The Stroke Crisis in Ageing Societies: Global Trends, Risk Factors, and Clinical Validation

The Stroke Crisis in Ageing Societies: Global Trends, Risk Factors, and Clinical Validation | 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 The Stroke Crisis in Ageing Societies: Global Trends, Risk Factors, and Clinical Validation Jing Tian, Xinhui Zhang, Dian Yu, Xinyi Chang, Yue Guo, Yi Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7589290/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background and Objectives: Stroke remains a major global public health challenge, particularly among the elderly population. This study aims to integrate MIMIC-IV clinical data with GBD 2021 data to analyse epidemiological trends and regional differences in stroke among people aged 70 and above, identify key risk factors, and predict future changes in incidence and mortality rates. Methods: The GBD 2021 database provides macro-epidemiological data on global stroke incidence, mortality, prevalence, and disability-adjusted life years (DALYs) from 1990 to 2021, while the MIMIC-IV database provides detailed clinical records for 2,144 stroke patients. Regional differences were assessed using the Socio-Demographic Index (SDI) for stratification. Three core statistical methods were employed: the Estimated Annual Percentage Change (EAPC) to quantify temporal trends in disease indicators, the Bayesian Age-Period-Cohort (BAPC) model to analyse the effects of ageing and predict the disease burden by 2040, and the Multivariate Cox Proportional Hazards Model to assess the impact of clinical risk factors on outcomes. Results: Clinical analysis of 2,144 ICU-treated stroke patients in the MIMIC-IV database revealed significantly higher 28-day mortality in those aged ≥70 years versus younger patients (22.0% vs. 12.7%, p<0.001), driven by heavier comorbidity burdens (hyperlipidaemia: 53.9% vs. 41.8%; diabetes: 36.4% vs. 30.0%) and poorer organ function (SOFA score: 5.0 vs. 4.0, p=0.037). Multivariable Cox regression confirmed age ≥70 years as an independent mortality risk factor (adjusted HR=1.36, 95% CI:1.05-1.76). In parallel, GBD 2021 data showed a 61% global rise in absolute stroke cases (1990-2021), disproportionately affecting Low-middle SDI regions. While High SDI regions achieved declining prevalence (EAPC=−0.53%), Middle SDI regions faced rising rates (EAPC=+0.42%), with East Asia exhibiting the sharpest incidence increase (EAPC=+1.05%). Hypertension dominated global stroke DALYs, followed by high LDL-cholesterol and fasting hyperglycaemia. Projections suggest a continued global decline in incidence and mortality by 2040, albeit at a slowing rate, attributed to accelerating population ageing. Conclusion: Significant regional disparities in the burden of stroke among the elderly are closely related to the level of the SDI and modifiable risk factors. High-SDI regions have reduced the burden of stroke through advanced intervention measures; Low-middle SDI regions urgently need strategic investment. stroke elderly population global burden of disease risk factors population ageing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Stroke has become a major challenge in the field of global public health. According to the latest report from the World Stroke Organisation, the global economic burden of stroke exceeds 890 billion US dollars (accounting for 0.66% of global GDP). From 1990 to 2021, the economic burden of stroke (measured by absolute case numbers) has significantly increased (stroke incidence increased by 70.0%, stroke mortality increased by 44.0%, stroke prevalence increased by 86.0%, and disability-adjusted life years (DALYs) increased by 32%), with approximately 85% of cases occurring in low- and middle-income countries [ 1 ] . Notably, age-specific epidemiological data indicate that the incidence of stroke is significantly higher in individuals aged 70 years and older compared to those aged 60–69 years [ 2 ] . This age-related increase in risk is associated with multiple pathophysiological mechanisms, including endothelial dysfunction, impaired blood-brain barrier integrity, and enhanced neuroinflammatory responses [ 3 ] . Currently, global population ageing is altering the epidemiological characteristics of stroke. Previous studies have shown that the clustering of traditional risk factors in people aged 70 and above has been on the rise from 1990 to 2020, and has been proven to significantly affect the risk of stroke in the elderly population [ 4 – 6 ] . More notably, age-specific risk factors such as frailty syndrome and polypharmacy are significantly associated with poor outcomes [ 7 , 8 ] . It is important to note that the burden of stroke exhibits marked regional disparities, which are closely linked to socioeconomic development. The Socio-Demographic Index (SDI), a composite indicator integrating per capita income, educational attainment, and fertility rates, provides a valuable framework for understanding these disparities [ 9 ] . Regions with different SDI levels (ranging from low to high) show distinct patterns in stroke incidence, mortality, and risk factor profiles, reflecting the influence of healthcare infrastructure, public health policies, and living conditions on disease outcomes [ 10 ] . Most existing epidemiological studies analyse the population aged over 60 as a whole [ 11 ] , and the Global Burden of Disease study has not yet systematically assessed the risk factor attribution scores for the population aged 70 and above [ 12 ] . In addition, there is a lack of long-term prediction models that take population structure changes into account [ 13 ] . Therefore, this study fills the research gap in the epidemiology of stroke in the elderly population based on MIMIC-IV and GBD 2021 data and provides scientific reference for stroke prevention and control in the global ageing society. Methodologies and materials Data sources The MIT Computational Physiology Laboratory established and maintained the large-scale MIMIC-IV database, which contained health-related data used in this retrospective investigation. This database contains comprehensive, excellent medical records of patients hospitalized to the Beth Israel Deaconess Medical Center's intensive care units. The authors were obliged to complete the training course to obtain access to the database. All clinical data for stroke patients were obtained from the MIMIC-IV database (See Appendix for the discharge process flow chart). Data on stroke from 1990 to 2021 were retrieved using the Global Health Data Exchange (GHDx) query tool. The dataset included information on mortality, incidence, and disability-adjusted life years (DALYs), as well as potential risk factors for stroke. The research methods used by the GBD have been extensively described in the existing literature. The SDI is a composite indicator that integrates per capita income distribution with the educational attainment of individuals aged 15 and older to represent regional economic conditions [ 9 ] . The SDI is scored on a scale from 0 to 1, with higher values indicating greater socioeconomic development. The regional classification based on SDI includes five categories: low ( 0.81) [ 14 ] . Statistical analysis Data are presented as mean ± standard deviation (SD) for normally distributed continuous variables or median [interquartile range, IQR] for non-normally distributed variables, and as frequencies (percentages) for categorical variables. Normality of distribution was assessed using the Shapiro-Wilk test. Group comparisons were performed using the chi-square test or Fisher's exact test for categorical variables, and the unpaired t-test or Mann-Whitney U test for continuous variables as appropriate. In the MIMIC - IV cohort, regarding the 28 - day mortality, we initially compared the Kaplan - Meier (K-M) survival curves. Subsequently, a multivariate Cox proportional hazards regression analysis was performed. Four sequential models were constructed: Model 1 (unadjusted), Model 2 (adjusted for gender and body mass index (BMI)), Model 3 (additionally adjusted for comorbidities based on Model 2), and Model 4 (comprehensively adjusted for demographic characteristics, comorbidities, organ failure scores, and vital signs). In GBD data, in order to analyze changes in Crude Rate (CR) over time, we utilized Estimated Annual Percentage Change (EAPC) values. The EAPC was determined using the formula y = α + βx + ε, where y represents the natural logarithm of the CR and x corresponds to the calendar year. Subsequently, the EAPC was calculated as 100 × (exp(β) − 1). A positive EAPC and its corresponding 95% confidence intervals (CIs) signifies an increasing trend in CR, whereas a negative EAPC and its corresponding 95% CIs indicate a decreasing trend in CR. For future burden prediction (up to 2040), we used Bayesian Age Period Queue (BAPC) model and Ensemble Nested Laplacian Approximation (INLA). This model decomposes time changes into age, period, and cohort effects, and generates predictions with 95% uncertainty intervals (UI) through Monte Carlo simulation. Risk factor attribution analysis is based on the GBD comparative risk assessment framework, which visualizes the percentage of attribution of 21 risk factors to stroke DALYs through heat maps MIMIC-IV analysis was conducted using R 4.5.0 software (survival package for Cox regression, ggplot2 package for plotting), while GBD analysis was based on R 4.5.0 (INLA package for running BAPC model, gbdR package for data processing) and WHO Health Equity Assessment Kit (HEAT). Results Clinical Characteristics of Elderly Stroke Patients (≥ 70 Years Old) The study included 2,144 stroke patients admitted to the intensive care unit (ICU), stratified according to the key age threshold of 70 years (Table 1 ). Baseline characteristics revealed significant differences between the elderly group (≥ 70 years old, n = 1,242) and the non-elderly group (< 70 years old, n = 902). Critically ill elderly stroke patients exhibited significantly higher comorbidity burdens, including type 2 diabetes (36.4% vs 30.0%, p = 0.002), CKD (27.5% vs 15.6%, p < 0.001), hyperlipidaemia (53.9% vs 41.8%, p < 0.001), and AHF (41.5% vs 24.5%, p < 0.001). At ICU admission, elderly patients presented with greater disease severity, evidenced by elevated organ failure scores: SOFA (median 5.0 vs 4.0, p = 0.037), SAPS II (42 vs 33, p < 0.001), and OASIS (35 vs 32, p < 0.001). This translated to poorer short-term outcomes, with higher in-hospital mortality (16.3% vs 12.2%, p = 0.008), ICU mortality (11.1% vs 8.1%, p = 0.021), and 28-day mortality (22.0% vs 12.7%, p < 0.001; Fig. 1 ). These findings highlight that ≥ 70-year-old stroke patients requiring ICU care constitute a high-risk subgroup with amplified resource demands due to compounded comorbidities and severity (Fig. 2 ). Table 1 Baseline Characteristics of Stroke Patients Stratified by Age Threshold (70 Years) Non-elderly group, n = 902 Elderly group, n = 1,242 p-value Age, years 61 (54, 66) 80 (74, 85) < 0.001 Gender, n (%) < 0.001 Male 574 (63.6%) 667 (53.7%) Female 328 (36.4%) 575 (46.3%) BMI, kg/m^2 28 (24, 33) 27 (24, 31) < 0.001 Comorbidities Hypertension, n (%) 454 (50.3%) 660 (53.1%) 0.199 Type 2 diabetes, n (%) 271 (30.0%) 452 (36.4%) 0.002 Type 1 diabetes, n (%) 27 (3.0%) 8 (0.6%) < 0.001 CKD, n (%) 141 (15.6%) 342 (27.5%) < 0.001 Hyperlipidaemia, n (%) 377 (41.8%) 669 (53.9%) < 0.001 AHF, n (%) 221 (24.5%) 515 (41.5%) < 0.001 AMI, n (%) 50 (5.5%) 118 (9.5%) < 0.001 COPD, n (%) 43 (4.8%) 119 (9.6%) < 0.001 ARF, n (%) 247 (27.4%) 431 (34.7%) < 0.001 Severity index SOFA 4.0 (2.0, 7.0) 5.0 (3.0, 7.0) 0.037 APS III 40 (28, 55) 43 (33, 59) < 0.001 SIRS 3.00 (2.00, 3.00) 3.00 (2.00, 3.00) 0.063 SAPS II 33 (26, 42) 42 (35, 50) < 0.001 OASIS 32 (26, 38) 35 (29, 41) < 0.001 GCS 15.0 (13.0, 15.0) 14.0 (12.0, 15.0) 0.005 Vital Signs HR, bpm 83 (74, 95) 81 (71, 93) 0.023 RR, bpm 17.0 (14.0, 21.0) 17.0 (14.0, 21.0) 0.795 SpO 2, % 99.00 (97.00, 100.00) 99.00 (96.00, 100.00) 0.056 SBP, mmHg 121 (105, 140) 123 (106, 141) 0.378 DBP, mmHg 68 (57, 80) 63 (53, 74) < 0.001 MV use, n (%) 570 (63.2%) 708 (57.0%) 0.004 MV time, hours 9 (0, 57) 6 (0, 31) < 0.001 length of hospital stay, days 10 (6, 17) 8 (5, 14) < 0.001 ICU length of stay, days 3.6 (1.9, 7.8) 3.1 (1.9, 6.1) 0.013 all-cause mortality, n (%) 285 (31.6%) 617 (49.7%) < 0.001 In-hospital death, n (%) 110 (12.2%) 202 (16.3%) 0.008 ICU dead, n (%) 73 (8.1%) 138 (11.1%) 0.021 CKD, Chronic Kidney Disease, AHF, Acute Heart Failure, AMI, Acute Myocardial Infarction, COPD, Chronic Obstructive Pulmonary Disease, ARF, Acute Renal Failure, SOFA, Sequential Organ Failure Assessment, APS III, Acute Physiology Score III, SIRS, Systemic Inflammatory Response Syndrome, SAPS II, Simplified Acute Physiology Score II, OASIS, Outcome and Assessment Information Set, GCS, Glasgow Coma Scale, HR, Heart Rate, RR, respiratory rate, SBP, Systolic blood pressure, DBP, Diastolic blood pressure, MV, Mechanical ventilation A. Prevalence of Key Comorbidities. B. Stratification of Disease Severity Indices Multivariable-adjusted hazard of 28-day mortality in elderly stroke patients Table 2 presents the multivariate Cox regression analysis of 28-day mortality in critically ill stroke patients. After full adjustment for clinical confounders in Model 4 (including organ failure scores, vital signs, and comorbidities), age ≥ 70 years remained independently associated with increased mortality risk, with an adjusted hazard ratio (HR) of 1.36 (95% CI: 1.05–1.76; p = 0.018). This elevated mortality risk, combined with extended resource utilization observed in elderly ICU patients (such as higher disease severity scores and comorbidity burden, as well as longer ICU stays), suggests amplified healthcare costs for this subgroup. Table 2 Multivariable Cox regression analysis of 28-day mortality in ICU-treated stroke patients Model 1 Model 2 Model 3 Model 4 HR (95%CI) p-value HR (95%CI) p-value HR (95%CI) p-value HR (95%CI) p-value Age Continuous 1.03 (1.02–1.03) < 0.001 1.01 (1.02–1.03) < 0.001 1.02 (1.01–1.03) < 0.001 1.01 (1.01–1.02) 0.004 Categories < 70 years Reference Reference Reference Reference Reference Reference Reference Reference ≥ 70 years 1.81 (1.45–2.25) < 0.001 1.63 (1.29–2.05) < 0.001 1.63 (1.29–2.05) < 0.001 1.36 (1.05–1.76) 0.018 Model 1: Crude Model 2: Adjust Gender, BMI Model 3: Adjust Gender, BMI, Hypertension, Type 2 diabetes, Type 1 diabetes, CKD, Hyperlipidaemia, AHF, AMI, COPD, ARF Model 4: Adjust Gender, BMI, Hypertension, Type 2 diabetes, Type 1 diabetes, CKD, Hyperlipidaemia, AHF, AMI, COPD, ARF, SOFA, APS III, SIRS, SAPS II, OASIS, GCS, HR, RR, SpO2, SBP, DBP The burden of stroke in people aged 70 and above in 2021 Global disease burden data (Table 3 ) indicate that between 1990 and 2021, the absolute number of global stroke cases (per 100,000 population) increased substantially from 135,201 to 327,672, while the age-standardized prevalence rate declined slightly (estimated annual percentage change, EAPC = -0.11%). Trends diverged regionally: High SDI regions showed the steepest decline (EAPC = -0.53%), contrasting with increased rates in Middle SDI regions (EAPC = + 0.42%). East Asia saw a 61% rise in crude incidence rate (CIR) – equivalent to an EAPC of + 1.05%. Mortality improvements were significant globally (EAPC = -1.75%), with high-income Asia Pacific achieving exceptional progress (EAPC = -4.3%), reducing mortality to 28% of its 1990 level. Low SDI regions lagged (EAPC = -0.81%), and sub-Saharan Africa faced persistent high burdens. Alarmingly, Southern sub-Saharan Africa recorded rising DALY rates (EAPC = + 0.63%). Table 3 Global stroke burden trends (1990–2021): Prevalence, incidence, mortality, and DALYs Location 1990 2021 EAPC_95%CI Number CR Number CR Prevalence Global 135201.07 [121908.72-150158.28] 6731.84 [6058.32-7486.86] 327671.96 [295042.07-363690.72] 6628.10 [5968.06-7356.68] -0.11 [-0.13 to -0.09] Socio-demographic index High SDI 55771.99 [50839.72-60898.93] 7978.36 [7266.82-8717.26] 102646.47 [95000.81-110905.5] 6917.04 [6410.71-7464.58] -0.53 [-0.60 to -0.47] High-middle SDI 33361.20 [29588.19-37573.03] 6418.73 [5682.91-7243.89] 83314.34 [74081.26-93268.99] 7089.89 [6304.81-7936.01] 0.27 [0.21 to 0.33] Middle SDI 27360.70 [23762.08-31452.04] 6037.65 [5227.63-6962.92] 97205.08 [85427.14-110416.23] 6938.92 [6090.33-7891.58] 0.42 [0.39 to 0.46] Low-middle SDI 12868.38 [11220.72-14731.95] 4914.36 [4270.04-5647.95] 32491.21 [28746.27-36692.22] 4664.40 [4115.28-5283.61] -0.19 [-0.21 to -0.16] Low SDI 5683.40 [5070.15-6345.50] 6235.28 [5539.45-6990.90] 11748.89 [10628.42-12923.62] 5515.02 [4967.37-6094.97] -0.43 [-0.47 to -0.39] Region High-income Asia Pacific 11315.43 [10247.61-12433.80] 10067.08 [9103.50-11080.70] 27345.13 [24959.31-29896.52] 7482.10 [6848.31-8161.12] -1.03 [-1.12 to -0.95] High-income North America 18378.52 [16088.45-20760.39] 7829.53 [6851.85-8844.61] 32381.36 [29201.94-35745.92] 7354.03 [6637.12-8109.77] -0.37 [-0.56 to -0.19] Western Europe 27579.80 [25503.72-29767.72] 7198.84 [6653.84-7774.82] 39296.15 [37113.90-41603.05] 5670.81 [5367.88-5992.28] -0.76 [-0.78 to -0.75] Australasia 1008.02 [954.07-1064.28] 6901.13 [6523.49-7293.18] 1919.75 [1818.85-2021.45] 5125.69 [4864.26-5391.11] -1.06 [-1.10 to -1.01] Andean Latin America 503.11 [468.13-539.39] 4928.16 [4583.44-5285.61] 1391.88 [1309.44-1477.08] 4208.27 [3961.71-4462.81] -0.58 [-0.62 to -0.54] Tropical Latin America 2755.59 [2355.98-3209.30] 6302.94 [5381.13-7357.71] 7055.95 [6087.21-8123.79] 4901.10 [4231.58-5638.22] -0.95 [-1.00 to -0.89] Central Latin America 2353.37 [2143.96-2578.60] 5905.48 [5371.98-6475.46] 6466.34 [5941.08-7057.45] 4690.72 [4312.37-5117.11] -0.85 [-0.90 to -0.80] Southern Latin America 1933.78 [1803.38-2063.45] 7335.49 [6831.33-7839.18] 3048.19 [2868.53-3226.79] 5488.54 [5169.43-5804.81] -1.02 [-1.06 to -0.98] Caribbean 717.38 [667.56–771.10] 4854.63 [4510.32-5228.27] 1452.85 [1353.71-1558.86] 4512.84 [4211.27-4834.47] -0.24 [-0.25 to -0.23] Central Europe 5545.41 [4951.06-6197.18] 6900.09 [6152.63-7724.11] 8950.65 [8104.41-9857.30] 5997.14 [5437.66-6597.67] -0.51 [-0.57 to -0.46] Eastern Europe 7829.10 [6646.78-9095.79] 5119.26 [4345.02-5944.58] 10817.42 [9241.79-12512.12] 5139.59 [4397.83-5935.62] 0.03 [-0.08 to 0.14] Central Asia 1438.17 [1303.60-1585.22] 6384.53 [5799.81-7023.79] 2205.73 [2025.19-2400.02] 6482.26 [5955.79-7053.65] 0.05 [0.01 to 0.09] North Africa and Middle East 3755.21 [3340.12-4205.44] 5138.88 [4555.67-5779.52] 10715.90 [9766.30-11720.22] 5271.50 [4794.17-5781.70] 0.08 [0.07 to 0.09] South