Global, Regional, and National Burden of Atrial Fibrillation and Atrial Flutter in the Working-Age Population from 1990 to 2021: A Systematic Analysis Based on 2021 Global Burden of Disease Data

Global, Regional, and National Burden of Atrial Fibrillation and Atrial Flutter in the Working-Age Population from 1990 to 2021: A Systematic Analysis Based on 2021 Global Burden of Disease Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global, Regional, and National Burden of Atrial Fibrillation and Atrial Flutter in the Working-Age Population from 1990 to 2021: A Systematic Analysis Based on 2021 Global Burden of Disease Data Yang Chao, Kong Youjing, Xiao Liu, Huang Xingxiao, Sun Qiuli, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6405324/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 Atrial fibrillation and atrial flutter impose a significant global health burden, particularly among individuals aged 20-64 years. This study analyzed data from the Global Burden of Disease 2021 database to estimate disease burden trends from 1990 to 2021. Key metrics included age-standardized incidence, prevalence, mortality, and disability-adjusted life years rates, stratified by age, sex, and Socio-Demographic Index. The methodologies encompassed descriptive analysis, age-period-cohort modeling, decomposition techniques, and Bayesian forecasting. From 1990 to 2021, age-standardized incidence rates increased by an estimated annual percentage change of 0.14, prevalence rates by 0.20, and disability rates by 0.08, while mortality rates declined by 0.16. By 2021, global incidence reached 47.05 per 100,000 population, prevalence 397.94, mortality 0.49, and disability 48.46. High-SDI regions exhibited the highest burden, with incidence at 61.01 and prevalence at 515.73 per 100,000, whereas low-SDI regions recorded the lowest incidence and prevalence at 35.32 and 282.73, respectively. Males consistently showed higher incidence, prevalence, and disability rates than females, with disease burden peaking in the 60-64 age group. Population growth contributed 52% to the rise in prevalent cases, surpassing aging and epidemiological factors. Projections to 2050 indicate declines in incidence to 45.18 and prevalence to 387.53 per 100,000, but mortality and disability rates are expected to rise to 0.51 and 49.29. High systolic blood pressure accounted for 13.04% of disability-adjusted life years globally, with contributions from high body mass index increasing across all SDI quintiles. Health inequalities narrowed between high- and low-SDI countries, with the slope index of inequality decreasing from 21.41 to 15.41 per 100,000 years and the concentration index shifting from 0.04 to -0.02. Critical priorities include optimizing screening protocols in high-SDI regions to reduce overdiagnosis, expanding hypertension control and anticoagulation access in low-SDI settings, and implementing workforce health surveillance targeting processed food consumption. Multisectoral strategies integrating real-time burden monitoring, salt-sugar regulation policies, and equitable technology distribution are essential to align with Sustainable Development Goals. This study underscores the necessity of region-specific interventions to mitigate economic productivity losses linked to atrial fibrillation and atrial flutter in the working-age population. Atrial fibrillation and flutter Working-age population Global burden of disease Disease burden trends Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Atrial fibrillation (AF) and atrial flutter (AFL), the most prevalent clinical tachyarrhythmias, impose a substantial global health burden through impaired quality of life, elevated risks of stroke and heart failure, and escalating healthcare expenditures [1] [2] [3] . While existing studies have extensively characterized AF/AFL epidemiology in elderly populations, the burden among working-age individuals (20-64 years) remains underexplored despite its distinct socioeconomic ramifications [4] . Notably, over 35% of global AF/AFL cases now occur in individuals under 65 years, with incidence rates in this demographic increasing 1.8-fold faster than in older adults since 2000 [5] [6] . This shift parallels socioeconomic transitions, including urbanization-driven metabolic syndrome and occupational stress, necessitating a life-course perspective to address early-onset cardiovascular risks [6] [7] . The working-age population faces unique challenges shaped by accelerated epidemiological transitions, occupational arrhythmogenic triggers, and systemic healthcare disparities [8] [6] . Hypertension prevalence has risen by 24% among individuals aged 30-50 in low-middle-income countries, while shift work elevates AF risk by 22% [6] . In parallel, anticoagulation coverage remains below 10% in rural sub-Saharan Africa, reflecting critical gaps in equitable care delivery [9] [10] . These factors drive a dynamic burden profile where high-income regions exhibit technology-driven overdiagnosis, whereas low-income settings are plagued by underdetection and treatment delays. Crucially, AF and AFL diminish individual productivity and strain national economies, costing an estimated $48 billion annually in lost workforce participation [6] [10] . However, current prevention strategies remain extrapolated from elderly cohorts, overlooking risk trajectories specific to younger populations and context-sensitive intervention thresholds. This study utilized data from the Global Burden of Disease Study 2021 to systematically analyze the epidemiology of AF and AFL in working-age populations from 1990 to 2021, with projections extended to 2050, thereby bridging this knowledge deficit. Multidimensional assessments of age, sex, Socio-Demographic Index (SDI), clinical subtypes, and severity were conducted at national, regional, and global levels, establishing an evidence-based policy framework. The findings delineate developmental patterns and socioeconomic determinants of the disease, providing actionable benchmarks for cardiovascular health strategies to achieve workforce preservation and Sustainable Development Goals. Methods 2.1 Data Sources and Extraction The data for this study were obtained from the Global Burden of Disease (GBD) 2021 database, which encompasses epidemiological data on 371 diseases and injuries across 204 countries and regions worldwide, covering the period from 1990 to 2021 [11] . Data were extracted using the GBD official data visualization platform (https://vizhub.healthdata.org/gbd-results/) and included the following dimensions: Geographical Levels: The data were categorized by global scope, Socio-Demographic Index (SDI) classifications (low, low-middle, middle, high-middle, and high SDI), 21 specific GBD regions, and 204 countries and territories [12] [13] . Demographic Characteristics: The target population for this study consisted of working-age individuals aged 20-65 years, stratified into 5-year age groups (20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64) and analyzed by sex (male, female, and overall). Due to the lack of data on AF and AFL for the age groups 20-24 and 25-29 in the GBD database, the analysis focused on the age range of 30-64 years. Core Indicators: The study extracted data on the incidence, prevalence, mortality rates, and disability-adjusted life years (DALYs) for AF and AFL, along with their corresponding 95% uncertainty intervals (UI). 2.2 Statistical Analysis Framework 2.2.1 Statistical Description of Disease Burden To eliminate the impact of age structure differences on the results, the disease burden data for the 30-64 age group were age-standardized using the GBD 2021 global standard population structure. The statistical indicators analyzed included the number of new cases, prevalent cases, deaths, DALYs, and their 95% UIs. Based on this, the following age-standardized metrics were calculated: age-standardized incidence rate (ASIR), age-standardized prevalence rate (ASPR), age-standardized mortality rate (ASMR), and age-standardized DALYs rate (ASDR). These metrics will be used to compare disease burden levels across different regions and populations. 2.2.2 Time Trend Analysis To assess the temporal trends in the disease burden of AF and AFL from 1990 to 2021, this study employed two methods: the estimated annual percentage change (EAPC) and Joinpoint regression analysis [14] [15] . The EAPC was calculated based on a log-linear regression model (ln(Y) = α + βX + ε), with the formula 100 × [exp(β) − 1], where β is the regression coefficient, X is the year, and Y is the natural logarithm of the age-standardized rate. The 95% confidence interval (CI) for the EAPC was used to evaluate the statistical significance of the trends; an EAPC greater than zero indicates an upward trend, while a value less than zero indicates a downward trend. Joinpoint regression analysis was conducted using the Joinpoint Regression Program version 4.9.1.0, employing Monte Carlo permutation tests to identify inflection points in the trends and calculating the average annual percentage change (AAPC) to characterize the overall trend and capture the nonlinear features of changes in disease burden. 2.2.3 Age-Period-Cohort Analysis This study utilized an age-period-cohort (APC) model framework to systematically decompose the factors contributing to changes in disease burden, including age effects, period effects, and cohort effects. The age effect reflects the natural variation in disease risk over an individual’s life cycle; the period effect captures the short-term impacts of external environmental factors (such as advancements in medical technology and policy interventions) on disease burden; and the cohort effect reveals long-term differences in disease burden among different birth cohorts due to exposure to specific risk factors. The model was implemented using R software (version 4.3.3), employing orthogonal decomposition to separate linear and nonlinear components and using weighted least squares (WLS) to estimate parameters. Model fit was assessed using the Wald χ² test [16] . 2.2.4 Decomposition Analysis of Disease Burden To quantify the contributions of population aging, population growth, and epidemiological changes to the disease burden of AF and AFL, this study employed demographic decomposition methods. Specifically, the changes in disease burden were decomposed into three main factors: changes in age structure, changes in population size, and epidemiological changes. By analyzing the relative contributions of these factors, the primary drivers of changes in disease burden were identified [17] [18] . 