety disorders were the most prevalent, while bipolar disorder and schizophrenia exhibited relatively stable incidence rates across SDI regions. However, schizophrenia contributed to a considerably greater disease burden (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).With the exception of high-SDI regions, which exhibited higher ASRs, the ASRs of psychiatric disorders varied only slightly across SDI regions. However, middle- and low-middle-SDI regions had the highest absolute number of cases and DALYs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Over time, the trends in psychiatric disorder burden remained consistent across all SDI regions (Fig. S2).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAcross the 21 GBD regions, from 1990 to 2021, the incidence and DALY burden of all four psychiatric disorders increased most rapidly in South Asia, Southeast Asia, North Africa and the Middle East, Western Sub-Saharan Africa, Eastern Sub-Saharan Africa, Tropical Latin America, and Central Latin America. These regions accounted for a growing proportion of the global burden. In contrast, densely populated East Asia experienced a decline in the incidence and DALYs of most psychiatric disorders, except for bipolar disorder and schizophrenia, where DALYs continued to rise.\\u003c/p\\u003e \\u003cp\\u003eRegarding ASRs, the age-standardized incidence rate (ASIR) and age-standardized disability-adjusted life year rate (ASDR) for bipolar disorder and schizophrenia remained relatively stable across all 21 regions. However, Tropical Latin America and Australasia recorded the highest ASIRs and ASDRs for bipolar disorder, while East Asia had the lowest. Schizophrenia ASIRs and ASDRs were highest in East Asia, Australasia, and High-income North America.\\u003c/p\\u003e \\u003cp\\u003eFor depression, Central Latin America, High-income North America, and Central Sub-Saharan Africa had the highest and increasing ASIRs and ASDRs. The fastest-growing ASDRs were observed in Tropical Latin America, Central Latin America, Andean Latin America, and High-income North America, while East Asia experienced the largest and fastest-growing ASIRs and ASDRs for depression. The estimated annual percentage change (EAPC) for depression was \\u0026minus;\\u0026thinsp;1.4 (95% CI: -1.65 to -1.16) for ASIRs and \\u0026minus;\\u0026thinsp;1.15 (95% CI: -1.34 to -0.95) for ASDRs, making East Asia the region with the most rapid decline. A more modest decline in ASIRs and ASDRs for depression was also observed in Oceania (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFigure\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e illustrates the global trend of psychiatric disorder burden over time in relation to SDI across countries. The prevalence of anxiety disorders increased worldwide, with DALYs rising in nearly all countries, except for a few. The most rapid increases occurred in countries with SDI\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.6, particularly Brazil, Portugal, and Paraguay. A similar trend was observed for depression, with incidence and DALYs increasing globally, peaking in Greenland. Compared to anxiety disorders, depression showed a weaker correlation between disease burden and SDI.For bipolar disorder and schizophrenia, ASRs remained largely unchanged across countries. Bipolar disorder prevalence increased, while DALY rates declined, particularly in high-SDI countries, with New Zealand exhibiting the fastest rise in both prevalence and DALYs. The burden of schizophrenia varied across countries, with high-SDI nations such as the Netherlands, the United States, and Denmark experiencing the most significant changes.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003e3. SDI Correlation Analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eOverall, the incidence and DALYs of the four aforementioned mental disorders were positively correlated with the SDI, increasing with higher SDI levels, particularly for anxiety disorders. At the regional level, the relationship between the incidence and DALYs of bipolar disorder and SDI followed a \\\"W\\\" pattern, with a significant decline at SDI\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.4, followed by a notable increase as SDI continued to rise. In contrast, schizophrenia exhibited a \\\"U\\\"-shaped relationship with SDI, with the lowest prevalence and DALYs observed at an SDI of 0.6. The disease burden of anxiety disorders steadily increased with SDI, whereas the burden of depression remained relatively stable at SDI\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.6 before rising sharply thereafter. At the national level, the incidence and DALY rates of all four disorders increased with SDI growth, with more pronounced