Asia 8935.05 [7440.23-10618.20] 3783.70 [3138.46-4515.43] 25525.06 [21767.19-29835.68] 3511.61 [2983.01-4121.15] -0.22 [-0.27 to -0.17] Southeast Asia 7549.72 [6661.59-8514.45] 6899.00 [6066.76-7810.65] 20749.07 [18638.31-23040.42] 6924.44 [6212.84-7700.60] -0.01 [-0.02 to 0.01] East Asia 25784.78 [21972.54-30243.42] 6746.90 [5722.47-7950.09] 112648.56 [97671.71-128917.80] 9234.46 [7993.81-10585.39] 1.05 [0.99 to 1.10] Oceania 68.31 [62.86–73.92] 7040.49 [6447.20-7660.33] 168.82 [157.31-180.27] 6404.79 [5953.84-6861.06] -0.3 [-0.33 to -0.28] Western Sub-Saharan Africa 3211.33 [2853.80-3601.22] 8227.59 [7280.39-9270.24] 6177.1 [5595.68-6809.18] 7786.97 [7025.39-8618.50] -0.21 [-0.22 to -0.2] Eastern Sub-Saharan Africa 2533.68 [2278.18-2810.65] 8593.08 [7683.18-9578.18] 5361.8 [4873.65-5891.02] 8069.08 [7304.26-8902.25] -0.25 [-0.27 to -0.22] Central Sub-Saharan Africa 680.13 [618.03-740.56] 8917.43 [8038.07-9786.78] 1472.62 [1353.27-1590.55] 8075.40 [7379.51-8774.08] -0.35 [-0.39 to -0.31] Southern Sub-Saharan Africa 1325.17 [1140.45-1538.80] 10465.64 [8985.58-12178.11] 2521.62 [2198.42-2879.24] 9822.31 [8529.12-11242.6] -0.26 [-0.29 to -0.23] Deaths Global 30090.27 [27543.40-31870.80] 1594.33 [1447.23-1691.65] 48433.30 [42583.69-52787.43] 979.70 [861.37-1067.78] -1.75 [-1.86 to -1.63] Socio-demographic index High SDI 6887.86 [6132.54-7257.87] 987.21 [875.41-1042.14] 6675.98 [5451.55-7330.49] 394.36 [327.71-430.44] -3.19 [-3.31 to -3.07] High-middle SDI 10581.01 [9701.68-11148.52] 2242.62 [2037.15-2368.57] 14534.97 [12635.3-16108.78] 1225.62 [1065.05-1358.78] -2.29 [-2.53 to -2.06] Middle SDI 7964.95 [7136.34-8811.76] 1963.58 [1747.26-2174.77] 17502.94 [15243.75-19458.04] 1308.92 [1136.09-1455.81] -1.41 [-1.54 to -1.28] Low-middle SDI 3352.17 [3032.60-3669.27] 1394.88 [1253.78-1532.50] 7341.97 [6625.65-7988.62] 1118.03 [1004.08-1218.82] -0.75 [-0.81 to -0.70] Low SDI 1259.71 [1125.26-1400.44] 1511.00 [1339.41-1691.76] 2329.24 [2066.62-2605.36] 1177.32 [1038.51–1321] -0.81 [-0.89 to -0.73] Region High-income Asia Pacific 1321.91 [1160.95-1409.71] 1261.28 [1094.01-1351.56] 1629.93 [1248.74-1853.1] 354.24 [280.65-397.91] -4.30 [-4.45 to -4.15] High-income North America 1281.79 [1100.99-1375.59] 539.27 [463.47-578.63] 1701.49 [1380.10-1868.19] 353.39 [290.27-386.08] -1.75 [-1.98 to -1.52] Western Europe 4345.35 [3864.36-4592.13] 1128.38 [999.22-1194.6] 2954.18 [2395.61-3249.63] 356.19 [293.27–389.80] -3.84 [-3.94 to -3.73] Australasia 116.17 [101.49-126.64] 845.67 [733.92-923.96] 131.38 [106.46-147.36] 307.51 [251.29-344.01] -3.43 [-3.52 to -3.34] Andean Latin America 74.23 [63.94–86.29] 731.95 [630.26-851.12] 137.15 [111.32-166.98] 407.39 [330.71-496.04] -2.15 [-2.34 to -1.95] Tropical Latin America 564.49 [513.03-592.87] 1424.51 [1278.17-1504.07] 813.36 [697.89-880.06] 555.96 [479.02-600.44] -2.81 [-2.90 to -2.72] Central Latin America 292.00 [271.96-304.12] 766.85 [710.61-800.22] 573.26 [499.06-638.89] 410.42 [358.36-457.06] -2.23 [-2.36 to -2.11] Southern Latin America 298.58 [273.60-317.73] 1192.74 [1085.57-1271.58] 269.26 [235.23–294.20] 461.47 [404.84-503.64] -2.75 [-2.89 to -2.61] Caribbean 152.11 [139.31-162.28] 1094.79 [997.66–1169.00] 245.39 [212.45-276.58] 729.56 [633.25-822.05] -1.32 [-1.38 to -1.26] Central Europe 1918.30 [1810.51-1983.90] 2497.08 [2336.69-2591.30] 1782.91 [1589.42-1918.25] 1150.19 [1027.07-1237.45] -2.86 [-3.01 to -2.70] Eastern Europe 3693.16 [3461.34-3806.92] 2577.18 [2394.04-2665.13] 3180.48 [2829.67-3451.98] 1425.44 [1269.90-1547.57] -2.67 [-3.13 to -2.22] Central Asia 391.27 [360.57-409.88] 1704.93 [1568.57-1787.43] 493.82 [442.02-538.24] 1449.85 [1294.80-1581.57] -0.83 [-1.11 to -0.56] North Africa and Middle East 1254.61 [1080.74-1409.24] 1933.08 [1654.93-2175.42] 2325.76 [2007.23-2619.24] 1228.39 [1056.33-1383.6] -1.47 [-1.58 to -1.37] South Asia 2227.01 [1936.84-2492.17] 1036.46 [897.13-1164.37] 5733.80 [5119.88-6338.14] 847.30 [752.93-938.38] -0.74 [-0.86 to -0.63] Southeast Asia 2024.99 [1766.63-2281.34] 1997.91 [1732.15-2262.97] 4723.37 [4194.16-5188.22] 1659.12 [1467.93-1825.17] -0.57 [-0.73 to -0.41] East Asia 8735.83 [7659.50-9806.73] 2756.83 [2398.45-3095.22] 19258.04 [16054.94-22356.85] 1688.34 [1403.79-1960.07] -1.69 [-1.96 to -1.42] Oceania 17.88 [14.53–21.50] 2024.06 [1647.32-2426.26] 40.50 [32.92–48.98] 1632.52 [1324.4-1975.17] -0.76 [-0.80 to -0.72] Western Sub-Saharan Africa 651.25 [567.73-742.34] 1797.61 [1558.67-2054.29] 1056.99 [920.64-1198.66] 1419.55 [1235.83-1608.71] -0.79 [-0.84 to -0.74] Eastern Sub-Saharan Africa 483.03 [426.6-545.91] 1738.39 [1515.59-1984.51] 816.73 [691.24–935.30] 1289.24 [1078.17-1484.24] -1.10 [-1.14 to -1.05] Central Sub-Saharan Africa 122.44 [96.72-153.35] 1761.68 [1389.38-2211.44] 266.39 [197.43-347.58] 1566.17 [1157.94-2055.52] -0.49 [-0.54 to -0.44] Southern Sub-Saharan Africa 123.88 [101.63-141.78] 1029.95 [842.47-1180.59] 299.09 [268.32-326.56] 1251.27 [1113.70-1370.28] 0.75 [0.25 to 1.24] Incidence Global 28152.39 [22887.91-34340.28] 1453.28 [1183.72-1768.28] 56389.95 [46069.51-67838.89] 1140.65 [931.89-1372.24] -1.54 [-2.48 to -0.59] Socio-demographic index High SDI 8128.41 [6541.56-9914.08] 1163.99 [936.52-1421.43] 10584.01 [8723.54-12660.9] 683.20 [560.86-819.89] -2.50 [-3.44 to -1.55] High-middle SDI 9019.60 [7238.69-11074.69] 1847.02 [1486.7-2260.2] 16796.28 [13572.45-20399.31] 1424.17 [1149.66-1731.37] -1.62 [-2.57 to -0.67] Middle SDI 6677.61 [5414.27-8167.44] 1600.95 [1301.32-1949.4] 19735.47 [16114.37-23919.11] 1440.19 [1177.61-1742.61] -1.12 [-2.06 to -0.17] Low-middle SDI 3111.68 [2548.41-3786.97] 1287.01 [1056.59-1559.55] 6982.79 [5775.97-8343.85] 1054.43 [873.92-1256.97] -1.40 [-2.34 to -0.45] Low SDI 1179.10 [967.43-1429.07] 1398.50 [1150.25-1684.93] 2240.26 [1859.25-2668.79] 1116.41 [929.62-1325.62] -1.43 [-2.37 to -0.49] Region High-income Asia Pacific 1482.76 [1144.13-1893.10] 1356.99 [1048.21-1730.53] 2400.72 [1967.61-2885.01] 611.61 [499.33-736.96] -3.62 [-4.63 to -2.6] High-income North America 2213.31 [1664.14-2860.77] 939.28 [706.61-1214.74] 2657.08 [2103.07-3301.22] 588.58 [465.11-731.71] -2.12 [-3.05 to -1.17] Western Europe 4707.96 [3903.65-5558.76] 1227.68 [1016.38-1451.29] 4735.86 [4060.25-5448.65] 639.39 [546.25-738.31] -2.87 [-3.81 to -1.93] Australasia 150.56 [129.45-172.15] 1053.69 [906.81-1204.61] 228.16 [190.6-267.25] 581.40 [484.39-681.54] -2.79 [-3.73 to -1.84] Andean Latin America 81.18 [67.01–97.69] 801.40 [661.80-964.16] 181.49 [151.11-214.92] 540.42 [449.67-640.08] -1.97 [-2.92 to -1.01] Tropical Latin America 517.48 [391.36-671.87] 1263.83 [960.34-1634.03] 1079.39 [837.83-1354.37] 742.53 [576.58–931.40] -2.43 [-3.37 to -1.47] Central Latin America 374.54 [303.97-455.78] 966.94 [786.79-1174.39] 896.68 [742.66-1070.18] 642.87 [532.51-767.22] -2.10 [-3.05 to -1.14] Southern Latin America 292.37 [244.58-346.72] 1137.97 [952.73-1348.98] 389.54 [327.36-455.56] 684.43 [574.52-800.95] -2.44 [-3.39 to -1.48] Caribbean 132.67 [111.78-155.83] 938.11 [791.15-1101.38] 253.46 [213.32-298.54] 761.25 [640.68-896.33] -1.30 [-2.23 to -0.35] Central Europe 1462.66 [1206.88-1738.57] 1883.56 [1557.00-2236.91] 1945.67 [1623.18-2275.62] 1273.73 [1060.36-1492.39] -1.99 [-2.93 to -1.05] Eastern Europe 2952.76 [2203.19-3833.64] 2030.07 [1521.99-2629.31] 3209.67 [2507.84-4051.59] 1453.15 [1130.76-1841.18] -1.76 [-2.68 to -0.83] Central Asia 404.16 [342.57-474.41] 1772.43 [1499.05-2083.68] 621.83 [530.73-720.14] 1834.52 [1565.31-2126.21] -0.39 [-1.31 to 0.54] North Africa and Middle East 869.85 [711.65-1053.6] 1309.02 [1076.18-1576.74] 2067.96 [1702.4-2482.99] 1062.88 [877.74-1272.95] -1.32 [-2.26 to -0.37] South Asia 2439.80 [1951.01-3027.01] 1140.85 [915.23-1408.91] 5832.55 [4748.89-7092.48] 860.77 [702.45-1044.16] -1.80 [-2.75 to -0.85] Southeast Asia 1685.88 [1395.82-2016.81] 1638.63 [1358.29-1956.93] 4157.38 [3472.74-4938.60] 1433.72 [1199.67-1699.85] -1.10 [-2.03 to -0.16] East Asia 7038.56 [5633.96–8704.00] 2095.78 [1679.12-2585.28] 23187.75 [18662.92-28583.95] 1943.31 [1566.37-2391.44] -1.06 [-2.01 to -0.11] Oceania 14.26 [11.94–16.82] 1573.76 [1322.84-1850.92] 33.06 [28.16–38.71] 1290.65 [1102.31-1507.23] -1.32 [-2.26 to -0.38] Western Sub-Saharan Africa 566.29 [463.43-689.58] 1539.07 [1262.67-1865.50] 991.42 [823.79-1180.07] 1305.38 [1086.9-1548.97] -1.15 [-2.08 to -0.21] Eastern Sub-Saharan Africa 464.35 [381.08-561.61] 1635.14 [1345.65-1968.05] 901.15 [746.64-1077.89] 1398.80 [1160.73-1670.13] -1.16 [-2.1 to -0.21] Central Sub-Saharan Africa 123.82 [101.25-149.96] 1704.45 [1401.28-2051.98] 253.43 [209.33-302.99] 1437.81 [1190.3-1715.79] -1.21 [-2.14 to -0.26] Southern Sub-Saharan Africa 177.19 [137.31-224.17] 1423.66 [1103.68-1799.19] 365.69 [285.96-460.98] 1463.69 [1146.86-1839.63] -0.58 [-1.52 to 0.36] DALY Global 459035.24 [425178.80-485509.47] 23314.06 [21478.67-24689.18] 723145.40 [648939.80-782761.26] 14627.67 [13126.65-15833.57] -1.68 [-1.8 to -1.57] Socio-demographic index High SDI 99908.25 [91169.89-105408.29] 14250.62 [12976.72-15048.88] 96352.56 [82554.53-105643.07] 6105.12 [5296.04-6668.05] -2.97 [-3.09 to -2.86] High-middle SDI 156837.38 [145105.26-165329.59] 31558.65 [29013.46-33323.49] 208443.37 [184409.49-230003.92] 17653.85 [15616.3-19486.23] -2.22 [-2.46 to -1.99] Middle SDI 126671.42 [114624.23-139625.44] 28932.85 [26045.96-31915.96] 265022.34 [233041.94-292917.72] 19242.75 [16880.13-21277.26] -1.43 [-1.54 to -1.31] Low-middle SDI 53834.23 [48940.9-58693.67] 21011.08 [19029.69-22970.53] 114846.83 [104871.18-124380.75] 16741.54 [15237.68-18154.64] -0.80 [-0.86 to -0.75] Low SDI 21132.65 [19031.42-23333.55] 23197.57 [20782.96-25743.57] 37791.21 [33710.25-42020.03] 17811.42 [15837.97-19851.06] -0.88 [-0.95 to -0.82] Region High-income Asia Pacific 19839.83 [17838.74-21112.46] 18199.34 [16231.92-19427.15] 23436.18 [19262.15-26340.30] 5740.23 [4828.84-6405.44] -3.94 [-4.09 to -3.79] High-income North America 19317.45 [17190.31-20845.39] 8179.04 [7284.08-8825.39] 25309.48 [21740.00-27811.82] 5512.88 [4776.32-6042.9] -1.63 [-1.83 to -1.43] Western Europe 60025.78 [54561.08-63238.86] 15529.10 [14087.77-16376.47] 39774.85 [33721.48-43676.23] 5201.49 [4473.63-5692.76] -3.66 [-3.77 to -3.55] Australasia 1676.99 [1503.90-1815.71] 11826.15 [10553.14-12826.79] 1784.31 [1504.59-1990.37] 4425.70 [3761.22-4930.52] -3.36 [-3.45 to -3.27] Andean Latin America 1113.15 [966.53-1289.2] 10916.9 [9477.69-12647.75] 2024.61 [1674.9-2434.02] 6092.33 [5038.85-7326.03] -2.16 [-2.36 to -1.96] Tropical Latin America 8570.25 [7913.53-8965.68] 20428.10 [18689.55-21447.13] 11866.12 [10489.47-12725.28] 8197.04 [7264.68-8780.63] -2.78 [-2.86 to -2.71] Central Latin America 4304.87 [4050.20-4483.77] 10947.09 [10265.47-11414.53] 8419.96 [7485.31-9333.31] 6097.83 [5429.32-6756.34] -2.15 [-2.26 to -2.04] Southern Latin America 4529.59 [4199.74-4800.72] 17528.16 [16183.17-18599.16] 3991.43 [3573.96-4333.6] 7032.88 [6316.22-7629.46] -2.72 [-2.84 to -2.59] Caribbean 2265.56 [2088.55-2416.04] 15730.63 [14455.24-16786.20] 3516.13 [3073.62-3945.07] 10771.86 [9426.32-12085.44] -1.25 [-1.3 to -1.20] Central Europe 27651.68 [26310.26-28592.66] 34933.50 [33058.30-36207.46] 24653.62 [22298.78-26422.99] 16199.38 [14672.13-17360.64] -2.87 [-3.03 to -2.70] Eastern Europe 52777.50 [49949.23-54384.40] 35393.43 [33292.71-36551.13] 43622.18 [39412.22-47168.57] 20001.44 [18099-21630.16] -2.58 [-3.03 to -2.14] Central Asia 5796.07 [5395.98-6067.23] 25409.33 [23644.81-26602.52] 7343.08 [6652.61-7977.67] 21541.85 [19492.68-23420.27] -0.88 [-1.17 to -0.59] North Africa and Middle East 18983.94 [16533.51-21239.29] 27581.62 [23907.04-30906.35] 34175.29 [29762.86–38319.00] 17411.34 [15122.60-19522.32] -1.54 [-1.63 to -1.45] South Asia 36558.82 [31998.00-40731.94] 15843.28 [13824.77-17696.19] 90940.09 [81825.55-100052.92] 12735.86 [11423.28-14034.01] -0.81 [-0.9 to -0.72] Southeast Asia 32029.49 [28215.93-35749.88] 30033.52 [26347.15-33655.63] 73185.58 [65685.53-79862.08] 24861.57 [22257.29-27156.85] -0.61 [-0.75 to -0.46] East Asia 140451.37 [123998.81-157355.55] 39480.62 [34686.98-44221.39] 289231.15 [243698.54-333010.3] 24365.63 [20492.77-28053.86] -1.66 [-1.89 to -1.43] Oceania 305.16 [250.35-364.98] 30832.65 [25345.28-36764.10] 663.78 [545.56-796.29] 24892.71 [20445.96-29857.19] -0.76 [-0.79 to -0.73] Western Sub-Saharan Africa 10514.61 [9265.00-11879.95] 27077.57 [23765.51-30653.94] 16734.65 [14699.25-18925.26] 21261.96 [18680.92-24017.88] -0.81 [-0.87 to -0.76] Eastern Sub-Saharan Africa 8216.48 [7302.65-9207.6] 27019.19 [23816.97-30469.44] 13403.91 [11561.15-15188.68] 19954.09 [17087.37-22693.76] -1.13 [-1.18 to -1.08] Central Sub-Saharan Africa 2145.41 [1716.79-2663.40] 27441.02 [21968.02-34082.15] 4382.02 [3298.6-5629.08] 23993.92 [18061.86-30953.46] -0.54 [-0.59 to -0.49] Southern Sub-Saharan Africa 1961.24 [1641.78-2224.35] 15680.34 [13102.73-17807.14] 4686.99 [4253.4-5086.78] 18545.42 [16737.13-20168.83] 0.63 [0.17 to 1.10] CR, Crude Rate Trends in disease burden from 1990 to 2021 From 1990 to 2020, epidemiological indicators of the global stroke burden showed significant temporal trends, as shown in Fig. 3 . crude prevalence rate (CPR), CIR, crude death rate (CDR), and DALYs all exhibited an overall downward trend across all SDI regions (High, Middle, and Low). However, the absolute burden for men has consistently been higher than that for women, indicating the persistent influence of gender differences in stroke risk. Notably, the extent of decline also varies across different SDI regions. The decline was most pronounced in High SDI regions, while Low SDI regions showed some improvement but still had a high baseline burden, highlighting the inequitable distribution of healthcare resources. Despite the overall improvement in global indicators, the burden of stroke increases exponentially with age. High SDI regions partially offset the risks associated with ageing through more effective interventions (such as hypertension management and acute-phase treatment), while low SDI regions, due to limited medical resources, saw a slower decline in the disease burden among the elderly population. The interaction between population ageing and regional development disparities continues to reshape the distribution of disease burden, and improvements over time have not fully eliminated the combined negative effects of age and SDI. 2021 Global Burden of Disease Distribution Map Figure 4 illustrates the global distribution of the prevalence, incidence, mortality, and DALY rate of stroke among the elderly population in 2021. The highest prevalence was in Botswana, the highest incidence was in Uzbekistan, Tajikistan, and Belarus, the highest mortality was in Montenegro and Serbia, and the highest DALY rate was in Montenegro and North Macedonia. A. Prevalence Rate. B. Incidence Rate. C. Mortality Rate. D. Disability-Adjusted Life Years (DALYs) Rate Age group and SDI regional stratification analysis Age-related trends and sociodemographic differences in incidence rates, mortality rates, and DALYs across High, Middle, and Low SDI regions are shown in Fig. 5 . All SDI regions exhibit a consistent upward trend in incidence rates with increasing age. However, the rates of increase vary significantly, with High SDI regions showing a relatively slower upward trend, while Low SDI regions exhibit a steeper upward trend. Mortality rates align with incidence trends, showing an upward trend across all SDI strata with increasing age. Significant differences exist in mortality rates, with Low SDI regions consistently recording the highest mortality rates. DALYs exhibit a progressive increase with age, reflecting the worsening of the disease burden. Notably, the accumulation of DALYs is slower in High SDI regions, while Low SDI regions exhibit a steeper upward trend. Ranking of risk factors Figure 6 shows the percentage of health loss attributable to various risk factors among individuals aged 70 and above in 2021, as a proportion of total DALYs. In most regions, high systolic blood pressure is the primary risk factor contributing to health loss, with values nearly uniformly at 10 across all regions, indicating its highly significant impact. High LDL cholesterol and high fasting plasma glucose also have a significant impact in most regions, with values typically ranging from 8 to 10. Low temperature, particularly in regions such as East Asia and Eastern Europe, has a notable impact, with values reaching 7 or higher. Diet-related factors, such as insufficient intake of whole grains, vegetables, and fruits, also have significant values in some regions. In regions such as East Asia, Eastern Europe, and Central Asia, the impact of hypertension, high LDL cholesterol, and high fasting plasma glucose is particularly pronounced, with values often reaching 10. In regions such as South Asia and Sub-Saharan Africa, the impact of tobacco, low temperature, and diet-related factors is relatively greater. Age-period-cohort effects and the role of ageing mechanisms Age-related biological cumulative effects are manifested by upward trends in both longitudinal and cross-sectional age curves, indicating that stroke incidence and mortality rates increase exponentially with age. This may reflect the cumulative effects of vascular aging and multiple comorbidities (Table 1 and Fig. 6 ), and also confirms that aging is an independent risk factor for stroke (Table 2 and Fig. 5 ), associated with age-related pathophysiological changes. However, societal progress moderates the impact of aging, with the decline in period RR and cohort RR reflecting how medical advancements and public health interventions have reduced stroke risk among age-matched populations over time. Specifically, later-born cohorts have lower risks even at advanced ages compared to earlier-born cohorts. However, in the progression of aging and societal progress, local drift increases are observed in older age groups (e.g., ≥ 80 years), suggesting that the physiological decline caused by ageing may eventually outweigh the benefits of medical interventions. Furthermore, the period bias reached its peak between 2000 and 2010, corresponding to the period of accelerated global ageing, reflecting the lag in the healthcare system's response to sudden changes in population structure. Additionally, cohort effects reveal early-life exposure patterns, with a decline in fitted cohort models suggesting that reduced risk in recent cohorts may be associated with improved early-life nutrition and reduced childhood infections. Specific details can be seen in Fig. 7 . Forecast for 2021–2040 It is projected that the global burden of stroke will undergo significant