Global Burden of Cardiovascular Disease Attributable to Lead Exposure: A Comprehensive Analysis from 1990 to 2021

Global Burden of Cardiovascular Disease Attributable to Lead Exposure: A Comprehensive Analysis from 1990 to 2021 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global Burden of Cardiovascular Disease Attributable to Lead Exposure: A Comprehensive Analysis from 1990 to 2021 kaleem maqsood, Akasha Fiaz, Mahnoor Fatima This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9571210/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Lead exposure is a significant environmental risk factor contributing to the global burden of cardiovascular disease (CVD). This study examines trends in CVD mortality and disability-adjusted life years (DALYs) attributable to lead exposure from 1990 to 2021 across 204 countries and territories, stratified by sex, socio-demographic index (SDI), and Global Burden of Disease (GBD) regions. Methods Data were extracted from the GBD 2021 study. Age-standardized mortality rate (ASMR) and DALY rates (ASDR) per 100,000 population were analyzed. Trends were assessed using estimated annual percentage change (EAPC) to evaluate temporal shifts. Results The global burden of CVDs due to lead exposure declined, with ASMR decreasing by -20.37% and ASDR by -27.55%. However, in 2021, global CVD mortality and DALYs attributable to lead exposure were 1476.24 and 30017.54 thousand, respectively. The corresponding ASMR and ASDR were highest in males, low-SDI, South Asia, and sub-Saharan Africa, as well as among countries such as Afghanistan, Egypt, and Sudan. In contrast, the High-SDI region, the High-income Asia-Pacific region, the Republic of Korea (-74.11% ASMR), and Ireland (-73.88% ASMR) showed the most significant declines. Among CVD subtypes, ischemic heart disease (IHD) and stroke demonstrated notable reductions, while AFF increased globally (+ 30.73% ASMR). Females exhibited a slightly greater percentage decline in mortality and DALYs than males. Conclusion Despite a global decline in CVD burden from lead exposure, substantial inequalities persist, with low-SDI regions and males experiencing the highest and rising ASR trends. The burden remains severe in South Asia, sub-Saharan Africa, and certain countries, while atrial fibrillation continues to rise globally. Addressing these disparities requires stronger lead mitigation policies, improved healthcare access, and targeted regional interventions. Epidemiology Lead Exposure Epidemiology CVDs Mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Cardiovascular diseases (CVDs) are among the leading health concerns worldwide owing to their increasing incidence and consequent disabilities and mortalities, with a heavy economic burden as they are an important contributor to the cost of medical care [ 1 ]. There was a 42.4% increase in CVD-related fatalities from 1990 to 2015 [ 2 ]. In 2016, CVDs contributed to almost 1/3rd of all mortalities worldwide [ 3 ]. In 2017, CVDs caused over 17 million mortalities, 35.6 million years-lived-with-disability, and 330 million years of life lost globally [ 4 ]. The deaths attributed to CVDs increased from 12.1 million people in 1990 to 18.6 million people in 2019 [ 5 ]. It was estimated that by 2030, CVDs would be responsible for over 23 million deaths worldwide. [ 6 ]. CVDs comprise ischemic heart disease (IHD) [ 7 ], stroke, heart failure, aortic aneurysm, atrial flutter and fibrillation, hypertension, peripheral arterial disease, rheumatic heart disease, and numerous other cardiac and vascular morbidities [ 8 , 9 ]. The global prevalence of IHD is rising. It affects almost 1.72% (126 million people) of the global population and has contributed to nine million mortalities worldwide [ 8 ]. A targeted approach to reducing CVDs burden and associated mortalities can be accomplished through a deeper understanding of sex- and region-specific trends. Accurately estimating CVDs risk factors can be instrumental in formulating efficient policies for public health. As societies continue to industrialize, heavy metal pollutants are becoming increasingly widespread. Lead is among the most prevalent environmental contaminants, and its exposure has constantly remained a major health concern for the public. Children can ingest it via contaminated soil, air, paints, and chip destruction dust [ 10 ]. Lead exposure has caused the deaths of around one million people and disability-adjusted life-years (DALYs) for 21.7 million people globally [ 11 ]. World Health Organization (WHO) reported that in 2016, 82% of lead-related deaths occurred among low- and middle-income populations in developing countries [ 12 ]. Lead is toxic because it accumulates in the human body with time and is detrimental at low concentrations [ 13 ]. It has been recognized for many years to cause fatal diseases like cancer and cardiovascular disorders, among others [ 14 ]. Considerable in-vitro and in-vivo evidence shows that lead exposure causes inflammation and oxidative stress and reduces bioavailability [ 15 ]. These mechanisms are major contributors to lead-linked vascular diseases [ 16 ]. Adverse effects of lead exposure on cardiac health include an augmented risk of cardiotoxicity, heart failure, arrhythmia, hypertension, and myocardial ischemia. Lead exposure can also cause vasoconstriction and vasodilation [ 17 ]. In studies involving animals and humans, exposure to low concentrations of lead for the long term has shown an elevated risk of persistent hypertension and cardiovascular mortality [ 18 ]. Cohort studies by the National Health and Nutrition Examination Survey (NHANES) have shown that blood lead levels and prevalence of peripheral artery disease (PAD) are associated [ 19 ]. Moreover, according to a population-level study including 179 participants, higher baseline blood lead levels predicted weakened left ventricular systolic function ten years later [ 20 ]. Estimating the impact of chronic lead exposure on the burden of major cardiovascular diseases is essential to implementing prevention policies. Therefore, we estimated trends in significant CVDs burden attributable to lead exposure based on the latest GBD data. This information is crucial for government and healthcare systems to make decisions for executing efficient prevention and alleviation programs. 2. METHODOLOGY 2.1. Data Source The data source for this study was the GBD 2021 database, which comprehensively estimates the burden of injuries and diseases across 204 territories and countries. This incorporates data from disease registries, national censuses, vital statistics, health service utilization, civil registration, and an extensive analysis of published literature on disease prevalence and incidence [ 21 ]. The GBD framework also provides comparative assessments for 87 risk factors across 21 regions globally. The Global Health Data Exchange (GHDx) results tool ( http://ghdx.healthdata.org/gbd-results-tool ) provided data on the overall CVDs burden and six CVD diseases among them are Aortic aneurysm (AA), Atrial fibrillation and flutter (AFF), Hypertensive heart disease (HHD), Ischemic heart disease IHD, Lower extremity peripheral arterial disease (LE-PAD) and stroke among them caused by lead exposure. The dataset comprises metrics stratified by sex, geographic region, SDI (SDI), and 204 territories and countries including mortality, DALYs, and age-standardized rates (ASRs). 2.2. Data Extraction and Processing From 1990 to 2021, data has been collected on lead exposure as a risk factor for CVDs on a global, regional, and country level. The Comparative Risk Assessment (CRA) paradigm was used to examine fatalities and DALYs associated with CVD. Research studies, national health surveys, and systematic reviews were used to determine blood lead levels. A cumulative blood lead index method was used to measure bone lead exposure. Bayesian meta-regression and spatiotemporal Gaussian process regression (ST-GPR) were employed to estimate missing exposure values and guarantee complete data coverage. 2.3. Data Analysis We analyzed age-standardized data on the burden of cardiovascular disorders due to lead exposure using death and DALYs metrics from the GBD 2021 database, spanning 1990 to 2021. The age-standardized death and DALYs data were stratified by year, sex, country, and region. The temporal trend of burden was found through EAPC, calculated by the Linear regression model, and applied to the natural log of SEV rate over time [ 22 , 23 ]. The overall temporal trend of mortality and disability burden was categorized as decreasing if the upper boundary of the confidence interval was less than 0, increasing if the lower boundary was greater than zero, and stable in other cases. Gender-wise trends in global exposure burden were assessed to observe gender-based disparities across the study period. Additionally, ASMR and ASDR of all countries for 1990 and 2021 were presented through choropleth to visualize the geographical and spatial distribution of burden [ 24 ]. All analyses were conducted using R software (version 4.2.2). All analyses of this study are adhered to the Guidelines for Accurate and Transparent Health Estimates Reporting. 3. RESULTS 3.1. Global Trends The GBD data for CVDs ASMR and ASDR due to lead exposure indicates a decreasing trend from 1990 to 2021. The number of deaths and DALYs due to CVDs in 2021 were 1,476.24 thousand and 30,017.54 thousand, respectively (Table 1 ). The ASMR for CVDS in 2021 was 17.82 per 100,000 population (95% CI: -2.11, 37.19), with a percentage change of -20.37% (EAPC =-0.76). The ASDR was 351.36 per 100,000 population (95% CI: -42.73, 727.75), with a percentage change of -27.55% (EAPC =-1.09) (Table 2 ). The aortic aneurysm (AA) showed a moderate decline in its impact over the study period. The ASR for AA deaths in 2021 was 0.01 per 100,000 population, showing a percentage change of -10.12% (EAPC =-0.48) (Table S1.1, Fig. 3 A) and for DALYs was 0.24, with a percentage change of -15.95% (EAPC =-0.74, Fig. 3 B) (Table S1.2). Atrial fibrillation and flutter (AFF) have shown an increasing global burden over the years. The ASR for AFF deaths in 2021 was 0.12 per 100,000 population, with a percentage change of 30.73% (EAPC 0.89) (Table S1.3, Fig. 3 A). The ASR for DALYs in 2021 was 2.70 per 100,000 population, showing a percentage change of 16.26% (EAPC = 0.50% (Table S1.4, Fig. 3 B). The global burden of hypertensive heart disease (HHD) decreased over the study period. The ASR for HHD deaths in 2021 was 3.92 per 100,000 population, with a percentage change of -22.04% (EAPC =-0.73) (Table S1.5, Fig. 3 A). The ASR for DALYs in 2021 was 69.93 per 100,000 population, showing a percentage change of -31.53% (EAPC =-1.21) (Table S1.6, Fig. 3 B). The global burden of ischemic heart disease (IHD) showed a declining trend from 1990 to 2021. The ASR for IHD deaths in 2021 was 7.11 per 100,000 population, with a percentage change of -10.05% (EAPC =-0.30) (Table S1.7, Fig. 3 A). The ASR for DALYs in 2021 was 138.57 per 100,000 population, showing a percentage change of -16.58% (EAPC=-0.58)​ (Table S1.8, Fig. 3 B). The global burden of lower extremity peripheral arterial disease (LE-PAD) declined from 1990 to 2021. The ASR for LE-PAD deaths in 2021 was 0.004 per 100,000 population, with a percentage change of -7.05% (EAPC=-0.28) (Table S1.9, Fig. 3 A). The ASR for DALYs in 2021 was 0.11 per 100,000 population, reflecting a percentage change of -9.32% (EAPC= -0.41%) (Table S1.10, Fig. 3 B). The global burden of stroke showed a declining trend from 1990 to 2021. The ASR for stroke deaths in 2021 was 6.65 per 100,000 population, with a percentage change of -28.73% (EAPC =-1.24) (Table S1.11, Fig. 3 A). The ASR for stroke-related DALYs in 2021 was 139.82 per 100,000 population, showing a percentage change of -34.66% (EAPC= -1.16)​ (Table S1.12, Fig. 3 B). 3.2. Gender-Based Trend Males had an ASR for CVDs deaths of 23.49 per 100,000 population (95% CI: -2.92, 7.23) in 2021, with 847.99 thousand deaths, a percentage change of -19.07% (EAPC =-0.71) (Table 1 , Fig. 1 A. in males had an ASR of 463.24 per 100,000 (95% CI: -58.69, 938.78), with 18180.43 thousand years, a percentage change of -26.42% (EAPC =-1.03) (Table 2 , Fig. 1 B). Females had an ASR for CVDs deaths of 13.42 per 100,000 population, with 628.24 thousand deaths, a percentage change of -22.78%, and an EAPC of -0.86% (Table 1 , Fig. 1 A). in females had an ASR of 255.67 per 100,000, with 11837.105 thousand DALYs, a percentage change of -29.57% (EAPC =-1.19) (Table 2 , Fig. 1 B). In 2021, the burden of CVD showed both increasing and decreasing trends across conditions. Deaths due to AA slightly increased in females (ASR 0.007 per 100,000, percentage change 2.80%, EAPC 0.13%), while they declined in males (ASR 0.019 per 100,000, percentage change − 18.01%, EAPC − 0.86%) (Table S1.1 and 1.2). AFF exhibited a rising trend for death and DALYs in both males (ASMR 0.14 per 100,000, percentage change 26.16%(EAPC = 0.79) and ASDR 3.36 per 100,000, percentage change 11.72 (EAPC = 0.40) and females (ASMR 0.10 per 100,000, percentage change 31.90%(EAPC = 0.92) and ASDR 2.19 per 100,000, percentage change 19.36 (EAPC = 0.55) (Table S1.3 and 1.4). All other conditions showed an overall decline in ASMR and ASDR burden. HHD had the largest decline in DALYs of both males (-34.00%, EAPC − 1.32%) and females ( -29.05%, EAPC − 1.09%). Similarly for, mortality in males (-25.33%, EAPC − 0.89%) and females (-19.26%, EAPC − 0.59%) (Table S1.5 and 1.6). 