The global, regional, and national burden of endocarditis from 1990 to 2021: an analysis of the global burden of disease study 2021

The global, regional, and national burden of endocarditis from 1990 to 2021: an analysis of the global burden of disease study 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 The global, regional, and national burden of endocarditis from 1990 to 2021: an analysis of the global burden of disease study 2021 Changjiang Deng, Yixin Xu, Ying Pan, Tingting Wu, Chao Fan, Zhihui Jiang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6404736/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 Infective endocarditis (IE) persists as a major public health challenge, shaped by demographic shifts and healthcare disparities. However, comprehensive analyses of its spatiotemporal epidemiological patterns and their linkage to structural inequities remain limited. Objective This study aimed to systematically quantify the global, regional, and national burden of IE from 1990 to 2021, evaluate socioeconomic inequalities, and forecast disease trajectories through 2035. Methods Utilizing Global Burden of Disease (GBD) 2021 data spanning 204 countries, we conducted decomposition analysis to disentangle disability-adjusted life years (DALYs) into demographic (aging, population growth) and epidemiological components, assessed socioeconomic disparities using Slope and Concentration Indices, and projected trends via Bayesian Age-Period-Cohort modeling. Results Global IE incidence surged by 135% between 1990 and 2021, with males disproportionately affected (1.37 times higher DALYs). High-income regions exhibited paradoxical elevation in incidence, while mortality rates declined (annual DALY reduction: −0.34%). Socioeconomic disparities moderated (Concentration Index: −0.20 to − 0.12), yet 40% of DALYs persisted in low-income populations. Decomposition identified population growth (87.9%) and aging (38.9%) as primary drivers. Projections indicated a 9.5% rise in incidence by 2035, contrasting with a projected 6.1% decline in mortality rates. Conclusions The escalating burden of IE is shaped by accelerating demographic pressures and entrenched healthcare inequities. Prioritizing context-specific interventions—including geriatric healthcare capacity-building, equitable resource distribution, and enhanced diagnostic access—is imperative to reduce preventable morbidity. Infective endocarditis Global Burden of Disease(GBD) Disability-adjusted life years (DALYs) Socioeconomic inequalities Decomposition analysis Bayesian Age-Period-Cohort modeling Figures Figure 1 Figure 2 Figure 3 Figure 4 Key Message What is already known on this topic Infective endocarditis (IE) burden has risen globally, disproportionately affecting high-income regions due to aging populations, prosthetic device use, and improved diagnostics. Socioeconomic disparities in outcomes are recognized but poorly quantified. Prior studies lacked comprehensive analyses of how demographic shifts, epidemiological transitions, and structural inequities collectively drive global burden heterogeneity. What this study adds This study quantifies population aging (38.9%) and growth (87.9%) as dominant drivers of IE disability-adjusted life years (DALYs) globally, with males experiencing 1.37-fold higher DALYs than females. Despite reduced inequality (Concentration Index: −0.20 to −0.12), 40% of DALYs persist in low-income populations. Bayesian modeling forecasts a 9.5% incidence rise by 2035 but predicts a 6.1% mortality decline, highlighting diverging trajectories. How this study might affect research, practice, or policy Findings prioritize geriatric healthcare capacity-building, equitable resource allocation for vulnerable populations, and gender-specific interventions. Projections underscore the need for early diagnostics and antimicrobial stewardship in aging societies. Policymakers should leverage decomposition insights to tailor regional strategies addressing demographic pressures and systemic inequities. Enhanced surveillance in under-resourced settings is critical for mitigating preventable morbidity. 1. Introduction Infective endocarditis (IE), a potentially life-threatening cardiovascular infection, continues to be a worldwide health problem, challenges notwithstanding the medical advance, with increasing incidence has been associated with aging of population and invasive cardiac interventions [ 1 , 2 ] . Worldwide, there are estimates of > 1 million cases per year, however, mortality remains unacceptably high in the low-resource settings, which can be in part attributed to the fragmented nature of patients’ care pathways [ 3 , 4 ] . Despite this paradox of better diagnostics and case fatality across high-income regions, absolute burdens are rising [ 5 ] , an outcome not caused by a traditional disease epidemic but by demographic transition and prosthetic device translation among a bulk of the burden, suggesting the independence of morbidity balance in different societies. This juxtaposition highlights the need for disentangling the demographic, epidemiological, and socioeconomic drivers of IE heterogeneity. Despite progress, there are critical knowledge gaps that inhibit equitable intervention. Partly, global analyses remain geographically fragmented, with low-income areas underreported for evidence-based comparisons due to unreliable surveillance. Second, socioeconomic differences in outcomes—specifically, delays in diagnosis and limited access to surgery—are well known but rarely quantified over time. These restrictions mask insights that could inform how population aging, healthcare infrastructure, and policy disparities interact to influence IE burden. To rectify these gaps, this study comprehensively assesses Global Burden of Disease (GBD) 2021 data (1990 to 2021) across 204 countries and territories. We leverage three methodological innovations: (1) decomposition analysis to separate trends in disability-adjusted life years (DALYs) into aging, population growth, and epidemiological components [ 6 ] ; (2) robust metrics of inequality (Slope Index of Inequality, Concentration Index) to capture socioeconomic differences [ 7 ] ; and (3) Bayesian Age-Period-Cohort modelling with Integrated Nested Laplace Approximation (INLA) to predict future burdens while circumventing data sparsity [ 8 ] . We also perform, to the best of our knowledge, the first simultaneous analysis of demographic determinants and cross-country inequity in IE burden, which advance a unified framework for guiding resource allocation. Our work pairs demographic precision with equity-focused analytics to identify areas of need to target avoidable morbidity in aging and under-resourced populations. 2. Methods 2.1 Date sources The GBD database has compiled and standardized epidemiological data from 204 countries and territories across 21 GBD regions since 1990. GBD 2021 offers comprehensive estimates for 371 health conditions and injuries, as well as 88 associated risk factors. All data were sourced from the Global Health Data Exchange (GHDx) query tool, accessed via https://vizhub.healthdata.org/gbd-results/ . Our dataset includes sex-stratified and age-stratified metrics—such as incidence, mortality, and DALYs—specific to infective endocarditis, aggregated across all 204 countries and territories in the GBD framework. 2.2 Statistical analysis This study quantified the burden of endocarditis across age, sex, temporal (years), and geographic (location) strata. Incidence, mortality, (DALYs, and estimated annual percentage change (EAPC) were employed to evaluate trends in disease morbidity and mortality. All estimates are reported with 95% uncertainty intervals (UIs). Statistical analyses utilized validated methodologies (e.g., generalized linear models, Bayesian age-period-cohort [BAPC] modeling), with significance defined at p < 0.05. Detailed methodological specifications, including calculations for equity indices (slope index of inequality, concentration index), decomposition frameworks, and BAPC parameters, are provided in subsequent sections. Analytical workflows were implemented using R software (v4.4.1) for data visualization. 2.2.2 Cross-country inequality analysis This study utilized the Slope Index of Inequality (SII) and the Concentration Index (CI), as outlined by the World Health Organization (WHO), to assess absolute and relative inequalities in the burden of endocarditis across 204 countries and territories from 1990 to 2021. SII quantifies absolute disparities by regressing disease burden (measured in DALYs) against the midpoint of the Socio-demographic Index (SDI)-ranked cumulative population distribution. Relative inequality was evaluated using the CI, derived via numerical integration of the area under the Lorenz curve, which matches the cumulative DALYs proportion to the population ranked by SDI. To mitigate bias from outliers and data heterogeneity, robust regression models (RLMs) replaced conventional linear regression (LM) in analyses, enhancing the reliability of inequality estimates. 2.2.3 Decomposition analysis Decomposition analysis was employed to identify factors contributing to temporal changes in age-standardized disease burden (measured in disability-adjusted life years, DALYs) between 1990 and 2021. This method quantifies the additive contributions of inter-group differences—specifically, disparities in age structure, epidemiological patterns, and population size—to variations in total DALYs. The analysis framework partitioned observed DALY changes into three components: 1) demographic shifts (population aging), 2) epidemiological transitions (disease incidence/prevalence), and 3) population growth effects, enabling precise attribution of each factor’s influence on overall burden trends. 2.2.3 BAPC model projection This study selected the BAPC model to project future disease burdens, given its ability to address complex, high-dimensional, and sparse data inherent in large-scale epidemiological datasets such as the GBD 2021. The BAPC model extends the generalized linear model (GLM) framework within a Bayesian paradigm, simultaneously modeling age, period, and cohort effects while incorporating temporal evolution smoothed via a second-order random walk prior. This approach yields accurate posterior estimates of burden trends. A key advantage of the BAPC model lies in its use of Integrated Nested Laplace Approximation (INLA), which facilitates efficient approximate Bayesian inference for estimating marginal posterior distributions. Unlike traditional Markov Chain Monte Carlo (MCMC) methods, INLA avoids convergence and mixing challenges common in high-dimensional settings, substantially improving computational efficiency without sacrificing accuracy. The model’s adaptable framework and robustness in capturing temporal trends in epidemiological data render it highly suitable for long-term burden projections. 3. Result 3.1 Global trends From 1990 to 2021, global endocarditis-related incidence, mortality, and DALYs increased significantly in absolute terms. However, their annual percentage changes exhibited divergent trends. While the incidence rate demonstrated an upward trajectory (EAPC = 1.00; 95% CI 0.93–1.08)(Table 1 ), mortality remained stable (EAPC = 0.19; 0.08–0.29)( Table S1 ), and DALYs displayed a decline (EAPC = − 0.34; −0.45 to − 0.23)( Table S2). Table 1 The incidence cases and rates for endocarditis in 1990/2021 and its temporal trends 1990 2021 1990–2021 counts rate counts rate EAPC(95%CI) Global 443552.34(375741.82,535453.03) 9.35(8.01,11.09) 1042477.45(893665.11,1204150.02) 12.61(10.84,14.55) 1.00(0.93,1.08) Male 233482.48(199067.28,280439.20) 10.11(8.68,11.87) 583279.86(502013.60,670184.75) 14.85(12.84,17.06) 1.31(1.22,1.41) Female 210069.86(176768.19,254383.32) 8.65(7.31,10.33) 459197.60(392156.84,535072.41) 10.58(9.05,12.28) 0.65(0.59,0.71) High SDI 101182.24(85942.50,119164.81) 10.54(8.98,12.50) 274389.71(236263.49,318813.61) 15.77(13.63,18.08) 1.23(1.09,1.37) High-middle SDI 110269.02(92249.37,132192.94) 10.86(9.18,12.99) 252068.57(212760.19,292725.01) 14.70(12.56,17.04) 1.04(0.99,1.09) Middle SDI 151150.15(127861.84,182316.07) 10.22(8.77,12.12) 328360.41(277094.33,382211.74) 12.81(10.94,14.87) 0.75(0.73,0.78) Low-middle SDI 51160.13(42853.70,62579.83) 5.56(4.72,6.61) 120841.97(103163.67,143214.71) 7.29(6.25,8.50) 0.88(0.86,0.90) Low SDI 29343.31(25292.61,35094.30) 6.61(5.78,7.70) 65870.51(57091.59,78624.68) 7.18(6.34,8.30) 0.26(0.22,0.29) Andean Latin America 2324.51(2000.83,2762.29) 7.79(6.77,9.08) 6579.12(5603.33,7730.42) 10.58(9.04,12.44) 0.98(0.93,1.02) Australasia 2419.38(1986.40,2869.92) 10.79(8.88,12.82) 7843.40(6589.95,9114.63) 16.65(14.20,19.47) 1.40(1.26,1.53) Caribbean 3390.59(2910.66,4042.51) 10.64(9.15,12.51) 6769.52(5879.67,7766.37) 13.46(11.74,15.50) 0.77(0.68,0.86) Central Asia 2615.31(2089.52,3247.51) 4.53(3.68,5.56) 4717.84(3888.63,5708.53) 5.33(4.43,6.43) 0.60(0.58,0.63) Central Europe 11004.12(8957.59,13145.91) 7.94(6.56,9.50) 21861.35(18282.29,25733.85) 12.16(10.26,14.18) 1.46(1.34,1.57) Central Latin America 9273.73(7717.22,11362.21) 6.99(5.85,8.37) 26424.11(22365.07,31035.42) 10.50(8.96,12.30) 1.29(1.14,1.44) Central Sub-Saharan Africa 3091.23(2621.56,3716.38) 6.99(6.06,8.14) 8342.47(7197.51,10065.51) 7.99(7.08,9.21) 0.44(0.36,0.51) East Asia 137198.57(113863.94,167537.33) 