changes from 2021 to 2040, with varying trends across different indicators. Figure 8 (A) and Fig. 8 (B) present the actual data on stroke incidence and mortality rates among individuals aged 70 and above from 1990 to 2021, as well as the projected trends beyond 2021. The data indicate that both incidence and mortality rates have shown a declining trend over the past 31 years, and are projected to continue declining in the future, though the rate of decline may gradually slow. By 2040, the lowest incidence and mortality rates are projected for the 70–74 age group (Death predictions and incidence predictions in supplementary materials). A. Incidence Rate. B. Mortality Rate. Discussion The global burden of stroke among adults aged 70 and older vividly illustrates the complex interplay between intrinsic ageing mechanisms and late-life diseases, as emphasised by Partridge [ 15 ] and his team. While advances in public health have extended lifespan, healthy longevity—that is, survival free from severe disability—has not kept pace [ 16 ] , placing unsustainable economic pressure on healthcare systems. Our MIMIC-IV analysis validated this disparity: elderly stroke patients faced significantly higher mortality rates (22.0% vs 12.7%, p < 0.001), more severe comorbidities, and over twice the length of ICU stays (median 7.3 days vs 3.1 days), directly leading to increased treatment costs per episode (estimated average medical costs per stroke ranging from 5,798.15 to 140,048 euros [ 17 ] ). This clinical vulnerability results in sustained high burdens, particularly in resource-limited settings, where over 90% of stroke deaths occur [ 18 ] . Crucially, irreversible ageing processes—vascular stiffening, cellular ageing, systemic inflammation [ 19 – 21 ] —interact with modifiable risk factors (e.g., hypertension, Fig. 6 ), further exacerbating the disease burden. By 2019, global stroke-related costs had exceeded 721 billion dollars [ 22 ] , with the economic burden particularly severe among those aged ≥ 70 years due to long-term disability care. However, research on the burden among the elderly population remains limited. Therefore, we integrated MIMIC-IV clinical models with GBD 2021 data to quantify global differences in incidence, mortality, and DALYs across different SDI strata, analyse the contribution of ageing to burden growth, and identify region-specific risk mitigation pathways. This study confirmed significant differences in stroke burden across regions with varying levels of SDI, with stroke prevalence and mortality rates being lower in High SDI regions compared to Low-middle SDI regions—a finding consistent with the results of Li et al.'s study [ 10 ] . This gradient difference likely reflects more developed medical infrastructure, more effective prevention programmes, and higher public awareness in High SDI regions [ 23 , 24 ] . Particularly concerning is the situation in East Asia, where the stroke incidence rate among adults aged 70 and older surged by 61% between 1990 and 2021 (EAPC + 1.05%), far exceeding the global downward trend (EAPC − 0.11%). This anomaly stems from a ‘double burden’: accelerated population ageing (e.g., 29% of the population aged 65 and older in Japan, 14% in China [ 25 ] ) and persistent metabolic risks (hypertension, hyperlipidaemia, and dietary patterns). Crucially, ageing leads to microcirculatory dysfunction and arteriosclerosis [ 26 ] , providing a pathological basis for stroke even in the absence of high-risk exposure. Hypertension and metabolic syndrome represent a cluster of risk phenotypes. In East Asian populations, the prevalence of arterial hypertension (35–45%) and visceral obesity is significantly elevated—both of which are closely associated with insulin resistance [ 27 ] —thereby forming a unique risk profile. Our MIMIC-IV analysis revealed this synergistic effect: elderly stroke patients commonly exhibit a ‘metabolic triad’ of hyperlipidaemia (53.9%), hypertension (53.1%), and type 2 diabetes (36.4%). This convergence accelerates the progression of atherosclerosis, primarily through physiological and pathological mechanisms such as plaque instability, blood flow obstruction, thrombosis, and plaque proliferation [ 28 – 30 ] . In the elderly population, these pathophysiological mechanisms exacerbate age-related vascular fragility, leading to increased stroke incidence and mortality. The MONICA project confirmed that the synergistic effect of smoking and hypertension is a key driver of incidence differences [ 31 ] . Crucially, the context of ‘ageing before affluence’ in East Asia exacerbates these risks—as reflected in our MIMIC-IV cohort by higher disease severity: SOFA score [5.0 (3.0–7.0) vs. 4.0 (2.0–7.0), p = 0.037], and increased in-hospital mortality (16.3% vs. 12.2%, p = 0.008). Our research indicates that elevated systolic blood pressure is the primary risk factor for stroke-related health loss in the global elderly population—a finding attributable to rapid urbanisation, changes in dietary patterns (such as high sodium intake), and insufficient physical activity [ 26 ] . This conclusion was validated by MIMIC-IV data: 53.1% of elderly stroke patients had hypertension, and the 28-day mortality risk for hypertensive patients was 63% higher than for non-hypertensive patients (adjusted hazard ratio [aHR] = 1.63, 95% confidence interval [CI]: 1.29–2.05). The Global Stroke Registry (despite methodological heterogeneity in definitions and time points) consistently validated hypertension as the primary risk factor (prevalence range: 45%-72%), followed by diabetes (18%-40%) and smoking (15%-35%) [ 32 – 34 ] . Notably, synergistic risk factors in specific environments warrant attention. Metabolic synergism primarily involves high LDL cholesterol and fasting blood glucose (particularly significant in East Asia and Eastern Europe, see Fig. 6 ), while environmental-behavioural risks primarily involve smoking and exposure to low environmental temperatures (particularly critical in South Asia and Sub-Saharan Africa). Although crude mortality rates increase with age among adults aged 70 and older, longitudinal data indicate a declining trend in relative risk—this paradox can be attributed to medical advancements such as endovascular thrombectomy [ 35 ] and community-based hypertension management programmes [ 36 ] . However, the GBD 2021 projections warn that the declining trend in mortality rates will slow due to accelerated population ageing, a trend exacerbated by the dual impact of COVID-19. During the COVID-19 pandemic, healthcare systems collapsed (with a 39% reduction in stroke imaging studies [ 37 ] , a 50–70% decrease in global hospitalisation rates [ 38 ] , and a 32% increase in delayed treatment [ 39 ] ; reduced emergency medical service calls related to stroke were associated with excess mortality [ 40 ] ); second, direct biological stimulation (severe COVID patients had a 3-fold increased risk of ischaemic stroke [ 41 ] ). This multidimensional crisis, stemming from resource allocation, avoidance of medical care, and virus-induced thrombosis, highlights critical vulnerabilities in an ageing society. Our research findings reveal a dual challenge. Population ageing will inevitably lead to an absolute increase in stroke incidence, while the long-term care needs of stroke survivors will place pressure on the healthcare system. This necessitates the establishment of a comprehensive ‘prevention-emergency care-rehabilitation’ management system [44, 45] , with priority intervention measures including strengthening community-based primary prevention (e.g., hypertension screening), optimising regional acute care networks (e.g., thrombectomy centres), and promoting cost-effective innovative measures (e.g., AI-guided remote rehabilitation [ 42 , 43 ] ). From an economic perspective, this burden will be exacerbated by the conflict between increasing rehabilitation needs and a declining working-age population (15–64 years) [48] . While the adoption of thrombolytic therapy has controlled costs per case, demographic pressures will increase cumulative expenditures by 40–60% by 2050 [ 12 ] . Therefore, developing equitable, cost-effective strategies, particularly resource allocation based on SDI stratification, is critical for sustainable stroke care. This study provides the first systematic assessment of stroke burden in the population aged ≥ 70 years, addressing a critical research gap. However, several limitations should be noted: the GBD estimates may underrepresent true disease burden in some regions, while the single-center MIMIC-IV data carry selection bias; the risk factor analysis omitted genetic markers and emerging environmental exposures; and the predictive models didn't account for public health emergencies. Future studies should integrate multi-source longitudinal data with molecular epidemiological approaches to better elucidate the biological mechanisms of aging-related stroke. Conclusion There are significant regional differences in the disease burden of stroke among the global elderly population, which is closely related to SDI and exposure to controllable risk factors. High SDI regions have significantly reduced the disease burden through advanced medical interventions, while Low-middle SDI regions urgently need strategic investment in two core areas (optimisation of medical resources, scaling up prevention programmes, and cost-effective innovation, etc.). Declarations Acknowledgements We sincerely acknowledge the contributions of the Global Burden of Disease Study 2021 collaborators and the MIMIC-IV database team for providing critical data resources that made this research possible. Author contributions All authors have made substantial contributions to this study. J T and Xh Z were responsible for the conceptualization and methodology of the research. D Y and Y G conducted the investigation and data curation. Y H and Xy C contributed to both aspects of the study. All authors were involved in (1) the conception and design of the study, or acquisition of data, or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content, and (3) final approval of the version to be submitted. Jing Tian accessed and verified the underlying data. The authors declare that this manuscript, including related data, figures, and tables, has not been previously published and is not under consideration elsewhere. Data availability No datasets were generated or analysed during the current study. Competing interests The authors declare no competing interests. Ethics approval declaration This study uses publicly available data from the Global Burden of Disease (GBD) database. The GBD data are anonymized and aggregated at the population level, and no individual-level information is included. Ethical approval and informed consent were not required for the use of these data. Access to the GBD database complies with all applicable data use agreements and guidelines provided by the Institute for Health Metrics and Evaluation (IHME). All clinical data in this study were obtained from the Medical Intensive Care Unit Information Market (MIMIC-IV) database, which was compiled from the electronic health records of the Beth Israel Deaconess Medical Centre (BIDMC) and is publicly accessible. The study author (J T) obtained the necessary authorisation to access the database. It is important to note that this study focuses on the analysis of a third-party open-access database approved by an institutional review board (IRB). Therefore, the IRB review process at our institution was deemed exempt. Human Ethics and Consent to Participate declarations Not applicable. Informed consent This study did not require informed consent, as it utilized publicly available data that did not contain confidential or personally identifiable information. Ethics The Institutional Review Board approved the exemption for this study because it used publicly available data that did not contain confidential or personally identifiable patient information. Ethics approval and consent to participate Not applicable. Acknowledgements Not applicable. Clinical trial number Not applicable. Consent for publication All authors agree to publish. Funding Outstanding Youth Project of Heilongjiang Natural Science Foundation (No. JQ2024H004). Harbin Medical University Young Talent Project. HMUMIF-24009. References FEIGIN V L, BRAININ M. World Stroke Organization: Global Stroke Fact Sheet 2025[J]. Int J Stroke. 2025;20(2):132–44. FEIGIN V L, OWOLABI M O FEIGINVL, et al. Pragmatic solutions to reduce the global burden of stroke: a World Stroke Organization–Lancet Neurology Commission[J]. Lancet Neurol. 2023;22(12):1160–206. RUNDEK T, TOLEA M, ARIKO T, et al. Vascular Cognitive Impairment (VCI)[J]. Neurotherapeutics. 2022;19(1):68–88. 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Supplementary Files S.zip Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Nov, 2025 Reviews received at journal 28 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviews received at journal 15 Oct, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers invited by journal 13 Oct, 2025 Editor invited by journal 17 Sep, 2025 Editor assigned by journal 13 Sep, 2025 Submission checks completed at journal 13 Sep, 2025 First submitted to journal 11 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7589290","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533951511,"identity":"b83d4d3f-6fd0-410d-a5aa-47780d7226bd","order_by":0,"name":"Jing Tian","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Tian","suffix":""},{"id":533951513,"identity":"8433a122-8040-4272-a8e9-145820bb25e2","order_by":1,"name":"Xinhui 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15:46:39","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":178273,"visible":true,"origin":"","legend":"","description":"","filename":"0d47353c2bb041cf8c1841dc3de7c0151structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7589290/v1/b2934760a371c63971cb2037.xml"},{"id":94474000,"identity":"23fb45ae-1a88-4095-ae0e-93f7fac0b67d","added_by":"auto","created_at":"2025-10-27 15:46:36","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":182606,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7589290/v1/ff9493b9cd0ee7d71b6d6791.html"},{"id":94474151,"identity":"fa4f63bf-7643-47c8-8256-a3ca8c20d3f4","added_by":"auto","created_at":"2025-10-27 15:47:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":112265,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier Survival Curves: Comparison of 28-Day Mortality Between Elderly (≥70 Years) and Non-elderly (\u0026lt;70 Years) Stroke Patients Admitted to ICU\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7589290/v1/ac4868d7b48a27224a85a534.jpg"},{"id":94473835,"identity":"f3f2586f-0689-4bc4-9ded-20a1cc8e52dc","added_by":"auto","created_at":"2025-10-27 15:45:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":569437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative Analysis of Comorbidity Burden and Disease Severity Scores Between Elderly (≥70 Years) and Non-elderly (\u0026lt;70 Years) ICU Stroke Patients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7589290/v1/9c22d77ffa95e62e32a62666.jpg"},{"id":94474040,"identity":"31a1bd8b-b43b-4e2b-972d-3e904ed5f336","added_by":"auto","created_at":"2025-10-27 15:46:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2240576,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal Trends of Global Stroke Burden (1990–2021): Prevalence, Incidence, Mortality, and DALYs Stratified by Socio-Demographic Index (SDI) and Gender\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7589290/v1/62bd262f05009465450bbe7e.jpg"},{"id":94474334,"identity":"5f141e16-c8bc-480b-9d0e-6420c6330c25","added_by":"auto","created_at":"2025-10-27 15:48:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":474658,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal Distribution of Stroke Burden in the Elderly (≥70 Years) Across 204 Countries and Regions in 2021\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7589290/v1/3bccb396a929660fff00bef0.png"},{"id":94474154,"identity":"9e402a0f-bc73-420e-bbcc-b43161028fbd","added_by":"auto","created_at":"2025-10-27 15:47:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1148127,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge-Specific Trends of Stroke Incidence, Mortality, and DALYs in 2021: Stratification by Socio-Demographic Index (High, Middle, Low SDI Regions)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7589290/v1/d974816479f46981a37b94a9.jpg"},{"id":94473741,"identity":"94853dc8-65b5-4115-8a73-37a4f9691271","added_by":"auto","created_at":"2025-10-27 15:45:26","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1391207,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of Risk Factor Attribution for Stroke DALYs in the Elderly (≥70 Years) Across 21 GBD Regions in 2021 (Percentage of Total DALYs)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7589290/v1/69fc75bdeb065f626cd43ec7.jpg"},{"id":94473745,"identity":"4dacbf4d-d846-4cb2-87bb-74c6f1a68087","added_by":"auto","created_at":"2025-10-27 15:45:28","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":690244,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge-Period-Cohort Effects on Stroke Incidence and Mortality in the Elderly (≥70 Years): Longitudinal and Cross-Sectional Analyses with Temporal Trends\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7589290/v1/64fd36bc007ed81f8788661b.jpg"},{"id":94474161,"identity":"d7142150-7ed6-4739-865e-339f048638fd","added_by":"auto","created_at":"2025-10-27 15:47:46","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":235146,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistorical Trends (1990–2021) and Projections (2021–2040) of Stroke Incidence and Mortality in the Elderly (≥70 Years)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. Incidence Rate. B. Mortality Rate.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7589290/v1/0376adb094b067616470d20b.jpg"},{"id":94490365,"identity":"6c7a6f5b-1870-4e28-b29d-e80df2150c84","added_by":"auto","created_at":"2025-10-27 17:09:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8999220,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7589290/v1/962e2903-8610-4224-838c-ae86d219960e.pdf"},{"id":94473594,"identity":"a54c57c5-8fb3-422c-861f-6c2e84af2a33","added_by":"auto","created_at":"2025-10-27 15:44:55","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":166664,"visible":true,"origin":"","legend":"","description":"","filename":"S.zip","url":"https://assets-eu.researchsquare.com/files/rs-7589290/v1/c4e72b08be97d2f3163413d8.