3.3. SDI Region trends The global burden of CVD showed significant variations across different SDI regions from 1990 to 2021. In High SDI regions, the ASR for CVD deaths in 2021 was lowest at 4.45 per 100,000 population (95% CI: -0.50, 9.42), with a percentage change of -53.81% and an EAPC of -2.63% (Table 1 , Fig. 2 A). The ASR for CVD-related DALYs in 2021 was also lowest at 84.54 per 100,000 population (95% CI: -9.58, 177.29), showing a percentage reduction of -55.32% (EAPC=-2.72) over the study period (Table 2 , Fig. 2 B). In Low SDI regions, the burden of CVD remained the highest, with an ASR for deaths in these regions of 35.10 per 100,000 population (95% CI: -3.63,68.01), showing a decline of -12.54% (EAPC=-0.41) over time (Table 1 , Fig. 2 A). The ASR for DALYs of 687.52 per 100,000 population (95% CI: -72.23, 1,352.17), reflecting a percentage change of -20.69% and an EAPC of -0.80% (Table 2 , Fig. 2 B). High SDI regions exhibited the most significant reductions in CVD mortality and DALYs rates, whereas Low SDI regions had the highest-burden but showed the slowest rate of decline. Among the diseases, AA showed the highest burden in low SDI regions, with an ASR for deaths at 0.02 per 100,000 population with a percentage change of 18.72% and an EAPC of 0.55% (Table S1.1). AFF had the highest burden in low-middle SDI regions, where the ASR for deaths reached 0.19 per 100,000 people. This condition demonstrated an increasing trend, with a percentage change of 57.06% and an EAPC of 1.53% (Table S1.3). Similarly, HHD had the highest burden in Low SDI regions, with an ASR for deaths of 10.63 per 100,000 population, showing a slight decline with a percentage change of -17.68% (EAPC = -0.63) (Table S1.5). IHD showed the highest burden in the Low-Middle SDI region, where the ASR for deaths reached 13.54 per 100,000 population, with a percentage change of 7.12% and an EAPC of 0.38% (Table S1.7). LE-PAD had the greatest impact in Low-SDI, where the ASR for deaths reached 0.01 per 100,000 people, with a percentage change of 41.18% and an EAPC of 1.14% (Table S1.9). Stroke exhibited the highest burden in Low SDI regions, where the ASR for deaths was 12.02 per 100,000 population, reflecting a notable decline with a percentage change of -22.56% and an EAPC of -0.88% (Table S1.11). While conditions like AFF showed increasing trends, significant declines were observed in AA, IHD, and stroke, particularly in lower SDI regions. 3.4. GBD Regions Trend The North Africa and Middle East region had the highest CVD mortality burden in 2021, with an ASMR of 40.09 per 100,000 population. Over time, the region experienced a 32.27% decrease in ASMR from 1990, with an EAPC (EAPC) of -1.27%, indicating a moderate decline (Table 1 , Fig. 2 A)​. Similarly, this region also showed the highest burden of DALYs due to CVD in 2021, with an ASR of 725.10 per 100,000 population. Over time, the region saw a 39.21% decrease in ASR, with an EAPC (EAPC) of -1.66%, indicating a significant decline in CVD-related health loss​ (Table 2 , Fig. 2 B). South Asia also had a significant CVD burden, with an ASMR of 29.21 per 100,000 population. However, the decline in ASMR from 1990 to 2021 was much smaller (-4.60%), and the EAPC of -0.07% suggests a very slow reduction (Table 1 , Fig. 2 A)​. For DALYs, it also showed an ASR of 587.60 per 100,000 people. However, the decline in ASR from 1990 to 2021 was only 14.62%, and the EAPC of -0.46% suggests a relatively slow reduction in DALYs (Table 2 , Fig. 2 B)​. Conversely, Southern Sub-Saharan Africa saw an increasing CVD mortality trend, with an ASMR of 18.42 per 100,000 population and a 22.01% increase in ASMR from 1990, along with a positive EAPC of 0.76%, indicating worsening conditions and persistent public health challenges (Table 1 , Fig. 2 A)​. Southern Sub-Saharan Africa recorded an increasing burden, with an ASR of 364.72 per 100,000 population, an 8.08% rise in DALYs since 1990, and a positive EAPC of 0.36%, indicating worsening health conditions​ overall, while regions like Western Europe and Southern Latin America experienced significant reductions in CVD mortality and disability (Table 2 , Fig. 2 B). In 2021, the burden of CVD varied across different regions, with notable trends in mortality and DALYs. AA had the highest burden in the Caribbean, where the ASMR (ASMR) was 0.03 per 100,000 population, and the DALYs ASR was 0.59 per 100,000 population. Meanwhile, Southern Sub-Saharan Africa experienced an increasing mortality trend of 6.40%, highlighting ongoing regional public health challenges (Table S1.1 and 1.2). AFF saw the highest percentage increase in burden in South Asia, where the ASMR reached 0.19 per 100,000 population, and the DALYs ASR was 4.25 per 100,000 population, reflecting a 74.48% increase in mortality over the study period (Table S1.3 and 1.4). Similarly, HHD had the highest burden in Central Sub-Saharan Africa, with an ASMR of 13.97 per 100,000 population and a DALYs ASR of 247.21 per 100,000 population, indicating a significant disease burden in the region (Table S1.5 and 1.6). For IHD, the North Africa and Middle East region experienced the highest burden, with an ASMR of 18.16 per 100,000 population and a DALYs ASR of 335.84 per 100,000 population, reinforcing the need for enhanced preventive and treatment strategies (Table S1.7 and 1.8). LE-PAD also had its highest burden in the Caribbean, with an ASMR of 0.02 per 100,000 population and a DALYs ASR of 0.36 per 100,000 population, alongside a 23.34% increase in ASMR, indicating a rising trend in the disease's impact (Table S1.9 and 1.10). Stroke had the highest burden in East Asia, where the ASMR reached 11.78 per 100,000 population, and the DALYs ASR was 220.96 per 100,000 population. Meanwhile, Southern Sub-Saharan Africa saw an 18.91% increase in mortality, highlighting the growing impact of stroke in the region (Table S1.11 and 1.12). These trends indicate a diverse global burden of CVD, with some regions showing increasing mortality and DALYs, while others continue to struggle with persistently high disease rates. 3.5. National Trends Among all nations, South Asian and Sub-Saharan African countries had the highest CVD burden due to lead exposure in 2021. For instance, Afghanistan had an ASMR of 98.11 per 100,000 (95% CI: -9.39, 192.06) and an ASDR of 1,948.18 per 100,000 (95% CI: -186.11, 3,885.22). Egypt and Sudan also reported high mortality burdens, with ASMRs of 87.60 with percentage change of -22.33 and 57.62 per 100,000 with percentage change of -28.37, respectively, reflecting the continued public health impact of lead exposure in these regions (Table S2.1, Fig. 4 A). Similarly, West and Central African countries such as Burkina Faso and Chad experienced an increase in mortality and DALYs, with Chad showing a 27.66% increase in ASMR and a 21.48% increase in ASDR from 1990 to 2021, highlighting a worsening trend despite global improvements. Lesotho had the highest increase in CVD mortality, with a 47.66% rise in ASMR and a 48.91% rise in ASDR, indicating a critical need for intervention (Table S2.1, Fig. 4 B). In contrast, due to lead exposure, high-income countries exhibited the most substantial reductions in CVD mortality and DALYs. Republic of Korea, Ireland, and Israel recorded the steepest declines in ASMR, decreasing by 74.11%, 73.88%, and 72.99%, respectively. Among the countries with the largest DALY reductions, the Republic of Korea, Ireland, and Singapore demonstrated the most progress, recording a 78.73% decrease in ASDR, Ireland a 77.57% decrease, and Singapore a 74.65% decrease. Despite global improvements, some nations saw little change or even increases in CVD mortality and DALYs due to lead exposure. Lesotho and Zimbabwe experienced minimal changes, with ASMR increases of 47.65% and 34.96%, respectively. For the ASDR percentage change, Lesotho showed the highest increase at 48.90% (Table S2.1). In 2021, the burden of CVD varied across countries, with significant trends observed in mortality and DALYs. AA had the highest burden in Armenia, with an ASMR of 0.050 per 100,000, reflecting an increase of 115.83%, ASDR of 0.54, and a percentage change of 81.01% (Table S2.2)​. AFF showed the highest burden in Honduras, where the ASMR reached 0.47 per 100,000, marking a rise of 88.10%​, while ASDR is 8.85, and the percentage change is 40.30 (Table S2.3). HHD was most prevalent in Afghanistan, with an ASMR of 33.94 per 100,000 and an ASDR of 624.91​ (Table S2.4). IHD was most prevalent in Egypt, with an ASMR of 40.35 per 100,000, a percentage change of -1.38, and ASDR of 739.80 and − 8.45% (Table S2.5)​. LE-PAD had the highest burden in Cuba, with an ASMR of 0.035 per 100,000, reflecting an increase of 56.31% for ASDR of 0.62 and a percentage change of 47.22%​ (Table S2.6). Stroke had the highest mortality burden in Afghanistan, where the ASMR was 27.04 per 100,000, with a decline of 21.45%, ASDR was 560.01, and percentage change was − 27.26 (Table S2.7)​. Table 1 Mortality data for CVD attributable to lead exposure across GBD regions presenting total numbers, ASRs per 100,000 for 1990 and 2021, percentage changes over time, and EAPC from 1990 to 2021." location No. 10 3 (95% UI) 1990 No. 10 3 (95% UI) 2021 ASR, per 100,000, 1990 ASR, per 100,000, 2021 percentage changes, %, 1990–2021 EAPC, %, 1990–2021 Global 796.30(-90.53,1670.93) 1476.24(-175.72,3080.40) 22.37(-2.55,46.91) 17.82(-2.11,37.19) -20.37(-26.76,-12.68) -0.76(-0.85,-0.67) SEX Male 451.13(-51.34,916.00) 847.99(-106.17,1710.76) 29.025(-3.307,59.009) 23.490(-2.917,47.231) -19.07(-8.30,-27.27) -0.71(-0.81,-0.60) Female 345.16(-39.13,737.66) 628.24(-69.54,1331.12) 17.376(-1.974,37.090) 13.417(-1.486,28.438) -22.78(-13.95,-30.83) -0.86(-0.94,-0.78) SDI Regions High SDI 105.85(-13.88,226.23) 109.79(-12.39,232.70) 9.63(-1.26,20.55) 4.45(-0.50,9.42) -53.81(-60.83,-50.65) -2.63(-2.70,-2.56) High-middle SDI 171.72(-20.06,368.83) 298.27(-35.64,625.65) 19.90(-2.34,42.57) 15.51(-1.84,32.46) -22.05(-31.34,-11.96) -0.92(-1.17,-0.66) Middle SDI 263.24(-26.53,542.23) 543.37(-65.29,1126.88) 31.59(-3.14,64.81) 23.40(-2.79,48.36) -25.92(-35.14,-13.56) -0.97(-1.05,-0.89) Low-middle SDI 180.68(-21.27,367.98) 385.80(-46.42,785.36) 34.97(-4.01,70.93) 31.43(-3.74,63.67) -10.14(-17.92,1.57) -0.29(-0.37,-0.20) Low SDI 73.92(-7.71,146.64) 137.75(-14.46,270.13) 40.13(-4.09,78.99) 35.10(-3.63,68.01) -12.54(-19.95,-1.20) -0.41(-0.51,-0.30) GBD Regions Andean Latin America 2.55(-0.24,5.30) 4.84(-0.48,10.13) 14.01(-1.28,29.01) 8.62(-0.84,18.03) -38.47(-49.23,-25.77) -1.62(-1.74,-1.51) Australasia 3.33(-0.46,7.12) 2.99(-0.39,6.36) 14.76(-2.04,31.53) 4.71(-0.62,10.01) -68.11(-71.80,-65.23) -3.90(-3.99,-3.80) Caribbean 7.44(-0.90,15.26) 12.68(-1.33,25.40) 31.03(-3.73,63.47) 23.21(-2.45,46.54) -25.22(-34.33,-14.71) -0.83(-0.90,-0.77) Central Asia 8.05(-1.02,17.40) 12.40(-1.50,26.11) 19.40(-2.45,41.86) 19.19(-2.33,40.40) -1.08(-10.75,9.24) -0.24(-0.63,0.15) Central Europe 26.43(-3.39,55.99) 29.87(-3.36,61.53) 19.51(-2.50,41.20) 12.46(-1.40,25.66) -36.16(-45.30,-30.34) -1.69(-1.85,-1.54) Central Latin America 15.69(-1.80,31.41) 35.11(-4.36,72.07) 22.61(-2.56,45.11) 14.94(-1.86,30.61) -33.93(-40.28,-25.69) -1.54(-1.68,-1.40) Central Sub-Saharan Africa 5.21(-0.45,10.69) 12.33(-1.08,25.39) 30.12(-2.47,62.00) 32.39(-2.68,66.07) 7.55(-14.52,37.12) 0.21(0.16,0.27) East Asia 242.03(-22.87,508.54) 487.56(-58.75,1022.55) 37.24(-3.37,78.06) 25.75(-3.09,53.89) -30.88(-44.05,-11.57) -1.15(-1.32,-0.98) Eastern Europe 30.55(-4.12,67.16) 38.92(-4.83,83.98) 12.26(-1.66,26.85) 10.83(-1.35,23.38) -11.63(-18.73,-3.16) -0.95(-1.55,-0.35) Eastern Sub-Saharan Africa 24.25(-2.06,47.72) 34.44(-2.98,67.43) 41.37(-3.32,81.83) 27.80(-2.30,53.72) -32.80(-41.27,-22.52) -1.47(-1.56,-1.39) High-income Asia Pacific 13.25(-1.60,28.90) 15.74(-1.82,33.40) 7.42(-0.89,16.12) 2.39(-0.28,5.12) -67.85(-71.34,-64.59) -3.71(-3.82,-3.59) High-income North America 33.37(-4.45,71.75) 34.60(-3.64,73.11) 9.20(-1.22,19.76) 4.80(-0.50,10.12) -47.82(-59.50,-42.72) -2.26(-2.38,-2.14) North Africa and Middle East 80.37(-8.66,162.06) 143.16(-15.11,289.02) 59.19(-6.22,119.07) 40.09(-4.18,80.50) -32.27(-38.91,-23.66) -1.27(-1.31,-1.23) Oceania 0.36(-0.04,0.75) 0.77(-0.08,1.67) 16.35(-1.63,34.32) 13.90(-1.39,29.88) -14.98(-28.94,4.17) -0.56(-0.64,-0.47) South Asia 149.24(-18.76,304.18) 368.56(-46.48,748.65) 30.61(-3.74,61.94) 29.21(-3.63,58.70) -4.60(-15.74,11.83) -0.07(-0.19,0.04) Southeast Asia 50.30(-5.53,105.61) 110.88(-13.28,234.51) 22.16(-2.44,46.66) 19.43(-2.33,41.07) -12.32(-23.49,3.84) -0.44(-0.62,-0.26) Southern Latin America 4.36(-0.47,9.28) 5.01(-0.47,10.65) 10.09(-1.09,21.46) 5.49(-0.52,11.66) -45.63(-54.49,-39.36) -1.77(-1.88,-1.67) Southern Sub-Saharan Africa 3.60(-0.31,7.28) 8.63(-0.76,17.33) 15.10(-1.27,30.76) 18.42(-1.61,36.31) 22.01(7.39,38.34) 0.76(0.25,1.28) Tropical Latin America 18.93(-2.21,39.24) 26.53(-2.96,54.31) 24.03(-2.78,49.68) 10.74(-1.19,21.99) -55.29(-58.15,-52.46) -2.46(-2.56,-2.35) Western Europe 57.69(-7.70,122.65) 54.98(-5.55,114.30) 9.70(-1.29,20.61) 4.41(-0.46,9.21) -54.54(-64.79,-49.40) -2.60(-2.71,-2.50) Western Sub-Saharan Africa 19.30(-1.88,40.66) 36.21(-3.81,71.37) 25.99(-2.54,54.59) 23.56(-2.54,46.20) -9.35(-22.83,7.12) -0.46(-0.64,-0.28) Table 2 DALYs data for CVD attributable to lead exposure across GBD regions presenting total numbers, ASRs per 100,000 for 1990 and 2021, percentage changes over time, and EAPC from 1990 to 2021." location No. 10 3 (95% UI) 1990 No. 10 3 (95% UI) 2021 ASR, per 100,000, 1990 ASR, per 100,000, 2021 percentage changes, %, 1990–2021 EAPC, %, 1990–2021 Global 19030.69(-2151.61,39936.97) 30017.54(-3660.04,62164.62) 484.95(-54.93,1016.51) 351.36(-42.73,727.75) -27.55(-33.43,-20.45) -1.09(-1.19,-0.99) SEX Male 11410.72(-1297.96,23203.75) 18180.43(-2315.55,36956.47) 629.581(-71.656,1280.875) 463.237(-58.699,938.780) -26.42(-17.09,-34.22) -1.03(-1.14,-0.92) Female 7619.96(-853.64,16361.33) 11837.10(-1327.53,24806.83) 362.997(-40.743,778.534) 255.674(-28.631,535.840) -29.57(-22.28,-36.79) -1.19(-1.28,-1.11) SDI Regions High SDI 2070.53(-274.16,4440.73) 1837.67(-210.61,3851.04) 189.22(-24.97,405.48) 84.54(-9.58,177.29) -55.32(-61.76,-52.56) -2.72(-2.77,-2.66) High-middle SDI 3839.56(-442.55,8229.71) 5284.02(-654.35,11150.35) 401.25(-46.42,858.16) 269.70(-33.27,569.36) -32.79(-40.64,-23.18) -1.47(-1.74,-1.19) Middle