12.25(10.39,14.86) 276167.93(225843.92,328814.13) 14.52(12.19,17.07) 0.57(0.51,0.62) Eastern Europe 20772.75(16998.57,24756.58) 8.16(6.78,9.70) 42342.44(35725.95,49589.96) 15.28(13.05,17.87) 2.37(2.23,2.52) Eastern Sub-Saharan Africa 11699.00(9972.44,14169.06) 7.56(6.58,8.84) 26305.33(22529.78,31919.22) 7.90(6.94,9.14) 0.07(0.03,0.11) High-income Asia Pacific 17676.53(14789.38,21465.96) 9.69(8.16,11.68) 46149.21(39169.42,54241.70) 12.82(10.80,14.99) 0.65(0.47,0.83) High-income North America 31012.54(26255.91,37181.83) 10.29(8.71,12.46) 83306.68(71986.43,96229.24) 15.54(13.68,17.82) 1.22(1.03,1.41) North Africa and Middle East 19271.36(16143.14,23520.75) 6.89(5.85,8.17) 47329.49(40042.38,56557.50) 8.88(7.59,10.40) 0.87(0.81,0.92) Oceania 433.91(371.19,518.74) 10.44(9.00,11.98) 1095.01(958.29,1268.36) 11.9(10.55,13.41) 0.52(0.49,0.56) South Asia 31497.71(25612.19,39462.72) 3.97(3.32,4.79) 89375.78(75120.69,107802.03) 5.52(4.62,6.55) 1.12(1.07,1.18) Southeast Asia 2864.517(2498.891-3286.605) 6.765(5.902–7.762) 6680.43(5889.92-7538.46) 5.83 (5.14–6.58) 0.81(0.73,0.88) Southern Latin America 5452.49(4706.47,6317.52) 11.71(10.14,13.49) 14803.82(13021.93,16591.31) 18.3(16.14,20.59) 1.36(1.20,1.51) Southern Sub-Saharan Africa 314.596(247.584-383.563) 2.179(1.715–2.657) 202.83(182.75-223.17) 2.08 (1.88–2.29) -0.35(-0.42,-0.28) Tropical Latin America 3587.81(2985.70,4369.76) 8.23(7.05,9.75) 5481.13(4639.81,6621.15) 7.70(6.57,9.07) 1.26(1.11,1.40) Western Europe 51578.76(43533.07,60080.52) 10.92(9.25,12.88) 134147.40(116364.20,154181.14) 17.2(14.98,19.78) 1.56(1.45,1.67) Western Sub-Saharan Africa 20412.03(17681.04,23927.32) 10.08(8.84,11.63) 39183.48(34219.09,45871.70) 8.60(7.56,9.87) -0.68(-0.73,-0.63) The number of new cases surged by 135%, rising from 443,552 (95% UI: 375,741–535,453) in 1990 to 1,042,477 (893,665–1,204,150) in 2021 (Table 1 ). Similarly, deaths increased by 111.1%, from 36,883 (31,646–40,522) to 77,844 (69,010–86,338) (Table S1 ), and DALYs rose by 55.7%, from 1,333,863 (1,056,184–1,508,950) to 2,076,413 (1,827,084–2,308,504) (Table S2). In 2021, a pronounced sex disparity was observed: males accounted for 583,280 cases (95% UI: 502,014–670,185) compared to 459,198 (392,157–535,072) in females (male-to-female ratio: 1.27:1) ( Table 1 ) . This disparity persisted in mortality (males: 40,094 [35,274–45,551]; females: 37,750 [31,155–43,488]; ratio: 1.06:1)(Table S1 ) and DALYs (males: 1,200,319 [1,007,093–1,393,512]; females: 876,094 [705,775–1,006,976]; ratio: 1.37:1)(Table S2). 3.2 Regional level 3.2.1 Incidence In 2021, Southern Latin America exhibited the highest age-standardized incidence rate (ASR) (18.30; 95% UI: 16.14–20.59), followed by Western Europe (17.20; 14.98–19.78) and Australasia (16.65; 14.20–19.47). In contrast, Southern Sub-Saharan Africa recorded the lowest ASR (2.08; 1.88–2.29). East Asia reported the greatest absolute case burden (276,168; 225,844–328,814), which starkly contrasted with Southern Sub-Saharan Africa (203; 183–223). Socioeconomic disparities were evident: high-SDI regions demonstrated an ASR of 15.77 (13.63–18.08), 2.2 times higher than low-SDI regions (7.18; 6.34–8.30). Geographically, Eastern Europe experienced the steepest increase in ASR (EAPC = 2.37; 95% CI: 2.23–2.52), followed by Western Europe (EAPC = 1.56; 1.45–1.67) and Central Europe (EAPC = 1.46; 1.34–1.57). Meanwhile, Western Sub-Saharan Africa showed the largest decline (EAPC = − 0.68; −0.73 to − 0.63) (Table 1 ). 3.2.2 Mortality In 2021, mortality rates were highest in Southern Latin America (18.30; 95% UI: 16.14–20.59), Western Europe (17.20; 14.98–19.78), and High-income North America (15.54; 13.68–17.82). In contrast, the lowest rates were observed in East Asia (0.17; 0.14–0.21), Central Asia (0.18; 0.15–0.20), and Andean Latin America (0.40; 0.32–0.49). Death counts similarly reflected geographic disparities, with the highest burdens in Western Europe (134,147; 116,364–154,181), South Asia (89,376; 75,121–107,802), and High-income North America (83,307; 71,986–96,229), and the lowest in Central Asia (149; 130–168), Southern Sub-Saharan Africa (203; 183–223), and Andean Latin America (247; 197–300). Socioeconomic stratification was pronounced: High-SDI regions exhibited both the highest mortality rate in 2021 (1.34; 1.18–1.44) and the strongest upward trend (EAPC = 0.88; 95% CI: 0.72–1.04), whereas Middle-SDI regions had the lowest rate (0.56; 0.49–0.71) alongside a significant decline (EAPC = − 0.83; 95% CI: −0.91 to − 0.74). Geographically, Eastern Europe experienced the steepest mortality increase (EAPC = 3.98; 3.64–4.31), while East Asia achieved the most substantial reduction (EAPC = − 2.82; −3.09 to − 2.55) (Table S1 ). 3.2.3 DALY In 2021, the highest DALY rates were observed in Oceania (75.41; 95% UI: 54.52–104.32), Eastern Europe (38.55; 35.78–41.49), and the Caribbean (39.15; 30.22–48.95), while the lowest rates occurred in East Asia (4.59; 3.72–6.22), Central Asia (6.39; 5.57–7.29), and Southern Sub-Saharan Africa (35.81; 30.26–45.86). Geographically, the largest absolute DALY burdens were reported in South Asia (401,831.73; 315,555.35–481,638.15), Southeast Asia (254,838.58; 202,734.28–361,941.27), and High-income North America (211,635.07; 195,397.99–223,604.68). By contrast, the lowest burdens were recorded in Australasia (11,314.77; 10,254.08–12,180.43), Andean Latin America (9,992.34; 7,992.11–12,380.36), and Oceania (9,093.30; 6,503.56–12,389.91). Socioeconomic disparities were evident: Low-SDI regions exhibited the highest DALY rate (40.71; 27.37–52.99), followed by High-SDI regions (28.62; 26.65–29.98). Notably, Eastern Europe experienced the steepest increase in DALY rates (EAPC = 3.68; 95% CI: 3.12–4.25), with similarly rising trends in Australasia (EAPC = 2.52; 2.11–2.94) and Western Europe (EAPC = 2.09; 1.69–2.49). Conversely, East Asia showed the most substantial decline (EAPC = − 5.01; 95% CI: −5.46 to − 4.55), followed by North Africa and the Middle East (EAPC = − 1.89; −1.92 to − 1.86) and Central Asia (EAPC = − 1.26; −1.54 to − 0.98) (Table S2). 3.3 National trends 3.3.1 Incidence In 2021, countries with the highest reported case counts included China (264,282; 95% UI: 216,083–315,405), India (70,294; 58,906–84,881), and the United States (74,668; 64,723–86,104), collectively constituting 49.7% of global cases. Smaller island nations demonstrated substantially lower burdens: the Cook Islands reported 4.68 cases (4.06–5.29), while Tokelau recorded 0.24 cases (0.21–0.27) (Fig. 1 A). The highest incidence rates occurred in Thailand (33.55; 29.76–37.89), Saint Lucia (26.83; 23.69–30.15), and Monaco (24.18; 21.13–27.55). Conversely, the lowest rates were observed in Tajikistan (4.31; 3.52–5.27), the Kyrgyz Republic (4.70; 3.89–5.70), and Mongolia (4.72; 3.84–5.64) (Figure S1 A). 3.3.2 Mortality In 2021, the United States reported the highest mortality burden (9,664; 95% UI: 8,447–10,371), followed by Japan (4,790; 3,616–5,494) and France (4,012; 3,407–4,498), collectively representing 26.2% of global deaths (Fig. 1 B). Conversely, Tokelau (0.03; 0.03–0.05) and Niue (0.04; 0.03–0.05) reported the lowest mortality figures. The highest mortality rates were observed in Switzerland (3.68; 2.98–4.11), the Netherlands (3.29; 2.83–3.62), and American Samoa (3.00; 1.43–4.39). In contrast, the lowest rates occurred in Tajikistan (0.03; 0.02–0.04), Azerbaijan (0.03; 0.02–0.04), and China (0.11; 0.08–0.15) (Figure S1 B). 3.3.2 DALY In 2021, the United States recorded the highest DALYs at 194,858 (95% UI: 179,626–206,136), followed by India (297,736; 231,113–360,859) and Brazil (82,085; 78,513–85,469), collectively constituting 43.7% (575,679 of 1,317,348) of global DALYs. By contrast, Tokelau (1.35; 1.04–1.83) and Niue (1.37; 1.07–1.89) exhibited negligible DALY burdens (Fig. 1 C). The highest DALY rates were observed in Tokelau (99.95; 76.93–135.08), the Republic of Madagascar (91.07; 56.51–135.06), and the Federated States of Micronesia (82.33; 54.57–117.72). Conversely, the lowest rates occurred in Azerbaijan (1.09; 0.70–1.63), Tajikistan (1.31; 0.89–1.86), and Armenia (3.14; 2.63–3.67) (Figure S1 C). 3.4 Age and sex patterns In 2021, incidence rates were consistently higher in males than in females across all age groups. The highest rates were observed in individuals aged ≥ 95 years (males: 189.09; 95% UI: 128.97–285.84; females: 208.10; 150.08–296.18). Absolute case numbers peaked among males aged 80–84 years (36,943; 95% UI: 27,423–47,720), whereas females aged ≥ 95 years exhibited a higher case count (8,196; 95% UI: 5,911–11,664) compared with males in the same age group (2,859; 95% UI: 1,950–4,322). Sex disparities in incidence intensified with age: the male-to-female rate ratio increased from 1.4 (< 5 years) to 1.8 (75–79 years), but reversed in absolute numbers among the oldest females (≥ 95 years: female-to-male ratio = 2.9) (Figure S2A). Mortality rates increased exponentially with age, peaking at 60.64 (95% UI: 46.81–68.39) for males and 82.95 (56.10–96.81) for females aged ≥ 95 years. Absolute deaths were highest among females aged ≥ 95 years (3,267; 95% UI: 2,209–3,812), followed by females aged 90–94 years (5,273; 3,836–6,088). Males exhibited higher mortality rates than females in most age groups. However, sex-specific absolute deaths shifted in older populations: females aged ≥ 85 years consistently exceeded males in death counts despite lower mortality rates (Figure S2B). In 2021, global DALY rates increased exponentially with age, peaking in the ≥ 95-year group (males: 497.14; 95% UI: 384.93–560.52; females: 675.25; 462.34–786.04). Absolute DALY counts were highest among females aged 85–89 years (55,574; 42,738–63,832), followed by females aged ≥ 90 years (46,325; 33,896–53,265), whereas males had peak burdens at 65–69 years (84,087; 69,537–102,807). Males exhibited higher DALY rates than females in most age groups under 75 years, but this pattern reversed in older age groups (≥ 95 years: female-to-male rate ratio = 1.36) (Figure S2C). 3.5 Burden of endocarditis The analysis revealed no consistent linear association between the SDI and age-standardized incidence rates (ASIR) of endocarditis across global regions. High-SDI regions exhibited divergent trajectories: High-income North America saw a substantial 51.1% increase in ASIR, while High-income Asia Pacific plateaued after 2010, and Western Europe maintained persistently elevated rates. Conversely, low-SDI regions (e.g., Central Sub-Saharan Africa) showed stability, with no significant temporal changes. Middle-SDI regions demonstrated heterogeneity; Southeast Asia consistently exceeded global averages, whereas North Africa and the Middle East experienced modest growth. Notably, certain high-SDI regions outpaced middle-SDI counterparts in ASIR growth, contradicting SDI-based projections (Figure S3A). Age-standardized death rates (ASDR) remained stable overall, marginally declining from 0.97 (95% UI: 0.85–1.07) to 0.96 (0.85–1.07), with no systematic correlation to SDI. High-SDI regions displayed marked disparities: High-income North America sustained elevated ASDR, while High-income Asia Pacific decreased from 0.82 (0.67–0.93) to 0.85 (0.68–0.97). Western Europe and Australasia retained high mortality despite advanced SDI levels, whereas low-SDI regions exhibited negligible progress (Figure S3B). DALY rates for endocarditis decreased from 28.32 (95% UI: 23.28–31.61) to 25.56 (22.34–28.37). High-SDI regions demonstrated contrasting trends: East Asia achieved the largest reduction, while High-income North America stagnated. Conversely, middle-SDI regions experienced significant increases, including Southern Sub-Saharan Africa (+ 25.6%), Central Europe (+ 64.7%), and the Caribbean (+ 34.8%). In low-SDI regions, Eastern Sub-Saharan Africa remained the highest-burden area despite a 27.5% decline in DALY rates (Figure S3C). 3.6 Cross-country inequality analysis We conducted a cross-country inequality analysis. The concentration index (C) for endocarditis-related DALYs demonstrated reduced socioeconomic inequality between 1990 and 2021, declining from − 0.20 (95% UI: −0.24 to − 0.16) to − 0.12 (− 0.16 to − 0.08) (Fig. 2 A). Despite this progress, DALYs remained disproportionately concentrated in lower SDI groups, as indicated by persistently negative C values. Absolute inequality, measured by the slope index of inequality (SII), narrowed significantly from − 37.03 (− 44.73 to − 29.33) to − 19.83 (− 27.15 to − 12.51) DALYs per 100,000 population during this period (Fig. 2 B). The negative SII values confirmed a persistently higher disease burden among socioeconomically disadvantaged populations. Cumulative fraction curves revealed that in 1990, the lowest 35% of the population (ranked by SDI) accounted for 50% of endocarditis DALYs; by 2021, this proportion shifted marginally to the lowest 40%. 