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Stroke Crisis in Ageing Societies: Global Trends, Risk Factors, and Clinical Validation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke has become a major challenge in the field of global public health. According to the latest report from the World Stroke Organisation, the global economic burden of stroke exceeds 890\u0026nbsp;billion US dollars (accounting for 0.66% of global GDP). From 1990 to 2021, the economic burden of stroke (measured by absolute case numbers) has significantly increased (stroke incidence increased by 70.0%, stroke mortality increased by 44.0%, stroke prevalence increased by 86.0%, and disability-adjusted life years (DALYs) increased by 32%), with approximately 85% of cases occurring in low- and middle-income countries\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Notably, age-specific epidemiological data indicate that the incidence of stroke is significantly higher in individuals aged 70 years and older compared to those aged 60\u0026ndash;69 years\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. This age-related increase in risk is associated with multiple pathophysiological mechanisms, including endothelial dysfunction, impaired blood-brain barrier integrity, and enhanced neuroinflammatory responses\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCurrently, global population ageing is altering the epidemiological characteristics of stroke. Previous studies have shown that the clustering of traditional risk factors in people aged 70 and above has been on the rise from 1990 to 2020, and has been proven to significantly affect the risk of stroke in the elderly population\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. More notably, age-specific risk factors such as frailty syndrome and polypharmacy are significantly associated with poor outcomes\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIt is important to note that the burden of stroke exhibits marked regional disparities, which are closely linked to socioeconomic development. The Socio-Demographic Index (SDI), a composite indicator integrating per capita income, educational attainment, and fertility rates, provides a valuable framework for understanding these disparities\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Regions with different SDI levels (ranging from low to high) show distinct patterns in stroke incidence, mortality, and risk factor profiles, reflecting the influence of healthcare infrastructure, public health policies, and living conditions on disease outcomes\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMost existing epidemiological studies analyse the population aged over 60 as a whole\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, and the Global Burden of Disease study has not yet systematically assessed the risk factor attribution scores for the population aged 70 and above\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In addition, there is a lack of long-term prediction models that take population structure changes into account\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Therefore, this study fills the research gap in the epidemiology of stroke in the elderly population based on MIMIC-IV and GBD 2021 data and provides scientific reference for stroke prevention and control in the global ageing society.\u003c/p\u003e"},{"header":"Methodologies and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData sources\u003c/h2\u003e\u003cp\u003eThe MIT Computational Physiology Laboratory established and maintained the large-scale MIMIC-IV database, which contained health-related data used in this retrospective investigation. This database contains comprehensive, excellent medical records of patients hospitalized to the Beth Israel Deaconess Medical Center's intensive care units. The authors were obliged to complete the training course to obtain access to the database. All clinical data for stroke patients were obtained from the MIMIC-IV database (See Appendix for the discharge process flow chart).\u003c/p\u003e\u003cp\u003eData on stroke from 1990 to 2021 were retrieved using the Global Health Data Exchange (GHDx) query tool. The dataset included information on mortality, incidence, and disability-adjusted life years (DALYs), as well as potential risk factors for stroke. The research methods used by the GBD have been extensively described in the existing literature. The SDI is a composite indicator that integrates per capita income distribution with the educational attainment of individuals aged 15 and older to represent regional economic conditions\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The SDI is scored on a scale from 0 to 1, with higher values indicating greater socioeconomic development. The regional classification based on SDI includes five categories: low (\u0026lt;\u0026thinsp;0.46), lower-middle (0.46\u0026ndash;0.60), middle (0.61\u0026ndash;0.69), upper-middle (0.70\u0026ndash;0.81), and high (\u0026gt;\u0026thinsp;0.81)\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for normally distributed continuous variables or median [interquartile range, IQR] for non-normally distributed variables, and as frequencies (percentages) for categorical variables. Normality of distribution was assessed using the Shapiro-Wilk test. Group comparisons were performed using the chi-square test or Fisher's exact test for categorical variables, and the unpaired t-test or Mann-Whitney U test for continuous variables as appropriate.\u003c/p\u003e\u003cp\u003eIn the MIMIC - IV cohort, regarding the 28 - day mortality, we initially compared the Kaplan - Meier (K-M) survival curves. Subsequently, a multivariate Cox proportional hazards regression analysis was performed. Four sequential models were constructed: Model 1 (unadjusted), Model 2 (adjusted for gender and body mass index (BMI)), Model 3 (additionally adjusted for comorbidities based on Model 2), and Model 4 (comprehensively adjusted for demographic characteristics, comorbidities, organ failure scores, and vital signs).\u003c/p\u003e\u003cp\u003eIn GBD data, in order to analyze changes in Crude Rate (CR) over time, we utilized Estimated Annual Percentage Change (EAPC) values. The EAPC was determined using the formula y\u0026thinsp;=\u0026thinsp;α\u0026thinsp;+\u0026thinsp;βx\u0026thinsp;+\u0026thinsp;ε, where y represents the natural logarithm of the CR and x corresponds to the calendar year. Subsequently, the EAPC was calculated as 100 \u0026times; (exp(β)\u0026thinsp;\u0026minus;\u0026thinsp;1). A positive EAPC and its corresponding 95% confidence intervals (CIs) signifies an increasing trend in CR, whereas a negative EAPC and its corresponding 95% CIs indicate a decreasing trend in CR.\u003c/p\u003e\u003cp\u003eFor future burden prediction (up to 2040), we used Bayesian Age Period Queue (BAPC) model and Ensemble Nested Laplacian Approximation (INLA). This model decomposes time changes into age, period, and cohort effects, and generates predictions with 95% uncertainty intervals (UI) through Monte Carlo simulation.\u003c/p\u003e\u003cp\u003eRisk factor attribution analysis is based on the GBD comparative risk assessment framework, which visualizes the percentage of attribution of 21 risk factors to stroke DALYs through heat maps\u003c/p\u003e\u003cp\u003eMIMIC-IV analysis was conducted using R 4.5.0 software (survival package for Cox regression, ggplot2 package for plotting), while GBD analysis was based on R 4.5.0 (INLA package for running BAPC model, gbdR package for data processing) and WHO Health Equity Assessment Kit (HEAT).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eClinical Characteristics of Elderly Stroke Patients (\u0026ge;\u0026thinsp;70 Years Old)\u003c/h2\u003e\u003cp\u003eThe study included 2,144 stroke patients admitted to the intensive care unit (ICU), stratified according to the key age threshold of 70 years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Baseline characteristics revealed significant differences between the elderly group (\u0026ge;\u0026thinsp;70 years old, n\u0026thinsp;=\u0026thinsp;1,242) and the non-elderly group (\u0026lt;\u0026thinsp;70 years old, n\u0026thinsp;=\u0026thinsp;902).\u003c/p\u003e\u003cp\u003eCritically ill elderly stroke patients exhibited significantly higher comorbidity burdens, including type 2 diabetes (36.4% vs 30.0%, p\u0026thinsp;=\u0026thinsp;0.002), CKD (27.5% vs 15.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hyperlipidaemia (53.9% vs 41.8%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and AHF (41.5% vs 24.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At ICU admission, elderly patients presented with greater disease severity, evidenced by elevated organ failure scores: SOFA (median 5.0 vs 4.0, p\u0026thinsp;=\u0026thinsp;0.037), SAPS II (42 vs 33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and OASIS (35 vs 32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This translated to poorer short-term outcomes, with higher in-hospital mortality (16.3% vs 12.2%, p\u0026thinsp;=\u0026thinsp;0.008), ICU mortality (11.1% vs 8.1%, p\u0026thinsp;=\u0026thinsp;0.021), and 28-day mortality (22.0% vs 12.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese findings highlight that \u0026ge;\u0026thinsp;70-year-old stroke patients requiring ICU care constitute a high-risk subgroup with amplified resource demands due to compounded comorbidities and severity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eBaseline Characteristics of Stroke Patients Stratified by Age Threshold (70 Years)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-elderly group, n\u0026thinsp;=\u0026thinsp;902\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eElderly group, n\u0026thinsp;=\u0026thinsp;1,242\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (54, 66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (74, 85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e574 (63.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e667 (53.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e328 (36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e575 (46.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m^2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (24, 33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (24, 31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e454 (50.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e660 (53.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType 2 diabetes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e271 (30.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e452 (36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType 1 diabetes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141 (15.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e342 (27.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidaemia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e377 (41.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e669 (53.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAHF, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e515 (41.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMI, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARF, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e247 (27.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e431 (34.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSeverity index\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.0 (2.0, 7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.0 (3.0, 7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPS III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (28, 55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (33, 59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00 (2.00, 3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00 (2.00, 3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAPS II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (26, 42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (35, 50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOASIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (26, 38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (29, 41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.0 (13.0, 15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.0 (12.0, 15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVital Signs\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR, bpm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (74, 95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81 (71, 93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRR, bpm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.0 (14.0, 21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.0 (14.0, 21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.795\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO\u003csub\u003e2, %\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.00 (97.00, 100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99.00 (96.00, 100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121 (105, 140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123 (106, 141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (57, 80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (53, 74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMV use, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e570 (63.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e708 (57.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMV time, hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (0, 57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (0, 31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elength of hospital stay, days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (6, 17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (5, 14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU length of stay, days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.6 (1.9, 7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.1 (1.9, 6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eall-cause mortality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e285 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e617 (49.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIn-hospital death, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e202 (16.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU dead, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73 (8.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCKD, Chronic Kidney Disease, AHF, Acute Heart Failure, AMI, Acute Myocardial Infarction, COPD, Chronic Obstructive Pulmonary Disease, ARF, Acute Renal Failure, SOFA, Sequential Organ Failure Assessment, APS III, Acute Physiology Score III, SIRS, Systemic Inflammatory Response Syndrome, SAPS II, Simplified Acute Physiology Score II, OASIS, Outcome and Assessment Information Set, GCS, Glasgow Coma Scale, HR, Heart Rate, RR, respiratory rate, SBP, Systolic blood pressure, DBP, Diastolic blood pressure, MV, Mechanical ventilation\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eA. Prevalence of Key Comorbidities. B. Stratification of Disease Severity Indices\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMultivariable-adjusted hazard of 28-day mortality in elderly stroke patients\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the multivariate Cox regression analysis of 28-day mortality in critically ill stroke patients. After full adjustment for clinical confounders in Model 4 (including organ failure scores, vital signs, and comorbidities), age\u0026thinsp;\u0026ge;\u0026thinsp;70 years remained independently associated with increased mortality risk, with an adjusted hazard ratio (HR) of 1.36 (95% CI: 1.05\u0026ndash;1.76; p\u0026thinsp;=\u0026thinsp;0.018). This elevated mortality risk, combined with extended resource utilization observed in elderly ICU patients (such as higher disease severity scores and comorbidity burden, as well as longer ICU stays), suggests amplified healthcare costs for this subgroup.\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\u003eMultivariable Cox regression analysis of 28-day mortality in ICU-treated stroke patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Continuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03 (1.02\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01 (1.02\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.02 (1.01\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.01 (1.01\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;70 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;70 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.81 (1.45\u0026ndash;2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.63 (1.29\u0026ndash;2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.63 (1.29\u0026ndash;2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.36 (1.05\u0026ndash;1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 1: Crude\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 2: Adjust Gender, BMI\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 3: Adjust Gender, BMI, Hypertension, Type 2 diabetes, Type 1 diabetes, CKD, Hyperlipidaemia, AHF, AMI, COPD, ARF\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 4: Adjust Gender, BMI, Hypertension, Type 2 diabetes, Type 1 diabetes, CKD, Hyperlipidaemia, AHF, AMI, COPD, ARF, SOFA, APS III, SIRS, SAPS II, OASIS, GCS, HR, RR, SpO2, SBP, DBP\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eThe burden of stroke in people aged 70 and above in 2021\u003c/h3\u003e\n\u003cp\u003eGlobal disease burden data (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicate that between 1990 and 2021, the absolute number of global stroke cases (per 100,000 population) increased substantially from 135,201 to 327,672, while the age-standardized prevalence rate declined slightly (estimated annual percentage change, EAPC = -0.11%). Trends diverged regionally: High SDI regions showed the steepest decline (EAPC = -0.53%), contrasting with increased rates in Middle SDI regions (EAPC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.42%). East Asia saw a 61% rise in crude incidence rate (CIR) \u0026ndash; equivalent to an EAPC of +\u0026thinsp;1.05%.\u003c/p\u003e\u003cp\u003eMortality improvements were significant globally (EAPC = -1.75%), with high-income Asia Pacific achieving exceptional progress (EAPC = -4.3%), reducing mortality to 28% of its 1990 level. Low SDI regions lagged (EAPC = -0.81%), and sub-Saharan Africa faced persistent high burdens. Alarmingly, Southern sub-Saharan Africa recorded rising DALY rates (EAPC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.63%).