SDI 6489.88(-661.57,13453.86) 10875.42(-1315.60,22606.99) 651.09(-65.84,1340.01) 426.18(-51.41,885.22) -34.54(-43.20,-23.62) -1.39(-1.47,-1.31) Low-middle SDI 4693.82(-559.50,9631.88) 8742.16(-1057.59,17882.17) 769.85(-90.67,1569.13) 630.82(-76.03,1287.66) -18.06(-25.26,-7.81) -0.60(-0.69,-0.52) Low SDI 1917.32(-201.23,3811.21) 3253.70(-342.03,6439.62) 866.89(-90.17,1718.09) 687.52(-72.23,1352.17) -20.69(-27.76,-11.57) -0.80(-0.89,-0.70) GBD Regions Andean Latin America 57.19(-5.49,119.07) 94.90(-9.59,200.31) 283.08(-26.83,586.09) 163.20(-16.45,344.12) -42.35(-52.31,-29.91) -1.85(-1.97,-1.72) Australasia 63.38(-8.87,135.40) 45.36(-6.20,96.16) 273.73(-38.26,584.30) 78.04(-10.62,165.28) -71.49(-74.18,-69.21) -4.27(-4.34,-4.19) Caribbean 167.45(-20.10,341.55) 255.89(-27.11,512.77) 652.70(-78.51,1331.50) 473.95(-50.22,950.09) -27.39(-36.73,-16.17) -0.94(-1.00,-0.88) Central Asia 177.99(-22.83,384.83) 251.84(-30.76,531.27) 392.29(-50.21,849.06) 342.25(-41.92,722.69) -12.76(-21.45,-2.91) -0.77(-1.21,-0.32) Central Europe 555.67(-72.35,1183.64) 488.89(-56.12,1009.09) 384.35(-49.94,817.62) 213.10(-24.44,440.42) -44.56(-51.65,-39.89) -2.20(-2.38,-2.03) Central Latin America 343.33(-40.67,693.80) 652.21(-82.81,1353.93) 432.47(-50.81,870.97) 267.35(-33.95,554.08) -38.18(-44.16,-30.70) -1.81(-1.94,-1.67) Central Sub-Saharan Africa 140.42(-12.24,289.17) 301.88(-27.45,619.28) 645.05(-55.13,1329.45) 620.22(-54.41,1264.16) -3.85(-23.25,20.86) -0.17(-0.24,-0.09) East Asia 5836.50(-567.39,12217.55) 9015.75(-1074.65,18928.42) 727.37(-68.76,1520.87) 436.63(-51.93,917.19) -39.97(-51.67,-23.45) -1.64(-1.79,-1.48) Eastern Europe 643.70(-86.67,1418.71) 714.40(-88.48,1531.60) 240.19(-32.38,528.57) 202.80(-25.10,433.75) -15.57(-22.45,-8.04) -1.19(-1.86,-0.52) Eastern Sub-Saharan Africa 614.86(-53.00,1218.62) 803.70(-71.08,1593.91) 863.23(-72.72,1705.60) 527.58(-45.86,1034.04) -38.88(-46.59,-29.85) -1.81(-1.91,-1.72) High-income Asia Pacific 281.27(-33.76,611.21) 237.76(-28.05,506.96) 145.04(-17.33,314.72) 44.73(-5.37,96.30) -69.16(-71.45,-66.41) -3.92(-3.99,-3.85) High-income North America 633.63(-83.48,1349.77) 604.67(-61.52,1251.09) 181.18(-23.64,385.04) 91.10(-8.98,187.89) -49.72(-61.12,-44.83) -2.30(-2.39,-2.21) North Africa and Middle East 1935.31(-214.63,3906.03) 3034.85(-325.41,6195.16) 1192.72(-130.11,2405.71) 725.10(-77.44,1468.23) -39.21(-45.29,-31.31) -1.66(-1.72,-1.60) Oceania 9.65(-0.94,20.45) 19.55(-1.86,42.33) 342.50(-34.04,718.91) 278.66(-27.23,602.94) -18.64(-33.18,1.82) -0.70(-0.79,-0.61) South Asia 3996.13(-508.30,8249.07) 8378.46(-1067.05,17223.64) 688.21(-86.29,1404.18) 587.60(-74.41,1201.49) -14.62(-24.24,-0.85) -0.46(-0.55,-0.37) Southeast Asia 1389.35(-152.18,2918.67) 2651.83(-313.52,5583.18) 522.38(-57.39,1095.61) 409.91(-48.58,864.92) -21.53(-31.96,-7.94) -0.80(-0.98,-0.63) Southern Latin America 97.18(-10.99,207.67) 89.49(-9.05,190.59) 213.56(-24.06,456.43) 101.10(-10.27,215.15) -52.66(-57.78,-47.99) -2.35(-2.43,-2.27) Southern Sub-Saharan Africa 93.55(-8.12,191.28) 201.05(-17.94,409.68) 337.45(-29.37,687.58) 364.72(-32.56,734.80) 8.08(-3.61,20.73) 0.36(-0.16,0.88) Tropical Latin America 470.75(-54.84,981.45) 539.86(-62.13,1112.87) 514.22(-60.16,1068.88) 211.99(-24.30,436.87) -58.77(-61.07,-56.60) -2.83(-2.94,-2.72) Western Europe 1036.83(-141.14,2213.24) 759.33(-83.56,1589.35) 178.98(-24.32,381.66) 69.42(-7.93,145.22) -61.21(-68.01,-57.95) -3.18(-3.28,-3.07) Western Sub-Saharan Africa 486.55(-46.33,1021.87) 875.88(-88.89,1733.27) 560.41(-54.04,1180.55) 469.39(-49.27,923.99) -16.24(-28.91,0.54) -0.72(-0.91,-0.52) 4. DISCUSSION The GBD data analysis from 1990 to 2021 demonstrates a notable decline in the burden of CVD worldwide, consistent with previous studies that have reported similar downward trends in CVD mortality and morbidity over recent decades [ 25 ]. The ASR of CVD deaths decreased by -20.37%, and DALYs declined by -27.55%, reinforcing earlier findings highlighting advancements in healthcare, preventive measures, and improved management strategies in reducing CVD burden [ 26 ]. Similar to earlier reports, IHD and stroke, the leading contributors to CVD-related morbidity and mortality, have shown a downward trajectory [ 27 ]. The ASR for IHD deaths decreased by -10.05%, and DALYs declined by -16.58%, while stroke-related deaths and DALYs exhibited an even greater reduction of -28.73% and − 34.66%, respectively [ 28 ]. These findings support previous studies that attribute declining stroke and IHD mortality to advances in thrombolytic therapy, revascularization techniques, and improved management of hypertension, smoking, and dietary risk factors [ 29 ]. HHD has also substantially reduced, with ASR declines of -22.04% for deaths and − 31.53% for DALYs. This is consistent with earlier research emphasizing the impact of improved blood pressure control through lifestyle interventions and the widespread use of antihypertensive medications [ 30 ]. Likewise, the mortality rates for AA and LE-PAD declined by -10.12% and − 7.05%, respectively, mirroring prior observations that associate these trends with enhanced screening, surgical interventions, and risk factor management, including smoking cessation and lipid control [ 31 ]. However, in contrast to the general decline in CVD burden, atrial fibrillation and flutter have shown an increasing trend, with a 30.73% rise in ASR for deaths and a 16.26% increase in ASR for DALYs [ 32 ]. This upward trajectory aligns with previous studies highlighting an aging global population, increased diagnostic sensitivity, and the rising prevalence of obesity, hypertension, and diabetes as key drivers of this burden. Despite advancements in anticoagulation therapy and arrhythmia management, earlier research also underscores the persistent challenges posed by atrial fibrillation in stroke prevention and healthcare resource utilization [ 33 ]. Males had an ASMR and ASDR for CVDs deaths, which shows that men continue to bear a greater burden of CVD than women. In contrast to earlier studies that indicated a more consistent fall across genders [ 34 ], the percentage decrease in mortality and DALYs over time has been marginally higher in females [ 35 ]. Although advancements have aided this trend in healthcare awareness and access, the gender gap in the total burden of CVD persists, underscoring the need for additional focused treatments, particularly for males, to close the gap further [ 36 ]. The current results show an increasing tendency in both sexes, with a more significant increase in females, in contrast to previous research that revealed a consistent prevalence of AFF [ 37 ]. This shows that AFF mainly affects men and points to sex-specific risk factors and growing diagnostic understanding [ 38 ]. HHD had the largest decline in DALYs of both males (-34.00%, EAPC − 1.32%) and females ( -29.05%, EAPC − 1.09%) coinciding with the previous studies and supports the benefits of better hypertension control [ 39 ]. Furthermore, the most recent data reveal a different pattern: a drop in males but a little increase in females compared to earlier studies that documented a consistent decline in AA-related mortality across genders [ 40 ]. The historical under-representation of women in clinical trials and delayed diagnosis could be the cause of this discrepancy. Significant disparities are revealed by the SDI-based analysis of CVD from 1990 to 2021. High SDI regions exhibit the largest reductions in mortality (-53.81%) and DALYs (-55.32%), which is consistent with research that attributes these declines to improvements in healthcare and preventive measures [ 41 , 42 ]. They attributed these findings to delayed diagnoses and healthcare inequality. Deaths from AFF increased significantly in Low-Middle SDI regions (57.06%; EAPC = 1.53%), which is consistent with research showing that aging populations and underdiagnosis are major causes [ 43 , 44 ]. According to studies that link poor risk factor control to disease burden, poorer SDI regions also saw increases in IHD and LE-PAD [ 42 ]. Decreases in AAs, HHD, and stroke, particularly in areas with poor SDI, support [ 45 ] findings on better management of hypertension. These enduring differences highlight the necessity of focused measures to improve early diagnosis and healthcare access in lower-income areas [ 46 ]. The GBD regional study (1990–2021) highlights significant differences in the burden of CVD. In 2021, North Africa and the Middle East found the greatest ASMR and ASDR. Despite this, the region experienced the largest decreases in DALYs (-39.21%) and ASMR (-32.27%), which is consistent with earlier research that links improved healthcare to a decrease in mortality [ 47 ]. South Asia, on the other hand, only had modest drops in DALYs (-14.62%) and ASMR (-4.60%), which is due to studies showing sluggish success in reducing CVD risk factors [ 48 ]. In line with previous findings on healthcare shortages, CVD trends worsened in Southern Sub-Saharan Africa, with rising DALYs (+ 8.08%) and ASMR (+ 22.01%) [ 49 ]. Whereas hypertensive cardiac disease continued to be most prevalent in Central Sub-Saharan Africa, AFF increased in South Asia (+ 74.48% ASMR). The Middle East and North Africa had the highest rates of IHD, which is consistent with earlier research that focused on risk factors unique to these regions [ 50 ]. Additionally, LE-PAD rose in the Caribbean (+ 23.34% ASMR), highlighting the necessity of specialized therapies. These results highlight how urgent it is to implement region-specific healthcare measures to reduce the rising rates of CVD death and disability. Lead exposure's effect on the burden of CVD varied greatly, with the highest death rates occurring in South Asia and Sub-Saharan Africa, especially in Afghanistan (ASMR: 98.11, ASDR: 1,948.18) and Egypt (ASMR: 87.60, -22.33%). In contrast, the biggest drops were seen in the Republic of Korea (-74.11% ASMR, -78.73% ASDR) and Ireland (-73.88% ASMR, -77.57% ASDR), which is following research that links lower lead exposure to better cardiovascular outcomes [ 51 ]. Lesotho (+ 47.66% ASMR, + 48.91% ASDR) and Chad (+ 27.66% ASMR, + 21.48% ASDR) had deteriorating trends despite global advancements, which is consistent with studies on chronic environmental lead exposure in low-income areas [ 52 ]. Similar to earlier research on the role of lead in vascular dysfunction, Armenia had the biggest increase in AA mortality (+ 115.83% ASMR). In contrast, Honduras had the highest increase in atrial fibrillation (+ 88.10% ASMR) [ 53 ]. According to previous research on lead-induced hypertension and ischemic events, Egypt had the largest burden of IHD (ASMR: 40.35, ASDR: 739.80), whereas Afghanistan had the highest burden of HHD (ASMR: 33.94, ASDR: 624.91). These results demonstrate the critical need for more stringent environmental laws and healthcare preventative initiatives in high-risk areas. This study has several limitations. First, the accuracy of GBD estimates depends on data quality, which varies across countries, particularly in low-income regions where underreporting and misclassification may affect CVD mortality and DALYs trends. Second, causality cannot be directly established, as lead exposure interacts with other risk factors, such as poor nutrition and healthcare access. Third, the study does not account for individual-level variations in exposure or genetic susceptibility, which may influence disease outcomes. Lastly, differences in healthcare infrastructure and prevention strategies across countries may impact CVD trends, limiting direct comparisons. 5. CONCLUSION The burden of CVDs attributable to lead exposure has notably declined in high-income countries, reflecting the success of environmental regulations and public health interventions. However, in low- and middle-income regions, particularly in South Asia and Sub-Saharan Africa, lead-related CVD burden remains a pressing public health challenge, as evidenced by persistently high mortality and DALYs. Despite some regions showing improvements, others, such as Lesotho and Chad, are experiencing worsening trends, underscoring the need for more effective and context-specific public health strategies. Strengthening environmental policies, improving healthcare access, and fostering international cooperation are crucial to reducing lead exposure globally. A concerted effort is necessary to address these regional disparities and mitigate the long-term cardiovascular consequences of lead toxicity, which is key to advancing public health and reducing the global burden of CVDs. 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American College of Cardiology Foundation Washington, DC. pp. 2529–2532 Fihn SD et al (2014) ACC/AHA/AATS/PCNA/SCAI/STS focused update of the guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines, and the American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. The Journal of thoracic and cardiovascular surgery, 2015. 