3.7 Decomposition analysis Decomposition analysis highlighted pronounced regional heterogeneity in the drivers of DALY changes (Fig. 3 ). Globally, population growth was the largest contributor to DALY increases (87.9%), followed by aging (38.9%), while epidemiological improvements significantly reduced the burden (− 26.8%). Regional patterns diverged markedly: in South Asia, aging and population growth acted synergistically to elevate DALYs (64.2% and 79.5%, respectively). High-income regions exhibited divergent trends. Western Europe experienced minimal influence from aging (10.7%) but substantial epidemiological improvements (− 51.0%), whereas High-income North America’s DALY rise was primarily driven by population growth (75.6%). Notably, Central Europe and Eastern Europe recorded negative contributions from aging (− 34.1% and − 39.3%, respectively), offset by positive epidemiological shifts of up to 103.1%. Low-SDI regions faced compounding risks due to population growth (67.7%) and aging (71.4%), while Middle-SDI regions paradoxically exhibited the most substantial epidemiological improvements (− 215.5%, − 151,973 DALYs). These findings underscore the critical influence of demographic dynamics and healthcare policies on disease burden trajectories. 3.8 Predictions of endocarditis from 2020 to 2035 Bayesian age-period-cohort analysis projected a significant rise in global ASIR of infective endocarditis, increasing from 12.66 (95% UI: 12.64–12.69) in 2021 to 13.66 (95% UI: 12.91–14.41) in 2035, representing a 9.5% increase. Conversely, the global age-standardized mortality rate (ASMR) is forecasted to decline from 0.98 (95% UI: 0.97–0.98) in 2021 to 0.92 (95% UI: 0.84–1.00) in 2035, reflecting a 6.1% reduction. Similarly, the ASDR is anticipated to decrease significantly during this period, dropping from 25.62 (95% UI: 25.58–25.65) to 23.18 (95% UI: 21.26–25.09), with a 9.5% decline (Fig. 4 ). 4. Discussion This study conducted a comprehensive analysis of endocarditis' global burden from 1990 to 2021 using the GBD 2021 dataset, encompassing 204 countries and 21 regions. We utilized advanced analytical methods, including SII and CI to assess socioeconomic disparities, decomposition analysis to identify demographic and epidemiological drivers, and Bayesian Age-Period-Cohort modeling for projections. Robust regression techniques reduced outlier impacts in inequality metrics, while age standardization enhanced cross-population comparability [ 9 ] . Key findings demonstrated a 135% rise in global endocarditis incidence since 1990, driven by population aging and growth, juxtaposed with a 34.4% reduction in age-standardized DALY rates [ 10 , 11 ] . Gender disparities persisted, with males exhibiting 1.37 times higher DALYs globally, whereas females ≥ 95 years faced elevated age-specific mortality [ 12 ] . High-SDI regions revealed paradoxical trends, such as increasing incidence in High-income North America despite advanced healthcare systems [ 13 ] . While socioeconomic inequality moderated, 40% of DALYs remained concentrated in low-SDI populations [ 14 ] . Projections indicate a 9.5% incidence rise by 2035, highlighting persistent challenges in disease transmission alongside declining mortality severity. Demographic changes and epidemiological transitions are responsible for the global rising endocarditis burden [ 15 ] . Higher incidence rates in aging populations − populations more vulnerable because of immunosenescence and comorbidities − drive rising incidence rates; population growth drives up absolute case numbers. Simultaneously, attenuation in the impact of diagnostics and clinical alertness, especially from high-SDI areas, probably fosters superior case identification without a corresponding rise in mortality levels [ 16 , 17 ] . Declining age-standardized DALY rates suggest innovations in medical management, with advances in surgical techniques, directed antibiotic therapy, and prevention that all decrease severity of illness and mortality. Persistent socioeconomic inequalities continue to highlight disparities in access to care, late diagnosis, and poor management in resource-constrained environments. Furthermore, regional differences must be considered into the context factor in which the study will be carried out encompassing differences in public health policies including healthcare infrastructure, socioeconomic determinants, and other variables that might interact with the response of COVID-19. These dynamics bring into focus the critical need for context-specific interventions to be developed for addressing the complex drivers of endocarditis burden [ 18 , 19 ] . Geographic disparities in the burden of endocarditis comorbidity correlate with regional drivers [ 20 , 21 ] . The high case counts in China and the United States are in line with aging populations and increasing use of invasive cardiac devices, while Thailand’s elevated ASIR probably represents greater case detection, owing to wide echocardiography utilization. By contrast, the low mortality rates in Tajikistan and China may indicate either systematic underdetection of fatal cases or effective antibiotic stewardship program efforts. The higher mortality in the United States correlates with increasing antibiotic resistance associated with the use of illicit intravenous drugs and the paradox of the higher mortality of Switzerland reflects delayed presentation in populations with universal healthcare access [ 22 ] . Biological susceptibility, comorbidity, and healthcare disparities are associated with age- and sex-specific variations in endocarditis incidence [ 23 – 25 ] . Higher incidence rates in older adults are associated with immunosenescence, cumulative comorbidities, and increased exposure to invasive medical procedures [ 26 ] . Men have a higher incidence in nearly all age groups, primarily due to their greater exposure to risk factors like IV drug use and cardiovascular disease. In contrast, historic female age-driven case and mortality rates are higher in absolute sex-specific terms due to longevity and more engagement with healthcare, resulting in substantial differences in disease detection and managed pathways into care in later stages of life. The uneven nature of the geographic burden of infective endocarditis is shaped by the interaction of the socioeconomic development, health care system capabilities, alongside changing demographic profiles [ 27 , 28 ] . High-SDI regions show further heterogeneous rates—from increasing incidence in North America to plateaued rates observed in the Asia-Pacific region—all reflective of the challenges related to disparities in access to healthcare, a rapidly aging population, and inequitable uptake of advanced diagnostic technologies. Continued high burdens in middle-SDI regions indicate ongoing systemic transitions in healthcare delivery and rising epidemiological risk profiles, and stagnant or falling rates in low-SDI regions most likely reflect underdiagnosis owing to poor healthcare infrastructure, not real epidemiological advances. Our cross-regional difference-in-difference analysis confirms this trend. Decreasing CI and SII reflect tangible progress in terms of the accessibility of health services, preventive measures and socioeconomic transformation in low- and middle-income nations. Persistently negative inequality indices, however, continue to depict systemic barriers to care endemic to low-SDI regions (eg, disparate health care infrastructure, diagnostic delays, low public health literacy, and restricted access to state-of-the-art therapies). These inequities highlight deep-rooted structural shortcomings that re-create global inequalities in disease burden. Similar to GBD 2021 trends in cardiovascular diseases, population aging (38.9%) and growth (87.9%) were identified as major global drivers of DALYs. However, regional differences show different causal pathways: the synergistic risks of population aging (64.2%) and population growth (79.5%) in South Asia highlight inadequacies in the ability of healthcare systems to respond to demographic changes, and the emphasis on population growth (75.6%) in High-income North America highlights the stresses placed on healthcare systems as a result of population pressure from immigration. The negative aging effect in Central Europe (− 34.1%) relates to the outmigration of elder subgroups and the greater availability of advanced cardiac interventions. As observed as unprecedented epidemiology reductions (-215.5%) in Middle-SDI regions through coordinated antibiotic-prophylaxis stewardship, while system underfunds in low-SDI regions, causing the structural cause and epidemiological risk of undiagnosed cases and undertreatment to fall into a vicious circle. However, the predicted 9.5% increase in ASIR indicates an accelerating demographic-driven aging and an increasing dependency on prosthetic valve implementation—the impact in the high SDI groups increased by improvements in diagnostic precision. In contrast, the 6.1% decline in the ASMR may reflect the embrace of multidisciplinary care frameworks and fourth-generation antistaphylococcal antibiotics. Persistent gaps in DALY reductions (− 9.5%) were nevertheless observed, consistent with Hu et al [ 29 ] . on prophylaxis and its systemic underfunding in low-income settings. This disparity highlights the divergent impact of antimicrobial stewardship programs—effective in reducing mortality in resource-rich settings—versus intermittent non-uniform healthcare reforms in regions challenged by propagation of aging-related epidemiological susceptibility. Shedding light on the underlying drivers that contribute to these trends has important implications for the theoretical and practical understanding of global and regional trends in endocarditis (as well as the drivers of these trends). Theoretically, it is deepening the concept of epidemiological framework in a systematic analysis by advanced indices like SII, CI, and the BAPC modeling. Methodologically, it explains demographic processes — such as aging and population growth — but identifies them as dominant drivers of disease burden, highlighting their importance for public health policy in aging populations. Likewise, the analysis unveils alarming disparities in disease burden across varying socioeconomic strata, highlighting the need for immediate equitable policy reform and targeted resource allocation to address systemic inequities. Improved modeling approaches generate projections that can bear direct implications for prioritization of healthcare at global and regional scales. Taken together, these results enable decision makers, clinicians, and public health authorities to accelerate evidence-based interventions, strengthen preventive strategies, and inform clinical practice guidelines for endocarditis that are better aligned with current burden. The main strengths of this study come from its methodologically strong design and analytically comprehensive framework that incorporates the latest epidemiological data from the GBD 2021 database across 204 countries and territories. Also, it is methodologically coherent, geographically and demographically broad, possesses advanced inequality metrics, and a high precision in predictive modeling. These components together provide a more nuanced understanding of endocarditis burden variation across global and regional contexts. Despite its methodological robustness, it is important to recognize the limitations of this study. First, our analysis is entirely reliant on the GBD 2021 dataset, which carries intrinsic variation in data quality and completeness by country, within countries potentially undermining reliability in low-resource contexts. Underreporting and misdiagnoses — typical in areas with little health care infrastructure — could lead to further underestimates of incidence and mortality rates. Second, there is inherent uncertainty in projections of future disease burden because future epidemiological trends are unpredictable, and advances in healthcare are not captured by the BAPC model, although the BAPC model improves predictive accuracy. Last, although the socioeconomic indicators usedSDI, while useful, it is an indirect proxy for healthcare access and may oversimplify the multiple socioeconomic causes of health inequalities. In summary, all future studies should focus on enhancing data quality by reinforcing surveillance mechanisms in scenarios with high underreporting potential where healthcare facilities are constrained, and ultimately improving the accuracy of estimates on the global endocarditis burden. In addition, ongoing methodological innovations in predictive modelling—like the incorporation of real-world data streams and healthcare innovation indicators—may improve forecasting precision, alleviating the uncertainty of long-term estimates. Last, the development of multidimensional socioeconomic indices that explicitly account for systemic barriers to care would enhance linkages between healthcare access and endocarditis outcomes, informing performance equity-focused interventions to reduce global health disparities. 