\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\u003eGlobal stroke burden trends (1990\u0026ndash;2021): Prevalence, incidence, mortality, and DALYs\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=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\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\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEAPC_95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNumber CR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eNumber CR\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\u003ePrevalence\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\u003eGlobal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135201.07 [121908.72-150158.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6731.84 [6058.32-7486.86]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e327671.96 [295042.07-363690.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6628.10 [5968.06-7356.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.11 [-0.13 to -0.09]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocio-demographic index\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\u003e55771.99 [50839.72-60898.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7978.36 [7266.82-8717.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e102646.47 [95000.81-110905.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6917.04 [6410.71-7464.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.53 [-0.60 to -0.47]\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\u003e33361.20 [29588.19-37573.03]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6418.73 [5682.91-7243.89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83314.34 [74081.26-93268.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7089.89 [6304.81-7936.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.27 [0.21 to 0.33]\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\u003e27360.70 [23762.08-31452.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6037.65 [5227.63-6962.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97205.08 [85427.14-110416.23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6938.92 [6090.33-7891.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.42 [0.39 to 0.46]\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\u003e12868.38 [11220.72-14731.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4914.36 [4270.04-5647.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32491.21 [28746.27-36692.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4664.40 [4115.28-5283.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.19 [-0.21 to -0.16]\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\u003e5683.40 [5070.15-6345.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6235.28 [5539.45-6990.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11748.89 [10628.42-12923.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5515.02 [4967.37-6094.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.43 [-0.47 to -0.39]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\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-income Asia Pacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11315.43 [10247.61-12433.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10067.08 [9103.50-11080.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27345.13 [24959.31-29896.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7482.10 [6848.31-8161.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.03 [-1.12 to -0.95]\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\u003e18378.52 [16088.45-20760.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7829.53 [6851.85-8844.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32381.36 [29201.94-35745.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7354.03 [6637.12-8109.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.37 [-0.56 to -0.19]\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\u003e27579.80 [25503.72-29767.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7198.84 [6653.84-7774.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39296.15 [37113.90-41603.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5670.81 [5367.88-5992.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.76 [-0.78 to -0.75]\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\u003e1008.02 [954.07-1064.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6901.13 [6523.49-7293.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1919.75 [1818.85-2021.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5125.69 [4864.26-5391.11]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.06 [-1.10 to -1.01]\u003c/p\u003e\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\u003e503.11 [468.13-539.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4928.16 [4583.44-5285.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1391.88 [1309.44-1477.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4208.27 [3961.71-4462.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.58 [-0.62 to -0.54]\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\u003e2755.59 [2355.98-3209.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6302.94 [5381.13-7357.71]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7055.95 [6087.21-8123.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4901.10 [4231.58-5638.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.95 [-1.00 to -0.89]\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\u003e2353.37 [2143.96-2578.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5905.48 [5371.98-6475.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6466.34 [5941.08-7057.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4690.72 [4312.37-5117.11]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.85 [-0.90 to -0.80]\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\u003e1933.78 [1803.38-2063.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7335.49 [6831.33-7839.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3048.19 [2868.53-3226.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5488.54 [5169.43-5804.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.02 [-1.06 to -0.98]\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\u003e717.38 [667.56\u0026ndash;771.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4854.63 [4510.32-5228.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1452.85 [1353.71-1558.86]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4512.84 [4211.27-4834.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.24 [-0.25 to -0.23]\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\u003e5545.41 [4951.06-6197.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6900.09 [6152.63-7724.11]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8950.65 [8104.41-9857.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5997.14 [5437.66-6597.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.51 [-0.57 to -0.46]\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\u003e7829.10 [6646.78-9095.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5119.26 [4345.02-5944.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10817.42 [9241.79-12512.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5139.59 [4397.83-5935.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03 [-0.08 to 0.14]\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\u003e1438.17 [1303.60-1585.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6384.53 [5799.81-7023.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2205.73 [2025.19-2400.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6482.26 [5955.79-7053.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05 [0.01 to 0.09]\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\u003e3755.21 [3340.12-4205.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5138.88 [4555.67-5779.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10715.90 [9766.30-11720.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5271.50 [4794.17-5781.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.08 [0.07 to 0.09]\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\u003e8935.05 [7440.23-10618.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3783.70 [3138.46-4515.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25525.06 [21767.19-29835.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3511.61 [2983.01-4121.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.22 [-0.27 to -0.17]\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\u003e7549.72 [6661.59-8514.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6899.00 [6066.76-7810.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20749.07 [18638.31-23040.42]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6924.44 [6212.84-7700.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.01 [-0.02 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\u003e25784.78 [21972.54-30243.42]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6746.90 [5722.47-7950.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112648.56 [97671.71-128917.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9234.46 [7993.81-10585.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.05 [0.99 to 1.10]\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\u003e68.31 [62.86\u0026ndash;73.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7040.49 [6447.20-7660.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e168.82 [157.31-180.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6404.79 [5953.84-6861.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.3 [-0.33 to -0.28]\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\u003e3211.33 [2853.80-3601.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8227.59 [7280.39-9270.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6177.1 [5595.68-6809.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7786.97 [7025.39-8618.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.21 [-0.22 to -0.2]\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\u003e2533.68 [2278.18-2810.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8593.08 [7683.18-9578.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5361.8 [4873.65-5891.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8069.08 [7304.26-8902.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.25 [-0.27 to -0.22]\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\u003e680.13 [618.03-740.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8917.43 [8038.07-9786.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1472.62 [1353.27-1590.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8075.40 [7379.51-8774.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.35 [-0.39 to -0.31]\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\u003e1325.17 [1140.45-1538.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10465.64 [8985.58-12178.11]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2521.62 [2198.42-2879.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9822.31 [8529.12-11242.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.26 [-0.29 to -0.23]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDeaths\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\u003eGlobal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30090.27 [27543.40-31870.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1594.33 [1447.23-1691.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48433.30 [42583.69-52787.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e979.70 [861.37-1067.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.75 [-1.86 to -1.63]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocio-demographic index\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\u003e6887.86 [6132.54-7257.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e987.21 [875.41-1042.14]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6675.98 [5451.55-7330.49]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e394.36 [327.71-430.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.19 [-3.31 to -3.07]\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\u003e10581.01 [9701.68-11148.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2242.62 [2037.15-2368.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14534.97 [12635.3-16108.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1225.62 [1065.05-1358.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.29 [-2.53 to -2.06]\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\u003e7964.95 [7136.34-8811.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1963.58 [1747.26-2174.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17502.94 [15243.75-19458.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1308.92 [1136.09-1455.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.41 [-1.54 to -1.28]\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\u003e3352.17 [3032.60-3669.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1394.88 [1253.78-1532.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7341.97 [6625.65-7988.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1118.03 [1004.08-1218.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.75 [-0.81 to -0.70]\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\u003e1259.71 [1125.26-1400.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1511.00 [1339.41-1691.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2329.24 [2066.62-2605.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1177.32 [1038.51\u0026ndash;1321]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.81 [-0.89 to -0.73]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\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-income Asia Pacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1321.91 [1160.95-1409.71]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1261.28 [1094.01-1351.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1629.93 [1248.74-1853.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e354.24 [280.65-397.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-4.30 [-4.45 to -4.15]\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\u003e1281.79 [1100.99-1375.59]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e539.27 [463.47-578.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1701.49 [1380.10-1868.19]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e353.39 [290.27-386.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.75 [-1.98 to -1.52]\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\u003e4345.35 [3864.36-4592.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1128.38 [999.22-1194.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2954.18 [2395.61-3249.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e356.19 [293.27\u0026ndash;389.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.84 [-3.94 to -3.73]\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\u003e116.17 [101.49-126.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e845.67 [733.92-923.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131.38 [106.46-147.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e307.51 [251.29-344.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.43 [-3.52 to -3.34]\u003c/p\u003e\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\u003e74.23 [63.94\u0026ndash;86.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e731.95 [630.26-851.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e137.15 [111.32-166.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e407.39 [330.71-496.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.15 [-2.34 to -1.95]\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\u003e564.49 [513.03-592.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1424.51 [1278.17-1504.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e813.36 [697.89-880.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e555.96 [479.02-600.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.81 [-2.90 to -2.72]\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\u003e292.00 [271.96-304.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e766.85 [710.61-800.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e573.26 [499.06-638.89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e410.42 [358.36-457.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.23 [-2.36 to -2.11]\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\u003e298.58 [273.60-317.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1192.74 [1085.57-1271.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e269.26 [235.23\u0026ndash;294.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e461.47 [404.84-503.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.75 [-2.89 to -2.61]\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\u003e152.11 [139.31-162.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1094.79 [997.66\u0026ndash;1169.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e245.39 [212.45-276.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e729.56 [633.25-822.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.32 [-1.38 to -1.26]\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\u003e1918.30 [1810.51-1983.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2497.08 [2336.69-2591.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1782.91 [1589.42-1918.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1150.19 [1027.07-1237.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.86 [-3.01 to -2.70]\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\u003e3693.16 [3461.34-3806.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2577.18 [2394.04-2665.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3180.48 [2829.67-3451.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1425.44 [1269.90-1547.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.67 [-3.13 to -2.22]\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\u003e391.27 [360.57-409.88]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1704.93 [1568.57-1787.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e493.82 [442.02-538.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1449.85 [1294.80-1581.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.83 [-1.11 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\u003e1254.61 [1080.74-1409.