increases observed at SDI\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.75(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003e4. Health Inequities\\u003c/h3\\u003e\\n\\u003cp\\u003eFrom 1990 to 2021, the slope indices for all four psychiatric disorders showed an upward trend, while the concentration indices declined (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eA/B). Specifically, the slope index for schizophrenia incidence increased from \\u0026minus;\\u0026thinsp;2.90 (95% CI: -4.26 to -1.55) in 1990 to -2.16 (95% CI: -3.55 to -0.78) in 2021, while the corresponding slope index for DALYs rose from 3.14 (95% CI: -7.60 to 13.88) to 5.84 (95% CI: -5.34 to 17.01). Similarly, the slope indices for the incidence of bipolar disorder, depression, and anxiety increased from 16.85 (95% CI: 9.00 to 24.69), -842.74 (95% CI: -1866.14 to 180.66), and 196.96 (95% CI: 131.14 to 262.78) to 17.97 (95% CI: 10.44 to 25.49), 465.39 (95% CI: -736.83 to 1667.60), and 330.90 (95% CI: 247.18 to 414.61), respectively. The corresponding slope indices for DALYs increased from 87.25 (95% CI: 54.75 to 119.75), -138.47 (95% CI: -276.91 to -0.03), and \\u0026minus;\\u0026thinsp;138.47 (95% CI: -276.91 to -0.03) to 88.51 (95% CI: 57.70 to 119.31), 39.84 (95% CI: -122.73 to 202.41), and 432.21 (95% CI: 329.22 to 535.21), respectively.Meanwhile, the concentration indices for the incidence of these four disorders decreased from \\u0026minus;\\u0026thinsp;0.08 (95% CI: -0.23 to 0.16), 0.02 (95% CI: -0.15 to 0.21), 0.15 (95% CI: -0.15 to 0.21), and 0.21 (95% CI: -0.15 to 0.21) to -0.10 (95% CI: -0.28 to 0.06), -0.09 (95% CI: -0.22 to 0.05), -0.06 (95% CI: -0.25 to 0.12), and \\u0026minus;\\u0026thinsp;0.08 (95% CI: -0.22 to 0.12), respectively. Likewise, the concentration index for DALYs also showed a downward trend, with schizophrenia decreasing from \\u0026minus;\\u0026thinsp;0.03 (95% CI: -0.21 to 0.26) to -0.04 (95% CI: -0.24 to 0.13), bipolar disorder from 0.09 (95% CI: -0.09 to 0.27) to -0.03 (95% CI: -0.16 to 0.11), depression from \\u0026minus;\\u0026thinsp;0.05 (95% CI: -0.23 to 0.20) to -0.08 (95% CI: -0.25 to 0.11), and anxiety disorders from 0.02 (95% CI: -0.16 to 0.26) to -0.02 (95% CI: -0.19 to 0.14).These findings highlight the complex changes in the global burden of mental disorders in the context of socioeconomic development, indicating that global health inequities have worsened over time and underscoring the urgent need to strengthen mental health services in low-SDI regions.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003e5. Decomposition Analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eGlobally, and particularly in low-SDI regions such as Africa, Latin America, South Asia, and Southeast Asia, population growth is the primary driver of the increasing burden of psychiatric disorders, excluding anxiety disorders. In East Asia, however, aging is the key factor, especially for schizophrenia and bipolar disorder. Aging has a negative impact on morbidity, contributing 112.57% and 91.72%, respectively, while positively influencing DALYs, with contributions of 76.3% and 139.99%.Regarding anxiety disorders, the global increase in both incidence and DALYs is largely attributed to population growth, which contributes 66% and 62.23%, respectively. However, epidemiological changes also play a significant role, contributing 35.85% and 33.75%, respectively. The impact of these changes becomes more pronounced as SDI increases, particularly in high-SDI areas, where epidemiological changes contribute 83.15% and 78.14%, respectively, and in high-middle-SDI regions, contributing 74.18% and 72.4%. In these regions, the contribution of population growth and aging is far less significant (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003e6. 2050 Projections\\u003c/h3\\u003e\\n\\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003e illustrates that the incidence and DALYs of anxiety disorders and depression are projected to increase significantly over the next decade, with a particularly sharp rise expected from 2030 onward. This trend suggests that anxiety disorders and depression will become increasingly prominent public health challenges. In contrast, although bipolar disorder and schizophrenia are also projected to rise, their growth will be more moderate, indicating that the burden from these disorders will increase at a slower pace. Overall, the burden of mental illness, particularly among women, is expected to continue rising, with anxiety and depression being of particular concern. This could have important implications for public health policy and resource allocation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig12\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eTo the