5. Conclusion This systematic analysis of global, regional, and national trends in infective endocarditis (IE) epidemiology from 1990 to 2021 identified an increasing global burden that can be largely attributed to demographic changes, specifically an increase in aging populations and uncontrolled population growth. While diagnostic advancements, along with expanded healthcare access—particularly in higher-income settings—have eased burdens, lagging regional disparities expose continuing healthcare inequalities and differential epidemiologic transitions across the globe. Country-specific evaluations revealed slow but inadequate progress towards inter- and intra-national inequality in socioeconomic determinants of health, reinforcing calls for context-specific approaches to inequality mitigation across low-SDI contexts. Demographic changes were shown to be the dominant drivers of rising incidence by more sophisticated decomposition analyses, despite the impact of epidemiological transition which decreased mortality and DALY rates. Projections over the long term (to 2035) indicate that the incidence will continue to increase but mortality and DALY rates will decline, providing key evidence for long-term health system infrastructure planning. Thus, policy makers need to make sure to introduce strategies that would aim to reduce demographic risk factors, build the healthcare capacity across the board and address the systemic inequities through equitable resource allocation. Together, these findings provide potential epidemiological evidence for driving proactive, equity-centered policies to sustainably reduce global IE burden. Declarations Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Interest The authors declare no relevant affiliations or financial relationships with any organization or entity that could influence or bias the content of this manuscript. Reviewer Disclosure Peer reviewers involved in this manuscript have no financial or non-financial relationships relevant to this work to disclose. Ethics Statement As this study constituted a retrospective analysis of publicly available, anonymized data from the GBD database, ethical approval and informed consent were not required. All analyses adhered to ethical guidelines for GBD research, which permit the use of de-identified datasets without institutional review board oversight. Acknowledgments The authors received no financial or technical support for this work. Author Contributions All authors contributed to the study design, data curation, analysis, and manuscript preparation. Specific contributions included: conceptualization (Changjiang Deng, Yixin Xu, Ying Pan, Tingting Wu); methodology (Chao Fan, Zhihui Jiang, Mingming Lv, Bingxin Bai); formal analysis (Zhiyan Du, Zhilong Wang, Adilai Adilijiang); and manuscript drafting and revision (Yingying Zheng, Xiang Xie). All authors approved the final manuscript and take responsibility for the integrity of the work. References Cahill TJ, Prendergast BD. Infective endocarditis. 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Supplementary Files TableS1.Thedeathcasesandratesforendocarditisin19902021anditstemporaltrends..docx TableS2.TheDALYcasesandratesforendocarditisin19902021anditstemporaltrends..docx FigureS1.IncidencerateAdeathrateBandDALYrateCofendocarditisforbothsexesin204countriesin2021..jpg FigureS2.GlobalcountsandincidenceAdeathBandDALYCratesofendocarditisbyageandsex2021.Errorbarsindicatethe95uncertaintyintervals95UIforincidenceAdeathBandDALYsC.Shading.jpg FigureS3.IncidencerateAdeathratesBDALYratesCforendocarditisfor21GlobalBurdenofDiseaseregionsfrom1990to2021bySDI.TheblacklinerepresentstheexpectedagestandardizedDALYratesbasedsolelyo.jpg Legend.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-6404736","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476499756,"identity":"02c63574-23a2-41cc-89d6-d186397c78f2","order_by":0,"name":"Changjiang Deng","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Changjiang","middleName":"","lastName":"Deng","suffix":""},{"id":476499757,"identity":"21307aad-157c-49e7-b411-f08519db2f59","order_by":1,"name":"Yixin Xu","email":"","orcid":"","institution":"First Affiliated Hospital of 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15:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6404736/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6404736/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85498020,"identity":"16e68d6b-62d3-4995-8c1a-299a73d294cd","added_by":"auto","created_at":"2025-06-26 14:09:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":246328,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence case(A), death case (B), and DALY case (C) of endocarditis for both sexes in 204 countries, in 2021.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6404736/v1/e03b95520fd2a33642827150.jpg"},{"id":85498023,"identity":"79898802-8010-4204-900b-308fe3eb7cfe","added_by":"auto","created_at":"2025-06-26 14:09:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":207469,"visible":true,"origin":"","legend":"\u003cp\u003eHealth inequality regression curves and concentration curves for the DALYs of endocarditis worldwide, 1990 and 2021.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6404736/v1/869caa6b695f6ddd8d2e4668.jpg"},{"id":85498904,"identity":"b8f3573f-33f5-4118-a269-16ba5603ba9b","added_by":"auto","created_at":"2025-06-26 14:17:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":131401,"visible":true,"origin":"","legend":"\u003cp\u003eDecomposition analysis of changes in the DALY number of endocarditis\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6404736/v1/6619fd508766bc88a3c2ffff.jpg"},{"id":85498022,"identity":"221eec99-e53c-4172-947b-0164cdc0ea18","added_by":"auto","created_at":"2025-06-26 14:09:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":142254,"visible":true,"origin":"","legend":"\u003cp\u003eThe temporal trends of age-standardized incidence rate (A), age-standardized death rate (B) and age-standardized DALY rate(C) for endocarditis from 1990 to 2035 at the global level.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6404736/v1/00e89ed5fa424647e0ebbb71.jpg"},{"id":87166653,"identity":"8ba7f60a-e3fe-484e-a3cf-ba14f10b1ade","added_by":"auto","created_at":"2025-07-21 06:32:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1760641,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6404736/v1/4d3b0ac6-0a58-43ad-a628-be269fdb2b36.pdf"},{"id":85498025,"identity":"6b118bd6-3d55-42b6-b68f-318eec175a7e","added_by":"auto","created_at":"2025-06-26 14:09:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21358,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.Thedeathcasesandratesforendocarditisin19902021anditstemporaltrends..docx","url":"https://assets-eu.researchsquare.com/files/rs-6404736/v1/5740ad793598bea1e2d0ed36.docx"},{"id":85500042,"identity":"072fb1e8-1403-4677-ac03-07f37ac65b6b","added_by":"auto","created_at":"2025-06-26 14:25:48","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21742,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.TheDALYcasesandratesforendocarditisin19902021anditstemporaltrends..docx","url":"https://assets-eu.researchsquare.com/files/rs-6404736/v1/57a268660d9ab86a4a68f6ce.docx"},{"id":85498905,"identity":"570a9e66-8605-4468-831a-ce0b02a68fba","added_by":"auto","created_at":"2025-06-26 14:17:48","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3100989,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.IncidencerateAdeathrateBandDALYrateCofendocarditisforbothsexesin204countriesin2021..jpg","url":"https://assets-eu.researchsquare.com/files/rs-6404736/v1/ca50c9aefa81c1e4d1ac1216.jpg"},{"id":85498033,"identity":"d5c560a9-8a75-4235-9012-e89904408949","added_by":"auto","created_at":"2025-06-26 14:09:49","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":5257401,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.GlobalcountsandincidenceAdeathBandDALYCratesofendocarditisbyageandsex2021.Errorbarsindicatethe95uncertaintyintervals95UIforincidenceAdeathBandDALYsC.Shading.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6404736/v1/c92ffc2295f2cdbbbb75d2f2.jpg"},{"id":85498044,"identity":"3470475e-4b76-4c4a-acb6-1a80efda71e3","added_by":"auto","created_at":"2025-06-26 14:09:49","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":4638236,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.IncidencerateAdeathratesBDALYratesCforendocarditisfor21GlobalBurdenofDiseaseregionsfrom1990to2021bySDI.TheblacklinerepresentstheexpectedagestandardizedDALYratesbasedsolelyo.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6404736/v1/ade4fc3638cbf82c7f433cad.jpg"},{"id":85498909,"identity":"cbae3025-7a22-4954-99dc-e18dbe1d4af3","added_by":"auto","created_at":"2025-06-26 14:17:49","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":10682,"visible":true,"origin":"","legend":"","description":"","filename":"Legend.docx","url":"https://assets-eu.researchsquare.com/files/rs-6404736/v1/6ab37ac1f216d32c9043d075.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The global, regional, and national burden of endocarditis from 1990 to 2021: an analysis of the global burden of disease study 2021","fulltext":[{"header":"Key Message","content":"\u003cp\u003e\u003cstrong\u003eWhat is already known on this topic\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInfective endocarditis (IE) burden has risen globally, disproportionately affecting high-income regions due to aging populations, prosthetic device use, and improved diagnostics. Socioeconomic disparities in outcomes are recognized but poorly quantified. Prior studies lacked comprehensive analyses of how demographic shifts, epidemiological transitions, and structural inequities collectively drive global burden heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat this study adds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study quantifies population aging (38.9%) and growth (87.9%) as dominant drivers of IE disability-adjusted life years (DALYs) globally, with males experiencing 1.37-fold higher DALYs than females. Despite reduced inequality (Concentration Index:\u0026nbsp;−0.20 to\u0026nbsp;−0.12), 40% of DALYs persist in low-income populations. Bayesian modeling forecasts a 9.5% incidence rise by 2035 but predicts a 6.1% mortality decline, highlighting diverging trajectories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow this study might affect research, practice, or policy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFindings prioritize geriatric healthcare capacity-building, equitable resource allocation for vulnerable populations, and gender-specific interventions. Projections underscore the need for early diagnostics and antimicrobial stewardship in aging societies. Policymakers should leverage decomposition insights to tailor regional strategies addressing demographic pressures and systemic inequities. Enhanced surveillance in under-resourced settings is critical for mitigating preventable morbidity.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eInfective endocarditis (IE), a\u0026ensp;potentially life-threatening cardiovascular infection, continues to be a worldwide health problem, challenges notwithstanding the medical advance, with increasing incidence has been associated with aging of population and invasive cardiac interventions\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Worldwide, there are estimates of \u0026gt;\u0026thinsp;1 million\u0026ensp;cases per year, however, mortality remains unacceptably high in the low-resource settings, which can be in part attributed to the fragmented nature of patients\u0026rsquo; care pathways\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Despite this paradox of better diagnostics and case fatality across high-income regions,\u0026ensp;absolute burdens are rising\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, an outcome not caused by a traditional disease epidemic but by demographic transition and prosthetic device translation among a bulk of the burden, suggesting the independence of morbidity balance in different societies. This juxtaposition highlights the need for disentangling the demographic, epidemiological,\u0026ensp;and socioeconomic drivers of IE heterogeneity.\u003c/p\u003e \u003cp\u003eDespite progress, there are critical knowledge gaps that inhibit\u0026ensp;equitable intervention. Partly, global analyses remain geographically fragmented, with low-income areas underreported for evidence-based comparisons due to unreliable\u0026ensp;surveillance. Second, socioeconomic differences in outcomes\u0026mdash;specifically, delays in diagnosis and limited access to surgery\u0026mdash;are well known but rarely\u0026ensp;quantified over time. These restrictions mask insights that could inform how population aging, healthcare infrastructure, and policy disparities interact\u0026ensp;to influence IE burden.\u003c/p\u003e \u003cp\u003eTo rectify these gaps, this study comprehensively assesses Global Burden of Disease (GBD) 2021 data (1990 to 2021)\u0026ensp;across 204 countries and territories. We leverage three methodological innovations: (1) decomposition analysis to separate trends\u0026ensp;in disability-adjusted life years (DALYs) into aging, population growth, and epidemiological components\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e; (2) robust metrics of inequality (Slope Index of Inequality, Concentration Index) to capture socioeconomic differences\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e; and (3) Bayesian Age-Period-Cohort modelling with Integrated Nested Laplace Approximation (INLA) to predict future burdens while circumventing data sparsity\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. We also perform, to the best of our knowledge, the first simultaneous analysis of demographic determinants\u0026ensp;and cross-country inequity in IE burden, which advance a unified framework for guiding resource allocation. Our work pairs demographic precision with equity-focused analytics to identify areas of\u0026ensp;need to target avoidable morbidity in aging and under-resourced populations.