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1933.08 [1654.93-2175.42]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2325.76 [2007.23-2619.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1228.39 [1056.33-1383.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.47 [-1.58 to -1.37]\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\u003e2227.01 [1936.84-2492.17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1036.46 [897.13-1164.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5733.80 [5119.88-6338.14]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e847.30 [752.93-938.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.74 [-0.86 to -0.63]\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\u003e2024.99 [1766.63-2281.34]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1997.91 [1732.15-2262.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4723.37 [4194.16-5188.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1659.12 [1467.93-1825.17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.57 [-0.73 to -0.41]\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\u003e8735.83 [7659.50-9806.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2756.83 [2398.45-3095.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19258.04 [16054.94-22356.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1688.34 [1403.79-1960.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.69 [-1.96 to -1.42]\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\u003e17.88 [14.53\u0026ndash;21.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2024.06 [1647.32-2426.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.50 [32.92\u0026ndash;48.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1632.52 [1324.4-1975.17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.76 [-0.80 to -0.72]\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\u003e651.25 [567.73-742.34]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1797.61 [1558.67-2054.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1056.99 [920.64-1198.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1419.55 [1235.83-1608.71]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.79 [-0.84 to -0.74]\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\u003e483.03 [426.6-545.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1738.39 [1515.59-1984.51]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e816.73 [691.24\u0026ndash;935.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1289.24 [1078.17-1484.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.10 [-1.14 to -1.05]\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\u003e122.44 [96.72-153.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1761.68 [1389.38-2211.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e266.39 [197.43-347.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1566.17 [1157.94-2055.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.49 [-0.54 to -0.44]\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\u003e123.88 [101.63-141.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1029.95 [842.47-1180.59]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e299.09 [268.32-326.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1251.27 [1113.70-1370.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.75 [0.25 to 1.24]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIncidence\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\u003eGlobal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28152.39 [22887.91-34340.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1453.28 [1183.72-1768.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56389.95 [46069.51-67838.89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1140.65 [931.89-1372.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.54 [-2.48 to -0.59]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocio-demographic index\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\u003e8128.41 [6541.56-9914.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1163.99 [936.52-1421.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10584.01 [8723.54-12660.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e683.20 [560.86-819.89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.50 [-3.44 to -1.55]\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\u003e9019.60 [7238.69-11074.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1847.02 [1486.7-2260.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16796.28 [13572.45-20399.31]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1424.17 [1149.66-1731.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.62 [-2.57 to -0.67]\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\u003e6677.61 [5414.27-8167.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1600.95 [1301.32-1949.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19735.47 [16114.37-23919.11]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1440.19 [1177.61-1742.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.12 [-2.06 to -0.17]\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\u003e3111.68 [2548.41-3786.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1287.01 [1056.59-1559.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6982.79 [5775.97-8343.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1054.43 [873.92-1256.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.40 [-2.34 to -0.45]\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\u003e1179.10 [967.43-1429.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1398.50 [1150.25-1684.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2240.26 [1859.25-2668.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1116.41 [929.62-1325.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.43 [-2.37 to -0.49]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\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-income Asia Pacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1482.76 [1144.13-1893.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1356.99 [1048.21-1730.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2400.72 [1967.61-2885.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e611.61 [499.33-736.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.62 [-4.63 to -2.6]\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\u003e2213.31 [1664.14-2860.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e939.28 [706.61-1214.74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2657.08 [2103.07-3301.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e588.58 [465.11-731.71]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.12 [-3.05 to -1.17]\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\u003e4707.96 [3903.65-5558.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1227.68 [1016.38-1451.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4735.86 [4060.25-5448.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e639.39 [546.25-738.31]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.87 [-3.81 to -1.93]\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\u003e150.56 [129.45-172.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1053.69 [906.81-1204.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e228.16 [190.6-267.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e581.40 [484.39-681.54]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.79 [-3.73 to -1.84]\u003c/p\u003e\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\u003e81.18 [67.01\u0026ndash;97.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e801.40 [661.80-964.16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e181.49 [151.11-214.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e540.42 [449.67-640.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.97 [-2.92 to -1.01]\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\u003e517.48 [391.36-671.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1263.83 [960.34-1634.03]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1079.39 [837.83-1354.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e742.53 [576.58\u0026ndash;931.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.43 [-3.37 to -1.47]\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\u003e374.54 [303.97-455.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e966.94 [786.79-1174.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e896.68 [742.66-1070.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e642.87 [532.51-767.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.10 [-3.05 to -1.14]\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\u003e292.37 [244.58-346.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1137.97 [952.73-1348.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e389.54 [327.36-455.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e684.43 [574.52-800.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.44 [-3.39 to -1.48]\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\u003e132.67 [111.78-155.83]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e938.11 [791.15-1101.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e253.46 [213.32-298.54]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e761.25 [640.68-896.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.30 [-2.23 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\u003e1462.66 [1206.88-1738.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1883.56 [1557.00-2236.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1945.67 [1623.18-2275.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1273.73 [1060.36-1492.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.99 [-2.93 to -1.05]\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\u003e2952.76 [2203.19-3833.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2030.07 [1521.99-2629.31]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3209.67 [2507.84-4051.59]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1453.15 [1130.76-1841.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.76 [-2.68 to -0.83]\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\u003e404.16 [342.57-474.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1772.43 [1499.05-2083.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e621.83 [530.73-720.14]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1834.52 [1565.31-2126.21]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.39 [-1.31 to 0.54]\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\u003e869.85 [711.65-1053.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1309.02 [1076.18-1576.74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2067.96 [1702.4-2482.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1062.88 [877.74-1272.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.32 [-2.26 to -0.37]\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\u003e2439.80 [1951.01-3027.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1140.85 [915.23-1408.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5832.55 [4748.89-7092.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e860.77 [702.45-1044.16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.80 [-2.75 to -0.85]\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\u003e1685.88 [1395.82-2016.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1638.63 [1358.29-1956.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4157.38 [3472.74-4938.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1433.72 [1199.67-1699.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.10 [-2.03 to -0.16]\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\u003e7038.56 [5633.96\u0026ndash;8704.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2095.78 [1679.12-2585.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23187.75 [18662.92-28583.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1943.31 [1566.37-2391.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.06 [-2.01 to -0.11]\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\u003e14.26 [11.94\u0026ndash;16.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1573.76 [1322.84-1850.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.06 [28.16\u0026ndash;38.71]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1290.65 [1102.31-1507.23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.32 [-2.26 to -0.38]\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\u003e566.29 [463.43-689.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1539.07 [1262.67-1865.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e991.42 [823.79-1180.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1305.38 [1086.9-1548.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.15 [-2.08 to -0.21]\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\u003e464.35 [381.08-561.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1635.14 [1345.65-1968.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e901.15 [746.64-1077.89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1398.80 [1160.73-1670.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.16 [-2.1 to -0.21]\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\u003e123.82 [101.25-149.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1704.45 [1401.28-2051.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e253.43 [209.33-302.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1437.81 [1190.3-1715.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.21 [-2.14 to -0.26]\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\u003e177.19 [137.31-224.17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1423.66 [1103.68-1799.19]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e365.69 [285.96-460.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1463.69 [1146.86-1839.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.58 [-1.52 to 0.36]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDALY\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\u003eGlobal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e459035.24 [425178.80-485509.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23314.06 [21478.67-24689.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e723145.40 [648939.80-782761.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14627.67 [13126.65-15833.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.68 [-1.8 to -1.57]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocio-demographic index\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\u003e99908.25 [91169.89-105408.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14250.62 [12976.72-15048.88]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96352.56 [82554.53-105643.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6105.12 [5296.04-6668.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.97 [-3.09 to -2.86]\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\u003e156837.38 [145105.26-165329.59]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31558.65 [29013.46-33323.49]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e208443.37 [184409.49-230003.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17653.85 [15616.3-19486.23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.22 [-2.46 to -1.99]\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\u003e126671.42 [114624.23-139625.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28932.85 [26045.96-31915.96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e265022.34 [233041.94-292917.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19242.75 [16880.13-21277.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.43 [-1.54 to -1.31]\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\u003e53834.23 [48940.9-58693.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21011.08 [19029.69-22970.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e114846.83 [104871.18-124380.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16741.54 [15237.68-18154.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.80 [-0.86 to -0.75]\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\u003e21132.65 [19031.42-23333.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23197.57 [20782.96-25743.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37791.21 [33710.25-42020.03]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17811.42 [15837.97-19851.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.88 [-0.95 to -0.82]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\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-income Asia Pacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19839.83 [17838.74-21112.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18199.34 [16231.92-19427.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23436.18 [19262.15-26340.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5740.23 [4828.84-6405.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.94 [-4.09 to -3.79]\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\u003e19317.45 [17190.31-20845.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8179.04 [7284.08-8825.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25309.48 [21740.00-27811.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5512.88 [4776.32-6042.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.63 [-1.83 to -1.43]\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\u003e60025.78 [54561.08-63238.86]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15529.10 [14087.77-16376.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39774.85 [33721.48-43676.23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5201.49 [4473.63-5692.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.66 [-3.77 to -3.55]\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\u003e1676.99 [1503.90-1815.71]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11826.15 [10553.14-12826.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1784.31 [1504.59-1990.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4425.70 [3761.22-4930.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.36 [-3.45 to -3.27]\u003c/p\u003e\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\u003e1113.15 [966.53-1289.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10916.9 [9477.69-12647.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2024.61 [1674.9-2434.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6092.33 [5038.85-7326.03]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.16 [-2.36 to -1.96]\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\u003e8570.25 [7913.53-8965.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20428.10 [18689.55-21447.