The Burden of Congenital Heart Disease in Asia and 34 Countries and Territories from 1990 to 2021: A Systematic Analysis Based on the Global Burden of Disease Study 2021

The Burden of Congenital Heart Disease in Asia and 34 Countries and Territories from 1990 to 2021: A Systematic Analysis Based on 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 Burden of Congenital Heart Disease in Asia and 34 Countries and Territories from 1990 to 2021: A Systematic Analysis Based on the Global Burden of Disease Study 2021 Haimei Zhang, Siqi Cao, Yanting Que, Yifang Dai, Meiying Cai, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7026873/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 One of the most prevalent birth defects worldwide, congenital heart disease (CHD) presents a serious risk to a child’s health. Although the survival rate of CHD has increased recently due to advancements in medical technology, the disease burden and incidence are still significant. Based on data from the Global Burden of Disease (GBD) study, this article examines the distribution characteristics and trends of CHD in regions with varying Socio-Demographic Index (SDI) levels. It does this by analyzing the disease burden indices of CHD in five Asian regions and the thirty-four countries and territories that are included from 1990 to 2021. Methods The GBD database covers 204 countries and regions and provides data on the incidence, mortality, and disability rates of 369 diseases and injuries from 1990 to 2021. Data on the prevalence, incidence, mortality, Disability-Adjusted Life Years (DALYs), years of life lost (YLL) and years lived with disability (YLD) of CHD were extracted from the GBD 2021 study for South, East, Southeast, and high-income Asia regions, encompassing the relevant countries and territories from 1990 to 2021. The Average Annual Percent Change (AAPC) in age-standardized rates of congenital heart disease was determined to assess temporal trends. Cohort proportions by age and gender were taken into account. The various SDI in five regions were used to assess the illness burden. Result Our research encompasses 34 countries and regions across five Asian subregions, conducting a detailed analysis of the disease burden related to CHD. The results indicate a significant reduction in mortality rates and case numbers between 1990 and 2021. Additionally, there has been a substantial decrease in YLLs and DALYs. However, Central Asia presents contrasting trends, suggesting that this region faces considerable challenges in the prevention and management of CHD. In 2021, Central Asia demonstrated markedly higher ASMR, ASIR, age-standardized YLLs rates, and age-standardized DALYs rates compared to the other four Asian subregions under investigation. In the same year, data from the five Asian subregions revealed that ASMR and ASPR were highest among children under the age of five. Moreover, across all age groups, the prevalence rate was consistently higher among males. From 1990 to 2021, high SDI countries experienced a notable decline in the proportion of childhood deaths attributable to CHD. Conclusion From 1990 to 2021, while the mortality rate of congenital heart disease (CHD) exhibited a downward trend in most regions and countries, significant disparities in CHD incidence and mortality rates persist across different countries. Many countries and regions still face substantial challenges in further reducing the disease burden. Consequently, it is imperative to enhance public health interventions and allocate additional resources to medical infrastructure, with the aim of improving patient prognosis and alleviating the burden of CHD. Notably, for countries with mid-to-low SDI, prioritizing policies that promote early diagnosis and comprehensive care is essential. congenital heart disease GBD (global burden of disease) prevalence mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Congenital Heart Disease (CHD) refers to structural and functional abnormalities of the heart that arise during embryonic development and represents one of the most prevalent birth defects globally [ 1 ] . Although there have been substantial improvements in survival rates for CHD patients due to advancements in medical technology, it continues to pose a significant threat to child health, particularly in low- and middle-income countries [ 2 ] . CHD not only severely compromises the physical health of patients but also imposes a considerable economic burden on families and society [ 3 ] . Therefore,early diagnosis and comprehensive care are crucial for reducing the incidence of CHD and improving patient outcomes. In recent years, driven by the continuous