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Date sources\u003c/h2\u003e \u003cp\u003eThe GBD database has compiled and standardized epidemiological data from 204 countries and territories across 21 GBD regions since 1990. GBD 2021 offers comprehensive estimates for 371 health conditions and injuries, as well as 88 associated risk factors. All data were sourced from the Global Health Data Exchange (GHDx) query tool, accessed via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Our dataset includes sex-stratified and age-stratified metrics\u0026mdash;such as incidence, mortality, and DALYs\u0026mdash;specific to infective endocarditis, aggregated across all 204 countries and territories in the GBD framework.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Statistical analysis\u003c/h2\u003e \u003cp\u003eThis study quantified the burden of endocarditis across age, sex, temporal (years), and geographic (location) strata. Incidence, mortality, (DALYs, and estimated annual percentage change (EAPC) were employed to evaluate trends in disease morbidity and mortality. All estimates are reported with 95% uncertainty intervals (UIs). Statistical analyses utilized validated methodologies (e.g., generalized linear models, Bayesian age-period-cohort [BAPC] modeling), with significance defined at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Detailed methodological specifications, including calculations for equity indices (slope index of inequality, concentration index), decomposition frameworks, and BAPC parameters, are provided in subsequent sections. Analytical workflows were implemented using R software (v4.4.1) for data visualization.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Cross-country inequality analysis\u003c/h2\u003e \u003cp\u003eThis study utilized the Slope Index of Inequality (SII) and the Concentration Index (CI), as outlined by the World Health Organization (WHO), to assess absolute and relative inequalities in the burden of endocarditis across 204 countries and territories from 1990 to 2021. SII quantifies absolute disparities by regressing disease burden (measured in DALYs) against the midpoint of the Socio-demographic Index (SDI)-ranked cumulative population distribution. Relative inequality was evaluated using the CI, derived via numerical integration of the area under the Lorenz curve, which matches the cumulative DALYs proportion to the population ranked by SDI. To mitigate bias from outliers and data heterogeneity, robust regression models (RLMs) replaced conventional linear regression (LM) in analyses, enhancing the reliability of inequality estimates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Decomposition analysis\u003c/h2\u003e \u003cp\u003eDecomposition analysis was employed to identify factors contributing to temporal changes in age-standardized disease burden (measured in disability-adjusted life years, DALYs) between 1990 and 2021. This method quantifies the additive contributions of inter-group differences\u0026mdash;specifically, disparities in age structure, epidemiological patterns, and population size\u0026mdash;to variations in total DALYs. The analysis framework partitioned observed DALY changes into three components: 1) demographic shifts (population aging), 2) epidemiological transitions (disease incidence/prevalence), and 3) population growth effects, enabling precise attribution of each factor\u0026rsquo;s influence on overall burden trends.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 BAPC model projection\u003c/h2\u003e \u003cp\u003eThis study selected the BAPC model to project future disease burdens, given its ability to address complex, high-dimensional, and sparse data inherent in large-scale epidemiological datasets such as the GBD 2021. The BAPC model extends the generalized linear model (GLM) framework within a Bayesian paradigm, simultaneously modeling age, period, and cohort effects while incorporating temporal evolution smoothed via a second-order random walk prior. This approach yields accurate posterior estimates of burden trends. A key advantage of the BAPC model lies in its use of Integrated Nested Laplace Approximation (INLA), which facilitates efficient approximate Bayesian inference for estimating marginal posterior distributions. Unlike traditional Markov Chain Monte Carlo (MCMC) methods, INLA avoids convergence and mixing challenges common in high-dimensional settings, substantially improving computational efficiency without sacrificing accuracy. The model\u0026rsquo;s adaptable framework and robustness in capturing temporal trends in epidemiological data render it highly suitable for long-term burden projections.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Global trends\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eFrom 1990 to 2021, global endocarditis-related incidence, mortality, and DALYs increased significantly in absolute terms. However, their annual percentage changes exhibited divergent trends. While the incidence rate demonstrated an upward trajectory (EAPC\u0026thinsp;=\u0026thinsp;1.00; 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.93\u0026ndash;1.08)(Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), mortality remained stable (EAPC\u0026thinsp;=\u0026thinsp;0.19; 0.08\u0026ndash;0.29)( Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e), and DALYs displayed a decline (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.34; \u0026minus;0.45 to \u0026minus;\u0026thinsp;0.23)( Table S2).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe incidence cases and rates for endocarditis in 1990/2021 and its temporal trends\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecounts\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003erate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecounts\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003erate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEAPC(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e443552.34(375741.82,535453.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.35(8.01,11.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1042477.45(893665.11,1204150.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.61(10.84,14.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00(0.93,1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e233482.48(199067.28,280439.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.11(8.68,11.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e583279.86(502013.60,670184.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.85(12.84,17.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.31(1.22,1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e210069.86(176768.19,254383.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.65(7.31,10.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e459197.60(392156.84,535072.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.58(9.05,12.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65(0.59,0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101182.24(85942.50,119164.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.54(8.98,12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e274389.71(236263.49,318813.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.77(13.63,18.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.23(1.09,1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110269.02(92249.37,132192.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.86(9.18,12.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e252068.57(212760.19,292725.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.70(12.56,17.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04(0.99,1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151150.15(127861.84,182316.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.22(8.77,12.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e328360.41(277094.33,382211.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.81(10.94,14.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75(0.73,0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51160.13(42853.70,62579.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.56(4.72,6.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120841.97(103163.67,143214.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.29(6.25,8.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88(0.86,0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29343.31(25292.61,35094.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.61(5.78,7.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65870.51(57091.59,78624.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.18(6.34,8.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26(0.22,0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2324.51(2000.83,2762.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.79(6.77,9.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6579.12(5603.33,7730.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.58(9.04,12.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98(0.93,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2419.38(1986.40,2869.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.79(8.88,12.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7843.40(6589.95,9114.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.65(14.20,19.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.40(1.26,1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3390.59(2910.66,4042.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.64(9.15,12.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6769.52(5879.67,7766.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.46(11.74,15.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77(0.68,0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2615.31(2089.52,3247.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.53(3.68,5.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4717.84(3888.63,5708.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.33(4.43,6.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60(0.58,0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11004.12(8957.59,13145.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.94(6.56,9.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21861.35(18282.29,25733.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.16(10.26,14.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.46(1.34,1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9273.73(7717.22,11362.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.99(5.85,8.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26424.11(22365.07,31035.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.50(8.96,12.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29(1.14,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3091.23(2621.56,3716.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.99(6.06,8.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8342.47(7197.51,10065.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.99(7.08,9.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44(0.36,0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137198.57(113863.94,167537.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.25(10.39,14.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e276167.93(225843.92,328814.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.52(12.19,17.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57(0.51,0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20772.75(16998.57,24756.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.16(6.78,9.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42342.44(35725.95,49589.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.28(13.05,17.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.37(2.23,2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11699.00(9972.44,14169.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.56(6.58,8.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26305.33(22529.78,31919.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.90(6.94,9.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07(0.03,0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17676.53(14789.38,21465.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.69(8.16,11.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46149.21(39169.42,54241.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.82(10.80,14.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65(0.47,0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31012.54(26255.91,37181.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.29(8.71,12.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83306.68(71986.43,96229.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.54(13.68,17.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.22(1.03,1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19271.36(16143.14,23520.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.89(5.85,8.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47329.49(40042.38,56557.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.88(7.59,10.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87(0.81,0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e433.91(371.19,518.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.44(9.00,11.