149(3): pp. e5-e23 Chugh SS et al (2014) Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation 129(8):837–847 Feigin VL, Norrving B, Mensah GA (2017) Global burden of stroke. Circul Res 120(3):439–448 Khawar MB et al (2026) Global, regional, and national trends in hypertensive heart disease burden due to high BMI: a 30-year analysis using GBD 2021 data with projections to 2035. Front Public Health 14:1701954 Benjamin EJ et al (2019) Heart disease and stroke statistics—2019 update: a report from the American Heart Association. Circulation 139(10):e56–e528 Prabhakaran D et al (2018) The changing patterns of cardiovascular diseases and their risk factors in the states of India: the Global Burden of Disease Study 1990–2016. Lancet Global Health 6(12):e1339–e1351 Souza M et al (2024) Global Epidemiology and Characteristics of Metabolic-associated Steatotic Liver Disease in Type 1 Diabetes Mellitus: An Updated Systematic Review and Meta-analysis. Clinical Gastroenterology and Hepatology Joseph P et al (2022) Cardiovascular disease, mortality, and their associations with modifiable risk factors in a multi-national South Asia cohort: a PURE substudy. Eur Heart J 43(30):2831–2840 Lanphear BP et al (2019) Low-level lead exposure and mortality in US adults: A population-based cohort study. J Australasian Coll Nutritional Environ Med 38(3):5–13 LeBrón AM et al (2019) The state of public health lead policies: Implications for urban health inequities and recommendations for health equity. Int J Environ Res Public Health 16(6):1064 Navas-Acien A et al (2007) Lead exposure and cardiovascular disease—a systematic review. Environ Health Perspect 115(3):472–482 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9571210","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":632113079,"identity":"7ac50a77-a099-471a-b34c-3465e56d3d58","order_by":0,"name":"kaleem maqsood","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACCQbmBgSPh8cGSDI2HsCvhRFZi0waSEsDKVpsDoNpvFok2w82PviYs02e7/zhgw/e5Jy3W9t+GGhLjU00Li3SPInNhjO33TaceSMt2XDOmdvJ284kArUcS8ttwKFFjiGxTZp3223GDTd4zKR5e24nmx0AamFsOIxbC//D9t9/t92233D+DFDLv3PJZucf4tciLZHYxsy47XbihgM5ZtI8PAfszG4QsEVyxsNmyd5tt5MhfuFJTjC7AbQlAY9fJM4nH/zwc9tt2z5wiPHY2ZudT3/44EONDU4tCHAAQiWCVSYQVI6kxZ4oxaNgFIyCUTCiAACwPWuvoqWY5gAAAABJRU5ErkJggg==","orcid":"","institution":"Institute of Zoology, University of the Punjab, Lahore 54590, Pakistan","correspondingAuthor":true,"prefix":"","firstName":"kaleem","middleName":"","lastName":"maqsood","suffix":""},{"id":632113080,"identity":"abafc277-5cc0-4c9a-8304-b1d188a2f933","order_by":1,"name":"Akasha Fiaz","email":"","orcid":"","institution":"Institute of Zoology, University of the Punjab, Lahore 54590, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Akasha","middleName":"","lastName":"Fiaz","suffix":""},{"id":632113081,"identity":"86e6a26e-348c-4a31-924b-9d8b5dd7075d","order_by":2,"name":"Mahnoor Fatima","email":"","orcid":"","institution":"Centre of Excellence in Molecular Biology (CEMB), University of the Punjab, Lahore, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Mahnoor","middleName":"","lastName":"Fatima","suffix":""}],"badges":[],"createdAt":"2026-04-30 03:05:59","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9571210/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9571210/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108420534,"identity":"3d558e3c-ace1-4973-8008-971feb86fa11","added_by":"auto","created_at":"2026-05-04 12:34:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59642,"visible":true,"origin":"","legend":"\u003cp\u003eGender-Based Trends in EAPC of Age-Standardized all cardiovascular disorders Mortality (A) and DALYS (B) Rates Due to Lead Exposure (1990–2021).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9571210/v1/54645c71def4a280a56962c3.png"},{"id":108493517,"identity":"9818d22f-eaa5-4684-95c4-87bd344fc0a8","added_by":"auto","created_at":"2026-05-05 10:00:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148294,"visible":true,"origin":"","legend":"\u003cp\u003eEAPC trend in GBD Regions of Age-Standardized cardiovascular disorders Mortality (A) and DALYs (B) Rates Due to Lead Exposure (1990–2021).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9571210/v1/0b01b5cfd578691ce542c2e4.png"},{"id":108420536,"identity":"b152034e-79da-4719-9e0f-b7fca33b4134","added_by":"auto","created_at":"2026-05-04 12:34:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":201368,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal trend of ASMR (A) and ASDR (B) per 100,000 of CVD from 1990–2021.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9571210/v1/e50633b43a3129c40b713432.png"},{"id":108492991,"identity":"63032027-faf5-4e6a-b3cf-afa8756b8b0b","added_by":"auto","created_at":"2026-05-05 09:59:13","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":691078,"visible":true,"origin":"","legend":"\u003cp\u003eThe global distribution of ASRs of CVD Mortality (A) and DALYs (B) in 204 countries for both sexes in 1990 and 2021.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9571210/v1/2f74b024baeb182519f5e9df.jpeg"},{"id":108495203,"identity":"dceb7211-579c-4f81-ba03-72c3c2970c74","added_by":"auto","created_at":"2026-05-05 10:09:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1506740,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9571210/v1/48929c78-1cc4-469f-8966-31859426770c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGlobal Burden of Cardiovascular Disease Attributable to Lead Exposure: A Comprehensive Analysis from 1990 to 2021\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eCardiovascular diseases (CVDs) are among the leading health concerns worldwide owing to their increasing incidence and consequent disabilities and mortalities, with a heavy economic burden as they are an important contributor to the cost of medical care [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. There was a 42.4% increase in CVD-related fatalities from 1990 to 2015 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 2016, CVDs contributed to almost 1/3rd of all mortalities worldwide [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In 2017, CVDs caused over 17\u0026nbsp;million mortalities, 35.6\u0026nbsp;million years-lived-with-disability, and 330\u0026nbsp;million years of life lost globally [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The deaths attributed to CVDs increased from 12.1\u0026nbsp;million people in 1990 to 18.6\u0026nbsp;million people in 2019 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It was estimated that by 2030, CVDs would be responsible for over 23\u0026nbsp;million deaths worldwide. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. CVDs comprise ischemic heart disease (IHD) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], stroke, heart failure, aortic aneurysm, atrial flutter and fibrillation, hypertension, peripheral arterial disease, rheumatic heart disease, and numerous other cardiac and vascular morbidities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The global prevalence of IHD is rising. It affects almost 1.72% (126\u0026nbsp;million people) of the global population and has contributed to nine million mortalities worldwide [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A targeted approach to reducing CVDs burden and associated mortalities can be accomplished through a deeper understanding of sex- and region-specific trends. Accurately estimating CVDs risk factors can be instrumental in formulating efficient policies for public health.\u003c/p\u003e \u003cp\u003eAs societies continue to industrialize, heavy metal pollutants are becoming increasingly widespread. Lead is among the most prevalent environmental contaminants, and its exposure has constantly remained a major health concern for the public. Children can ingest it via contaminated soil, air, paints, and chip destruction dust [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Lead exposure has caused the deaths of around one million people and disability-adjusted life-years (DALYs) for 21.7\u0026nbsp;million people globally [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. World Health Organization (WHO) reported that in 2016, 82% of lead-related deaths occurred among low- and middle-income populations in developing countries [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLead is toxic because it accumulates in the human body with time and is detrimental at low concentrations [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It has been recognized for many years to cause fatal diseases like cancer and cardiovascular disorders, among others [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Considerable in-vitro and in-vivo evidence shows that lead exposure causes inflammation and oxidative stress and reduces bioavailability [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These mechanisms are major contributors to lead-linked vascular diseases [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Adverse effects of lead exposure on cardiac health include an augmented risk of cardiotoxicity, heart failure, arrhythmia, hypertension, and myocardial ischemia. Lead exposure can also cause vasoconstriction and vasodilation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In studies involving animals and humans, exposure to low concentrations of lead for the long term has shown an elevated risk of persistent hypertension and cardiovascular mortality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Cohort studies by the National Health and Nutrition Examination Survey (NHANES) have shown that blood lead levels and prevalence of peripheral artery disease (PAD) are associated [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, according to a population-level study including 179 participants, higher baseline blood lead levels predicted weakened left ventricular systolic function ten years later [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEstimating the impact of chronic lead exposure on the burden of major cardiovascular diseases is essential to implementing prevention policies. Therefore, we estimated trends in significant CVDs burden attributable to lead exposure based on the latest GBD data. This information is crucial for government and healthcare systems to make decisions for executing efficient prevention and alleviation programs.\u003c/p\u003e"},{"header":"2. METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data Source\u003c/h2\u003e \u003cp\u003eThe data source for this study was the GBD 2021 database, which comprehensively estimates the burden of injuries and diseases across 204 territories and countries. This incorporates data from disease registries, national censuses, vital statistics, health service utilization, civil registration, and an extensive analysis of published literature on disease prevalence and incidence [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The GBD framework also provides comparative assessments for 87 risk factors across 21 regions globally. The Global Health Data Exchange (GHDx) results tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ghdx.healthdata.org/gbd-results-tool\u003c/span\u003e\u003cspan address=\"http://ghdx.healthdata.org/gbd-results-tool\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) provided data on the overall CVDs burden and six CVD diseases among them are Aortic aneurysm (AA), Atrial fibrillation and flutter (AFF), Hypertensive heart disease (HHD), Ischemic heart disease IHD, Lower extremity peripheral arterial disease (LE-PAD) and stroke among them caused by lead exposure. The dataset comprises metrics stratified by sex, geographic region, SDI (SDI), and 204 territories and countries including mortality, DALYs, and age-standardized rates (ASRs).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Extraction and Processing\u003c/h2\u003e \u003cp\u003eFrom 1990 to 2021, data has been collected on lead exposure as a risk factor for CVDs on a global, regional, and country level. The Comparative Risk Assessment (CRA) paradigm was used to examine fatalities and DALYs associated with CVD.\u003c/p\u003e \u003cp\u003eResearch studies, national health surveys, and systematic reviews were used to determine blood lead levels. A cumulative blood lead index method was used to measure bone lead exposure.\u003c/p\u003e \u003cp\u003eBayesian meta-regression and spatiotemporal Gaussian process regression (ST-GPR) were employed to estimate missing exposure values and guarantee complete data coverage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data Analysis\u003c/h2\u003e \u003cp\u003eWe analyzed age-standardized data on the burden of cardiovascular disorders due to lead exposure using death and DALYs metrics from the GBD 2021 database, spanning 1990 to 2021. The age-standardized death and DALYs data were stratified by year, sex, country, and region.\u003c/p\u003e \u003cp\u003eThe temporal trend of burden was found through EAPC, calculated by the Linear regression model, and applied to the natural log of SEV rate over time [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The overall temporal trend of mortality and disability burden was categorized as decreasing if the upper boundary of the confidence interval was less than 0, increasing if the lower boundary was greater than zero, and stable in other cases.\u003c/p\u003e \u003cp\u003eGender-wise trends in global exposure burden were assessed to observe gender-based disparities across the study period. Additionally, ASMR and ASDR of all countries for 1990 and 2021 were presented through choropleth to visualize the geographical and spatial distribution of burden [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll analyses were conducted using R software (version 4.2.2). All analyses of this study are adhered to the Guidelines for Accurate and Transparent Health Estimates Reporting.