advancement of medical technology, the diagnosis and treatment of CHD have been markedly improved [ 4 ] . For instance, the development of prenatal screening technologies has enabled the identification of many complex cardiac defects prior to birth, thereby facilitating early intervention. Moreover, minimally invasive surgical techniques and advanced interventional treatments have substantially enhanced both surgical success rates and patients’quality of life [ 5 ] . Nevertheless, while high-income countries have achieved significant progress in CHD management and treatment, low- and middle-income countries continue to encounter numerous challenges in diagnosing and treating CHD, such as constrained medical resources, a shortage of specialized medical personnel, and limited awareness of CHD [ 6 ] . The GBD study presents a comprehensive framework for assessing the global burden of diseases [ 7 ] . This analysis employed CHD data from the GBD 2021, which is openly accessible to the public. It encompasses CHD related data across different age groups in the Asian region impacted by CHD from 1990 to 2021. The study detailed estimates of CHD incidence, mortality, and disability. These data not only help to understand the global epidemiological trends of CHD but also provide a scientific basis for formulating public health policies and resource allocation. This research aimed to explore the temporal, economic, and gender trends in the burden of congenital heart disease in 34 countries in the Asian region, thereby offering an evidence-based foundation for CHD prevention and treatment strategies. 2. Methods 2.1. Data acquisition and download The 2021 Global Burden of Disease (GBD) study extensively assessed health damages related to 369 specific diseases, injuries, and disabilities, as well as 88 risk factors. It covered 204 countries and regions, using the latest epidemiological data and standardized methods [ 7 , 8 ] . 2.2. Burden description We have been chosen East Asia region includes China, Taiwan (Province of China) and Democratic People’s Republic of Korea. South Asia region includes Bangladesh, Bhutan, India, Nepal, Pakistan. Southeast Asia region includes Cambodia, Indonesia, Lao People’s Democratic Republic, Malaysia, Maldives, Mauritius, Myanmar, Philippines, Seychelles, Sri Lanka, Thailand, Timor-Leste, Viet Nam. High-income Asia-Pacific region includes Brunei Darussalam, Japan, Republic of Korea and Singapore. Central Asia region includes Armenia, Azerbaijan, Uzbekistan, Georgia, Kazakhstan, Mongolia, Kyrgyzstan, Tajikistan, Turkmenistan. We conducted a comprehensive assessment of CHD prevalence, incidence, mortality, years of life lost (YLL), years lived with disability (YLD), and disability-adjusted life years (DALY) for five Asian regions from 1990 to 2021, extracted from the GBD 2021 dataset. DALYs are calculated by summing two components: YLL from premature mortality and YLD associated with the health condition. The computational formulas were applied as follows: (1) YLL = Number of deaths × Standard life expectancy at the age of death; (2) YLD = Prevalence of the condition × Disability weight; Where disability weights, derived through systematic expert consensus in the GBD study framework, quantify health loss on a scale from 0 (representing optimal health) to 1 (equivalent to fatal outcomes).The Socio-demographic Index (SDI) [ 9 ] measures a country's or region's development level using indicators such as fertility rates, education levels, and per capita income. The SDI ranges from 0 to 1, with higher values indicating greater socioeconomic development [ 10 ] . 2.3. Joinpoint regression analysis Joinpoint regression analysis, performed using the Joinpoint regression program version 5.3.0, is a widely used statistical method in epidemiological studies to assess changes in disease prevalence, incidence or mortality rates over time. This method effectively identified and quantified critical points of change in the time series data for CHD burden. It allowed for the calculation of the annual percent change (APC) along with its 95% confidence interval (CI), providing a clear depiction of disease burden trends within specific time segments. Additionally, the average annual percent change (AAPC) was computed to offer a comprehensive overview of the overall trend throughout the study period from 1990 to 2021 [ 11 ] . Statistically, the interpretation of APC and AAPC estimates is as follows: an APC or AAPC estimate with a 95% CI lower bound above zero indicates an increasing trend; an APC or AAPC estimate with a 95% CI upper bound below zero suggests a decreasing trend; and a 95% CI that includes zero for the APC or AAPC implies a stable trend [ 12 ] . 