2.2.5 Bayesian Age-Period-Cohort (BAPC) Analysis This study employed a BAPC model to forecast the trends of AF and AFL from 2022 to 2050. The advantage of this model lies in its use of a second-order random walk before smooth age, period, and cohort effects, effectively avoiding overfitting. By utilizing the Integrated Nested Laplace Approximation (INLA) method, this study efficiently calculated the marginal posterior distributions, circumventing the computational bottlenecks associated with traditional Markov Chain Monte Carlo (MCMC) methods. Finally, the robustness of the model predictions was evaluated using cross-validation and other methods to ensure the reliability of the forecasted results [19] . 2.2.6 Health Inequality and Frontier Analysis To assess both absolute and relative health inequalities in disease burden, this study utilized the Slope Index of Inequality (SII) and the Concentration Index (CII). The SII was calculated by regressing the DALYs rate against the SDI, with the midpoint of the cumulative population distribution sorted by SDI used in the regression analysis. The CII was computed by matching the cumulative proportion of DALYs with the cumulative population distribution sorted by SDI and numerically integrating the area under the Lorenz curve [20] . Additionally, a frontier analysis was conducted, based on SDI, to construct a frontier model using ASDR. This model aims to identify the theoretically achievable minimum ASDR for each country or region at different levels of development. By quantifying the gap between the actual disease burden and the potential minimum burden, it highlights areas for improvement. To ensure the robustness of the analysis, locally weighted regression combined with local polynomial regression methods were employed, using varying smoothing spans to generate smooth boundary lines that capture the nonlinear relationship between SDI and ASDR. Furthermore, 100 bootstrap resampling iterations were performed, and the average ASDR for each SDI value was calculated to ensure the reliability of the analysis [20] . 2.2.7 Correlation Analysis This study used Spearman’s rank correlation analysis to explore the relationship between SDI and ASR. To control for the false positive results caused by multiple comparisons, the Benjamini-Hochberg method was applied to adjust the false discovery rate (FDR) (FDR < 0.05). Additionally, the local weighted scatterplot smoothing (LOWESS) method was employed to fit nonlinear trends, further elucidating the complex association between SDI and disease burden. Results 3.1 Global Burden of AF and AFL in the Working-Age Population According to data from 2021, the ASIR for atrial AF and AFL was 47.05 (95% UI: 29.73, 71.26) per 100,000, the ASPR was 397.94 (95% UI: 282.14, 558.51) per 100,000, the ASMR was 0.49 (95% UI: 0.44, 0.53) per 100,000, and the ASDR was 48.46 (95% UI: 34.29, 66.42) per 100,000 years. This indicates that in 2021, there were approximately 1,599,232.83 (95% UI: 1,009,695.25, 2,423,709.76) new cases of AF and AFL globally, with a total of 13,592,619.06 (95% UI: 9,645,950.95, 19,057,302.82) existing cases. The number of deaths among the working-age population due to AF and AFL was 16,700.33 (95% UI: 14,939.08, 18,116.07), resulting in a total of 1,655,235.79 (95% UI: 1,171,507.95, 2,267,266.09) DALYs. From 1990 to 2021, the ASIR, ASPR, and ASDR for AF and AFL in the working-age population showed significant increases (with EAPCs and their 95% CIs all being positive), while the ASMR exhibited a significant decline (with EAPCs and their 95% CIs all being negative) (Table 1 and Figure S1). 3.2 Regional Burden of AF and AFL in the Working-Age Population Among the five SDI regions in 2021, the working-age population in high SDI regions had the highest ASIR (61.01 (95% UI: 43.99, 82.97) per 100,000), ASPR (515.73 (95% UI: 407.04, 657.96) per 100,000), ASMR (0.57 (95% UI: 0.55, 0.59) per 100,000), and ASDR (60.52 (95% UI: 44.75, 79.26) per 100,000 years). In contrast, low SDI regions had the lowest ASIR (35.32 (95% UI: 20.83, 56.11) per 100,000), ASPR (282.73 (95% UI: 187.24, 417.68) per 100,000), and ASDR (39.59 (95% UI: 27.40, 55.46) per 100,000 years), as well as the lowest ASMR in high-middle SDI regions (0.41 (95% UI: 0.37, 0.46) per 100,000 years). From 1990 to 2021, both ASIR and ASPR significantly increased across all five SDI regions. The ASMR in low-middle SDI regions showed a significant increase, while ASMR in high-SDI and low-SDI regions did not change significantly. Conversely, ASMR in high-middle SDI and middle SDI regions significantly decreased. The ASDR in high SDI and high-middle SDI regions showed no significant change, while the remaining three SDI regions exhibited significant increases in ASDR (Table 1 and Figure S1). In the 21 GBD regions, Australasia had the highest ASIR (73.59 (95% UI: 44.65, 115.28) per 100,000), ASPR (639.10 (95% UI: 438.73, 910.22) per 100,000), ASMR (1.29 (95% UI: 0.86, 1.80) per 100,000), and ASDR (78.19 (95% UI: 56.00, 106.43) per 100,000 years) in 2021. In contrast, North Africa and the Middle East had the lowest ASIR (24.12 (95% UI: 14.51, 37.55) per 100,000), ASPR (196.06 (95% UI: 132.60, 284.17) per 100,000), ASDR (29.19 (95% UI: 22.08, 38.72) per 100,000 years), and Western Sub-Saharan Africa had the lowest ASMR (0.31 (95% UI: 0.20, 0.39) per 100,000). From 1990 to 2021, ASIR showed no significant change in five regions, while three regions experienced significant declines; the remaining regions exhibited significant increases, with East Asia showing the most pronounced increase and Southern Latin America the most significant decrease. ASPR showed no significant change in five regions, with two regions experiencing significant declines, while the remaining regions exhibited significant increases, particularly in East Asia, which had the most notable increase, and Southern Latin America, which had the most significant decrease. ASMR showed no significant change in eight regions, with seven regions experiencing significant declines, while the remaining regions exhibited significant increases, particularly in Southern Sub-Saharan Africa, which had the most pronounced increase, and High-income Asia Pacific, which had the most significant decrease. ASDR showed no significant change in seven regions, with four regions experiencing significant declines, while the remaining regions exhibited significant increases, particularly in Eastern Europe, which had the most notable increase, and High-income Asia Pacific, which had the most significant decrease (Table 1 and Figure S1). 3.3 National Burden of AF and AFL in the Working-Age Population In 2021, Sweden had the highest ASIR (107.64 (95% UI: 64.61, 170.24) per 100,000) and ASPR (851.01 (95% UI: 567.93, 1,226.51) per 100,000), while Nauru had the highest ASMR (2.55 (95% UI: 1.51, 3.76) per 100,000) and ASDR (130.25 (95% UI: 83.97, 181.31) per 100,000 years). In contrast, Turkey had the lowest ASIR (16.83 (95% UI: 11.91, 23.14) per 100,000), ASPR (145.25 (95% UI: 115.78, 185.46) per 100,000), and ASDR (22.62 (95% UI: 16.46, 30.67) per 100,000 years), while Singapore had the lowest ASMR (0.19 (95% UI: 0.17, 0.21) per 100,000) (Figure 1, Table S1). From 1990 to 2021, Austria exhibited the most significant increases in ASIR, while Lesotho showed the most notable increases in ASPR, ASMR, and ASDR. Conversely, Turkey showed the most significant declines in ASIR, and Lebanon exhibited the most notable declines in ASPR, ASMR, and ASDR (Table S1 and Figure S2). 3.4 Age-Sex-Time Trends in the Disease Burden of AF and AFL in the Working-Age Population Gender-age analysis showed that both males and females experienced an increase in the ASR of AF and AFL with advancing age. Males exhibited slightly higher ASIR, ASPR, and ASDR than females, while the ASMR was similar between the genders (Figure 2). After accounting for the effects of period and cohort factors, the age effect analysis revealed a continuous increase in the ASR of AF and AFL with age (Figures S3A-D). In terms of absolute numbers, both the number of new cases and deaths increased with age, with males having higher values than females. Gender-time analysis indicated that, from 1990 to 2021, both male and female ASIR, ASPR, and ASDR showed a gradual upward trend across the globe and within all five SDI regions, while ASMR exhibited a gradual decline, except the low-middle SDI region, where ASMR showed a slow increase (Figures S4- S7). Age-time analysis further showed that from 1990 to 2021, all age groups exhibited a slow upward trend in ASIR, ASPR, and ASDR. In contrast, ASMR showed a slow decline over this period (Figures S8 -11). Moreover, the disease burden of AF and AFL in the working-age population was primarily concentrated in the 60-64-year age group. After controlling for age and cohort factors, period effect analysis revealed that ASIR, ASPR, and ASDR initially increased, then decreased, and then increased again, with inflection points occurring in 2005 and 2015. In contrast, ASMR showed a consistent decline year after year (Figures S3E-H). Cohort effect analysis demonstrated that later-born cohorts had higher ASIR, ASPR, and ASDR than earlier-born cohorts, while ASMR exhibited the opposite trend (Figures S3I-L). Joinpoint regression analysis results indicated that, from 1990 to 2021, overall, ASIR (AAPC = 0.052, 95% CI: 0.044, 0.059), ASPR (AAPC = 0.610, 95% CI: 0.553, 0.667), and ASDR (AAPC = 0.034, 95% CI: 0.029, 0.039) exhibited significant increasing trends, whereas ASMR (AAPC = -0.000, 95% CI: -0.001, -0.000) showed a significant decreasing trend. Specifically, inflection points for ASIR and ASPR occurred in 1997/1996, 2005, and 2015, with both showing significant increases after 2015. ASMR experienced inflection points in 1994, 2005, and 2008, with a significant decline from 2008 onwards. ASDR showed inflection points in 1999, 2005, and 2013, and no significant upward or downward trend was observed after 2013 (Figure 3 and Table S2). 