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1095.01(958.29,1268.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.9(10.55,13.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52(0.49,0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31497.71(25612.19,39462.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.97(3.32,4.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89375.78(75120.69,107802.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.52(4.62,6.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.12(1.07,1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2864.517(2498.891-3286.605)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.765(5.902\u0026ndash;7.762)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6680.43(5889.92-7538.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.83 (5.14\u0026ndash;6.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81(0.73,0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5452.49(4706.47,6317.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.71(10.14,13.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14803.82(13021.93,16591.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.3(16.14,20.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36(1.20,1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e314.596(247.584-383.563)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.179(1.715\u0026ndash;2.657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e202.83(182.75-223.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.08 (1.88\u0026ndash;2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.35(-0.42,-0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTropical Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3587.81(2985.70,4369.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.23(7.05,9.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5481.13(4639.81,6621.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.70(6.57,9.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.26(1.11,1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51578.76(43533.07,60080.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.92(9.25,12.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134147.40(116364.20,154181.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.2(14.98,19.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.56(1.45,1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20412.03(17681.04,23927.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.08(8.84,11.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39183.48(34219.09,45871.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.60(7.56,9.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.68(-0.73,-0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe number of new cases surged by 135%, rising from 443,552 (95% UI: 375,741\u0026ndash;535,453) in 1990 to 1,042,477 (893,665\u0026ndash;1,204,150) in 2021 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Similarly, deaths increased by 111.1%, from 36,883 (31,646\u0026ndash;40,522) to 77,844 (69,010\u0026ndash;86,338) (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e), and DALYs rose by 55.7%, from 1,333,863 (1,056,184\u0026ndash;1,508,950) to 2,076,413 (1,827,084\u0026ndash;2,308,504) (Table S2).\u003c/p\u003e\n \u003cp\u003eIn 2021, a pronounced sex disparity was observed: males accounted for 583,280 cases (95% UI: 502,014\u0026ndash;670,185) compared to 459,198 (392,157\u0026ndash;535,072) in females (male-to-female ratio: 1.27:1)\u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. This disparity persisted in mortality (males: 40,094 [35,274\u0026ndash;45,551]; females: 37,750 [31,155\u0026ndash;43,488]; ratio: 1.06:1)(Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e) and DALYs (males: 1,200,319 [1,007,093\u0026ndash;1,393,512]; females: 876,094 [705,775\u0026ndash;1,006,976]; ratio: 1.37:1)(Table S2).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Regional level\u003c/h2\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Incidence\u003c/h2\u003e\n \u003cp\u003eIn 2021, Southern Latin America exhibited the highest age-standardized incidence rate (ASR) (18.30; 95% UI: 16.14\u0026ndash;20.59), followed by Western Europe (17.20; 14.98\u0026ndash;19.78) and Australasia (16.65; 14.20\u0026ndash;19.47). In contrast, Southern Sub-Saharan Africa recorded the lowest ASR (2.08; 1.88\u0026ndash;2.29). East Asia reported the greatest absolute case burden (276,168; 225,844\u0026ndash;328,814), which starkly contrasted with Southern Sub-Saharan Africa (203; 183\u0026ndash;223). Socioeconomic disparities were evident: high-SDI regions demonstrated an ASR of 15.77 (13.63\u0026ndash;18.08), 2.2 times higher than low-SDI regions (7.18; 6.34\u0026ndash;8.30). Geographically, Eastern Europe experienced the steepest increase in ASR (EAPC\u0026thinsp;=\u0026thinsp;2.37; 95% CI: 2.23\u0026ndash;2.52), followed by Western Europe (EAPC\u0026thinsp;=\u0026thinsp;1.56; 1.45\u0026ndash;1.67) and Central Europe (EAPC\u0026thinsp;=\u0026thinsp;1.46; 1.34\u0026ndash;1.57). Meanwhile, Western Sub-Saharan Africa showed the largest decline (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.68; \u0026minus;0.73 to \u0026minus;\u0026thinsp;0.63) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Mortality\u003c/h2\u003e\n \u003cp\u003eIn 2021, mortality rates were highest in Southern Latin America (18.30; 95% UI: 16.14\u0026ndash;20.59), Western Europe (17.20; 14.98\u0026ndash;19.78), and High-income North America (15.54; 13.68\u0026ndash;17.82). In contrast, the lowest rates were observed in East Asia (0.17; 0.14\u0026ndash;0.21), Central Asia (0.18; 0.15\u0026ndash;0.20), and Andean Latin America (0.40; 0.32\u0026ndash;0.49). Death counts similarly reflected geographic disparities, with the highest burdens in Western Europe (134,147; 116,364\u0026ndash;154,181), South Asia (89,376; 75,121\u0026ndash;107,802), and High-income North America (83,307; 71,986\u0026ndash;96,229), and the lowest in Central Asia (149; 130\u0026ndash;168), Southern Sub-Saharan Africa (203; 183\u0026ndash;223), and Andean Latin America (247; 197\u0026ndash;300). Socioeconomic stratification was pronounced: High-SDI regions exhibited both the highest mortality rate in 2021 (1.34; 1.18\u0026ndash;1.44) and the strongest upward trend (EAPC\u0026thinsp;=\u0026thinsp;0.88; 95% CI: 0.72\u0026ndash;1.04), whereas Middle-SDI regions had the lowest rate (0.56; 0.49\u0026ndash;0.71) alongside a significant decline (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.83; 95% CI: \u0026minus;0.91 to \u0026minus;\u0026thinsp;0.74). Geographically, Eastern Europe experienced the steepest mortality increase (EAPC\u0026thinsp;=\u0026thinsp;3.98; 3.64\u0026ndash;4.31), while East Asia achieved the most substantial reduction (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.82; \u0026minus;3.09 to \u0026minus;\u0026thinsp;2.55) (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.3 DALY\u003c/h2\u003e\n \u003cp\u003eIn 2021, the highest DALY rates were observed in Oceania (75.41; 95% UI: 54.52\u0026ndash;104.32), Eastern Europe (38.55; 35.78\u0026ndash;41.49), and the Caribbean (39.15; 30.22\u0026ndash;48.95), while the lowest rates occurred in East Asia (4.59; 3.72\u0026ndash;6.22), Central Asia (6.39; 5.57\u0026ndash;7.29), and Southern Sub-Saharan Africa (35.81; 30.26\u0026ndash;45.86). Geographically, the largest absolute DALY burdens were reported in South Asia (401,831.73; 315,555.35\u0026ndash;481,638.15), Southeast Asia (254,838.58; 202,734.28\u0026ndash;361,941.27), and High-income North America (211,635.07; 195,397.99\u0026ndash;223,604.68). By contrast, the lowest burdens were recorded in Australasia (11,314.77; 10,254.08\u0026ndash;12,180.43), Andean Latin America (9,992.34; 7,992.11\u0026ndash;12,380.36), and Oceania (9,093.30; 6,503.56\u0026ndash;12,389.91). Socioeconomic disparities were evident: Low-SDI regions exhibited the highest DALY rate (40.71; 27.37\u0026ndash;52.99), followed by High-SDI regions (28.62; 26.65\u0026ndash;29.98). Notably, Eastern Europe experienced the steepest increase in DALY rates (EAPC\u0026thinsp;=\u0026thinsp;3.68; 95% CI: 3.12\u0026ndash;4.25), with similarly rising trends in Australasia (EAPC\u0026thinsp;=\u0026thinsp;2.52; 2.11\u0026ndash;2.94) and Western Europe (EAPC\u0026thinsp;=\u0026thinsp;2.09; 1.69\u0026ndash;2.49). Conversely, East Asia showed the most substantial decline (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.01; 95% CI: \u0026minus;5.46 to \u0026minus;\u0026thinsp;4.55), followed by North Africa and the Middle East (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.89; \u0026minus;1.92 to \u0026minus;\u0026thinsp;1.86) and Central Asia (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.26; \u0026minus;1.54 to \u0026minus;\u0026thinsp;0.98) (Table S2).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 National trends\u003c/h2\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Incidence\u003c/h2\u003e\n \u003cp\u003eIn 2021, countries with the highest reported case counts included China (264,282; 95% UI: 216,083\u0026ndash;315,405), India (70,294; 58,906\u0026ndash;84,881), and the United States (74,668; 64,723\u0026ndash;86,104), collectively constituting 49.7% of global cases. Smaller island nations demonstrated substantially lower burdens: the Cook Islands reported 4.68 cases (4.06\u0026ndash;5.29), while Tokelau recorded 0.24 cases (0.21\u0026ndash;0.27) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). The highest incidence rates occurred in Thailand (33.55; 29.76\u0026ndash;37.89), Saint Lucia (26.83; 23.69\u0026ndash;30.15), and Monaco (24.18; 21.13\u0026ndash;27.55). Conversely, the lowest rates were observed in Tajikistan (4.31; 3.52\u0026ndash;5.27), the Kyrgyz Republic (4.70; 3.89\u0026ndash;5.70), and Mongolia (4.72; 3.84\u0026ndash;5.64) (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eA).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 Mortality\u003c/h2\u003e\n \u003cp\u003eIn 2021, the United States reported the highest mortality burden (9,664; 95% UI: 8,447\u0026ndash;10,371), followed by Japan (4,790; 3,616\u0026ndash;5,494) and France (4,012; 3,407\u0026ndash;4,498), collectively representing 26.2% of global deaths (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). Conversely, Tokelau (0.03; 0.03\u0026ndash;0.05) and Niue (0.04; 0.03\u0026ndash;0.05) reported the lowest mortality figures. The highest mortality rates were observed in Switzerland (3.68; 2.98\u0026ndash;4.11), the Netherlands (3.29; 2.83\u0026ndash;3.62), and American Samoa (3.00; 1.43\u0026ndash;4.39). In contrast, the lowest rates occurred in Tajikistan (0.03; 0.02\u0026ndash;0.04), Azerbaijan (0.03; 0.02\u0026ndash;0.04), and China (0.11; 0.08\u0026ndash;0.15) (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eB).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 DALY\u003c/h2\u003e\n \u003cp\u003eIn 2021, the United States recorded the highest DALYs at 194,858 (95% UI: 179,626\u0026ndash;206,136), followed by India (297,736; 231,113\u0026ndash;360,859) and Brazil (82,085; 78,513\u0026ndash;85,469), collectively constituting 43.7% (575,679 of 1,317,348) of global DALYs. By contrast, Tokelau (1.35; 1.04\u0026ndash;1.83) and Niue (1.37; 1.07\u0026ndash;1.89) exhibited negligible DALY burdens (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). The highest DALY rates were observed in Tokelau (99.95; 76.93\u0026ndash;135.08), the Republic of Madagascar (91.07; 56.51\u0026ndash;135.06), and the Federated States of Micronesia (82.33; 54.57\u0026ndash;117.72). Conversely, the lowest rates occurred in Azerbaijan (1.09; 0.70\u0026ndash;1.63), Tajikistan (1.31; 0.89\u0026ndash;1.86), and Armenia (3.14; 2.63\u0026ndash;3.67) (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eC).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Age and sex patterns\u003c/h2\u003e\n \u003cp\u003eIn 2021, incidence rates were consistently higher in males than in females across all age groups. The highest rates were observed in individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;95 years (males: 189.09; 95% UI: 128.97\u0026ndash;285.84; females: 208.10; 150.08\u0026ndash;296.18). Absolute case numbers peaked among males aged 80\u0026ndash;84 years (36,943; 95% UI: 27,423\u0026ndash;47,720), whereas females aged\u0026thinsp;\u0026ge;\u0026thinsp;95 years exhibited a higher case count (8,196; 95% UI: 5,911\u0026ndash;11,664) compared with males in the same age group (2,859; 95% UI: 1,950\u0026ndash;4,322). Sex disparities in incidence intensified with age: the male-to-female rate ratio increased from 1.4 (\u0026lt;\u0026thinsp;5 years) to 1.8 (75\u0026ndash;79 years), but reversed in absolute numbers among the oldest females (\u0026ge;\u0026thinsp;95 years: female-to-male ratio\u0026thinsp;=\u0026thinsp;2.9) (Figure S2A).\u003c/p\u003e\n \u003cp\u003eMortality rates increased exponentially with age, peaking at 60.64 (95% UI: 46.81\u0026ndash;68.39) for males and 82.95 (56.10\u0026ndash;96.81) for females aged\u0026thinsp;\u0026ge;\u0026thinsp;95 years. Absolute deaths were highest among females aged\u0026thinsp;\u0026ge;\u0026thinsp;95 years (3,267; 95% UI: 2,209\u0026ndash;3,812), followed by females aged 90\u0026ndash;94 years (5,273; 3,836\u0026ndash;6,088). Males exhibited higher mortality rates than females in most age groups. However, sex-specific absolute deaths shifted in older populations: females aged\u0026thinsp;\u0026ge;\u0026thinsp;85 years consistently exceeded males in death counts despite lower mortality rates (Figure S2B).