best of our knowledge, our study is the first to analyze and compare the four most common or impactful psychiatric disorders in women of reproductive age globally. Our findings show that while the incidence and DALYs for anxiety, depression, bipolar disorder, and schizophrenia in women of childbearing age have all increased modestly at the global level from 1990 to 2021, and the corresponding ASRs have generally trended downward, anxiety disorders have shown rising rates of episodes and DALYs in nearly all countries, and depression has followed a similar pattern in most countries, particularly in higher-SDI regions. Furthermore, there are significant differences in the burden of these four mental disorders across age groups, regions, and countries, with varying influencing factors in each region.\\u003c/p\\u003e \\u003cp\\u003eIn 2021, our study found that anxiety, depression, bipolar disorder, and schizophrenia together accounted for 45,134,365 DALYs among women of childbearing age globally, with the respective proportions being 36.4%, 46.6%, 6.2%, and 10.7%. These findings suggest that depression and anxiety disorders continue to be the most prevalent psychiatric disorders among women of childbearing age, consistent with previous population-wide studies. Additionally, both depression and anxiety disorders showed a significant increase in 2020, a trend confirmed by two recent studies. The COVID-19 pandemic in 2020, combined with increased uncertainty about health risks, rising unemployment, economic downturns, geographic lockdowns, and shifts in lifestyles, all contributed to heightened feelings of despair, loneliness, anxiety, and even depression.\\u003c/p\\u003e \\u003cp\\u003eOur study revealed distinct age-dependent patterns in psychiatric disorder prevalence: depression predominated in older adults, anxiety and bipolar disorders were more prevalent among adolescents (15\\u0026ndash;19 years), whereas schizophrenia incidence peaked in the 20\\u0026ndash;24 age cohort. These distributions likely stem from multifactorial interactions involving neurotransmitter imbalances, genetic predispositions, regional brain dysfunction, and environmental stressors (Sullivan et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e; Alexopoulos, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Casey et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Stein and Stein, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Katon, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Diniz et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; American Psychological Association, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Cale et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Mechanistically, age-related central nervous system decline in older adults reduces serotonin (5-HT), dopamine (DA), and norepinephrine (NE) levels, impairing mood regulation and elevating depression risk (Alexopoulos, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e). In adolescents, neurodevelopmental immaturity\\u0026mdash;particularly in frontal/temporal lobes and hippocampal volumes\\u0026mdash;coupled with dysregulated 5-HT, γ-aminobutyric acid (GABA), and cortisol levels, compromises emotional stability, predisposing to anxiety and bipolar disorders (Casey et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Stein and Stein, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). Concurrent psychosocial stressors differentially affect age groups: older adults face elevated depression risks due to social isolation (post-retirement, widowhood, or \\\"empty nest\\\" syndrome) and comorbidities with neurodegenerative (e.g., Alzheimer\\u0026rsquo;s, Parkinson\\u0026rsquo;s) and chronic metabolic diseases (e.g., hypertension, diabetes) (Katon, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Diniz et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Conversely, younger populations exhibit heightened anxiety susceptibility attributable to academic pressure, occupational demands, and interpersonal conflicts characteristic of modern fast-paced lifestyles (American Psychological Association, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eOur study highlights significant regional and global disparities in psychiatric disorders among women of childbearing age. Notably, high-income North America, Latin America, and Australasia exhibit the highest and most rapidly increasing burden of psychiatric disorders, a trend identified and prioritized in public health agendas two decades ago (Kohn et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e). Conversely, East Asia reports the lowest rates of anxiety, depression, and bipolar disorder, as well as the lowest DALYs. Specifically, depression prevalence in East Asia