3.5 The Association Between the Disease Burden of AF and AFL in the Working-Age Population and SDI A significant correlation was observed between the ASIR, ASPR, and ASDR of AF and AFL in the working-age population and the SDI (p < 0.05), which was consistent across 21 GBD regions and 204 countries. As SDI increased, ASIR, ASPR, and ASDR gradually rose, while ASMR showed no significant change (Figure 4 and Figure S12). The correlation coefficients and their p-values are provided in Table S3. Specifically, at the regional level, the correlation between ASIR and ASPR was most pronounced in Australasia, while the correlation between ASMR and ASDR was most evident in Oceania. At the national level, Sweden, Germany, and Israel had significantly higher ASIR and ASPR compared to other countries, while Nauru, the Marshall Islands, and Micronesia had significantly higher ASMR and ASDR than other nations. The EAPC of ASMR and ASDR also demonstrated a significant correlation with SDI (p < 0.05). Specifically, the EAPC for ASMR and ASDR showed an initial increase followed by a decrease as SDI rose, with inflection points occurring at an SDI around 0.5. As SDI increased, the negative EAPC values indicated that the rate of decline in ASMR and ASDR initially slowed and then accelerated. In contrast, the EAPC for ASIR and ASPR showed a weaker correlation with SDI (p > 0.05), and no significant changes were observed in the EAPC of these rates as SDI increased (Figure S13). Health inequality analysis further revealed significant absolute and relative inequalities in ASDR between SDI levels. The slope index indicated that, from 1990 to 2021, the gap in ASDR between countries with the highest and lowest SDI decreased from 21.41 (95% UI: 15.05, 27.78) per 100,000 years to 15.41 (95% UI: 8.65, 22.17) per 100,000 years (Figure S14), suggesting a reduction in absolute inequality between high and low SDI countries, with the disease burden primarily concentrated in high SDI nations. Meanwhile, the CII decreased from 0.04 (95% CI: 0.01, 0.08) in 1990 to -0.02 (95% CI: -0.06, 0.02) in 2021 (Figure S15), indicating an improvement in relative inequality between high and low SDI countries. Frontier analysis results showed that the 15 countries and regions with the greatest gap in disease burden compared to the theoretical frontier were Nauru, the Marshall Islands, Micronesia, Tuvalu, the Northern Mariana Islands, Montenegro, Niue, Tokelau, Greenland, Samoa, American Samoa, Vanuatu, Fiji, Germany, and Sweden, with potential improvements in actual burden ranging from 62.80 to 107.42 (Figure S16). Among low SDI countries (SDI 0.85), the countries with the largest frontier differences were the United States of America, Austria, Denmark, Sweden, and Germany. 3.6 Decomposition Analysis, Future Projections, and Attributable Risk Factors Globally, both population growth and aging have had a positive impact on the ASR of AF and AFL, while epidemiological changes have led to a reduction in the ASMR but an increase in the ASIR, ASPR, and ASDR. At the regional level, population growth has generally driven an increase in the ASR for AF and AFL, while the impacts of aging and epidemiological changes have varied across regions. Specifically, aging had the most significant effect on ASR increase in Middle SDI regions, while its impact was most noticeable in reducing ASIR and ASPR in Western Europe and reducing ASMR and ASDR in Eastern Europe. Epidemiological changes had the largest impact on increasing ASIR and ASPR in East Asia, on increasing ASMR in Low-middle SDI regions, and on increasing ASDR in Middle SDI regions. Meanwhile, epidemiological changes had the most significant effect on reducing ASIR, ASPR, and ASDR in High-income Asia Pacific regions and on reducing ASMR in East Asia (Figure S17 and Table S4). Based on future projections, from 2022 to 2050, the ASIR and ASPR for AF and AFL are expected to decline gradually, while the ASMR and ASDR are expected to rise slowly (Figure 5). By 2050, the projected ASIR will be 45.18 (95% UI: 39.21, 51.15) per 100,000, the ASPR will be 387.53 (95% UI: 345.36, 429.71) per 100,000, the ASMR will be 0.51 (95% UI: 0.41, 0.60) per 100,000, and the ASDR will be 49.29 (95% UI: 43.71, 54.88) per 100,000 years (Table S5). The trends in ASR across different age groups are similar to the overall trend, with the most significant decreases in ASIR and ASPR observed in the 55-59 and 60-64 age groups, while these groups also showed the most notable increases in ASMR and ASDR (Figures S18 -21). Currently, six main attributable risk factors for AF and AFL have been identified. Globally, the highest proportion of DALYs due to AF and AFL is attributed to high systolic blood pressure (13.04%), and this proportion is the highest across all five SDI regions (Figure S22). From 1990 to 2021, the proportion of AF and AFL attributed to high body mass index has gradually increased both globally and in all five SDI regions. The proportion of AF and AFL attributable to high systolic blood pressure has increased in Middle SDI, Low-middle SDI, and Low SDI regions, while changes in other regions were not significant. The proportion of AF and AFL attributable to smoking has gradually decreased across the globe and in all five SDI regions. The trends in the proportions of other attributable risk factors have not shown significant changes (Figure S23). Discussion Our study revealed that from 1990 to 2021, the global burden of AF and AFL among working-age populations (30-64 years) demonstrated complex evolving trends: While ASIR, ASPR, and ASDR showed significant increases, ASMR exhibited a paradoxical decline. This dual-track progression reflects stratified patterns across nations with varying socioeconomic development levels - high SDI regions demonstrate an epidemiological profile characterized by coexisting high incidence and low mortality, driven by diagnostic advancements and population aging, whereas low SDI regions face challenges of underdiagnosis and elevated disability rates due to healthcare resource deficiencies and suboptimal risk factor management. These findings highlight the nonlinear association between disease control capacity and economic development levels. This study reveals marked regional disparities in the 2021 burden of AF and AFL among working-age populations: High-SDI regions such as Australia and Oceania exhibited the highest global incidence, prevalence, and disability rates, whereas low-SDI regions like Western Sub-Saharan Africa demonstrated accelerated growth in disability rates despite lower absolute values. Longitudinal analyses showed universal increases in incidence and prevalence across all SDI quintiles, with East Asia recording the most substantial growth driven by urbanization-induced metabolic syndrome [21] [22] and psychosocial stress [23] [24] , while Southern Latin America achieved counter-trend declines through public health interventions like hypertension screening [25] [26] [27] . Mortality and disability patterns exhibited greater heterogeneity: high-SDI regions achieved mortality reductions through ambulatory ECG monitoring [28] and anticoagulation therapies [29] [30] , yet paradoxically maintained elevated disability rates, reflecting prolonged survival with complication-related disabilities [31] . Conversely, low-SDI regions displayed artificially suppressed mortality metrics due to diagnostic under-capture [32] , while escalating disability rates revealed dual crises of treatment delays and uncontrolled risk factors. Sub-Saharan Africa experienced anomalous mortality increases from stroke care deficiencies [33] and HIV-associated cardiomyopathy comorbidities [34] . While this study demonstrates country-level variations in disease burden determinants, Sweden's high-incidence-low-mortality pattern likely arises from comprehensive ECG surveillance [35] [36] , whereas Nauru exhibits dual deterioration driven by extreme obesity prevalence [37] [38] and healthcare desertification [39] . Turkey's family physician program [40] effectively controlled incidence rates, while Singapore's universal health coverage achieved the world's lowest mortality [41] . These cases collectively illustrate the multifactorial interplay of demographic structure, metabolic risks, healthcare accessibility, and social policies in shaping disease burden. Further demonstrates significant three-dimensional heterogeneity in atrial fibrillation AF and AFL burden across age, sex, and temporal dimensions. Males exhibited higher incidence, prevalence, and disability rates than women, attributable to occupational stress [42] , smoking prevalence [43] , and testosterone's pro-arrhythmic effects [44] , though mortality convergence suggests a dynamic equilibrium between improved stroke management in males and atypical symptom presentation in females. All metrics showed age-dependent escalation peaking at 60-64 years, reflecting the biological accumulation of comorbidities like arteriosclerosis [45] , while higher disease burden in later-born cohorts indicates long-term risks from generational exposure to high-salt/high-fat diets [46] [47] and environmental pollutants [48] . Temporal evolution revealed significant increases in global incidence, prevalence, and disability rates from 1990 to 2021, with inflection mechanisms reflecting public health-technological interplay: intensified hypertension management [49] moderated growth trends, while wearable device proliferation [50] [51] increased asymptomatic case detection. Novel oral anticoagulants [52] and ABC pathway implementation [53] drove sustained mortality declines since 2008, though low-middle SDI regions experienced paradoxical mortality increases due to anticoagulation therapy deficits. This study further elucidates the tripartite mechanisms driving AF and AFL burden evolution through demographic transition, epidemiological shifts, and risk factor polarization. Globally, population growth continues to elevate age-standardized incidence, prevalence, and mortality rates, with the 60-64 age cohort constituting the core affected population. While diagnostic advancements and comorbidity management optimization reduced mortality, they paradoxically increased incidence and disability rates through enhanced case detection and over-intervention in asymptomatic AF, creating a "technological burden shift" phenomenon [54] [55] . Projections indicate that by 2050, prevalence rates will gradually decline to 387.53 per 100,000, yet mortality and disability rates will persistently rise, reflecting the "prevention-treatment imbalance" dilemma. Although hypertension management contributed substantially to burden mitigation [56] , its diminishing marginal returns alongside obesity control [57] fail to offset cumulative AF and AFL risks. Notably, the 55-64 age group faces an escalating disability crisis, imposing substantial socioeconomic burdens. Notwithstanding these findings, our study has inherent limitations. While the GBD methodology demonstrates robustness, its precision depends on source data quality, and statistical modeling in data-scarce regions may introduce systematic biases. Furthermore, the absence of age-stratified AF and AFL data for the 20-29 age group in GBD databases renders disease burden in this critical demographic stratum an unexplored domain within predictive models. In conclusion, this study synthesizes key findings through cohort effects revealing earlier chronic disease onset