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11866.12 [10489.47-12725.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8197.04 [7264.68-8780.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.78 [-2.86 to -2.71]\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\u003e4304.87 [4050.20-4483.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10947.09 [10265.47-11414.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8419.96 [7485.31-9333.31]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6097.83 [5429.32-6756.34]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.15 [-2.26 to -2.04]\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\u003e4529.59 [4199.74-4800.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17528.16 [16183.17-18599.16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3991.43 [3573.96-4333.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7032.88 [6316.22-7629.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.72 [-2.84 to -2.59]\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\u003e2265.56 [2088.55-2416.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15730.63 [14455.24-16786.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3516.13 [3073.62-3945.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10771.86 [9426.32-12085.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.25 [-1.3 to -1.20]\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\u003e27651.68 [26310.26-28592.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34933.50 [33058.30-36207.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24653.62 [22298.78-26422.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16199.38 [14672.13-17360.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.87 [-3.03 to -2.70]\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\u003e52777.50 [49949.23-54384.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35393.43 [33292.71-36551.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43622.18 [39412.22-47168.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20001.44 [18099-21630.16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.58 [-3.03 to -2.14]\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\u003e5796.07 [5395.98-6067.23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25409.33 [23644.81-26602.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7343.08 [6652.61-7977.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21541.85 [19492.68-23420.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.88 [-1.17 to -0.59]\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\u003e18983.94 [16533.51-21239.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27581.62 [23907.04-30906.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34175.29 [29762.86\u0026ndash;38319.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17411.34 [15122.60-19522.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.54 [-1.63 to -1.45]\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\u003e36558.82 [31998.00-40731.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15843.28 [13824.77-17696.19]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90940.09 [81825.55-100052.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12735.86 [11423.28-14034.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.81 [-0.9 to -0.72]\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\u003e32029.49 [28215.93-35749.88]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30033.52 [26347.15-33655.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73185.58 [65685.53-79862.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24861.57 [22257.29-27156.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.61 [-0.75 to -0.46]\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\u003e140451.37 [123998.81-157355.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39480.62 [34686.98-44221.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e289231.15 [243698.54-333010.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24365.63 [20492.77-28053.86]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.66 [-1.89 to -1.43]\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\u003e305.16 [250.35-364.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30832.65 [25345.28-36764.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e663.78 [545.56-796.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24892.71 [20445.96-29857.19]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.76 [-0.79 to -0.73]\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\u003e10514.61 [9265.00-11879.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27077.57 [23765.51-30653.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16734.65 [14699.25-18925.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21261.96 [18680.92-24017.88]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.81 [-0.87 to -0.76]\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\u003e8216.48 [7302.65-9207.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27019.19 [23816.97-30469.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13403.91 [11561.15-15188.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19954.09 [17087.37-22693.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.13 [-1.18 to -1.08]\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\u003e2145.41 [1716.79-2663.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27441.02 [21968.02-34082.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4382.02 [3298.6-5629.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23993.92 [18061.86-30953.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.54 [-0.59 to -0.49]\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\u003e1961.24 [1641.78-2224.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15680.34 [13102.73-17807.14]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4686.99 [4253.4-5086.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18545.42 [16737.13-20168.83]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.63 [0.17 to 1.10]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eCR, Crude Rate\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eTrends in disease burden from 1990 to 2021\u003c/h3\u003e\n\u003cp\u003eFrom 1990 to 2020, epidemiological indicators of the global stroke burden showed significant temporal trends, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. crude prevalence rate (CPR), CIR, crude death rate (CDR), and DALYs all exhibited an overall downward trend across all SDI regions (High, Middle, and Low). However, the absolute burden for men has consistently been higher than that for women, indicating the persistent influence of gender differences in stroke risk. Notably, the extent of decline also varies across different SDI regions. The decline was most pronounced in High SDI regions, while Low SDI regions showed some improvement but still had a high baseline burden, highlighting the inequitable distribution of healthcare resources.\u003c/p\u003e\u003cp\u003eDespite the overall improvement in global indicators, the burden of stroke increases exponentially with age. High SDI regions partially offset the risks associated with ageing through more effective interventions (such as hypertension management and acute-phase treatment), while low SDI regions, due to limited medical resources, saw a slower decline in the disease burden among the elderly population. The interaction between population ageing and regional development disparities continues to reshape the distribution of disease burden, and improvements over time have not fully eliminated the combined negative effects of age and SDI.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e2021 Global Burden of Disease Distribution Map\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the global distribution of the prevalence, incidence, mortality, and DALY rate of stroke among the elderly population in 2021. The highest prevalence was in Botswana, the highest incidence was in Uzbekistan, Tajikistan, and Belarus, the highest mortality was in Montenegro and Serbia, and the highest DALY rate was in Montenegro and North Macedonia.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eA. Prevalence Rate. B. Incidence Rate. C. Mortality Rate. D. Disability-Adjusted Life Years (DALYs) Rate\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003eAge group and SDI regional stratification analysis\u003c/h2\u003e\u003cp\u003eAge-related trends and sociodemographic differences in incidence rates, mortality rates, and DALYs across High, Middle, and Low SDI regions are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. All SDI regions exhibit a consistent upward trend in incidence rates with increasing age. However, the rates of increase vary significantly, with High SDI regions showing a relatively slower upward trend, while Low SDI regions exhibit a steeper upward trend. Mortality rates align with incidence trends, showing an upward trend across all SDI strata with increasing age. Significant differences exist in mortality rates, with Low SDI regions consistently recording the highest mortality rates. DALYs exhibit a progressive increase with age, reflecting the worsening of the disease burden. Notably, the accumulation of DALYs is slower in High SDI regions, while Low SDI regions exhibit a steeper upward trend.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eRanking of risk factors\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the percentage of health loss attributable to various risk factors among individuals aged 70 and above in 2021, as a proportion of total DALYs. In most regions, high systolic blood pressure is the primary risk factor contributing to health loss, with values nearly uniformly at 10 across all regions, indicating its highly significant impact. High LDL cholesterol and high fasting plasma glucose also have a significant impact in most regions, with values typically ranging from 8 to 10. Low temperature, particularly in regions such as East Asia and Eastern Europe, has a notable impact, with values reaching 7 or higher. Diet-related factors, such as insufficient intake of whole grains, vegetables, and fruits, also have significant values in some regions. In regions such as East Asia, Eastern Europe, and Central Asia, the impact of hypertension, high LDL cholesterol, and high fasting plasma glucose is particularly pronounced, with values often reaching 10. In regions such as South Asia and Sub-Saharan Africa, the impact of tobacco, low temperature, and diet-related factors is relatively greater.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eAge-period-cohort effects and the role of ageing mechanisms\u003c/h2\u003e\u003cp\u003eAge-related biological cumulative effects are manifested by upward trends in both longitudinal and cross-sectional age curves, indicating that stroke incidence and mortality rates increase exponentially with age. This may reflect the cumulative effects of vascular aging and multiple comorbidities (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), and also confirms that aging is an independent risk factor for stroke (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), associated with age-related pathophysiological changes. However, societal progress moderates the impact of aging, with the decline in period RR and cohort RR reflecting how medical advancements and public health interventions have reduced stroke risk among age-matched populations over time. Specifically, later-born cohorts have lower risks even at advanced ages compared to earlier-born cohorts. However, in the progression of aging and societal progress, local drift increases are observed in older age groups (e.g., \u0026ge;\u0026thinsp;80 years), suggesting that the physiological decline caused by ageing may eventually outweigh the benefits of medical interventions. Furthermore, the period bias reached its peak between 2000 and 2010, corresponding to the period of accelerated global ageing, reflecting the lag in the healthcare system's response to sudden changes in population structure. Additionally, cohort effects reveal early-life exposure patterns, with a decline in fitted cohort models suggesting that reduced risk in recent cohorts may be associated with improved early-life nutrition and reduced childhood infections. Specific details can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eForecast for 2021\u0026ndash;2040\u003c/h2\u003e\u003cp\u003eIt is projected that the global burden of stroke will undergo significant changes from 2021 to 2040, with varying trends across different indicators. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e \u003cb\u003e(A) and\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e \u003cb\u003e(B)\u003c/b\u003e present the actual data on stroke incidence and mortality rates among individuals aged 70 and above from 1990 to 2021, as well as the projected trends beyond 2021. The data indicate that both incidence and mortality rates have shown a declining trend over the past 31 years, and are projected to continue declining in the future, though the rate of decline may gradually slow. By 2040, the lowest incidence and mortality rates are projected for the 70\u0026ndash;74 age group (Death predictions and incidence predictions in supplementary materials).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eA. Incidence Rate. B. Mortality Rate.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe global burden of stroke among adults aged 70 and older vividly illustrates the complex interplay between intrinsic ageing mechanisms and late-life diseases, as emphasised by Partridge\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003eand his team. While advances in public health have extended lifespan, healthy longevity\u0026mdash;that is, survival free from severe disability\u0026mdash;has not kept pace\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, placing unsustainable economic pressure on healthcare systems. Our MIMIC-IV analysis validated this disparity: elderly stroke patients faced significantly higher mortality rates (22.0% vs 12.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), more severe comorbidities, and over twice the length of ICU stays (median 7.3 days vs 3.1 days), directly leading to increased treatment costs per episode (estimated average medical costs per stroke ranging from 5,798.15 to 140,048 euros\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e). This clinical vulnerability results in sustained high burdens, particularly in resource-limited settings, where over 90% of stroke deaths occur\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Crucially, irreversible ageing processes\u0026mdash;vascular stiffening, cellular ageing, systemic inflammation\u003csup\u003e[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;interact with modifiable risk factors (e.g., hypertension, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), further exacerbating the disease burden. By 2019, global stroke-related costs had exceeded 721\u0026nbsp;billion dollars\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, with the economic burden particularly severe among those aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years due to long-term disability care. However, research on the burden among the elderly population remains limited. Therefore, we integrated MIMIC-IV clinical models with GBD 2021 data to quantify global differences in incidence, mortality, and DALYs across different SDI strata, analyse the contribution of ageing to burden growth, and identify region-specific risk mitigation pathways.\u003c/p\u003e\u003cp\u003eThis study confirmed significant differences in stroke burden across regions with varying levels of SDI, with stroke prevalence and mortality rates being lower in High SDI regions compared to Low-middle SDI regions\u0026mdash;a finding consistent with the results of Li et al.'s study\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. This gradient difference likely reflects more developed medical infrastructure, more effective prevention programmes, and higher public awareness in High SDI regions\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Particularly concerning is the situation in East Asia, where the stroke incidence rate among adults aged 70 and older surged by 61% between 1990 and 2021 (EAPC\u0026thinsp;+\u0026thinsp;1.05%), far exceeding the global downward trend (EAPC \u0026minus;\u0026thinsp;0.11%). This anomaly stems from a \u0026lsquo;double burden\u0026rsquo;: accelerated population ageing (e.g., 29% of the population aged 65 and older in Japan, 14% in China\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e) and persistent metabolic risks (hypertension, hyperlipidaemia, and dietary patterns). Crucially, ageing leads to microcirculatory dysfunction and arteriosclerosis\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, providing a pathological basis for stroke even in the absence of high-risk exposure.\u003c/p\u003e\u003cp\u003eHypertension and metabolic syndrome represent a cluster of risk phenotypes. In East Asian populations, the prevalence of arterial hypertension (35\u0026ndash;45%) and visceral obesity is significantly elevated\u0026mdash;both of which are closely associated with insulin resistance\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;thereby forming a unique risk profile. Our MIMIC-IV analysis revealed this synergistic effect: elderly stroke patients commonly exhibit a \u0026lsquo;metabolic triad\u0026rsquo; of hyperlipidaemia (53.9%), hypertension (53.1%), and type 2 diabetes (36.4%). This convergence accelerates the progression of atherosclerosis, primarily through physiological and pathological mechanisms such as plaque instability, blood flow obstruction, thrombosis, and plaque proliferation\u003csup\u003e[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. In the elderly population, these pathophysiological mechanisms exacerbate age-related vascular fragility, leading to increased stroke incidence and mortality. The MONICA project confirmed that the synergistic effect of smoking and hypertension is a key driver of incidence differences\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Crucially, the context of \u0026lsquo;ageing before affluence\u0026rsquo; in East Asia exacerbates these risks\u0026mdash;as reflected in our MIMIC-IV cohort by higher disease severity: SOFA score [5.0 (3.0\u0026ndash;7.0) vs. 4.0 (2.0\u0026ndash;7.0), p\u0026thinsp;=\u0026thinsp;0.037], and increased in-hospital mortality (16.3% vs. 12.2%, p\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e\u003cp\u003eOur research indicates that elevated systolic blood pressure is the primary risk factor for stroke-related health loss in the global elderly population\u0026mdash;a finding attributable to rapid urbanisation, changes in dietary patterns (such as high sodium intake), and insufficient physical activity\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This conclusion was validated by MIMIC-IV data: 53.1% of elderly stroke patients had hypertension, and the 28-day mortality risk for hypertensive patients was 63% higher than for non-hypertensive patients (adjusted hazard ratio [aHR]\u0026thinsp;=\u0026thinsp;1.63, 95% confidence interval [CI]: 1.29\u0026ndash;2.05). The Global Stroke Registry (despite methodological heterogeneity in definitions and time points) consistently validated hypertension as the primary risk factor (prevalence range: 45%-72%), followed by diabetes (18%-40%) and smoking (15%-35%)\u003csup\u003e[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Notably, synergistic risk factors in specific environments warrant attention. Metabolic synergism primarily involves high LDL cholesterol and fasting blood glucose (particularly significant in East Asia and Eastern Europe, see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), while environmental-behavioural risks primarily involve smoking and exposure to low environmental temperatures (particularly critical in South Asia and Sub-Saharan Africa).