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Global Trends\u003c/h2\u003e \u003cp\u003eThe GBD data for CVDs ASMR and ASDR due to lead exposure indicates a decreasing trend from 1990 to 2021. The number of deaths and DALYs due to CVDs in 2021 were 1,476.24 thousand and 30,017.54 thousand, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The ASMR for CVDS in 2021 was 17.82 per 100,000 population (95% CI: -2.11, 37.19), with a percentage change of -20.37% (EAPC =-0.76). The ASDR was 351.36 per 100,000 population (95% CI: -42.73, 727.75), with a percentage change of -27.55% (EAPC =-1.09) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe \u003cb\u003eaortic aneurysm (AA)\u003c/b\u003e showed a moderate decline in its impact over the study period. The ASR for AA deaths in 2021 was 0.01 per 100,000 population, showing a percentage change of -10.12% (EAPC =-0.48) (Table S1.1, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and for DALYs was 0.24, with a percentage change of -15.95% (EAPC =-0.74, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) (Table S1.2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAtrial fibrillation and flutter (AFF)\u003c/b\u003e have shown an increasing global burden over the years. The ASR for AFF deaths in 2021 was 0.12 per 100,000 population, with a percentage change of 30.73% (EAPC 0.89) (Table S1.3, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The ASR for DALYs in 2021 was 2.70 per 100,000 population, showing a percentage change of 16.26% (EAPC\u0026thinsp;=\u0026thinsp;0.50% (Table S1.4, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe global burden of \u003cb\u003ehypertensive heart disease (HHD)\u003c/b\u003e decreased over the study period. The ASR for HHD deaths in 2021 was 3.92 per 100,000 population, with a percentage change of -22.04% (EAPC =-0.73) (Table S1.5, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The ASR for DALYs in 2021 was 69.93 per 100,000 population, showing a percentage change of -31.53% (EAPC =-1.21) (Table S1.6, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe global burden of \u003cb\u003eischemic heart disease (IHD)\u003c/b\u003e showed a declining trend from 1990 to 2021. The ASR for IHD deaths in 2021 was 7.11 per 100,000 population, with a percentage change of -10.05% (EAPC =-0.30) (Table S1.7, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The ASR for DALYs in 2021 was 138.57 per 100,000 population, showing a percentage change of -16.58% (EAPC=-0.58)​ (Table S1.8, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe global burden of \u003cb\u003elower extremity peripheral arterial disease (LE-PAD)\u003c/b\u003e declined from 1990 to 2021. The ASR for LE-PAD deaths in 2021 was 0.004 per 100,000 population, with a percentage change of -7.05% (EAPC=-0.28) (Table S1.9, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The ASR for DALYs in 2021 was 0.11 per 100,000 population, reflecting a percentage change of -9.32% (EAPC= -0.41%) (Table S1.10, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe global burden of \u003cb\u003estroke\u003c/b\u003e showed a declining trend from 1990 to 2021. The ASR for stroke deaths in 2021 was 6.65 per 100,000 population, with a percentage change of -28.73% (EAPC =-1.24) (Table S1.11, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The ASR for stroke-related DALYs in 2021 was 139.82 per 100,000 population, showing a percentage change of -34.66% (EAPC= -1.16)​ (Table S1.12, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Gender-Based Trend\u003c/h2\u003e \u003cp\u003eMales had an ASR for CVDs deaths of 23.49 per 100,000 population (95% CI: -2.92, 7.23) in 2021, with 847.99 thousand deaths, a percentage change of -19.07% (EAPC =-0.71) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. in males had an ASR of 463.24 per 100,000 (95% CI: -58.69, 938.78), with 18180.43 thousand years, a percentage change of -26.42% (EAPC =-1.03) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eFemales had an ASR for CVDs deaths of 13.42 per 100,000 population, with 628.24 thousand deaths, a percentage change of -22.78%, and an EAPC of -0.86% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). in females had an ASR of 255.67 per 100,000, with 11837.105 thousand DALYs, a percentage change of -29.57% (EAPC =-1.19) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eIn 2021, the burden of CVD showed both increasing and decreasing trends across conditions. Deaths due to AA slightly increased in females (ASR 0.007 per 100,000, percentage change 2.80%, EAPC 0.13%), while they declined in males (ASR 0.019 per 100,000, percentage change\u0026thinsp;\u0026minus;\u0026thinsp;18.01%, EAPC\u0026thinsp;\u0026minus;\u0026thinsp;0.86%) (Table S1.1 and 1.2). AFF exhibited a rising trend for death and DALYs in both males (ASMR 0.14 per 100,000, percentage change 26.16%(EAPC\u0026thinsp;=\u0026thinsp;0.79) and ASDR 3.36 per 100,000, percentage change 11.72 (EAPC\u0026thinsp;=\u0026thinsp;0.40) and females (ASMR 0.10 per 100,000, percentage change 31.90%(EAPC\u0026thinsp;=\u0026thinsp;0.92) and ASDR 2.19 per 100,000, percentage change 19.36 (EAPC\u0026thinsp;=\u0026thinsp;0.55) (Table S1.3 and 1.4).\u003c/p\u003e \u003cp\u003eAll other conditions showed an overall decline in ASMR and ASDR burden. HHD had the largest decline in DALYs of both males (-34.00%, EAPC\u0026thinsp;\u0026minus;\u0026thinsp;1.32%) and females ( -29.05%, EAPC\u0026thinsp;\u0026minus;\u0026thinsp;1.09%). Similarly for, mortality in males (-25.33%, EAPC\u0026thinsp;\u0026minus;\u0026thinsp;0.89%) and females (-19.26%, EAPC\u0026thinsp;\u0026minus;\u0026thinsp;0.59%) (Table S1.5 and 1.6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. SDI Region trends\u003c/h2\u003e \u003cp\u003eThe global burden of CVD showed significant variations across different SDI regions from 1990 to 2021. In High SDI regions, the ASR for CVD deaths in 2021 was lowest at 4.45 per 100,000 population (95% CI: -0.50, 9.42), with a percentage change of -53.81% and an EAPC of -2.63% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The ASR for CVD-related DALYs in 2021 was also lowest at 84.54 per 100,000 population (95% CI: -9.58, 177.29), showing a percentage reduction of -55.32% (EAPC=-2.72) over the study period (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eIn Low SDI regions, the burden of CVD remained the highest, with an ASR for deaths in these regions of 35.10 per 100,000 population (95% CI: -3.63,68.01), showing a decline of -12.54% (EAPC=-0.41) over time (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The ASR for DALYs of 687.52 per 100,000 population (95% CI: -72.23, 1,352.17), reflecting a percentage change of -20.69% and an EAPC of -0.80% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eHigh SDI regions exhibited the most significant reductions in CVD mortality and DALYs rates, whereas Low SDI regions had the highest-burden but showed the slowest rate of decline.\u003c/p\u003e \u003cp\u003eAmong the diseases, \u003cb\u003eAA\u003c/b\u003e showed the highest burden in low SDI regions, with an ASR for deaths at 0.02 per 100,000 population with a percentage change of 18.72% and an EAPC of 0.55% (Table S1.1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAFF\u003c/b\u003e had the highest burden in low-middle SDI regions, where the ASR for deaths reached 0.19 per 100,000 people. This condition demonstrated an increasing trend, with a percentage change of 57.06% and an EAPC of 1.53% (Table S1.3). Similarly, \u003cb\u003eHHD\u003c/b\u003e had the highest burden in Low SDI regions, with an ASR for deaths of 10.63 per 100,000 population, showing a slight decline with a percentage change of -17.68% (EAPC = -0.63) (Table S1.5).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIHD\u003c/b\u003e showed the highest burden in the Low-Middle SDI region, where the ASR for deaths reached 13.54 per 100,000 population, with a percentage change of 7.12% and an EAPC of 0.38% (Table S1.7). \u003cb\u003eLE-PAD\u003c/b\u003e had the greatest impact in Low-SDI, where the ASR for deaths reached 0.01 per 100,000 people, with a percentage change of 41.18% and an EAPC of 1.14% (Table S1.9).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStroke\u003c/b\u003e exhibited the highest burden in Low SDI regions, where the ASR for deaths was 12.02 per 100,000 population, reflecting a notable decline with a percentage change of -22.56% and an EAPC of -0.88% (Table S1.11). While conditions like AFF showed increasing trends, significant declines were observed in AA, IHD, and stroke, particularly in lower SDI regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4. GBD Regions Trend\u003c/h2\u003e \u003cp\u003eThe North Africa and Middle East region had the highest CVD mortality burden in 2021, with an ASMR of 40.09 per 100,000 population. Over time, the region experienced a 32.27% decrease in ASMR from 1990, with an EAPC (EAPC) of -1.27%, indicating a moderate decline (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA)​. Similarly, this region also showed the highest burden of DALYs due to CVD in 2021, with an ASR of 725.10 per 100,000 population. Over time, the region saw a 39.21% decrease in ASR, with an EAPC (EAPC) of -1.66%, indicating a significant decline in CVD-related health loss​ (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eSouth Asia also had a significant CVD burden, with an ASMR of 29.21 per 100,000 population. However, the decline in ASMR from 1990 to 2021 was much smaller (-4.60%), and the EAPC of -0.07% suggests a very slow reduction (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA)​. For DALYs, it also showed an ASR of 587.60 per 100,000 people. However, the decline in ASR from 1990 to 2021 was only 14.62%, and the EAPC of -0.46% suggests a relatively slow reduction in DALYs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB)​.\u003c/p\u003e \u003cp\u003eConversely, Southern Sub-Saharan Africa saw an increasing CVD mortality trend, with an ASMR of 18.42 per 100,000 population and a 22.01% increase in ASMR from 1990, along with a positive EAPC of 0.76%, indicating worsening conditions and persistent public health challenges (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA)​. Southern Sub-Saharan Africa recorded an increasing burden, with an ASR of 364.72 per 100,000 population, an 8.08% rise in DALYs since 1990, and a positive EAPC of 0.36%, indicating worsening health conditions​ overall, while regions like Western Europe and Southern Latin America experienced significant reductions in CVD mortality and disability (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eIn 2021, the burden of CVD varied across different regions, with notable trends in mortality and DALYs. AA had the highest burden in the Caribbean, where the ASMR (ASMR) was 0.03 per 100,000 population, and the DALYs ASR was 0.59 per 100,000 population. Meanwhile, Southern Sub-Saharan Africa experienced an increasing mortality trend of 6.40%, highlighting ongoing regional public health challenges (Table S1.1 and 1.2).\u003c/p\u003e \u003cp\u003eAFF saw the highest percentage increase in burden in South Asia, where the ASMR reached 0.19 per 100,000 population, and the DALYs ASR was 4.25 per 100,000 population, reflecting a 74.48% increase in mortality over the study period (Table S1.3 and 1.4). Similarly, HHD had the highest burden in Central Sub-Saharan Africa, with an ASMR of 13.97 per 100,000 population and a DALYs ASR of 247.21 per 100,000 population, indicating a significant disease burden in the region (Table S1.5 and 1.6).\u003c/p\u003e \u003cp\u003eFor IHD, the North Africa and Middle East region experienced the highest burden, with an ASMR of 18.16 per 100,000 population and a DALYs ASR of 335.84 per 100,000 population, reinforcing the need for enhanced preventive and treatment strategies (Table S1.7 and 1.8). LE-PAD also had its highest burden in the Caribbean, with an ASMR of 0.02 per 100,000 population and a DALYs ASR of 0.36 per 100,000 population, alongside a 23.34% increase in ASMR, indicating a rising trend in the disease's impact (Table S1.9 and 1.10).\u003c/p\u003e \u003cp\u003eStroke had the highest burden in East Asia, where the ASMR reached 11.78 per 100,000 population, and the DALYs ASR was 220.96 per 100,000 population. Meanwhile, Southern Sub-Saharan Africa saw an 18.91% increase in mortality, highlighting the growing impact of stroke in the region (Table S1.11 and 1.12). These trends indicate a diverse global burden of CVD, with some regions showing increasing mortality and DALYs, while others continue to struggle with persistently high disease rates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5. National Trends\u003c/h2\u003e \u003cp\u003eAmong all nations, South Asian and Sub-Saharan African countries had the highest CVD burden due to lead exposure in 2021. For instance, Afghanistan had an ASMR of 98.11 per 100,000 (95% CI: -9.39, 192.06) and an ASDR of 1,948.18 per 100,000 (95% CI: -186.11, 3,885.22). Egypt and Sudan also reported high mortality burdens, with ASMRs of 87.60 with percentage change of -22.33 and 57.62 per 100,000 with percentage change of -28.37, respectively, reflecting the continued public health impact of lead exposure in these regions (Table S2.1, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eSimilarly, West and Central African countries such as Burkina Faso and Chad experienced an increase in mortality and DALYs, with Chad showing a 27.66% increase in ASMR and a 21.48% increase in ASDR from 1990 to 2021, highlighting a worsening trend despite global improvements. Lesotho had the highest increase in CVD mortality, with a 47.66% rise in ASMR and a 48.91% rise in ASDR, indicating a critical need for intervention (Table S2.1, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eIn contrast, due to lead exposure, high-income countries exhibited the most substantial reductions in CVD mortality and DALYs. Republic of Korea, Ireland, and Israel recorded the steepest declines in ASMR, decreasing by 74.11%, 73.88%, and 72.99%, respectively.\u003c/p\u003e \u003cp\u003eAmong the countries with the largest DALY reductions, the Republic of Korea, Ireland, and Singapore demonstrated the most progress, recording a 78.73% decrease in ASDR, Ireland a 77.57% decrease, and Singapore a 74.65% decrease.\u003c/p\u003e \u003cp\u003eDespite global improvements, some nations saw little change or even increases in CVD mortality and DALYs due to lead exposure. Lesotho and Zimbabwe experienced minimal changes, with ASMR increases of 47.65% and 34.96%, respectively. For the ASDR percentage change, Lesotho showed the highest increase at 48.90% (Table S2.1).\u003c/p\u003e \u003cp\u003eIn 2021, the burden of CVD varied across countries, with significant trends observed in mortality and DALYs. AA had the highest burden in Armenia, with an ASMR of 0.050 per 100,000, reflecting an increase of 115.83%, ASDR of 0.54, and a percentage change of 81.01% (Table S2.2)​. AFF showed the highest burden in Honduras, where the ASMR reached 0.47 per 100,000, marking a rise of 88.10%​, while ASDR is 8.85, and the percentage change is 40.30 (Table S2.3).