and period effects demonstrating regional technological disparities, necessitating the establishment of precision prevention systems integrating life-cycle management, gender-specific screening, and regional resource optimization. Risk factor transitions further reflect latent capital globalization influences: the food industry's salt-sugar hegemony accelerates processed food penetration, directly elevating systolic blood pressure and body mass index [58] [59] . Confronting the "high-cost, low-efficiency" phase of chronic disease management, traditional risk interventions exhibit diminishing mitigation efficacy, urgently requiring the integration of emerging risks like microplastics and light pollution [60] . Breakthrough strategies demand multidimensional approaches: implementing "hypertension-diabetes" comorbidity screening to disrupt risk pathways, legislating salt-sugar content regulation in processed foods, and aligning climate policy with health governance. Ultimately, curbing the global expansion of risk commodities, achieving equitable distribution of wearable technologies, and establishing cross-generational disability insurance systems are imperative to overcome metabolic traps entrenched by capital-driven paradigms and mitigate disease-associated economic losses. Declarations Data availability The article's data were derived from sources in the public domain: Institute for Health Metrics and Evaluation, at http://ghdx.healthdata.org/gbd-results-tool, accessed on March 2025. Acknowledgments Funding This research was supported by the Science and Technology Foundation of Zhejiang Province of China (Grant No.2020C03018), Grant of Westlake Laboratory of Life Sciences and Biomedicine (Grant No. XHSYS-02), and Zhejiang Chinese Medical University Postgraduate Scientific Research Fund Project（Grant No. 2023YKJ15). Author contributions Yang Chao, Kong Youjing, and Liu Xiao contributed equally as co-first authors. Yang Chao led the study design, data curation, and statistical modeling. Kong Youjing performed the decomposition analysis and health inequality assessments and drafted the manuscript. Liu Xiao conducted the age-period-cohort analysis, Bayesian forecasting, and visualization of results. Huang Xingxiao and Sun Qiuli contributed to data validation, regional burden interpretation, and risk factor attribution analysis. Wang Hanxin and Yu Minjun assisted in literature review, methodological refinement, and supplementary analyses. Gao Beibei (co-corresponding author) supervised the statistical framework, coordinated revisions, and validated policy recommendations. Huang Jinyu (lead corresponding author) conceptualized the study, secured funding, provided critical intellectual input, and finalized the manuscript for submission. All authors reviewed and approved the final version of the manuscript. References Brundel BJJM, Ai X, Hills MT, et al. Atrial fibrillation. Nat Rev Dis Primers. 2022 Apr 7;8(1):21. Calkins H. Important Differences Exist Between Atrial Fibrillation and Atrial Flutter in Atrial Remodeling. J Am Coll Cardiol. 2020 Jul 28;76(4):389-390. Elliott AD, Middeldorp ME, Van Gelder IC, et al. Epidemiology and modifiable risk factors for atrial fibrillation. 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Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. 2017 Apr 1;3(4):524-548. Luo L. Assessing validity and application scope of the intrinsic estimator approach to the age-period-cohort problem. Demography. 2013 Dec;50(6):1945-67. Hu W, Fang L, Zhang H, et al. Global disease burden of COPD from 1990 to 2019 and prediction of future disease burden trends in China. Public Health. 2022 Jul;208:89-97. Bai Z, Han J, An J, , et al. The global, regional, and national patterns of change in the burden of congenital birth defects, 1990-2021: an analysis of the global burden of disease study 2021 and forecast to 2040. EClinicalMedicine. 2024 Oct 4;77:102873. Fong TCT, Ho RTH, Yip PSF. Effects of urbanization on metabolic syndrome via dietary intake and physical activity in Chinese adults: Multilevel mediation analysis with latent centering. Soc Sci Med. 2019 Aug;234:112372. Ranasinghe P, Mathangasinghe Y, Jayawardena R, et al. Prevalence and trends of metabolic syndrome among adults in the asia-pacific region: a systematic review. BMC Public Health. 2017 Jan 21;17(1):101. Cheung T, Fong KH, Xiang YT. The impact of urbanization on youth mental health in Hong Kong. Curr Opin Psychiatry. 2024 May 1;37(3):172-176. Inoue Y, Howard AG, Yazawa A, et al. Relative deprivation of assets defined at multiple geographic scales, perceived stress and self-rated health in China. Health Place. 2019 Jul;58:102117. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. Lancet. 2021 Sep 11;398(10304):957-980. Brant LCC, Miranda JJ, Carrillo-Larco RM, et al. Epidemiology of cardiometabolic health in Latin America and strategies to address disparities. Nat Rev Cardiol. 2024 Dec;21(12):849-864. Ouriques Martins SC, Sacks C, Hacke W, et al. Priorities to reduce the burden of stroke in Latin American countries. Lancet Neurol. 2019 Jul;18(7):674-683. Sandau KE, Funk M, Auerbach A, et al. Update to Practice Standards for Electrocardiographic Monitoring in Hospital Settings: A Scientific Statement From the American Heart Association. Circulation. 2017 Nov 7;136(19):e273-e344. Healey JS, Lopes RD, Granger CB, et al. Apixaban for Stroke Prevention in Subclinical Atrial Fibrillation. N Engl J Med. 2024 Jan 11;390(2):107-117. O'Neal WT, Sandesara PB, Claxton JS, et al. Provider Specialty, Anticoagulation Prescription Patterns, and Stroke Risk in Atrial Fibrillation. J Am Heart Assoc. 2018 Mar 10;7(6):e007943. Brant LCC, Ribeiro ALP. Cardiovascular health: a global primordial need. Heart. 2018 Aug;104(15):1232-1233. Le Goff D, Barais M, Perraud G, et al. Innovative cardiovascular primary prevention population-based strategies: a 2-year hybrid type 1 implementation randomised control trial (RCT) which evaluates behavioural change conducted by community champions compared with brief advice only from the SPICES project (scaling-up packages of interventions for cardiovascular disease prevention in selected sites in Europe and sub-Saharan Africa). BMC Public Health. 2021 Jul 19;21(1):1422. Adoukonou T, Kossi O, Fotso Mefo P, et al. Stroke case fatality in sub-Saharan Africa: Systematic review and meta-analysis. Int J Stroke. 2021 Oct;16(8):902-916. So-Armah K, Benjamin LA, Bloomfield GS, et al. HIV and cardiovascular disease. Lancet HIV. 2020 Apr;7(4):e279-e293. Magnusson P, Koyi H, Mattsson G. A protocol for a prospective observational study using chest and thumb ECG: transient ECG assessment in stroke evaluation (TEASE) in Sweden. BMJ Open. 2018 Apr 3;8(4):e019933. Varma N, Braunschweig F, Burri H, et al. Remote monitoring of cardiac implantable electronic devices and disease management. Europace. 2023 Aug 2;25(9):euad233. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet. 2017 Dec 16;390(10113):2627-2642. Ampofo AG, Boateng EB. Beyond 2020: Modelling obesity and diabetes prevalence. Diabetes Res Clin Pract. 2020 Sep;167:108362. McCall C. Urgent medical attention needed for people on Nauru. Lancet. 2018 Oct 27;392(10157):1507-1508. Ozsahin AK. Family practice in Turkey. Glob Health Promot. 2014 Mar;21(1):59-62. Tan CC, Lam CSP, Matchar DB, et al. Singapore's health-care system: key features, challenges, and shifts. Lancet. 2021 Sep 18;398(10305):1091-1104. Sultan-Taïeb H, Villeneuve T, Chastang JF, et al. Burden of cardiovascular diseases and depression attributable to psychosocial work exposures in 28 European countries. Eur J Public Health. 2022 Aug 1;32(4):586-592. Staerk L, Sherer JA, Ko D, et al. Atrial Fibrillation: Epidemiology, Pathophysiology, and Clinical Outcomes. Circ Res. 2017 Apr 28;120(9):1501-1517. Elagizi A, Gersh FL, Lavie CJ, et al. Testosterone and cardiovascular health. Eur Heart J. 2024 Jan 7;45(2):139-141. Corban MT, Toya T, Ahmad A, et al. Atrial Fibrillation and Endothelial Dysfunction: A Potential Link? Mayo Clin Proc. 2021 Jun;96(6):1609-1621. Yu Q, Zhao L, Tang T, et al. Estimates and trends in death and disability from atrial fibrillation/atrial flutter due to high sodium intake, China, 1990 to 2019. BMC Cardiovasc Disord. 2025 Jan 25;25(1):49. Maggioni AP, Poli G, Mannucci PM. Impact of Dietary Fats on Cardiovascular Disease with a Specific Focus on Omega-3 Fatty Acids. J Clin Med. 2022 Nov 9;11(22):6652. Ma Y, Su B, Li D, et al. Air pollution, genetic susceptibility, and the risk of atrial fibrillation: A large prospective cohort study. Proc Natl Acad Sci USA. 2023 Aug 8;120(32):e2302708120. Gawałko M, Linz D. Atrial Fibrillation Detection and Management in Hypertension. Hypertension. 2023 Mar;80(3):523-533. January CT, Wann LS, Calkins H, et al. 2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society in Collaboration With the Society of Thoracic Surgeons. Circulation. 2019 Jul 9;140(2):e125-e151. Perez MV, Mahaffey KW, Hedlin H, et al. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med. 2019 Nov 14;381(20):1909-1917. Ruff CT, Giugliano RP, Braunwald E, et al. Comparison of the efficacy and safety of new oral anticoagulants with warfarin in patients with atrial fibrillation: a meta-analysis of randomised trials. Lancet. 2014 Mar 15;383(9921):955-62. Lip GYH, Proietti M, Potpara T, et al. Atrial fibrillation and stroke prevention: 25 years of research at EP Europace journal. Europace. 2023 Aug 2;25(9):euad226. Nagarajan VD, Lee SL, Robertus JL, et al. Artificial intelligence in the diagnosis and management of arrhythmias. Eur Heart J. 2021 Oct 7;42(38):3904-3916. Poole JE, Bahnson TD, Monahan KH, et al. Recurrence of Atrial Fibrillation After Catheter Ablation or Antiarrhythmic Drug Therapy in the CABANA Trial. J Am Coll Cardiol. 2020 Jun 30;75(25):3105-3118. Middeldorp ME, Ariyaratnam JP, Kamsani SH, et al. Hypertension and atrial fibrillation. J Hypertens. 2022 Dec 1;40(12):2337-2352. Pouwels S, Topal B, Knook MT, et al. Interaction of obesity and atrial fibrillation: an overview of pathophysiology and clinical management. Expert Rev Cardiovasc Ther. 2019 Mar;17(3):209-223. Juul F, Vaidean G, Parekh N. Ultra-processed Foods and Cardiovascular Diseases: Potential Mechanisms of Action. Adv Nutr. 2021 Oct 1;12(5):1673-1680. Sun Y, Yu B, Yu Y, et al. Sweetened Beverages, Genetic Susceptibility, and Incident Atrial Fibrillation: A Prospective Cohort Study. Circ Arrhythm Electrophysiol. 2024 Mar;17(3):e012145. Lu Y, Sun Y, Cai L, et al. Non-traditional risk factors for atrial fibrillation: epidemiology, mechanisms, and strategies. Eur Heart J. 2025 Mar 3;46(9):784-804. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Table1.