\u003c/p\u003e\n \u003cp\u003eIn 2021, global DALY rates increased exponentially with age, peaking in the \u0026ge;\u0026thinsp;95-year group (males: 497.14; 95% UI: 384.93\u0026ndash;560.52; females: 675.25; 462.34\u0026ndash;786.04). Absolute DALY counts were highest among females aged 85\u0026ndash;89 years (55,574; 42,738\u0026ndash;63,832), followed by females aged\u0026thinsp;\u0026ge;\u0026thinsp;90 years (46,325; 33,896\u0026ndash;53,265), whereas males had peak burdens at 65\u0026ndash;69 years (84,087; 69,537\u0026ndash;102,807). Males exhibited higher DALY rates than females in most age groups under 75 years, but this pattern reversed in older age groups (\u0026ge;\u0026thinsp;95 years: female-to-male rate ratio\u0026thinsp;=\u0026thinsp;1.36) (Figure S2C).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Burden of endocarditis\u003c/h2\u003e\n \u003cp\u003eThe analysis revealed no consistent linear association between the SDI and age-standardized incidence rates (ASIR) of endocarditis across global regions. High-SDI regions exhibited divergent trajectories: High-income North America saw a substantial 51.1% increase in ASIR, while High-income Asia Pacific plateaued after 2010, and Western Europe maintained persistently elevated rates. Conversely, low-SDI regions (e.g., Central Sub-Saharan Africa) showed stability, with no significant temporal changes. Middle-SDI regions demonstrated heterogeneity; Southeast Asia consistently exceeded global averages, whereas North Africa and the Middle East experienced modest growth. Notably, certain high-SDI regions outpaced middle-SDI counterparts in ASIR growth, contradicting SDI-based projections (Figure S3A).\u003c/p\u003e\n \u003cp\u003eAge-standardized death rates (ASDR) remained stable overall, marginally declining from 0.97 (95% UI: 0.85\u0026ndash;1.07) to 0.96 (0.85\u0026ndash;1.07), with no systematic correlation to SDI. High-SDI regions displayed marked disparities: High-income North America sustained elevated ASDR, while High-income Asia Pacific decreased from 0.82 (0.67\u0026ndash;0.93) to 0.85 (0.68\u0026ndash;0.97). Western Europe and Australasia retained high mortality despite advanced SDI levels, whereas low-SDI regions exhibited negligible progress (Figure S3B).\u003c/p\u003e\n \u003cp\u003eDALY rates for endocarditis decreased from 28.32 (95% UI: 23.28\u0026ndash;31.61) to 25.56 (22.34\u0026ndash;28.37). High-SDI regions demonstrated contrasting trends: East Asia achieved the largest reduction, while High-income North America stagnated. Conversely, middle-SDI regions experienced significant increases, including Southern Sub-Saharan Africa (+\u0026thinsp;25.6%), Central Europe (+\u0026thinsp;64.7%), and the Caribbean (+\u0026thinsp;34.8%). In low-SDI regions, Eastern Sub-Saharan Africa remained the highest-burden area despite a 27.5% decline in DALY rates (Figure S3C).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Cross-country inequality analysis\u003c/h2\u003e\n \u003cp\u003eWe conducted a cross-country inequality analysis. The concentration index (C) for endocarditis-related DALYs demonstrated reduced socioeconomic inequality between 1990 and 2021, declining from \u0026minus;\u0026thinsp;0.20 (95% UI: \u0026minus;0.24 to \u0026minus;\u0026thinsp;0.16) to \u0026minus;\u0026thinsp;0.12 (\u0026minus;\u0026thinsp;0.16 to \u0026minus;\u0026thinsp;0.08) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Despite this progress, DALYs remained disproportionately concentrated in lower SDI groups, as indicated by persistently negative C values. Absolute inequality, measured by the slope index of inequality (SII), narrowed significantly from \u0026minus;\u0026thinsp;37.03 (\u0026minus;\u0026thinsp;44.73 to \u0026minus;\u0026thinsp;29.33) to \u0026minus;\u0026thinsp;19.83 (\u0026minus;\u0026thinsp;27.15 to \u0026minus;\u0026thinsp;12.51) DALYs per 100,000 population during this period (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). The negative SII values confirmed a persistently higher disease burden among socioeconomically disadvantaged populations. Cumulative fraction curves revealed that in 1990, the lowest 35% of the population (ranked by SDI) accounted for 50% of endocarditis DALYs; by 2021, this proportion shifted marginally to the lowest 40%.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Decomposition analysis\u003c/h2\u003e\n \u003cp\u003eDecomposition analysis highlighted pronounced regional heterogeneity in the drivers of DALY changes (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Globally, population growth was the largest contributor to DALY increases (87.9%), followed by aging (38.9%), while epidemiological improvements significantly reduced the burden (\u0026minus;\u0026thinsp;26.8%). Regional patterns diverged markedly: in South Asia, aging and population growth acted synergistically to elevate DALYs (64.2% and 79.5%, respectively). High-income regions exhibited divergent trends. Western Europe experienced minimal influence from aging (10.7%) but substantial epidemiological improvements (\u0026minus;\u0026thinsp;51.0%), whereas High-income North America\u0026rsquo;s DALY rise was primarily driven by population growth (75.6%). Notably, Central Europe and Eastern Europe recorded negative contributions from aging (\u0026minus;\u0026thinsp;34.1% and \u0026minus;\u0026thinsp;39.3%, respectively), offset by positive epidemiological shifts of up to 103.1%. Low-SDI regions faced compounding risks due to population growth (67.7%) and aging (71.4%), while Middle-SDI regions paradoxically exhibited the most substantial epidemiological improvements (\u0026minus;\u0026thinsp;215.5%, \u0026minus;\u0026thinsp;151,973 DALYs). These findings underscore the critical influence of demographic dynamics and healthcare policies on disease burden trajectories.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8 Predictions of endocarditis from 2020 to 2035\u003c/h2\u003e\n \u003cp\u003eBayesian age-period-cohort analysis projected a significant rise in global ASIR of infective endocarditis, increasing from 12.66 (95% UI: 12.64\u0026ndash;12.69) in 2021 to 13.66 (95% UI: 12.91\u0026ndash;14.41) in 2035, representing a 9.5% increase. Conversely, the global age-standardized mortality rate (ASMR) is forecasted to decline from 0.98 (95% UI: 0.97\u0026ndash;0.98) in 2021 to 0.92 (95% UI: 0.84\u0026ndash;1.00) in 2035, reflecting a 6.1% reduction. Similarly, the ASDR is anticipated to decrease significantly during this period, dropping from 25.62 (95% UI: 25.58\u0026ndash;25.65) to 23.18 (95% UI: 21.26\u0026ndash;25.09), with a 9.5% decline (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study conducted a comprehensive analysis of endocarditis' global burden from 1990 to 2021 using the GBD 2021 dataset, encompassing 204 countries and 21 regions. We utilized advanced analytical methods, including SII and CI to assess socioeconomic disparities, decomposition analysis to identify demographic and epidemiological drivers, and Bayesian Age-Period-Cohort modeling for projections. Robust regression techniques reduced outlier impacts in inequality metrics, while age standardization enhanced cross-population comparability\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eKey findings demonstrated a 135% rise in global endocarditis incidence since 1990, driven by population aging and growth, juxtaposed with a 34.4% reduction in age-standardized DALY rates\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Gender disparities persisted, with males exhibiting 1.37 times higher DALYs globally, whereas females\u0026thinsp;\u0026ge;\u0026thinsp;95 years faced elevated age-specific mortality\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. High-SDI regions revealed paradoxical trends, such as increasing incidence in High-income North America despite advanced healthcare systems\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. While socioeconomic inequality moderated, 40% of DALYs remained concentrated in low-SDI populations\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Projections indicate a 9.5% incidence rise by 2035, highlighting persistent challenges in disease transmission alongside declining mortality severity.\u003c/p\u003e \u003cp\u003eDemographic changes\u0026ensp;and epidemiological transitions are responsible for the global rising endocarditis burden\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Higher incidence rates in aging populations\u0026thinsp;\u0026minus;\u0026thinsp;populations more vulnerable because of immunosenescence and comorbidities\u0026thinsp;\u0026minus;\u0026thinsp;drive rising incidence rates; population growth drives up\u0026ensp;absolute case numbers. Simultaneously, attenuation in the impact of diagnostics and clinical alertness,\u0026ensp;especially from high-SDI areas, probably fosters superior case identification without a corresponding rise in mortality levels\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Declining age-standardized DALY rates suggest innovations in medical management, with advances in surgical techniques, directed antibiotic therapy, and prevention that all decrease severity of illness and\u0026ensp;mortality. Persistent socioeconomic\u0026ensp;inequalities continue to highlight disparities in access to care, late diagnosis, and poor management in resource-constrained environments. Furthermore, regional differences must be considered into the context factor in\u0026ensp;which the study will be carried out encompassing differences in public health policies including healthcare infrastructure, socioeconomic determinants, and other variables that might interact with the response of COVID-19. These dynamics bring into\u0026ensp;focus the critical need for context-specific interventions to be developed for addressing the complex drivers of endocarditis burden\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGeographic disparities in the\u0026ensp;burden of endocarditis comorbidity correlate with regional drivers\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The high case counts in China and the\u0026ensp;United States are in line with aging populations and increasing use of invasive cardiac devices, while Thailand\u0026rsquo;s elevated ASIR probably represents greater case detection, owing to wide echocardiography utilization. By contrast, the low mortality rates in Tajikistan and China may indicate either systematic underdetection of fatal cases or\u0026ensp;effective antibiotic stewardship program efforts. The higher mortality in the United States correlates with increasing antibiotic resistance associated with the use of illicit intravenous drugs and the paradox of the higher mortality of Switzerland reflects delayed presentation in populations with\u0026ensp;universal healthcare access\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBiological susceptibility, comorbidity, and healthcare\u0026ensp;disparities are associated with age- and sex-specific variations in endocarditis incidence\u003csup\u003e[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Higher incidence rates in older adults are associated with immunosenescence, cumulative\u0026ensp;comorbidities, and increased exposure to invasive medical procedures\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Men have a higher incidence in nearly all age groups, primarily due\u0026ensp;to their greater exposure to risk factors like IV drug use and cardiovascular disease. In contrast, historic female age-driven case and mortality rates are higher in absolute sex-specific terms due to longevity and\u0026ensp;more engagement with healthcare, resulting in substantial differences in disease detection and managed pathways into care in later stages of life.\u003c/p\u003e \u003cp\u003eThe uneven nature of the geographic burden of infective endocarditis is shaped by the interaction of the\u0026ensp;socioeconomic development, health care system capabilities, alongside changing demographic profiles\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. High-SDI regions show further heterogeneous rates\u0026mdash;from increasing\u0026ensp;incidence in North America to plateaued rates observed in the Asia-Pacific region\u0026mdash;all reflective of the challenges related to disparities in access to healthcare, a rapidly aging population, and inequitable uptake of advanced diagnostic technologies. Continued high burdens in\u0026ensp;middle-SDI regions indicate ongoing systemic transitions in healthcare delivery and rising epidemiological risk profiles, and stagnant or falling rates in low-SDI regions most likely reflect underdiagnosis owing to poor healthcare infrastructure, not real epidemiological advances.\u003c/p\u003e \u003cp\u003eOur\u0026ensp;cross-regional difference-in-difference analysis confirms this trend. Decreasing CI and SII reflect tangible progress in terms of the accessibility of health services, preventive measures and socioeconomic transformation in\u0026ensp;low- and middle-income nations. Persistently negative inequality indices, however, continue to depict systemic barriers to care endemic to low-SDI regions (eg, disparate health care infrastructure, diagnostic delays, low public health literacy, and\u0026ensp;restricted access to state-of-the-art therapies). These inequities highlight deep-rooted structural shortcomings that re-create global inequalities in\u0026ensp;disease burden.