\u003c/p\u003e \u003cp\u003eHHD was most prevalent in Afghanistan, with an ASMR of 33.94 per 100,000 and an ASDR of 624.91​ (Table S2.4). IHD was most prevalent in Egypt, with an ASMR of 40.35 per 100,000, a percentage change of -1.38, and ASDR of 739.80 and \u0026minus;\u0026thinsp;8.45% (Table S2.5)​. LE-PAD had the highest burden in Cuba, with an ASMR of 0.035 per 100,000, reflecting an increase of 56.31% for ASDR of 0.62 and a percentage change of 47.22%​ (Table S2.6). Stroke had the highest mortality burden in Afghanistan, where the ASMR was 27.04 per 100,000, with a decline of 21.45%, ASDR was 560.01, and percentage change was \u0026minus;\u0026thinsp;27.26 (Table S2.7)​.\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\u003eMortality data for CVD attributable to lead exposure across GBD regions presenting total numbers, ASRs per 100,000 for 1990 and 2021, percentage changes over time, and EAPC from 1990 to 2021.\"\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003elocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. 10\u003csup\u003e3\u003c/sup\u003e (95% UI) 1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. 10\u003csup\u003e3\u003c/sup\u003e (95% UI) 2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASR, per 100,000, 1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASR, per 100,000, 2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epercentage changes, %, 1990\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEAPC, %, 1990\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e796.30(-90.53,1670.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e1476.24(-175.72,3080.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e22.37(-2.55,46.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e17.82(-2.11,37.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-20.37(-26.76,-12.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.76(-0.85,-0.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSEX\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e451.13(-51.34,916.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e847.99(-106.17,1710.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e29.025(-3.307,59.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e23.490(-2.917,47.231)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-19.07(-8.30,-27.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.71(-0.81,-0.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e345.16(-39.13,737.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e628.24(-69.54,1331.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e17.376(-1.974,37.090)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e13.417(-1.486,28.438)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-22.78(-13.95,-30.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.86(-0.94,-0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSDI Regions\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e105.85(-13.88,226.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e109.79(-12.39,232.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e9.63(-1.26,20.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e4.45(-0.50,9.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-53.81(-60.83,-50.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.63(-2.70,-2.56)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e171.72(-20.06,368.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e298.27(-35.64,625.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e19.90(-2.34,42.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e15.51(-1.84,32.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-22.05(-31.34,-11.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.92(-1.17,-0.66)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e263.24(-26.53,542.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e543.37(-65.29,1126.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e31.59(-3.14,64.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e23.40(-2.79,48.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-25.92(-35.14,-13.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.97(-1.05,-0.89)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e180.68(-21.27,367.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e385.80(-46.42,785.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e34.97(-4.01,70.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e31.43(-3.74,63.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10.14(-17.92,1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.29(-0.37,-0.20)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e73.92(-7.71,146.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e137.75(-14.46,270.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e40.13(-4.09,78.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e35.10(-3.63,68.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-12.54(-19.95,-1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.41(-0.51,-0.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGBD Regions\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e2.55(-0.24,5.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e4.84(-0.48,10.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e14.01(-1.28,29.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e8.62(-0.84,18.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-38.47(-49.23,-25.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.62(-1.74,-1.51)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e3.33(-0.46,7.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e2.99(-0.39,6.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e14.76(-2.04,31.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e4.71(-0.62,10.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-68.11(-71.80,-65.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.90(-3.99,-3.80)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e7.44(-0.90,15.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e12.68(-1.33,25.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e31.03(-3.73,63.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e23.21(-2.45,46.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-25.22(-34.33,-14.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.83(-0.90,-0.77)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e8.05(-1.02,17.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e12.40(-1.50,26.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e19.40(-2.45,41.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e19.19(-2.33,40.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.08(-10.75,9.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.24(-0.63,0.15)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e26.43(-3.39,55.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e29.87(-3.36,61.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e19.51(-2.50,41.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e12.46(-1.40,25.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-36.16(-45.30,-30.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.69(-1.85,-1.54)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e15.69(-1.80,31.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e35.11(-4.36,72.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e22.61(-2.56,45.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e14.94(-1.86,30.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-33.93(-40.28,-25.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.54(-1.68,-1.40)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e5.21(-0.45,10.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e12.33(-1.08,25.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e30.12(-2.47,62.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e32.39(-2.68,66.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.55(-14.52,37.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21(0.16,0.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e242.03(-22.87,508.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e487.56(-58.75,1022.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e37.24(-3.37,78.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e25.75(-3.09,53.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-30.88(-44.05,-11.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.15(-1.32,-0.98)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e30.55(-4.12,67.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e38.92(-4.83,83.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e12.26(-1.66,26.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e10.83(-1.35,23.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-11.63(-18.73,-3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.95(-1.55,-0.35)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e24.25(-2.06,47.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e34.44(-2.98,67.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e41.37(-3.32,81.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e27.80(-2.30,53.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-32.80(-41.27,-22.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.47(-1.56,-1.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e13.25(-1.60,28.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e15.74(-1.82,33.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e7.42(-0.89,16.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2.39(-0.28,5.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-67.85(-71.34,-64.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.71(-3.82,-3.59)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e33.37(-4.45,71.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e34.60(-3.64,73.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e9.20(-1.22,19.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e4.80(-0.50,10.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-47.82(-59.50,-42.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.26(-2.38,-2.14)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e80.37(-8.66,162.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e143.16(-15.11,289.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e59.19(-6.22,119.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e40.09(-4.18,80.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-32.27(-38.91,-23.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.27(-1.31,-1.23)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e0.36(-0.04,0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e0.77(-0.08,1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e16.35(-1.63,34.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e13.90(-1.39,29.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-14.98(-28.94,4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.56(-0.64,-0.47)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e149.24(-18.76,304.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e368.56(-46.48,748.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e30.61(-3.74,61.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e29.21(-3.63,58.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.60(-15.74,11.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.07(-0.19,0.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoutheast Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e50.30(-5.53,105.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e110.88(-13.28,234.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e22.16(-2.44,46.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e19.43(-2.33,41.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-12.32(-23.49,3.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.44(-0.62,-0.26)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e4.36(-0.47,9.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e5.01(-0.47,10.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10.09(-1.09,21.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e5.49(-0.52,11.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-45.63(-54.49,-39.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.77(-1.88,-1.67)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e3.60(-0.31,7.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e8.63(-0.76,17.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e15.10(-1.27,30.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e18.42(-1.61,36.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.01(7.39,38.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76(0.25,1.28)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e18.93(-2.21,39.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e26.53(-2.96,54.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e24.03(-2.78,49.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e10.74(-1.19,21.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-55.29(-58.15,-52.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.46(-2.56,-2.35)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e57.69(-7.70,122.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e54.98(-5.55,114.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e9.70(-1.29,20.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e4.41(-0.46,9.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-54.54(-64.79,-49.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.60(-2.71,-2.50)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e19.30(-1.88,40.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e36.21(-3.81,71.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e25.99(-2.54,54.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e23.56(-2.54,46.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-9.35(-22.83,7.