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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ASIR; B. ASPR; C. ASMR; D. ASDR)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6405324/v1/f47133d85e13f7e2101add4e.png"},{"id":84324022,"identity":"d1a36211-2924-4187-ba83-4ebdfb0c4da3","added_by":"auto","created_at":"2025-06-10 14:50:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":650441,"visible":true,"origin":"","legend":"\u003cp\u003eAge-sex-time analysis of the disease burden of AF and AFL (A. ASIR; B. ASPR; C. ASMR; D. ASDR)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6405324/v1/e7227af6b0f1491ef12d51d4.png"},{"id":84324020,"identity":"eb0b47f0-8c67-4939-937e-eb8efe315381","added_by":"auto","created_at":"2025-06-10 14:50:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":535837,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint analysis of the disease burden of AF and AFL (A. ASIR; B. ASPR; C. ASMR; D. ASDR)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6405324/v1/671ab03fe80e3f94baf101b9.png"},{"id":84324024,"identity":"0e5e31d5-9756-45ac-a2a2-bd7171be0fa9","added_by":"auto","created_at":"2025-06-10 14:50:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":849518,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between age-standardized rates (ASR) and Socio-Demographic Index (SDI) for AF and AFL across 21 regions (A. ASIR; B. ASPR; C. ASMR; D. ASDR)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6405324/v1/7e0115d6c3964a168dbe8733.png"},{"id":84324021,"identity":"620ca40e-879b-4f8f-9ed0-ad4e8e85d386","added_by":"auto","created_at":"2025-06-10 14:50:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":399839,"visible":true,"origin":"","legend":"\u003cp\u003eForecasting analysis of age-standardized rates (ASR) for AF and AFL (A. ASIR; B. ASPR; C. ASMR; D. ASDR)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6405324/v1/0bd0247de88795abbd0afd87.png"},{"id":102409611,"identity":"bd3e52a7-5050-4212-9b78-c91c3c87511e","added_by":"auto","created_at":"2026-02-11 11:43:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5908008,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6405324/v1/3cc80228-92e7-4c0e-b59c-460877b04580.pdf"},{"id":84324025,"identity":"c5aa2ebf-1fed-490b-a79a-4a8bfa4ee89e","added_by":"auto","created_at":"2025-06-10 14:50:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32852430,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6405324/v1/1cd2e2038395bcc6aa3a1619.docx"},{"id":84324018,"identity":"d3d38039-b01d-499c-b06f-47654efd3681","added_by":"auto","created_at":"2025-06-10 14:50:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23664,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6405324/v1/3683c127836d74707eeb8de0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global, Regional, and National Burden of Atrial Fibrillation and Atrial Flutter in the Working-Age Population from 1990 to 2021: A Systematic Analysis Based on 2021 Global Burden of Disease Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAtrial fibrillation (AF) and atrial flutter (AFL), the most prevalent clinical tachyarrhythmias, impose a substantial global health burden through impaired quality of life, elevated risks of stroke and heart failure, and escalating healthcare expenditures\u003csup\u003e[1]\u003c/sup\u003e\u003csup\u003e[2]\u003c/sup\u003e\u003csup\u003e[3]\u003c/sup\u003e. While existing studies have extensively characterized AF/AFL epidemiology in elderly populations, the burden among working-age individuals (20-64 years) remains underexplored despite its distinct socioeconomic ramifications\u003csup\u003e[4]\u003c/sup\u003e. Notably, over 35% of global AF/AFL cases now occur in individuals under 65 years, with incidence rates in this demographic increasing 1.8-fold faster than in older adults since 2000\u003csup\u003e[5]\u003c/sup\u003e\u003csup\u003e[6]\u003c/sup\u003e. This shift parallels socioeconomic transitions, including urbanization-driven metabolic syndrome and occupational stress, necessitating a life-course perspective to address early-onset cardiovascular risks\u003csup\u003e[6]\u003c/sup\u003e\u003csup\u003e[7]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe working-age population faces unique challenges shaped by accelerated epidemiological transitions, occupational arrhythmogenic triggers, and systemic healthcare disparities\u003csup\u003e[8]\u003c/sup\u003e\u003csup\u003e[6]\u003c/sup\u003e. Hypertension prevalence has risen by 24% among individuals aged 30-50 in low-middle-income countries, while shift work elevates AF risk by 22%\u003csup\u003e[6]\u003c/sup\u003e. In parallel, anticoagulation coverage remains below 10% in rural sub-Saharan Africa, reflecting critical gaps in equitable care delivery\u003csup\u003e[9]\u003c/sup\u003e\u003csup\u003e[10]\u003c/sup\u003e. These factors drive a dynamic burden profile where high-income regions exhibit technology-driven overdiagnosis, whereas low-income settings are plagued by underdetection and treatment delays. Crucially, AF and AFL diminish individual productivity and strain national economies, costing an estimated $48 billion annually in lost workforce participation\u003csup\u003e[6]\u003c/sup\u003e\u003csup\u003e[10]\u003c/sup\u003e. However, current prevention strategies remain extrapolated from elderly cohorts, overlooking risk trajectories specific to younger populations and context-sensitive intervention thresholds.\u003c/p\u003e\n\u003cp\u003eThis study utilized data from the Global Burden of Disease Study 2021 to systematically analyze the epidemiology of AF and AFL in working-age populations from 1990 to 2021, with projections extended to 2050, thereby bridging this knowledge deficit. Multidimensional assessments of age, sex, Socio-Demographic Index (SDI), clinical subtypes, and severity were conducted at national, regional, and global levels, establishing an evidence-based policy framework. The findings delineate developmental patterns and socioeconomic determinants of the disease, providing actionable benchmarks for cardiovascular health strategies to achieve workforce preservation and Sustainable Development Goals.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data Sources and Extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study were obtained from the Global Burden of Disease (GBD) 2021 database, which encompasses epidemiological data on 371 diseases and injuries across 204 countries and regions worldwide, covering the period from 1990 to 2021\u003csup\u003e[11]\u003c/sup\u003e. Data were extracted using the GBD official data visualization platform (https://vizhub.healthdata.org/gbd-results/) and included the following dimensions: Geographical Levels: The data were categorized by global scope, Socio-Demographic Index (SDI) classifications (low, low-middle, middle, high-middle, and high SDI), 21 specific GBD regions, and 204 countries and territories\u003csup\u003e[12]\u003c/sup\u003e\u003csup\u003e[13]\u003c/sup\u003e. Demographic Characteristics: The target population for this study consisted of working-age individuals aged 20-65 years, stratified into 5-year age groups (20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64) and analyzed by sex (male, female, and overall). Due to the lack of data on AF and AFL for the age groups 20-24 and 25-29 in the GBD database, the analysis focused on the age range of 30-64 years. Core Indicators: The study extracted data on the incidence, prevalence, mortality rates, and disability-adjusted life years (DALYs) for AF and AFL, along with their corresponding 95% uncertainty intervals (UI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Statistical Analysis Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1 Statistical Description of Disease Burden\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo eliminate the impact of age structure differences on the results, the disease burden data for the 30-64 age group were age-standardized using the GBD 2021 global standard population structure. The statistical indicators analyzed included the number of new cases, prevalent cases, deaths, DALYs, and their 95% UIs. Based on this, the following age-standardized metrics were calculated: age-standardized incidence rate (ASIR), age-standardized prevalence rate (ASPR), age-standardized mortality rate (ASMR), and age-standardized DALYs rate (ASDR). These metrics will be used to compare disease burden levels across different regions and populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 Time Trend Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the temporal trends in the disease burden of AF and AFL from 1990 to 2021, this study employed two methods: the estimated annual percentage change (EAPC) and Joinpoint regression analysis\u003csup\u003e[14]\u003c/sup\u003e\u003csup\u003e[15]\u003c/sup\u003e. The EAPC was calculated based on a log-linear regression model (ln(Y) = \u0026alpha; + \u0026beta;X + \u0026epsilon;), with the formula 100 \u0026times; [exp(\u0026beta;) \u0026minus; 1], where \u0026beta; is the regression coefficient, X is the year, and Y is the natural logarithm of the age-standardized rate. The 95% confidence interval (CI) for the EAPC was used to evaluate the statistical significance of the trends; an EAPC greater than zero indicates an upward trend, while a value less than zero indicates a downward trend. Joinpoint regression analysis was conducted using the Joinpoint Regression Program version 4.9.1.0, employing Monte Carlo permutation tests to identify inflection points in the trends and calculating the average annual percentage change (AAPC) to characterize the overall trend and capture the nonlinear features of changes in disease burden.