\u003c/p\u003e\u003cp\u003eAlthough crude mortality rates increase with age among adults aged 70 and older, longitudinal data indicate a declining trend in relative risk\u0026mdash;this paradox can be attributed to medical advancements such as endovascular thrombectomy\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e and community-based hypertension management programmes\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. However, the GBD 2021 projections warn that the declining trend in mortality rates will slow due to accelerated population ageing, a trend exacerbated by the dual impact of COVID-19. During the COVID-19 pandemic, healthcare systems collapsed (with a 39% reduction in stroke imaging studies\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, a 50\u0026ndash;70% decrease in global hospitalisation rates\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e, and a 32% increase in delayed treatment\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e; reduced emergency medical service calls related to stroke were associated with excess mortality\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e); second, direct biological stimulation (severe COVID patients had a 3-fold increased risk of ischaemic stroke\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e). This multidimensional crisis, stemming from resource allocation, avoidance of medical care, and virus-induced thrombosis, highlights critical vulnerabilities in an ageing society.\u003c/p\u003e\u003cp\u003eOur research findings reveal a dual challenge. Population ageing will inevitably lead to an absolute increase in stroke incidence, while the long-term care needs of stroke survivors will place pressure on the healthcare system. This necessitates the establishment of a comprehensive \u0026lsquo;prevention-emergency care-rehabilitation\u0026rsquo; management system\u003csup\u003e[44, 45]\u003c/sup\u003e, with priority intervention measures including strengthening community-based primary prevention (e.g., hypertension screening), optimising regional acute care networks (e.g., thrombectomy centres), and promoting cost-effective innovative measures (e.g., AI-guided remote rehabilitation\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e). From an economic perspective, this burden will be exacerbated by the conflict between increasing rehabilitation needs and a declining working-age population (15\u0026ndash;64 years)\u003csup\u003e[48]\u003c/sup\u003e. While the adoption of thrombolytic therapy has controlled costs per case, demographic pressures will increase cumulative expenditures by 40\u0026ndash;60% by 2050\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Therefore, developing equitable, cost-effective strategies, particularly resource allocation based on SDI stratification, is critical for sustainable stroke care.\u003c/p\u003e\u003cp\u003eThis study provides the first systematic assessment of stroke burden in the population aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years, addressing a critical research gap. However, several limitations should be noted: the GBD estimates may underrepresent true disease burden in some regions, while the single-center MIMIC-IV data carry selection bias; the risk factor analysis omitted genetic markers and emerging environmental exposures; and the predictive models didn't account for public health emergencies. Future studies should integrate multi-source longitudinal data with molecular epidemiological approaches to better elucidate the biological mechanisms of aging-related stroke.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThere are significant regional differences in the disease burden of stroke among the global elderly population, which is closely related to SDI and exposure to controllable risk factors. High SDI regions have significantly reduced the disease burden through advanced medical interventions, while Low-middle SDI regions urgently need strategic investment in two core areas (optimisation of medical resources, scaling up prevention programmes, and cost-effective innovation, etc.).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely acknowledge the contributions of the Global Burden of Disease Study 2021 collaborators and the MIMIC-IV database team for providing critical data resources that made this research possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have made substantial contributions to this study. J T and Xh Z were responsible for the conceptualization and methodology of the research. D Y and Y G conducted the investigation and data curation. Y H and Xy C contributed to both aspects of the study. All authors were involved in (1) the conception and design of the study, or acquisition of data, or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content, and (3) final approval of the version to be submitted. Jing Tian accessed and verified the underlying data. The authors declare that this manuscript, including related data, figures, and tables, has not been previously published and is not under consideration elsewhere.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study uses publicly available data from the Global Burden of Disease (GBD) database. The GBD data are anonymized and aggregated at the population level, and no individual-level information is included. Ethical approval and informed consent were not required for the use of these data. Access to the GBD database complies with all applicable data use agreements and guidelines provided by the Institute for Health Metrics and Evaluation (IHME).\u003c/p\u003e\n\u003cp\u003eAll clinical data in this study were obtained from the Medical Intensive Care Unit Information Market (MIMIC-IV) database, which was compiled from the electronic health records of the Beth Israel Deaconess Medical Centre (BIDMC) and is publicly accessible. The study author (J T) obtained the necessary authorisation to access the database. It is important to note that this study focuses on the analysis of a third-party open-access database approved by an institutional review board (IRB). Therefore, the IRB review process at our institution was deemed exempt.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not require informed consent, as it utilized publicly available data that did not contain confidential or personally identifiable information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Board approved the exemption for this study because it used publicly available data that did not contain confidential or personally identifiable patient information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agree to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOutstanding Youth Project of Heilongjiang Natural Science Foundation (No. JQ2024H004).\u003c/p\u003e\n\u003cp\u003eHarbin Medical University Young Talent Project. HMUMIF-24009.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFEIGIN V L, BRAININ M. World Stroke Organization: Global Stroke Fact Sheet 2025[J]. Int J Stroke. 2025;20(2):132\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFEIGIN V L, OWOLABI M O FEIGINVL, et al. Pragmatic solutions to reduce the global burden of stroke: a World Stroke Organization\u0026ndash;Lancet Neurology Commission[J]. Lancet Neurol. 2023;22(12):1160\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRUNDEK T, TOLEA M, ARIKO T, et al. Vascular Cognitive Impairment (VCI)[J]. Neurotherapeutics. 2022;19(1):68\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSESHADRI SM, WOLF P A M. Lifetime risk of stroke and dementia: current concepts, and estimates from the Framingham Study[J]. 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Ageing Res Rev. 2018;41:18\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePOMATTO L C D, DAVIES K J A. The role of declining adaptive homeostasis in ageing[J]. J Physiol. 2017;595(24):7275\u0026ndash;309.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCOLLABORATORS GS. Global, regional, and national burden of stroke and its risk factors, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet Neurol. 2021;20(10):795\u0026ndash;820.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePandian JD, Gall SL. Prevention of stroke: a global perspective[J]. Lancet. 2018;392(10154):1269\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCHIMATIRO GL, RHODA AJ. Scoping review of acute stroke care management and rehabilitation in low and middle-income countries[J]. BMC Health Serv Res. 2019;19(1):789.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAMUTHAVALLI TJ, MIKTON C, HARWOOD R H et al. The UN Decade of healthy ageing: strengthening measurement for monitoring health and wellbeing of older people[J]. Age Ageing, 2022,51(7).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCAVAGNA P, LEPLAY C, N GUETTA R, et al. Hypertension treatment in sub-Saharan Africa: a systematic review[J]. Cardiovasc J Afr. 2023;34(5):49\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCHANDALIA M, ABATE N, GARG A, et al. Relationship between generalized and upper body obesity to insulin resistance in Asian Indian men[J]. J Clin Endocrinol Metab. 1999;84(7):2329\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWONG K S GAOS, CHAN Y L, et al. Mechanisms of acute cerebral infarctions in patients with middle cerebral artery stenosis: A diffusion-weighted imaging and microemboli monitoring study[J]. Ann Neurol. 2002;52(1):74\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eL\u0026Oacute;PEZ-CANCIO E, MATHEUS M G, ROMANO J G, et al. Infarct Patterns, Collaterals and Likely Causative Mechanisms of Stroke in Symptomatic Intracranial Atherosclerosis[J]. Cerebrovasc Dis. 2014;37(6):417\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCHEN L H, SPAGNOLO-ALLENDE A, YANG D, et al. Epidemiology, Pathophysiology, and Imaging of Atherosclerotic Intracranial Disease[J]. Stroke. 2024;55(2):311\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSTEGMAYR B, ASPLUND K, KUULASMAA K et al. stroke-incidence\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e-and-mortality-correlated-to-stroke-risk-factors-in-the-who-monica-project[J]\u003c/span\u003e\u003cspan address=\"http://-and-mortality-correlated-to-stroke-risk-factors-in-the-who-monica-project[J]\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Stroke, 1997,28(7): 1367\u0026ndash;1374.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHONG K, BANG O Y, KANG D, et al. Stroke Statistics in Korea: Part I. Epidemiology and Risk Factors: A Report from the Korean Stroke Society and Clinical Research Center for Stroke[J]. J Stroke. 2013;15(1):2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMASKEY A, PARAJULI M, KOHLI SC. A study of risk factors of stroke in patients admitted in Manipal Teaching Hospital, Pokhara[J]. Kathmandu Univ Med J. 2011;9(36):244.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVENKETASUBRAMANIAN N, YOON B W PANDIANJ, et al. Stroke Epidemiology in South, East, and South-East Asia: A Review[J]. J Stroke. 2017;19(3):286\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoyal M, Menon BK, van Zwam WH. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials[J]. Lancet,387(10029): 1723\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNORRVING B FEIGINVL, GEORGE M, G, et al. Prevention of stroke: a strategic global imperative[J]. Nat Reviews Neurol. 2016;12(9):501\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKANSAGRA A P, GOYAL M S HAMILTONS, et al. Collateral Effect of Covid-19 on Stroke Evaluation in the United States[J]. N Engl J Med. 2020;383(4):400\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Stroke Organization. WSO. The Global Impact of COVID-19 on Stroke - Survey Report from Prof[J]. Marc Fischer, WSO President-Elect; 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNOGUEIRA R G, ABDALKADER M. Global impact of COVID-19 on stroke care[J]. Int J Stroke. 2021;16(5):573\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSHARMA R, KUOHN L R, WEINBERGER D M, et al. Excess Cerebrovascular Mortality in the United States During the COVID-19 Pandemic[J]. Stroke. 2021;52(2):563\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKIM YE, PARK HUHK. Association Between Vaccination and Acute Myocardial Infarction and Ischemic Stroke After COVID-19 Infection[J]. JAMA. 2022;328(9):887\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKIM J, KIM E, LEE S, et al. Use of cortical hemodynamic responses in digital therapeutics for upper limb rehabilitation in patients with stroke[J]. J Neuroeng Rehabil. 2024;21(1):115.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZHOU Y, XIE H, LI X, et al. Predicting upper limb motor recovery in subacute stroke patients via fNIRS-measured cerebral functional responses induced by robotic training[J]. J Neuroeng Rehabil. 2024;21(1):226.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"stroke, elderly population, global burden of disease, risk factors, population ageing","lastPublishedDoi":"10.21203/rs.3.rs-7589290/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7589290/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Objectives: \u003c/strong\u003eStroke remains a major global public health challenge, particularly among the elderly population. This study aims to integrate MIMIC-IV clinical data with GBD 2021 data to analyse epidemiological trends and regional differences in stroke among people aged 70 and above, identify key risk factors, and predict future changes in incidence and mortality rates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe GBD 2021 database provides macro-epidemiological data on global stroke incidence, mortality, prevalence, and disability-adjusted life years (DALYs) from 1990 to 2021, while the MIMIC-IV database provides detailed clinical records for 2,144 stroke patients. Regional differences were assessed using the Socio-Demographic Index (SDI) for stratification. Three core statistical methods were employed: the Estimated Annual Percentage Change (EAPC) to quantify temporal trends in disease indicators, the Bayesian Age-Period-Cohort (BAPC) model to analyse the effects of ageing and predict the disease burden by 2040, and the Multivariate Cox Proportional Hazards Model to assess the impact of clinical risk factors on outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eClinical analysis of 2,144 ICU-treated stroke patients in the MIMIC-IV database revealed significantly higher 28-day mortality in those aged ≥70 years versus younger patients (22.0% vs. 12.7%, p\u0026lt;0.001), driven by heavier comorbidity burdens (hyperlipidaemia: 53.9% vs. 41.8%; diabetes: 36.4% vs. 30.0%) and poorer organ function (SOFA score: 5.0 vs. 4.0, p=0.037). Multivariable Cox regression confirmed age ≥70 years as an independent mortality risk factor (adjusted HR=1.36, 95% CI:1.05-1.76). In parallel, GBD 2021 data showed a 61% global rise in absolute stroke cases (1990-2021), disproportionately affecting Low-middle SDI regions. While High SDI regions achieved declining prevalence (EAPC=−0.53%), Middle SDI regions faced rising rates (EAPC=+0.42%), with East Asia exhibiting the sharpest incidence increase (EAPC=+1.05%). Hypertension dominated global stroke DALYs, followed by high LDL-cholesterol and fasting hyperglycaemia. Projections suggest a continued global decline in incidence and mortality by 2040, albeit at a slowing rate, attributed to accelerating population ageing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eSignificant regional disparities in the burden of stroke among the elderly are closely related to the level of the SDI and modifiable risk factors. High-SDI regions have reduced the burden of stroke through advanced intervention measures; Low-middle SDI regions urgently need strategic investment.\u003c/p\u003e","manuscriptTitle":"The Stroke Crisis in Ageing Societies: Global Trends, Risk Factors, and Clinical Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 14:31:38","doi":"10.21203/rs.3.rs-7589290/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-07T11:29:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T23:50:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303716571962420890056246601398773481568","date":"2025-10-28T22:30:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-15T11:13:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100368371702043253984092209282499829552","date":"2025-10-13T13:04:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-13T12:14:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-17T16:17:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-13T06:23:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-13T06:23:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-09-11T07:53:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3ae355bc-d9bd-4d39-9b80-7ba35b8f234d","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-23T02:08:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 14:31:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7589290","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7589290","identity":"rs-7589290","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}