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.46(-0.64,-0.28)\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\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\u003eDALYs data for CVD attributable to lead exposure across GBD regions presenting total numbers, ASRs per 100,000 for 1990 and 2021, percentage changes over time, and EAPC from 1990 to 2021.\"\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003elocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. 10\u003csup\u003e3\u003c/sup\u003e (95% UI) 1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. 10\u003csup\u003e3\u003c/sup\u003e (95% UI) 2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASR, per 100,000, 1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASR, per 100,000, 2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epercentage changes, %, 1990\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEAPC, %, 1990\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e19030.69(-2151.61,39936.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e30017.54(-3660.04,62164.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e484.95(-54.93,1016.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e351.36(-42.73,727.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-27.55(-33.43,-20.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.09(-1.19,-0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSEX\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e11410.72(-1297.96,23203.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e18180.43(-2315.55,36956.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e629.581(-71.656,1280.875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e463.237(-58.699,938.780)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-26.42(-17.09,-34.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.03(-1.14,-0.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e7619.96(-853.64,16361.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e11837.10(-1327.53,24806.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e362.997(-40.743,778.534)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e255.674(-28.631,535.840)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-29.57(-22.28,-36.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.19(-1.28,-1.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSDI Regions\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e2070.53(-274.16,4440.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e1837.67(-210.61,3851.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e189.22(-24.97,405.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e84.54(-9.58,177.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-55.32(-61.76,-52.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.72(-2.77,-2.66)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e3839.56(-442.55,8229.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e5284.02(-654.35,11150.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e401.25(-46.42,858.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e269.70(-33.27,569.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-32.79(-40.64,-23.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.47(-1.74,-1.19)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e6489.88(-661.57,13453.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e10875.42(-1315.60,22606.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e651.09(-65.84,1340.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e426.18(-51.41,885.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-34.54(-43.20,-23.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.39(-1.47,-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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e4693.82(-559.50,9631.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e8742.16(-1057.59,17882.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e769.85(-90.67,1569.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e630.82(-76.03,1287.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-18.06(-25.26,-7.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.60(-0.69,-0.52)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e1917.32(-201.23,3811.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e3253.70(-342.03,6439.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e866.89(-90.17,1718.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e687.52(-72.23,1352.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-20.69(-27.76,-11.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.80(-0.89,-0.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGBD Regions\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean Latin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e57.19(-5.49,119.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e94.90(-9.59,200.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e283.08(-26.83,586.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e163.20(-16.45,344.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-42.35(-52.31,-29.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.85(-1.97,-1.72)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e63.38(-8.87,135.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e45.36(-6.20,96.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e273.73(-38.26,584.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e78.04(-10.62,165.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-71.49(-74.18,-69.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.27(-4.34,-4.19)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e167.45(-20.10,341.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e255.89(-27.11,512.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e652.70(-78.51,1331.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e473.95(-50.22,950.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-27.39(-36.73,-16.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.94(-1.00,-0.88)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e177.99(-22.83,384.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e251.84(-30.76,531.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e392.29(-50.21,849.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e342.25(-41.92,722.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-12.76(-21.45,-2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.77(-1.21,-0.32)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e555.67(-72.35,1183.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e488.89(-56.12,1009.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e384.35(-49.94,817.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e213.10(-24.44,440.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-44.56(-51.65,-39.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.20(-2.38,-2.03)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e343.33(-40.67,693.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e652.21(-82.81,1353.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e432.47(-50.81,870.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e267.35(-33.95,554.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-38.18(-44.16,-30.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.81(-1.94,-1.67)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e140.42(-12.24,289.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e301.88(-27.45,619.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e645.05(-55.13,1329.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e620.22(-54.41,1264.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-3.85(-23.25,20.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.17(-0.24,-0.09)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e5836.50(-567.39,12217.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e9015.75(-1074.65,18928.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e727.37(-68.76,1520.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e436.63(-51.93,917.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-39.97(-51.67,-23.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.64(-1.79,-1.48)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e643.70(-86.67,1418.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e714.40(-88.48,1531.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e240.19(-32.38,528.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e202.80(-25.10,433.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-15.57(-22.45,-8.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.19(-1.86,-0.52)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e614.86(-53.00,1218.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e803.70(-71.08,1593.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e863.23(-72.72,1705.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e527.58(-45.86,1034.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-38.88(-46.59,-29.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.81(-1.91,-1.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e281.27(-33.76,611.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e237.76(-28.05,506.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e145.04(-17.33,314.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e44.73(-5.37,96.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-69.16(-71.45,-66.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.92(-3.99,-3.85)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e633.63(-83.48,1349.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e604.67(-61.52,1251.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e181.18(-23.64,385.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e91.10(-8.98,187.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-49.72(-61.12,-44.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.30(-2.39,-2.21)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e1935.31(-214.63,3906.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e3034.85(-325.41,6195.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e1192.72(-130.11,2405.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e725.10(-77.44,1468.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-39.21(-45.29,-31.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.66(-1.72,-1.60)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e9.65(-0.94,20.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e19.55(-1.86,42.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e342.50(-34.04,718.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e278.66(-27.23,602.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-18.64(-33.18,1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.70(-0.79,-0.61)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e3996.13(-508.30,8249.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e8378.46(-1067.05,17223.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e688.21(-86.29,1404.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e587.60(-74.41,1201.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-14.62(-24.24,-0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.46(-0.55,-0.37)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e1389.35(-152.18,2918.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e2651.83(-313.52,5583.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e522.38(-57.39,1095.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e409.91(-48.58,864.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-21.53(-31.96,-7.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.80(-0.98,-0.63)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e97.18(-10.99,207.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e89.49(-9.05,190.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e213.56(-24.06,456.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e101.10(-10.27,215.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-52.66(-57.78,-47.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.35(-2.43,-2.27)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e93.55(-8.12,191.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e201.05(-17.94,409.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e337.45(-29.37,687.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e364.72(-32.56,734.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e8.08(-3.61,20.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36(-0.16,0.88)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e470.75(-54.84,981.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e539.86(-62.13,1112.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e514.22(-60.16,1068.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e211.99(-24.30,436.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-58.77(-61.07,-56.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.83(-2.94,-2.72)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e1036.83(-141.14,2213.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e759.33(-83.56,1589.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e178.98(-24.32,381.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e69.42(-7.93,145.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-61.21(-68.01,-57.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.18(-3.28,-3.07)\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=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e486.55(-46.33,1021.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e875.88(-88.89,1733.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e560.41(-54.04,1180.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e469.39(-49.27,923.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e-16.24(-28.91,0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.72(-0.91,-0.