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3 Age-Period-Cohort Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized an age-period-cohort (APC) model framework to systematically decompose the factors contributing to changes in disease burden, including age effects, period effects, and cohort effects. The age effect reflects the natural variation in disease risk over an individual\u0026rsquo;s life cycle; the period effect captures the short-term impacts of external environmental factors (such as advancements in medical technology and policy interventions) on disease burden; and the cohort effect reveals long-term differences in disease burden among different birth cohorts due to exposure to specific risk factors. The model was implemented using R software (version 4.3.3), employing orthogonal decomposition to separate linear and nonlinear components and using weighted least squares (WLS) to estimate parameters. Model fit was assessed using the Wald\u0026nbsp;\u0026chi;\u0026sup2;\u0026nbsp;test\u003csup\u003e[16]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.4 Decomposition Analysis of Disease Burden\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo quantify the contributions of population aging, population growth, and epidemiological changes to the disease burden of AF and AFL, this study employed demographic decomposition methods. Specifically, the changes in disease burden were decomposed into three main factors: changes in age structure, changes in population size, and epidemiological changes. By analyzing the relative contributions of these factors, the primary drivers of changes in disease burden were identified\u003csup\u003e[17]\u003c/sup\u003e\u003csup\u003e[18]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.5 Bayesian Age-Period-Cohort (BAPC) Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a BAPC model to forecast the trends of AF and AFL from 2022 to 2050. The advantage of this model lies in its use of a second-order random walk before smooth age, period, and cohort effects, effectively avoiding overfitting. By utilizing the Integrated Nested Laplace Approximation (INLA) method, this study efficiently calculated the marginal posterior distributions, circumventing the computational bottlenecks associated with traditional Markov Chain Monte Carlo (MCMC) methods. Finally, the robustness of the model predictions was evaluated using cross-validation and other methods to ensure the reliability of the forecasted results\u003csup\u003e[19]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.6 Health Inequality and Frontier Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess both absolute and relative health inequalities in disease burden, this study utilized the Slope Index of Inequality (SII) and the Concentration Index (CII). The SII was calculated by regressing the DALYs rate against the SDI, with the midpoint of the cumulative population distribution sorted by SDI used in the regression analysis. The CII was computed by matching the cumulative proportion of DALYs with the cumulative population distribution sorted by SDI and numerically integrating the area under the Lorenz curve\u003csup\u003e[20]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAdditionally, a frontier analysis was conducted, based on SDI, to construct a frontier model using ASDR. This model aims to identify the theoretically achievable minimum ASDR for each country or region at different levels of development. By quantifying the gap between the actual disease burden and the potential minimum burden, it highlights areas for improvement. To ensure the robustness of the analysis, locally weighted regression combined with local polynomial regression methods were employed, using varying smoothing spans to generate smooth boundary lines that capture the nonlinear relationship between SDI and ASDR. Furthermore, 100 bootstrap resampling iterations were performed, and the average ASDR for each SDI value was calculated to ensure the reliability of the analysis\u003csup\u003e[20]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.7 Correlation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used Spearman\u0026rsquo;s rank correlation analysis to explore the relationship between SDI and ASR. To control for the false positive results caused by multiple comparisons, the Benjamini-Hochberg method was applied to adjust the false discovery rate (FDR) (FDR \u0026lt; 0.05). Additionally, the local weighted scatterplot smoothing (LOWESS) method was employed to fit nonlinear trends, further elucidating the complex association between SDI and disease burden.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Global Burden of AF and AFL in the Working-Age Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to data from 2021, the ASIR for atrial AF and AFL was 47.05 (95% UI: 29.73, 71.26) per 100,000, the ASPR was 397.94 (95% UI: 282.14, 558.51) per 100,000, the ASMR was 0.49 (95% UI: 0.44, 0.53) per 100,000, and the ASDR was 48.46 (95% UI: 34.29, 66.42) per 100,000 years. This indicates that in 2021, there were approximately 1,599,232.83 (95% UI: 1,009,695.25, 2,423,709.76) new cases of AF and AFL globally, with a total of 13,592,619.06 (95% UI: 9,645,950.95, 19,057,302.82) existing cases. The number of deaths among the working-age population due to AF and AFL was 16,700.33 (95% UI: 14,939.08, 18,116.07), resulting in a total of 1,655,235.79 (95% UI: 1,171,507.95, 2,267,266.09) DALYs. From 1990 to 2021, the ASIR, ASPR, and ASDR for AF and AFL in the working-age population showed significant increases (with EAPCs and their 95% CIs all being positive), while the ASMR exhibited a significant decline (with EAPCs and their 95% CIs all being negative) (Table 1 and Figure S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Regional Burden of AF and AFL in the Working-Age Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the five SDI regions in 2021, the working-age population in high SDI regions had the highest ASIR (61.01 (95% UI: 43.99, 82.97) per 100,000), ASPR (515.73 (95% UI: 407.04, 657.96) per 100,000), ASMR (0.57 (95% UI: 0.55, 0.59) per 100,000), and ASDR (60.52 (95% UI: 44.75, 79.26) per 100,000 years). In contrast, low SDI regions had the lowest ASIR (35.32 (95% UI: 20.83, 56.11) per 100,000), ASPR (282.73 (95% UI: 187.24, 417.68) per 100,000), and ASDR (39.59 (95% UI: 27.40, 55.46) per 100,000 years), as well as the lowest ASMR in high-middle SDI regions (0.41 (95% UI: 0.37, 0.46) per 100,000 years). From 1990 to 2021, both ASIR and ASPR significantly increased across all five SDI regions. The ASMR in low-middle SDI regions showed a significant increase, while ASMR in high-SDI and low-SDI regions did not change significantly. Conversely, ASMR in high-middle SDI and middle SDI regions significantly decreased. The ASDR in high SDI and high-middle SDI regions showed no significant change, while the remaining three SDI regions exhibited significant increases in ASDR (Table 1 and Figure S1).\u003c/p\u003e\n\u003cp\u003eIn the 21 GBD regions, Australasia had the highest ASIR (73.59 (95% UI: 44.65, 115.28) per 100,000), ASPR (639.10 (95% UI: 438.73, 910.22) per 100,000), ASMR (1.29 (95% UI: 0.86, 1.80) per 100,000), and ASDR (78.19 (95% UI: 56.00, 106.43) per 100,000 years) in 2021. In contrast, North Africa and the Middle East had the lowest ASIR (24.12 (95% UI: 14.51, 37.55) per 100,000), ASPR (196.06 (95% UI: 132.60, 284.17) per 100,000), ASDR (29.19 (95% UI: 22.08, 38.72) per 100,000 years), and Western Sub-Saharan Africa had the lowest ASMR (0.31 (95% UI: 0.20, 0.39) per 100,000). From 1990 to 2021, ASIR showed no significant change in five regions, while three regions experienced significant declines; the remaining regions exhibited significant increases, with East Asia showing the most pronounced increase and Southern Latin America the most significant decrease. ASPR showed no significant change in five regions, with two regions experiencing significant declines, while the remaining regions exhibited significant increases, particularly in East Asia, which had the most notable increase, and Southern Latin America, which had the most significant decrease. ASMR showed no significant change in eight regions, with seven regions experiencing significant declines, while the remaining regions exhibited significant increases, particularly in Southern Sub-Saharan Africa, which had the most pronounced increase, and High-income Asia Pacific, which had the most significant decrease. ASDR showed no significant change in seven regions, with four regions experiencing significant declines, while the remaining regions exhibited significant increases, particularly in Eastern Europe, which had the most notable increase, and High-income Asia Pacific, which had the most significant decrease (Table 1 and Figure S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 National Burden of AF and AFL in the Working-Age Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 2021, Sweden had the highest ASIR (107.64 (95% UI: 64.61, 170.24) per 100,000) and ASPR (851.01 (95% UI: 567.93, 1,226.51) per 100,000), while Nauru had the highest ASMR (2.55 (95% UI: 1.51, 3.76) per 100,000) and ASDR (130.25 (95% UI: 83.97, 181.31) per 100,000 years). In contrast, Turkey had the lowest ASIR (16.83 (95% UI: 11.91, 23.14) per 100,000), ASPR (145.25 (95% UI: 115.78, 185.46) per 100,000), and ASDR (22.62 (95% UI: 16.46, 30.67) per 100,000 years), while Singapore had the lowest ASMR (0.19 (95% UI: 0.17, 0.21) per 100,000) (Figure 1, Table S1). From 1990 to 2021, Austria exhibited the most significant increases in ASIR, while Lesotho showed the most notable increases in ASPR, ASMR, and ASDR. Conversely, Turkey showed the most significant declines in ASIR, and Lebanon exhibited the most notable declines in ASPR, ASMR, and ASDR (Table S1 and \u0026nbsp;Figure S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Age-Sex-Time Trends in the Disease Burden of AF and AFL in the Working-Age Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGender-age analysis showed that both males and females experienced an increase in the ASR of AF and AFL with advancing age. Males exhibited slightly higher ASIR, ASPR, and ASDR than females, while the ASMR was similar between the genders (Figure 2). After accounting for the effects of period and cohort factors, the age effect analysis revealed a continuous increase in the ASR of AF and AFL with age (Figures S3A-D). In terms of absolute numbers, both the number of new cases and deaths increased with age, with males having higher values than females. Gender-time analysis indicated that, from 1990 to 2021, both male and female ASIR, ASPR, and ASDR showed a gradual upward trend across the globe and within all five SDI regions, while ASMR exhibited a gradual decline, except the low-middle SDI region, where ASMR showed a slow increase (Figures S4- S7). Age-time analysis further showed that from 1990 to 2021, all age groups exhibited a slow upward trend in ASIR, ASPR, and ASDR. In contrast, ASMR showed a slow decline over this period (Figures S8 -11). Moreover, the disease burden of AF and AFL in the working-age population was primarily concentrated in the 60-64-year age group. After controlling for age and cohort factors, period effect analysis revealed that ASIR, ASPR, and ASDR initially increased, then decreased, and then increased again, with inflection points occurring in 2005 and 2015. In contrast, ASMR showed a consistent decline year after year (Figures S3E-H). Cohort effect analysis demonstrated that later-born cohorts had higher ASIR, ASPR, and ASDR than earlier-born cohorts, while ASMR exhibited the opposite trend (Figures S3I-L).