\u003c/p\u003e \u003cp\u003eSimilar to GBD 2021 trends in cardiovascular diseases,\u0026ensp;population aging (38.9%) and growth (87.9%) were identified as major global drivers of DALYs. However, regional differences show different causal pathways: the synergistic risks of population aging (64.2%) and population growth (79.5%) in South Asia highlight inadequacies in the ability of healthcare systems to respond to demographic changes, and the emphasis on population growth (75.6%) in High-income North America highlights the stresses placed on healthcare systems\u0026ensp;as a result of population pressure from immigration. The negative aging effect in Central Europe (\u0026minus;\u0026thinsp;34.1%) relates to the outmigration of elder subgroups\u0026ensp;and the greater availability of advanced cardiac interventions. As observed as unprecedented epidemiology reductions (-215.5%) in Middle-SDI regions through coordinated antibiotic-prophylaxis stewardship, while system underfunds in low-SDI regions, causing the structural\u0026ensp;cause and epidemiological risk of undiagnosed cases and undertreatment to fall into a vicious circle.\u003c/p\u003e \u003cp\u003eHowever, the predicted 9.5% increase in ASIR indicates an accelerating demographic-driven aging and an increasing dependency on prosthetic valve implementation\u0026mdash;the impact in the\u0026ensp;high SDI groups increased by improvements in diagnostic precision. In contrast, the 6.1% decline in the ASMR\u0026ensp;may reflect the embrace of multidisciplinary care frameworks and fourth-generation antistaphylococcal antibiotics. Persistent\u0026ensp;gaps in DALY reductions (\u0026minus;\u0026thinsp;9.5%) were nevertheless observed, consistent with Hu et al\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. on prophylaxis and its systemic underfunding in low-income\u0026ensp;settings. This disparity highlights the divergent impact of antimicrobial stewardship programs\u0026mdash;effective in reducing mortality in resource-rich settings\u0026mdash;versus intermittent non-uniform\u0026ensp;healthcare reforms in regions challenged by propagation of aging-related epidemiological susceptibility.\u003c/p\u003e \u003cp\u003eShedding light on the underlying drivers that contribute to these trends has important implications for the theoretical and practical understanding of global and regional trends in endocarditis (as\u0026ensp;well as the drivers of these trends). Theoretically, it is deepening the concept of epidemiological framework in a systematic analysis\u0026ensp;by advanced indices like SII, CI, and the BAPC modeling. Methodologically, it explains\u0026ensp;demographic processes \u0026mdash; such as aging and population growth \u0026mdash; but identifies them as dominant drivers of disease burden, highlighting their importance for public health policy in aging populations. Likewise, the analysis unveils alarming disparities in disease burden across varying socioeconomic strata, highlighting the need\u0026ensp;for immediate equitable policy reform and targeted resource allocation to address systemic inequities. Improved modeling approaches\u0026ensp;generate projections that can bear direct implications for prioritization of healthcare at global and regional scales. Taken together, these results enable decision makers, clinicians, and public health authorities to accelerate evidence-based interventions, strengthen preventive strategies, and inform clinical practice guidelines for endocarditis\u0026ensp;that are better aligned with current burden.\u003c/p\u003e \u003cp\u003eThe main strengths of this study come from its methodologically strong\u0026ensp;design and analytically comprehensive framework that incorporates the latest epidemiological data from the GBD 2021 database across 204 countries and territories. Also, it is methodologically coherent, geographically and demographically broad, possesses advanced inequality metrics, and a\u0026ensp;high precision in predictive modeling. These components together provide a more nuanced understanding of endocarditis burden\u0026ensp;variation across global and regional contexts.\u003c/p\u003e \u003cp\u003eDespite its methodological robustness, it is important to recognize\u0026ensp;the limitations of this study. First, our analysis is entirely\u0026ensp;reliant on the GBD 2021 dataset, which carries intrinsic variation in data quality and completeness by country, within countries potentially undermining reliability in low-resource contexts. Underreporting and misdiagnoses \u0026mdash; typical in areas with little health care infrastructure \u0026mdash; could lead to further\u0026ensp;underestimates of incidence and mortality rates. Second,\u0026ensp;there is inherent uncertainty in projections of future disease burden because future epidemiological trends are unpredictable, and advances in healthcare are not captured by the BAPC model, although the BAPC model improves predictive accuracy. Last, although the\u0026ensp;socioeconomic indicators usedSDI, while useful, it is an indirect proxy for healthcare access and may oversimplify the multiple socioeconomic causes of health inequalities.\u003c/p\u003e \u003cp\u003eIn summary, all future studies should focus on enhancing data quality by reinforcing surveillance mechanisms in scenarios with high underreporting potential where healthcare facilities are constrained, and ultimately improving the accuracy of estimates on the global\u0026ensp;endocarditis burden. In addition, ongoing methodological innovations in predictive modelling\u0026mdash;like the incorporation of\u0026ensp;real-world data streams and healthcare innovation indicators\u0026mdash;may improve forecasting precision, alleviating the uncertainty of long-term estimates. Last, the development of multidimensional socioeconomic indices that explicitly account for systemic barriers to care would enhance linkages between healthcare access and endocarditis outcomes, informing performance equity-focused interventions to reduce global\u0026ensp;health disparities.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis systematic analysis of global, regional, and national trends in infective endocarditis (IE) epidemiology from 1990 to 2021 identified an increasing global burden that\u0026ensp;can be largely attributed to demographic changes, specifically an increase in aging populations and uncontrolled population growth. While diagnostic advancements, along with expanded healthcare access\u0026mdash;particularly in higher-income settings\u0026mdash;have eased burdens, lagging regional disparities expose\u0026ensp;continuing healthcare inequalities and differential epidemiologic transitions across the globe. Country-specific evaluations revealed slow but inadequate progress towards inter- and intra-national inequality in socioeconomic determinants of health, reinforcing\u0026ensp;calls for context-specific approaches to inequality mitigation across low-SDI contexts. Demographic changes were\u0026ensp;shown to be the dominant drivers of rising incidence by more sophisticated decomposition analyses, despite the impact of epidemiological transition which decreased mortality and DALY rates. Projections over the long term (to 2035) indicate that the incidence will continue to increase but mortality and DALY\u0026ensp;rates will decline, providing key evidence for long-term health system infrastructure planning. Thus, policy makers need to make sure to introduce strategies that would aim to reduce demographic\u0026ensp;risk factors, build the healthcare capacity across the board and address the systemic inequities through equitable resource allocation. Together, these findings provide potential epidemiological evidence for driving proactive, equity-centered policies to sustainably reduce global IE\u0026ensp;burden.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no relevant affiliations or financial relationships with any organization or entity that could influence or bias the content of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReviewer Disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeer reviewers involved in this manuscript have no financial or non-financial relationships relevant to this work to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs this study constituted a retrospective analysis of publicly available, anonymized data from the GBD database, ethical approval and informed consent were not required. All analyses adhered to ethical guidelines for GBD research, which permit the use of de-identified datasets without institutional review board oversight.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial or technical support for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study design, data curation, analysis, and manuscript preparation. Specific contributions included: conceptualization (Changjiang Deng, Yixin Xu, Ying Pan, Tingting Wu); methodology (Chao Fan, Zhihui Jiang, Mingming Lv, Bingxin Bai); formal analysis (Zhiyan Du, Zhilong Wang, Adilai Adilijiang); and manuscript drafting and revision (Yingying Zheng, Xiang Xie). All authors approved the final manuscript and take responsibility for the integrity of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCahill TJ, Prendergast BD. Infective endocarditis. Lancet. 2015;387(10021):882\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(15)00067-7\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(15)00067-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOtto CM, Nishimura RA, Bonow RO, et al. 2020 ACC/AHA Guideline for the Management of Patients with Valvular Heart Disease. 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Transnational inequities in cardiovascular diseases from 1990 to 2019: exploration based on the global burden of disease study 2019. Front Public Health. 2024;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2024.1322574\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2024.1322574\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[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":"Infective endocarditis, Global Burden of Disease(GBD), Disability-adjusted life years (DALYs), Socioeconomic inequalities, Decomposition analysis, Bayesian Age-Period-Cohort modeling","lastPublishedDoi":"10.21203/rs.3.rs-6404736/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6404736/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInfective endocarditis (IE) persists as a major public health challenge, shaped by demographic shifts and healthcare disparities. However, comprehensive analyses of its spatiotemporal epidemiological patterns and their linkage to structural inequities remain limited.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to systematically quantify the global, regional, and national burden of IE from 1990 to 2021, evaluate socioeconomic inequalities, and forecast disease trajectories through 2035.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUtilizing Global Burden of Disease (GBD) 2021 data spanning 204 countries, we conducted decomposition analysis to disentangle disability-adjusted life years (DALYs) into demographic (aging, population growth) and epidemiological components, assessed socioeconomic disparities using Slope and Concentration Indices, and projected trends via Bayesian Age-Period-Cohort modeling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eGlobal IE incidence surged by 135% between 1990 and 2021, with males disproportionately affected (1.37 times higher DALYs). High-income regions exhibited paradoxical elevation in incidence, while mortality rates declined (annual DALY reduction: \u0026minus;0.34%). Socioeconomic disparities moderated (Concentration Index: \u0026minus;0.20 to \u0026minus;\u0026thinsp;0.12), yet 40% of DALYs persisted in low-income populations. Decomposition identified population growth (87.9%) and aging (38.9%) as primary drivers. Projections indicated a 9.5% rise in incidence by 2035, contrasting with a projected 6.1% decline in mortality rates.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe escalating burden of IE is shaped by accelerating demographic pressures and entrenched healthcare inequities. Prioritizing context-specific interventions\u0026mdash;including geriatric healthcare capacity-building, equitable resource distribution, and enhanced diagnostic access\u0026mdash;is imperative to reduce preventable morbidity.\u003c/p\u003e","manuscriptTitle":"The global, regional, and national burden of endocarditis from 1990 to 2021: an analysis of the global burden of disease study 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-26 14:09:44","doi":"10.21203/rs.3.rs-6404736/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":"213cd828-9a17-4196-a8a6-a30ba0761775","owner":[],"postedDate":"June 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-21T06:23:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-26 14:09:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6404736","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6404736","identity":"rs-6404736","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}