2.4. Statistics analysis The prevalence, incidence, mortality, YLLs, YLDs, and DALYs was represented as a projection for every 100,000 persons individuals in the populace, inclusive of its 95% UI. Their age-standard rate was used to eliminate the impact of differences in age structure among various populations on rates, thereby making data comparable across different time points or populations. All data analysis and visualizations were performed via R language programming (version 4.4.2, R Development Core Team). p-value of less than 0.05 was considered statistically significant. 3. Result 3.1. Regional level 3.1.1. Regional burden and trends Table 1 Prevalent cases, incident cases, death cases, YLDs, YLL, DALYs for congenital heart disease in 1990 and 2021 for both sexes and their age-standardized rates in east Asia, high-income Asia Pacific, south Asia, Southeast Asia region, Central Asia East Asia High-income Asia Pacific South Asia Southeast Asia Central Asia Counts Age-standard adjust rate per 100,000 Counts Age-standard adjust rate per 100,000 Counts Age-standard adjust rate per 100,000 Counts Age-standard adjust rate per 100,000 Counts Age-standard adjust rate per 100,000 1990 Deaths 127882 (82117,176229) 10.98 (7.01,15.14) 4302 (3538,4795) 3.86 (3.14,4.34) 115427 (72640,154224) 7.41 (4.67,9.87) 46652 (23424,62790) 8.03 (4.06,10.8) 4237 (3679,4788) 4.61 (4.02,5.2) DALYs 11180599 (7172266,15360042) 962.04 (614.3,1322.94) 374999 (307491,424327) 344.78 (279.67,392.25) 10254281 (6526703,13707205) 652.5 (414.83,870.95) 4133016 (2062756,5556044) 708.51 (354.57,951.89) 385254 (335781,435341) 417.57 (363.7,471.6) YLDs 151290 (84912,237579) 12.47 (7.09,19.59) 29887 (16491,46781) 20.16 (11.39,31) 160851 (96884,243971) 11.82 (6.91,18.08) 63581 (36995,98438) 11.86 (6.77,18.43) 14893 (8617,23209) 18.34 (10.38,28.76) YLL 11029309 (7015899,15231588) 949.57 (601.34,1312.37) 345112 (280554,387833) 324.62 (262.04,367.18) 10093429 (6355779,13514636) 640.68 (402.22,856.32) 4069435 (2010586,5488366) 696.65 (344.87,939.83) 370361 (320496,419157) 399.23 (346.3,451.31) Prevalence 2297423 (2016404,2547966) 189.43 (166.35,210.34) 508797 (451039,564854) 317.65 (284.45,351.28) 2320460 (2040136,2595536) 179.43 (159.06,199.59) 917081 (805694,1019274) 177.6 (156.19,197.24) 230075 (201956,258401) 294.09 (258.24,329.31) Incidence 452156 (348091,599753) 39.42 (30.35,52.29) 28831 (23823,35778) 30.36 (25.09,37.68) 629918 (493661,816392) 38.21 (29.95,49.52) 209335 (163445,272853) 35.32 (27.58,46.04) 45189 (36153,56330) 47.88 (38.31,59.69) 2021 Deaths 20838 (16840,25797) 2.71 (2.15,3.45) 760 (618,964) 0.79 (0.62,1.04) 57260 (41723,78497) 3.63 (2.62,5.02) 23227 (19107,28530) 4.09 (3.36,5.04) 4629 (3673,5683) 4.68 (3.72,5.74) DALYs 1782703 (1440585,2196783) 241.17 (191.48,305.04) 76963 (61679,96224) 82.88 (68.11,103.25) 5102106 (3703401,7010944) 324.04 (234.07,448.15) 2053890 (1683426,2518299) 364.01 (298.07,447.05) 418325 (332291,513640) 423.52 (336.81,519.76) YLDs 164415 (92153,253356) 14.41 (8.46,21.84) 27237 (14235,43355) 20.21 (11.44,30.74) 225720 (131421,342403) 12.85 (7.58,19.44) 77762 (42889,120893) 12.13 (6.84,18.83) 19299 (10759,30014) 19.85 (11.07,30.88) YLLs 1618288 (1289564,2025532) 226.76 (176.37,291.34) 49726 (39632,64660) 62.67 (48.79,83.37) 4876386 (3513945,6805460) 311.19 (222.88,436.98) 1976127 (1622750,2436158) 351.88 (288.23,435.18) 399025 (314011,493138) 403.67 (317.88,498.44) Prevalence 2424302 (2134904,2699200) 195.8 (173.55,216.6) 502672 (442568,560253) 312.02 (280.79,343.6) 3330810 (2964168,3697815) 187.82 (167.12,209.07) 1185575 (1041838,1313767) 179.93 (158.29,199.55) 296863 (262771,330687) 306 (271.06,340.42) Incidence 179635 (136692,242919) 32.56 (24.78,44.03) 15941 (13114,19793) 27.9 (22.95,34.64) 597007 (469727,777530) 39.43 (31.03,51.36) 182739 (141503,238879) 33.89 (26.24,44.3) 49458 (39873,61684) 50.36 (40.6,62.81) Table 1 . Data in parentheses represent the 95% uncertainty intervals In 2021, research on CHD in East Asia, high-income Asia Pacific, South Asia, Southeast Asia, and Central Asia revealed that South Asia had the highest number of deaths, at 57,260 (95% UI: 41,723 − 78,497), with an age-standardized mortality rate (ASMR) of 3.63 (95% UI: 2.62–5.02). Southeast Asia had the second-highest number of deaths, at 23,227 (95% UI: 19,107 − 28,530), but its ASMR was higher at 4.09 (95% UI: 3.36–5.04). Despite having a lower ASMR compared to Southeast Asia, South Asia's large population base resulted in a higher number of death cases. East Asia ranked third in terms of death cases, with 20,838 (95% UI: 16,840 − 25,797) and an ASMR of 2.71 (95% UI: 2.15–3.45). In