has declined significantly since 1990, driven largely by a\\u0026thinsp;\\u0026gt;\\u0026thinsp;12% reduction in major depressive disorder cases in China (Tian et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). While direct evidence for these regional differences is limited, contextual factors provide plausible explanations. For instance, Japan\\u0026rsquo;s mental health policies\\u0026mdash;such as capping out-of-pocket costs for psychiatric care at 10% of total expenses and including transcranial magnetic stimulation therapy in public healthcare\\u0026mdash;have significantly improved access to treatment. Consequently, according to Kawakami (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) and the Japan Local Life Support Association (JLSA, 2018), consultation rates for major depression have more than doubled in Japan. Moreover, depression is increasingly recognized as a societal indicator in Japanese media, fostering greater public awareness and resilience. Similarly, China and South Korea demonstrate elevated psychological resilience, potentially contributing to lower mental health burdens (Qin et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). In contrast, North America\\u0026rsquo;s economic prosperity masks stark social inequalities, where mental health issues are exacerbated by high living costs, healthcare expenses, and inadequate social support for low-income populations (Sareen et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Latin America faces distinct challenges, including economic instability, poverty, and unemployment, which elevate anxiety and depression rates (Lund et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). Cultural norms, such as \\u0026ldquo;machismo,\\u0026rdquo; further hinder timely mental health interventions, particularly among men. Compounding these issues are pervasive social stressors, including political unrest, over-urbanization, gang violence, high crime rates, drug trade, and child abuse, which collectively sustain high psychiatric disorder prevalence in the region (Sapag et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Mascayano et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Peen et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Libby et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e). The global burden of anxiety and depression among women of childbearing age is rising rapidly, particularly in high-income regions of North America, Latin America, and parts of Africa. To address this growing issue, effective public health policies must be developed urgently. East Asian countries offer potential models, providing valuable insights through substantial governmental funding for healthcare, well-established primary mental health care systems, and a supportive social environment.Additionally, special attention is needed for adolescents and older adults, who are particularly vulnerable to psychiatric disorders due to neurodevelopmental changes, cognitive decline, and multidimensional social disadvantages. For these populations, fostering a supportive and engaging environment, along with adequate social support, is crucial in mitigating exposure to harmful external stressors.\\u003c/p\\u003e \\u003cp\\u003eWhile our study highlights the importance of psychosomatic health in women of reproductive age, it has several limitations. First, our analysis primarily accounts for health-related losses, while broader welfare losses associated with mental illness are not included due to the complexity of quantification. Second, diagnosing and categorizing mental disorders remains a clinical challenge, particularly in low-SDI regions, where data quality may be inconsistent. Additionally, economic and cultural barriers often prevent individuals in these areas from seeking medical treatment, further complicating accurate assessment.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, our study underscores the persistent and increasing burden of psychiatric disorders among women of reproductive age worldwide, with notable regional disparities, especially in anxiety and depression. The lack of significant improvement over time suggests that current prevention and treatment strategies have yet to yield substantial real-world benefits. However, by leveraging big data, our study offers a comprehensive analysis of the factors contributing to these regional variations. Identifying these key determinants can serve as a foundation for targeted interventions aimed at reducing the global impact of psychiatric disorders in this vulnerable population.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eFunding information:\\u003c/h2\\u003e\\n\\u003cp\\u003eNo funding was received for this study.