\u003c/p\u003e\n\u003cp\u003eJoinpoint regression analysis results indicated that, from 1990 to 2021, overall, ASIR (AAPC = 0.052, 95% CI: 0.044, 0.059), ASPR (AAPC = 0.610, 95% CI: 0.553, 0.667), and ASDR (AAPC = 0.034, 95% CI: 0.029, 0.039) exhibited significant increasing trends, whereas ASMR (AAPC = -0.000, 95% CI: -0.001, -0.000) showed a significant decreasing trend. Specifically, inflection points for ASIR and ASPR occurred in 1997/1996, 2005, and 2015, with both showing significant increases after 2015. ASMR experienced inflection points in 1994, 2005, and 2008, with a significant decline from 2008 onwards. ASDR showed inflection points in 1999, 2005, and 2013, and no significant upward or downward trend was observed after 2013 (Figure 3 and Table S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 The Association Between the Disease Burden of AF and AFL in the Working-Age Population and SDI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA significant correlation was observed between the ASIR, ASPR, and ASDR of AF and AFL in the working-age population and the SDI (p \u0026lt; 0.05), which was consistent across 21 GBD regions and 204 countries. As SDI increased, ASIR, ASPR, and ASDR gradually rose, while ASMR showed no significant change (Figure 4 and Figure S12). The correlation coefficients and their p-values are provided in Table S3. Specifically, at the regional level, the correlation between ASIR and ASPR was most pronounced in Australasia, while the correlation between ASMR and ASDR was most evident in Oceania. At the national level, Sweden, Germany, and Israel had significantly higher ASIR and ASPR compared to other countries, while Nauru, the Marshall Islands, and Micronesia had significantly higher ASMR and ASDR than other nations.\u003c/p\u003e\n\u003cp\u003eThe EAPC of ASMR and ASDR also demonstrated a significant correlation with SDI (p \u0026lt; 0.05). Specifically, the EAPC for ASMR and ASDR showed an initial increase followed by a decrease as SDI rose, with inflection points occurring at an SDI around 0.5. As SDI increased, the negative EAPC values indicated that the rate of decline in ASMR and ASDR initially slowed and then accelerated. In contrast, the EAPC for ASIR and ASPR showed a weaker correlation with SDI (p \u0026gt; 0.05), and no significant changes were observed in the EAPC of these rates as SDI increased (Figure S13).\u003c/p\u003e\n\u003cp\u003eHealth inequality analysis further revealed significant absolute and relative inequalities in ASDR between SDI levels. The slope index indicated that, from 1990 to 2021, the gap in ASDR between countries with the highest and lowest SDI decreased from 21.41 (95% UI: 15.05, 27.78) per 100,000 years to 15.41 (95% UI: 8.65, 22.17) per 100,000 years (Figure S14), suggesting a reduction in absolute inequality between high and low SDI countries, with the disease burden primarily concentrated in high SDI nations. Meanwhile, the CII decreased from 0.04 (95% CI: 0.01, 0.08) in 1990 to -0.02 (95% CI: -0.06, 0.02) in 2021 (Figure S15), indicating an improvement in relative inequality between high and low SDI countries.\u003c/p\u003e\n\u003cp\u003eFrontier analysis results showed that the 15 countries and regions with the greatest gap in disease burden compared to the theoretical frontier were Nauru, the Marshall Islands, Micronesia, Tuvalu, the Northern Mariana Islands, Montenegro, Niue, Tokelau, Greenland, Samoa, American Samoa, Vanuatu, Fiji, Germany, and Sweden, with potential improvements in actual burden ranging from 62.80 to 107.42 (Figure S16). Among low SDI countries (SDI \u0026lt; 0.50), the countries with the smallest frontier differences included Somalia, Benin, Chad, Mali, and Niger; while in high SDI countries (SDI \u0026gt; 0.85), the countries with the largest frontier differences were the United States of America, Austria, Denmark, Sweden, and Germany.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Decomposition Analysis, Future Projections, and Attributable Risk Factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlobally, both population growth and aging have had a positive impact on the ASR of AF and AFL, while epidemiological changes have led to a reduction in the ASMR but an increase in the ASIR, ASPR, and ASDR. At the regional level, population growth has generally driven an increase in the ASR for AF and AFL, while the impacts of aging and epidemiological changes have varied across regions. Specifically, aging had the most significant effect on ASR increase in Middle SDI regions, while its impact was most noticeable in reducing ASIR and ASPR in Western Europe and reducing ASMR and ASDR in Eastern Europe. Epidemiological changes had the largest impact on increasing ASIR and ASPR in East Asia, on increasing ASMR in Low-middle SDI regions, and on increasing ASDR in Middle SDI regions. Meanwhile, epidemiological changes had the most significant effect on reducing ASIR, ASPR, and ASDR in High-income Asia Pacific regions and on reducing ASMR in East Asia (Figure S17 and Table S4).\u003c/p\u003e\n\u003cp\u003eBased on future projections, from 2022 to 2050, the ASIR and ASPR for AF and AFL are expected to decline gradually, while the ASMR and ASDR are expected to rise slowly (Figure 5). By 2050, the projected ASIR will be 45.18 (95% UI: 39.21, 51.15) per 100,000, the ASPR will be 387.53 (95% UI: 345.36, 429.71) per 100,000, the ASMR will be 0.51 (95% UI: 0.41, 0.60) per 100,000, and the ASDR will be 49.29 (95% UI: 43.71, 54.88) per 100,000 years (Table S5). The trends in ASR across different age groups are similar to the overall trend, with the most significant decreases in ASIR and ASPR observed in the 55-59 and 60-64 age groups, while these groups also showed the most notable increases in ASMR and ASDR (Figures S18 -21).\u003c/p\u003e\n\u003cp\u003eCurrently, six main attributable risk factors for AF and AFL have been identified. Globally, the highest proportion of DALYs due to AF and AFL is attributed to high systolic blood pressure (13.04%), and this proportion is the highest across all five SDI regions (Figure S22). From 1990 to 2021, the proportion of AF and AFL attributed to high body mass index has gradually increased both globally and in all five SDI regions. The proportion of AF and AFL attributable to high systolic blood pressure has increased in Middle SDI, Low-middle SDI, and Low SDI regions, while changes in other regions were not significant. The proportion of AF and AFL attributable to smoking has gradually decreased across the globe and in all five SDI regions. The trends in the proportions of other attributable risk factors have not shown significant changes (Figure S23).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study revealed that from 1990 to 2021, the global burden of AF and AFL among working-age populations (30-64 years) demonstrated complex evolving trends: While ASIR, ASPR, and ASDR showed significant increases, ASMR exhibited a paradoxical decline. This dual-track progression reflects stratified patterns across nations with varying socioeconomic development levels - high SDI regions demonstrate an epidemiological profile characterized by coexisting high incidence and low mortality, driven by diagnostic advancements and population aging, whereas low SDI regions face challenges of underdiagnosis and elevated disability rates due to healthcare resource deficiencies and suboptimal risk factor management. These findings highlight the nonlinear association between disease control capacity and economic development levels.\u003c/p\u003e\n\u003cp\u003eThis study reveals marked regional disparities in the 2021 burden of AF and AFL among working-age populations: High-SDI regions such as Australia and Oceania exhibited the highest global incidence, prevalence, and disability rates, whereas low-SDI regions like Western Sub-Saharan Africa demonstrated accelerated growth in disability rates despite lower absolute values. Longitudinal analyses showed universal increases in incidence and prevalence across all SDI quintiles, with East Asia recording the most substantial growth driven by urbanization-induced metabolic syndrome\u003csup\u003e[21]\u003c/sup\u003e\u003csup\u003e[22]\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand psychosocial stress\u003csup\u003e[23]\u003c/sup\u003e\u003csup\u003e[24]\u003c/sup\u003e, while Southern Latin America achieved counter-trend declines through public health interventions like hypertension screening\u003csup\u003e[25]\u003c/sup\u003e\u003csup\u003e[26]\u003c/sup\u003e\u003csup\u003e[27]\u003c/sup\u003e. Mortality and disability patterns exhibited greater heterogeneity: high-SDI regions achieved mortality reductions through ambulatory ECG monitoring\u003csup\u003e[28]\u003c/sup\u003e and anticoagulation therapies\u003csup\u003e[29]\u003c/sup\u003e\u003csup\u003e[30]\u003c/sup\u003e, yet paradoxically maintained elevated disability rates, reflecting prolonged survival with complication-related disabilities\u003csup\u003e[31]\u003c/sup\u003e. Conversely, low-SDI regions displayed artificially suppressed mortality metrics due to diagnostic under-capture\u003csup\u003e[32]\u003c/sup\u003e, while escalating disability rates revealed dual crises of treatment delays and uncontrolled risk factors. Sub-Saharan Africa experienced anomalous mortality increases from stroke care deficiencies\u003csup\u003e[33]\u003c/sup\u003e and HIV-associated cardiomyopathy comorbidities\u003csup\u003e[34]\u003c/sup\u003e. While this study demonstrates country-level variations in disease burden determinants, Sweden\u0026apos;s high-incidence-low-mortality pattern likely arises from comprehensive ECG surveillance\u003csup\u003e[35]\u003c/sup\u003e\u003csup\u003e[36]\u003c/sup\u003e, whereas Nauru exhibits dual deterioration driven by extreme obesity prevalence\u003csup\u003e[37]\u003c/sup\u003e\u003csup\u003e[38]\u003c/sup\u003e and healthcare desertification\u003csup\u003e[39]\u003c/sup\u003e. Turkey\u0026apos;s family physician program\u003csup\u003e[40]\u003c/sup\u003e effectively controlled incidence rates, while Singapore\u0026apos;s universal health coverage achieved the world\u0026apos;s lowest mortality\u003csup\u003e[41]\u003c/sup\u003e. These cases collectively illustrate the multifactorial interplay of demographic structure, metabolic risks, healthcare accessibility, and social policies in shaping disease burden.