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe GBD data analysis from 1990 to 2021 demonstrates a notable decline in the burden of CVD worldwide, consistent with previous studies that have reported similar downward trends in CVD mortality and morbidity over recent decades [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The ASR of CVD deaths decreased by -20.37%, and DALYs declined by -27.55%, reinforcing earlier findings highlighting advancements in healthcare, preventive measures, and improved management strategies in reducing CVD burden [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Similar to earlier reports, IHD and stroke, the leading contributors to CVD-related morbidity and mortality, have shown a downward trajectory [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The ASR for IHD deaths decreased by -10.05%, and DALYs declined by -16.58%, while stroke-related deaths and DALYs exhibited an even greater reduction of -28.73% and \u0026minus;\u0026thinsp;34.66%, respectively [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These findings support previous studies that attribute declining stroke and IHD mortality to advances in thrombolytic therapy, revascularization techniques, and improved management of hypertension, smoking, and dietary risk factors [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHHD has also substantially reduced, with ASR declines of -22.04% for deaths and \u0026minus;\u0026thinsp;31.53% for DALYs. This is consistent with earlier research emphasizing the impact of improved blood pressure control through lifestyle interventions and the widespread use of antihypertensive medications [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Likewise, the mortality rates for AA and LE-PAD declined by -10.12% and \u0026minus;\u0026thinsp;7.05%, respectively, mirroring prior observations that associate these trends with enhanced screening, surgical interventions, and risk factor management, including smoking cessation and lipid control [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, in contrast to the general decline in CVD burden, atrial fibrillation and flutter have shown an increasing trend, with a 30.73% rise in ASR for deaths and a 16.26% increase in ASR for DALYs [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This upward trajectory aligns with previous studies highlighting an aging global population, increased diagnostic sensitivity, and the rising prevalence of obesity, hypertension, and diabetes as key drivers of this burden. Despite advancements in anticoagulation therapy and arrhythmia management, earlier research also underscores the persistent challenges posed by atrial fibrillation in stroke prevention and healthcare resource utilization [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMales had an ASMR and ASDR for CVDs deaths, which shows that men continue to bear a greater burden of CVD than women. In contrast to earlier studies that indicated a more consistent fall across genders [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], the percentage decrease in mortality and DALYs over time has been marginally higher in females [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Although advancements have aided this trend in healthcare awareness and access, the gender gap in the total burden of CVD persists, underscoring the need for additional focused treatments, particularly for males, to close the gap further [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current results show an increasing tendency in both sexes, with a more significant increase in females, in contrast to previous research that revealed a consistent prevalence of AFF [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This shows that AFF mainly affects men and points to sex-specific risk factors and growing diagnostic understanding [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHHD had the largest decline in DALYs of both males (-34.00%, EAPC\u0026thinsp;\u0026minus;\u0026thinsp;1.32%) and females ( -29.05%, EAPC\u0026thinsp;\u0026minus;\u0026thinsp;1.09%) coinciding with the previous studies and supports the benefits of better hypertension control [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Furthermore, the most recent data reveal a different pattern: a drop in males but a little increase in females compared to earlier studies that documented a consistent decline in AA-related mortality across genders [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The historical under-representation of women in clinical trials and delayed diagnosis could be the cause of this discrepancy.\u003c/p\u003e \u003cp\u003eSignificant disparities are revealed by the SDI-based analysis of CVD from 1990 to 2021. High SDI regions exhibit the largest reductions in mortality (-53.81%) and DALYs (-55.32%), which is consistent with research that attributes these declines to improvements in healthcare and preventive measures [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. They attributed these findings to delayed diagnoses and healthcare inequality.\u003c/p\u003e \u003cp\u003eDeaths from AFF increased significantly in Low-Middle SDI regions (57.06%; EAPC\u0026thinsp;=\u0026thinsp;1.53%), which is consistent with research showing that aging populations and underdiagnosis are major causes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. According to studies that link poor risk factor control to disease burden, poorer SDI regions also saw increases in IHD and LE-PAD [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Decreases in AAs, HHD, and stroke, particularly in areas with poor SDI, support [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] findings on better management of hypertension. These enduring differences highlight the necessity of focused measures to improve early diagnosis and healthcare access in lower-income areas [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe GBD regional study (1990\u0026ndash;2021) highlights significant differences in the burden of CVD. In 2021, North Africa and the Middle East found the greatest ASMR and ASDR. Despite this, the region experienced the largest decreases in DALYs (-39.21%) and ASMR (-32.27%), which is consistent with earlier research that links improved healthcare to a decrease in mortality [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. South Asia, on the other hand, only had modest drops in DALYs (-14.62%) and ASMR (-4.60%), which is due to studies showing sluggish success in reducing CVD risk factors [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn line with previous findings on healthcare shortages, CVD trends worsened in Southern Sub-Saharan Africa, with rising DALYs (+\u0026thinsp;8.08%) and ASMR (+\u0026thinsp;22.01%) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Whereas hypertensive cardiac disease continued to be most prevalent in Central Sub-Saharan Africa, AFF increased in South Asia (+\u0026thinsp;74.48% ASMR). The Middle East and North Africa had the highest rates of IHD, which is consistent with earlier research that focused on risk factors unique to these regions [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Additionally, LE-PAD rose in the Caribbean (+\u0026thinsp;23.34% ASMR), highlighting the necessity of specialized therapies. These results highlight how urgent it is to implement region-specific healthcare measures to reduce the rising rates of CVD death and disability.\u003c/p\u003e \u003cp\u003eLead exposure's effect on the burden of CVD varied greatly, with the highest death rates occurring in South Asia and Sub-Saharan Africa, especially in Afghanistan (ASMR: 98.11, ASDR: 1,948.18) and Egypt (ASMR: 87.60, -22.33%). In contrast, the biggest drops were seen in the Republic of Korea (-74.11% ASMR, -78.73% ASDR) and Ireland (-73.88% ASMR, -77.57% ASDR), which is following research that links lower lead exposure to better cardiovascular outcomes [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLesotho (+\u0026thinsp;47.66% ASMR, +\u0026thinsp;48.91% ASDR) and Chad (+\u0026thinsp;27.66% ASMR, +\u0026thinsp;21.48% ASDR) had deteriorating trends despite global advancements, which is consistent with studies on chronic environmental lead exposure in low-income areas [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Similar to earlier research on the role of lead in vascular dysfunction, Armenia had the biggest increase in AA mortality (+\u0026thinsp;115.83% ASMR). In contrast, Honduras had the highest increase in atrial fibrillation (+\u0026thinsp;88.10% ASMR) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. According to previous research on lead-induced hypertension and ischemic events, Egypt had the largest burden of IHD (ASMR: 40.35, ASDR: 739.80), whereas Afghanistan had the highest burden of HHD (ASMR: 33.94, ASDR: 624.91). These results demonstrate the critical need for more stringent environmental laws and healthcare preventative initiatives in high-risk areas.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the accuracy of GBD estimates depends on data quality, which varies across countries, particularly in low-income regions where underreporting and misclassification may affect CVD mortality and DALYs trends. Second, causality cannot be directly established, as lead exposure interacts with other risk factors, such as poor nutrition and healthcare access. Third, the study does not account for individual-level variations in exposure or genetic susceptibility, which may influence disease outcomes. Lastly, differences in healthcare infrastructure and prevention strategies across countries may impact CVD trends, limiting direct comparisons.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThe burden of CVDs attributable to lead exposure has notably declined in high-income countries, reflecting the success of environmental regulations and public health interventions. However, in low- and middle-income regions, particularly in South Asia and Sub-Saharan Africa, lead-related CVD burden remains a pressing public health challenge, as evidenced by persistently high mortality and DALYs. Despite some regions showing improvements, others, such as Lesotho and Chad, are experiencing worsening trends, underscoring the need for more effective and context-specific public health strategies. Strengthening environmental policies, improving healthcare access, and fostering international cooperation are crucial to reducing lead exposure globally. A concerted effort is necessary to address these regional disparities and mitigate the long-term cardiovascular consequences of lead toxicity, which is key to advancing public health and reducing the global burden of CVDs.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFuster V (2019) \u003cem\u003eThe Global Burden of Cardiovascular Diseases and Risk Factors: 2020 and beyond.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMensah GA et al (2015) Mortality from cardiovascular diseases in sub-Saharan Africa, 1990\u0026ndash;2013: a systematic analysis of data from the Global Burden of Disease Study 2013. 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Environ Health Perspect 115(3):472\u0026ndash;482\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of the Punjab","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lead Exposure, Epidemiology, CVDs, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-9571210/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9571210/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLead exposure is a significant environmental risk factor contributing to the global burden of cardiovascular disease (CVD). This study examines trends in CVD mortality and disability-adjusted life years (DALYs) attributable to lead exposure from 1990 to 2021 across 204 countries and territories, stratified by sex, socio-demographic index (SDI), and Global Burden of Disease (GBD) regions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were extracted from the GBD 2021 study. Age-standardized mortality rate (ASMR) and DALY rates (ASDR) per 100,000 population were analyzed. Trends were assessed using estimated annual percentage change (EAPC) to evaluate temporal shifts.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe global burden of CVDs due to lead exposure declined, with ASMR decreasing by -20.37% and ASDR by -27.55%. However, in 2021, global CVD mortality and DALYs attributable to lead exposure were 1476.24 and 30017.54 thousand, respectively. The corresponding ASMR and ASDR were highest in males, low-SDI, South Asia, and sub-Saharan Africa, as well as among countries such as Afghanistan, Egypt, and Sudan. In contrast, the High-SDI region, the High-income Asia-Pacific region, the Republic of Korea (-74.11% ASMR), and Ireland (-73.88% ASMR) showed the most significant declines. Among CVD subtypes, ischemic heart disease (IHD) and stroke demonstrated notable reductions, while AFF increased globally (+\u0026thinsp;30.73% ASMR). Females exhibited a slightly greater percentage decline in mortality and DALYs than males.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDespite a global decline in CVD burden from lead exposure, substantial inequalities persist, with low-SDI regions and males experiencing the highest and rising ASR trends. The burden remains severe in South Asia, sub-Saharan Africa, and certain countries, while atrial fibrillation continues to rise globally. Addressing these disparities requires stronger lead mitigation policies, improved healthcare access, and targeted regional interventions.\u003c/p\u003e","manuscriptTitle":"Global Burden of Cardiovascular Disease Attributable to Lead Exposure: A Comprehensive Analysis from 1990 to 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 12:34:38","doi":"10.21203/rs.3.rs-9571210/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e5bd4b4-fc24-42eb-b04d-e482407097a4","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67280531,"name":"Epidemiology"}],"tags":[],"updatedAt":"2026-05-04T12:34:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 12:34:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9571210","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9571210","identity":"rs-9571210","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}