\\u003c/p\\u003e\\n\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\n\\u003cp\\u003eX.W. conducted formal analysis, developed the methodology, and implemented the software; X.W. and Y.K. performed data curation; Y.K. and Y.J. handled visualization; X.W., Y.K., and Y.J. wrote the original draft; X.L. provided project administration, supervision, and validation; all authors (X.W., Y.K., X.L., and Y.J.) contributed to conceptualization and writing\\u0026mdash;review and editing; X.L. is the corresponding author.\\u003c/p\\u003e\\n\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\n\\u003cp\\u003eAcknowledgmentsWe thank the works by the GBD 2021 Diseases and Injuries Collaborators.\\u003c/p\\u003e\\n\\u003ch2\\u003e\\u0026nbsp;\\u003c/h2\\u003e\\n\\u003ch3\\u003eData availability\\u003c/h3\\u003e\\n\\u003cp\\u003eThe data used in this study can be derived from the GBD 2021 (Available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://vizhub.healthdata.org/gbd-results/\\u003c/span\\u003e\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003eClinical trial number: not applicable.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAlexopoulos GS (2005) Depression in the elderly. Lancet 365(9475):1961\\u0026ndash;1970. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S0140-6736(05)66665-2\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0140-6736(05)66665-2\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAmerican Psychological Association (2018) \\u003cem\\u003eStress in America: Generation Z\\u003c/em\\u003e. Washington, DC: American Psychological Association. Retrieved from \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.apa.org/news/press/releases/stress/2018/stress-gen-z.pdf\\u003c/span\\u003e\\u003cspan address=\\\"https://www.apa.org/news/press/releases/stress/2018/stress-gen-z.pdf\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBachmann S (2018) Epidemiology of suicide and the psychiatric perspective. Int Journal Environ Res Public Health 15(7):1425. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/ijerph15071425\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/ijerph15071425\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCale JA, Chauhan EJ, Cleaver JJ, Fusciardi AR, McCann S, Waters HC, Žavbi J, King MV (2024) GABAergic and inflammatory changes in the frontal cortex following neonatal PCP plus isolation rearing, as a dual-hit neurodevelopmental model for schizophrenia. Mol Neurobiol 61(9):6968\\u0026ndash;6983. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s12035-024-03987-y\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s12035-024-03987-y\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCantwell R (2021) Mental disorder in pregnancy and the early postpartum. Anaesthesia 76(4):76\\u0026ndash;83. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/anae.15424\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/anae.15424\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCasey BJ, Jones RM, Hare TA (2008) The adolescent brain. Ann N Y Acad Sci 1124(1):111\\u0026ndash;126. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1196/annals.1440.010\\u003c/span\\u003e\\u003cspan address=\\\"10.1196/annals.1440.010\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDiniz BS, Butters MA, Albert SM, Dew MA, Reynolds CF III (2013) Late-life depression and risk of vascular dementia and Alzheimer's disease: Systematic review and meta-analysis of community-based cohort studies. Br J Psychiatry 202(5):329\\u0026ndash;335. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1192/bjp.bp.112.118307\\u003c/span\\u003e\\u003cspan address=\\\"10.1192/bjp.bp.112.118307\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGBD 2019 Mental Disorders Collaborators (2022) Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990\\u0026ndash;2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry 9(2):137\\u0026ndash;150. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S2215-0366(21)00395-3\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S2215-0366(21)00395-3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGBD 2021 Diseases and Injuries Collaborators (2024) Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990\\u0026ndash;2021: A systematic analysis for the Global Burden of Disease Study 2021. Lancet 403(10440):2133\\u0026ndash;2161. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S0140-6736(24)00757-8\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0140-6736(24)00757-8\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGrande I, Berk M, Birmaher B, Vieta E (2016) Bipolar disorder. Lancet 387(10027):1561\\u0026ndash;1572. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S0140-6736(15)00241-X\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0140-6736(15)00241-X\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eIran Subnational Mental Health GBD Collaborators (2024) National and subnational burden of mental disorders in Iran (1990\\u0026ndash;2019): Findings of the Global Burden of Disease 2019 study. Lancet Global Health 12 (12), e1984-e1992. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S2214-109X(24)00342-5\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S2214-109X(24)00342-5\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJapan Local Life Support Association (2018) Japan's medical system that supports individuals with mental disorders. Available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://jlsa-net.jp/sei/seihin-iryou/?utm_source\\u003c/span\\u003e\\u003cspan address=\\\"https://jlsa-net.jp/sei/seihin-iryou/?utm_source\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJauhar S, Johnstone M, McKenna PJ (2022) Schizophrenia. \\u003cem\\u003eLance\\u003c/em\\u003et, \\u003cem\\u003e399\\u003c/em\\u003e (10323), 473\\u0026ndash;486. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S0140-6736(21)01730-X\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0140-6736(21)01730-X\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJones I, Chandra PS, Dazzan P, Howard LM (2014) Bipolar disorder, affective psychosis, and schizophrenia in pregnancy and the post-partum period. Lancet 384(9956):1789\\u0026ndash;1799. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S0140-6736(14)61278-2\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0140-6736(14)61278-2\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKaton WJ (2011) Epidemiology and treatment of depression in patients with chronic medical illness. Dialog Clin Neurosci 13(1):7\\u0026ndash;23. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.31887/DCNS.2011.13.1/wkaton\\u003c/span\\u003e\\u003cspan address=\\\"10.31887/DCNS.2011.13.1/wkaton\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKawakami N (2014) A large-scale epidemiological study on the prevalence of mental disorders in Japan: World Mental Health Japan Survey Second. Available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://mhlw-grants.niph.go.jp/project/22776?utm_source\\u003c/span\\u003e\\u003cspan address=\\\"https://mhlw-grants.niph.go.jp/project/22776?utm_source\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKohn R, Levav I, de Almeida JM, Vicente B, Andrade L, Caraveo-Anduaga JJ, Saxena S, Saraceno B (2005) Los trastornos mentales en Am\\u0026eacute;rica Latina y el Caribe: Asunto prioritario para la salud p\\u0026uacute;blica [Mental disorders in Latin America and the Caribbean: A public health priority]. Revista Panam de Salud P\\u0026uacute;blica 18(4\\u0026ndash;5):229\\u0026ndash;240. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1590/s1020-49892005000900002\\u003c/span\\u003e\\u003cspan address=\\\"10.1590/s1020-49892005000900002\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLauron S, Plasse C, Vaysset M, Pereira B, D'Incan M, Rondepierre F, Jalenques I (2023) Prevalence and odds of depressive and anxiety disorders and symptoms in children and adults with alopecia areata: A systematic review and meta-analysis. JAMA Dermatology 159(3):281\\u0026ndash;288. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1001/jamadermatol.2022.6085\\u003c/span\\u003e\\u003cspan address=\\\"10.1001/jamadermatol.2022.6085\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLibby AM, Orton HD, Novins DK, Beals J, Manson SM, AI-SUPERPFP Team (2005) Childhood physical and sexual abuse and subsequent depressive and anxiety disorders for two American Indian tribes. Psychol Med 35(3):329\\u0026ndash;340. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1017/s0033291704003599\\u003c/span\\u003e\\u003cspan address=\\\"10.1017/s0033291704003599\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLund C, Breen A, Flisher AJ, Kakuma R, Corrigall J, Joska JA, Swartz L, Patel V (2010) Poverty and common mental disorders in low and middle income countries: A systematic review. Soc Sci Med 71(3):517\\u0026ndash;528. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.socscimed.2010.04.027\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.socscimed.2010.04.027\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMascayano F, Tapia T, Schilling S, Alvarado R, Tapia E, Lips W, Yang LH (2016) Stigma toward mental illness in Latin America and the Caribbean: A systematic review. Braz J Psychiatry 38(1):73\\u0026ndash;85. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1590/1516-4446-2015-1652\\u003c/span\\u003e\\u003cspan address=\\\"10.1590/1516-4446-2015-1652\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOpio JN, Munn Z, Aromataris E (2022) Prevalence of mental disorders in Uganda: A systematic review and meta-analysis. Psychiatr Q 93(1):199\\u0026ndash;226. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s11126-021-09941-8\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s11126-021-09941-8\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePai N, Vella SL, Castle D (2022) A comparative review of the epidemiology of mental disorders in Australia and India. Asia-Pacific Psychiatry 14(4):e12517. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/appy.12517\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/appy.12517\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePeen J, Schoevers RA, Beekman AT, Dekker J (2010) The current status of urban-rural differences in psychiatric disorders. Acta Psychiatr Scand 121(2):84\\u0026ndash;93. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/j.1600-0447.2009.01438.x\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/j.1600-0447.2009.01438.x\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eQin C, Lee M, Deng J, Lee Y, You M, Liu J (2025) Mental health and psychological resilience amid the spread of the Omicron variant: A comparison between China and Korea. Front Public Health 12:1451318. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3389/fpubh.2024.1451318\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fpubh.2024.1451318\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSafiri S, Noori M, Nejadghaderi SA, Shamekh A, Sullman MJM, Collins GS, Kolahi AA (2024) The burden of schizophrenia in the Middle East and North Africa region, 1990\\u0026ndash;2019. Sci Rep 14(1):9720. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41598-024-59905-8\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41598-024-59905-8\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSapag JC, Sena BF, Bustamante IV, Bobbili SJ, Velasco PR, Mascayano F, Alvarado R, Khenti A (2018) Stigma towards mental illness and substance use issues in primary health care: Challenges and opportunities for Latin America. Glob Public Health 13(10):1468\\u0026ndash;1480. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/17441692.2017.1356347\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/17441692.2017.1356347\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSareen J, Afifi TO, McMillan KA, Asmundson GJ (2011) Relationship between household income and mental disorders: Findings from a population-based longitudinal study. Arch Gen Psychiatry 68(4):419\\u0026ndash;427. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1001/archgenpsychiatry.2011.15\\u003c/span\\u003e\\u003cspan address=\\\"10.1001/archgenpsychiatry.2011.15\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eStein MB, Stein DJ (2008) Social anxiety disorder. Lancet 371(9618):1115\\u0026ndash;1125. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S0140-6736(08)60488-2\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0140-6736(08)60488-2\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSullivan PF, Kendler KS, Neale MC (2003) Schizophrenia as a complex trait: Evidence from a meta-analysis of twin studies. Arch Gen Psychiatry 60(12):1187\\u0026ndash;1192. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1001/archpsyc.60.12.1187\\u003c/span\\u003e\\u003cspan address=\\\"10.1001/archpsyc.60.12.1187\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTian W, Yan G, Xiong S, Zhang J, Peng J, Zhang X, Zhou Y, Liu T, Zhang Y, Ye P, Zhao W, Tian M (2025) Burden of depressive and anxiety disorders in China and its provinces, 1990\\u0026ndash;2021: Findings from the Global Burden of Disease Study 2021. Br J Psychiatry 1\\u0026ndash;11. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1192/bjp.2024.267\\u003c/span\\u003e\\u003cspan address=\\\"10.1192/bjp.2024.267\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVigo D, Thornicroft G, Atun R (2016) Estimating the true global burden of mental illness. Lancet Psychiatry 3(2):171\\u0026ndash;178. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S2215-0366(15)00505-2\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S2215-0366(15)00505-2\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWorld Health Organization (2024), August 29 \\u003cem\\u003eSuicide\\u003c/em\\u003e. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.who.int/news-room/fact-sheets/detail/suicide\\u003c/span\\u003e\\u003cspan address=\\\"https://www.who.int/news-room/fact-sheets/detail/suicide\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWorrall S, Pike O, Christiansen P, Jackson L, De Pascalis L, Harrold JA, Fallon V, Silverio SA (2025) Psychosocial experiences of pregnant women during the COVID-19 pandemic: A UK-wide study of prevalence rates and risk factors for clinically relevant depression and anxiety. J Psychosom Obstet Gynecol 46(1):2459619. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/0167482X.2025.2459619\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/0167482X.2025.2459619\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang J, Liu Y, Zhang X (2025) The burden of mental disorders, substance use disorders and self-harm among young people in Asia, 2019\\u0026ndash;2021: Findings from the global burden of disease study 2021. Psychiatry Res 345:116370. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.psychres.2025.116370\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.psychres.2025.116370\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"mental disorders, burden of disease, Deaths, DALYs\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7970352/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7970352/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground and Objective\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMental disorders are a leading cause of years lived with disability (YLDs) and disability-adjusted life years (DALYs) worldwide, with high prevalence and severe consequences, including elevated suicide rates. These issues remain a priority for the World Health Organization. Depression, anxiety, bipolar disorder, and schizophrenia are among the most common and impactful mental disorders, and women of reproductive age experience unique physical and psychological stressors, increasing their vulnerability. This study aims to comprehensively analyze the global burden of these four mental disorders in this population, identify potential influencing factors, and project future trends.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eData from the Global Burden of Disease (GBD) 2021 database were used to assess the prevalence and DALYs of depression, anxiety, bipolar disorder, and schizophrenia among women of reproductive age. Trends were analyzed across different age groups, Socio-Demographic Index (SDI) regions, GBD regions, and countries. Correlations between disease burden indicators and SDI were examined, along with regional disparities. Decomposition analysis was conducted to assess potential factors contributing to the observed changes in disease burden. Future trends were projected using the Bayesian-Aperiodic-People-Cohort (BAPC) model.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBetween 1990 and 2021, the age-standardized incidence and DALY rates of anxiety, depression, bipolar disorder, and schizophrenia among women of reproductive age remained relatively stable. However, the absolute number of cases and DALYs increased, particularly for anxiety and depression. By 2021, the estimated number of cases and DALYs were 18,962,131 and 16,448,344 for anxiety, 133,248,593 and 21,042,424 for depression, 826,397 and 2,806,894 for bipolar disorder, and 513,255 and 4,836,703 for schizophrenia, respectively. The corresponding ASRs were 976.14/100,000 and 844.05/100,000 for anxiety, 6,808.01/100,000 and 1,073.5/100,000 for depression, 43.16/100,000 and 143.77/100,000 for bipolar disorder, and 26.71/100,000 and 243.46/100,000 for schizophrenia. Adolescents had the highest incidence and DALYs for anxiety, bipolar disorder, and schizophrenia, whereas depression incidence and DALYs increased with age. North America and Latin America exhibited the highest and fastest-growing burdens, while East Asia had the lowest burden, largely influenced by an aging population. Health disparities in mental illness burdens persisted over time. Projections indicate a substantial increase in anxiety and depression cases and DALYs among women of reproductive age in the coming decade.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe global burden of depression, anxiety, bipolar disorder, and schizophrenia among women of reproductive age continues to rise, particularly for anxiety and depression. Significant health disparities persist, necessitating urgent and targeted interventions.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Analysis and Projections of the Global Burden of Anxiety, Depression, Bipolar Disorder, and Schizophrenia Among Women of Reproductive Age (1990–2021)\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-08 18:09:03\",\"doi\":\"10.21203/rs.3.rs-7970352/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"094de996-3ce6-4146-bf12-7b5319acbc4d\",\"owner\":[],\"postedDate\":\"January 8th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-01-08T18:09:04+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-08 18:09:03\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7970352\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7970352\",\"identity\":\"rs-7970352\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}