\u003c/p\u003e\n\u003cp\u003eFurther demonstrates significant three-dimensional heterogeneity in atrial fibrillation AF and AFL burden across age, sex, and temporal dimensions. Males exhibited higher incidence, prevalence, and disability rates than women, attributable to occupational stress\u003csup\u003e[42]\u003c/sup\u003e, smoking prevalence\u003csup\u003e[43]\u003c/sup\u003e, and testosterone\u0026apos;s pro-arrhythmic effects\u003csup\u003e[44]\u003c/sup\u003e, though mortality convergence suggests a dynamic equilibrium between improved stroke management in males and atypical symptom presentation in females. All metrics showed age-dependent escalation peaking at 60-64 years, reflecting the biological accumulation of comorbidities like arteriosclerosis\u003csup\u003e[45]\u003c/sup\u003e, while higher disease burden in later-born cohorts indicates long-term risks from generational exposure to high-salt/high-fat diets\u003csup\u003e[46]\u003c/sup\u003e\u003csup\u003e[47]\u003c/sup\u003e and environmental pollutants\u003csup\u003e[48]\u003c/sup\u003e. Temporal evolution revealed significant increases in global incidence, prevalence, and disability rates from 1990 to 2021, with inflection mechanisms reflecting public health-technological interplay: intensified hypertension management\u003csup\u003e[49]\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003emoderated growth trends, while wearable device proliferation\u003csup\u003e[50]\u003c/sup\u003e\u003csup\u003e[51]\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eincreased asymptomatic case detection. Novel oral anticoagulants\u003csup\u003e[52]\u003c/sup\u003e and ABC pathway implementation\u003csup\u003e[53]\u003c/sup\u003e drove sustained mortality declines since 2008, though low-middle SDI regions experienced paradoxical mortality increases due to anticoagulation therapy deficits.\u003c/p\u003e\n\u003cp\u003eThis study further elucidates the tripartite mechanisms driving AF and AFL burden evolution through demographic transition, epidemiological shifts, and risk factor polarization. Globally, population growth continues to elevate age-standardized incidence, prevalence, and mortality rates, with the 60-64 age cohort constituting the core affected population. While diagnostic advancements and comorbidity management optimization reduced mortality, they paradoxically increased incidence and disability rates through enhanced case detection and over-intervention in asymptomatic AF, creating a \u0026quot;technological burden shift\u0026quot; phenomenon\u003csup\u003e[54]\u003c/sup\u003e\u003csup\u003e[55]\u003c/sup\u003e. Projections indicate that by 2050, prevalence rates will gradually decline to 387.53 per 100,000, yet mortality and disability rates will persistently rise, reflecting the \u0026quot;prevention-treatment imbalance\u0026quot; dilemma. Although hypertension management contributed substantially to burden mitigation\u003csup\u003e[56]\u003c/sup\u003e, its diminishing marginal returns alongside obesity control\u003csup\u003e[57]\u003c/sup\u003e fail to offset cumulative AF and AFL risks. Notably, the 55-64 age group faces an escalating disability crisis, imposing substantial socioeconomic burdens.\u003c/p\u003e\n\u003cp\u003eNotwithstanding these findings, our study has inherent limitations. While the GBD methodology demonstrates robustness, its precision depends on source data quality, and statistical modeling in data-scarce regions may introduce systematic biases. Furthermore, the absence of age-stratified AF and AFL data for the 20-29 age group in GBD databases renders disease burden in this critical demographic stratum an unexplored domain within predictive models.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study synthesizes key findings through cohort effects revealing earlier chronic disease onset and period effects demonstrating regional technological disparities, necessitating the establishment of precision prevention systems integrating life-cycle management, gender-specific screening, and regional resource optimization. Risk factor transitions further reflect latent capital globalization influences: the food industry\u0026apos;s salt-sugar hegemony accelerates processed food penetration, directly elevating systolic blood pressure and body mass index\u003csup\u003e[58]\u003c/sup\u003e\u003csup\u003e[59]\u003c/sup\u003e. Confronting the \u0026quot;high-cost, low-efficiency\u0026quot; phase of chronic disease management, traditional risk interventions exhibit diminishing mitigation efficacy, urgently requiring the integration of emerging risks like microplastics and light pollution\u003csup\u003e[60]\u003c/sup\u003e. Breakthrough strategies demand multidimensional approaches: implementing \u0026quot;hypertension-diabetes\u0026quot; comorbidity screening to disrupt risk pathways, legislating salt-sugar content regulation in processed foods, and aligning climate policy with health governance. Ultimately, curbing the global expansion of risk commodities, achieving equitable distribution of wearable technologies, and establishing cross-generational disability insurance systems are imperative to overcome metabolic traps entrenched by capital-driven paradigms and mitigate disease-associated economic losses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe article\u0026apos;s data were derived from sources in the public domain: Institute for Health Metrics and Evaluation, at http://ghdx.healthdata.org/gbd-results-tool, accessed on March 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Science and Technology Foundation of Zhejiang Province of China (Grant No.2020C03018), Grant of Westlake Laboratory of Life Sciences and Biomedicine (Grant No. XHSYS-02), and Zhejiang Chinese Medical University Postgraduate Scientific Research Fund Project（Grant No. 2023YKJ15).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYang Chao, Kong Youjing, and Liu Xiao\u003c/strong\u003e contributed equally as co-first authors. \u003cstrong\u003eYang Chao\u0026nbsp;\u003c/strong\u003eled the study design, data curation, and statistical modeling. \u003cstrong\u003eKong Youjing\u003c/strong\u003e performed the decomposition analysis and health inequality assessments and drafted the manuscript. \u003cstrong\u003eLiu Xiao\u0026nbsp;\u003c/strong\u003econducted the age-period-cohort analysis, Bayesian forecasting, and visualization of results. \u003cstrong\u003eHuang Xingxiao and Sun Qiuli\u003c/strong\u003e contributed to data validation, regional burden interpretation, and risk factor attribution analysis.\u003cstrong\u003e\u0026nbsp;Wang Hanxin and Yu Minjun\u003c/strong\u003e assisted in literature review, methodological refinement, and supplementary analyses. \u003cstrong\u003eGao Beibei\u003c/strong\u003e (co-corresponding author) supervised the statistical framework, coordinated revisions, and validated policy recommendations.\u003cstrong\u003e\u0026nbsp;Huang Jinyu\u003c/strong\u003e (lead corresponding author) conceptualized the study, secured funding, provided critical intellectual input, and finalized the manuscript for submission.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBrundel BJJM, Ai X, Hills MT, et al. 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Non-traditional risk factors for atrial fibrillation: epidemiology, mechanisms, and strategies. Eur Heart J. 2025 Mar 3;46(9):784-804. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\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":"Atrial fibrillation and flutter, Working-age population, Global burden of disease, Disease burden trends","lastPublishedDoi":"10.21203/rs.3.rs-6405324/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6405324/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Atrial fibrillation and atrial flutter impose a significant global health burden, particularly among individuals aged 20-64 years. This study analyzed data from the Global Burden of Disease 2021 database to estimate disease burden trends from 1990 to 2021. Key metrics included age-standardized incidence, prevalence, mortality, and disability-adjusted life years rates, stratified by age, sex, and Socio-Demographic Index. The methodologies encompassed descriptive analysis, age-period-cohort modeling, decomposition techniques, and Bayesian forecasting. From 1990 to 2021, age-standardized incidence rates increased by an estimated annual percentage change of 0.14, prevalence rates by 0.20, and disability rates by 0.08, while mortality rates declined by 0.16. By 2021, global incidence reached 47.05 per 100,000 population, prevalence 397.94, mortality 0.49, and disability 48.46. High-SDI regions exhibited the highest burden, with incidence at 61.01 and prevalence at 515.73 per 100,000, whereas low-SDI regions recorded the lowest incidence and prevalence at 35.32 and 282.73, respectively. Males consistently showed higher incidence, prevalence, and disability rates than females, with disease burden peaking in the 60-64 age group. Population growth contributed 52% to the rise in prevalent cases, surpassing aging and epidemiological factors. Projections to 2050 indicate declines in incidence to 45.18 and prevalence to 387.53 per 100,000, but mortality and disability rates are expected to rise to 0.51 and 49.29. High systolic blood pressure accounted for 13.04% of disability-adjusted life years globally, with contributions from high body mass index increasing across all SDI quintiles. Health inequalities narrowed between high- and low-SDI countries, with the slope index of inequality decreasing from 21.41 to 15.41 per 100,000 years and the concentration index shifting from 0.04 to -0.02. Critical priorities include optimizing screening protocols in high-SDI regions to reduce overdiagnosis, expanding hypertension control and anticoagulation access in low-SDI settings, and implementing workforce health surveillance targeting processed food consumption. Multisectoral strategies integrating real-time burden monitoring, salt-sugar regulation policies, and equitable technology distribution are essential to align with Sustainable Development Goals. This study underscores the necessity of region-specific interventions to mitigate economic productivity losses linked to atrial fibrillation and atrial flutter in the working-age population.","manuscriptTitle":"Global, Regional, and National Burden of Atrial Fibrillation and Atrial Flutter in the Working-Age Population from 1990 to 2021: A Systematic Analysis Based on 2021 Global Burden of Disease Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 14:49:59","doi":"10.21203/rs.3.rs-6405324/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":"29444a14-06a7-4f8e-a7e7-021e86aed12e","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T11:42:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 14:49:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6405324","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6405324","identity":"rs-6405324","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}