contrast, both Central Asia and the high-income Asia Pacific region saw a significant reduction in death cases (Table 1 ). From 1990 to 2021, there was a marked decline in both the number of death cases and the ASMR for CHD in East Asia, the high-income Asia Pacific region, South Asia, and Southeast Asia. Specifically, East Asia saw a reduction of approximately 84.5% in death cases, from 127,882 to 20,838, and a decrease of about 75.3% in ASMR, from 10.98 to 2.71. The high-income Asia Pacific region experienced a decrease of around 82.3% in death cases, from 4,302 to 760, and a reduction of about 79.5% in ASMR, from 3.86 to 0.79. South Asia witnessed a decline of approximately 50.4% in death cases, from 115,427 to 57,260, and a decrease of about 50.9% in ASMR, from 7.41 to 3.63. Southeast Asia had a reduction of around 49.8% in death cases, from 46,652 to 23,227, and a decrease of about 49.1% in ASMR, from 8.03 to 4.09(Table 1 ). However, Central Asia showed a different trend. The number of death cases increased by about 9.2%, from 4,237 to 4,629, and the ASMR rose by approximately 1.5%, from 4.61 to 4.68(Table 1 ). In 2021, the five Asian regions showed significant differences in disability-adjusted life years(DALYs) for CHD. South Asia had the highest DALYs at 5,102,106 (95% UI: 3,703,401-7,010,944), with an age-standardized DALYs rate of 324.04 (95% UI: 234.07-448.15). Southeast Asia had the second-highest DALYs at 2,053,890 (95% UI: 1,683,426-2,518,299), and the highest age-standardized DALYs rate of 364.01 (95% UI: 298.07-447.05). East Asia had the third-highest DALYs at 1,782,703 (95% UI: 1,440,585-2,196,783), with a relatively low age-standardized DALYs rate of 241.17 (95% UI: 191.48-305.04). The high-income Asia Pacific region had the lowest DALYs and age-standardized DALYs rate, with 76,963 DALYs (95% UI: 61,679 − 96,224) and a rate of 82.88 (95% UI: 68.11-103.25). Central Asia had the fourth-highest DALYs at 418,325 (95% UI: 332,291–513,640), with a high age-standardized DALYs rate of 423.52 (95% UI: 336.81-519.76), (Table 1 ). In the High-income Asia Pacific region, the age-standardized prevalence rate (ASPR) of CHD saw a modest decline. Specifically, the ASPR decreased from 317.65 per 100,000 in 1990 (UI95%: 284.45–351.28) to 312.02 per 100,000 in 2021 (95% UI: 280.79–343.6), representing a reduction of 1.77%. Conversely, the remaining four regions experienced increases in their respective ASPR. Notably, East Asia witnessed a 3.36% rise, South Asia saw a 4.68% increase, Central Asia had a 4.05% climb, and Southeast Asia registered a 1.31% upturn (Table 1 ). Between 1990 and 2021, the age-standardized incidence rates (ASIR) of CHD exhibited diverse trajectories across five Asian regions. In East Asia, the ASIR experienced the most substantial decline, plummeting from 39.42 (95%UI: 30.35, 52.29) per 100,000 to 32.56 (95%UI: 24.78, 44.03) per 100,000, a reduction of approximately 17.35%. The High-income Asia Pacific region witnessed a moderate decline, with the ASIR dropping from 30.36 (95%UI: 25.09, 37.68) per 100,000 to 27.9 (95%UI: 22.95, 34.64) per 100,000, representing a decrease of about 7.44%. In Southeast Asia, the ASIR experienced a slight decline, falling from 35.32 (95%UI: 27.58, 46.04) per 100,000 to 33.89 (95%UI: 26.24, 44.3) per 100,000, a decline of approximately 3.48%. Conversely, the ASIR in Central Asia and South Asia witnessed an increase, rising by about 5.18% and 3.19%, respectively (Table 1 ). 3.1.2. Temporal joinpoint analysis in regional level Between 1990 and 2021, East Asia and the High-income Asia Pacific region have demonstrated a notable decrease in ASMR, ASIR and DALYs for congenital heart disease. This trend likely signifies advancements in medical technology and the effectiveness of public health initiatives within these regions. The marked reduction in ASMR and DALYs in East Asia. Conversely, while South Asia, Southeast Asia, and Central Asia have also experienced a downward trend in mortality rates and DALYs, the improvements are less significant(Fig. 1 and supplementary S1). Furthermore, the trajectories of YLDs and YLLs underscore the varying degrees of challenge that different regions encounter in disease management and health promotion efforts. The declining YLDs and YLLs in East Asia suggest successful strides in mitigating the disease's impact on quality of life and in prolonging healthy lifespans. In contrast, the rise in YLDs and YLLs observed in South Asia, Southeast Asia, and Central Asia could signal shortcomings in the realms of disease prevention, treatment, and rehabilitation. These regions require more intensive public health interventions and a more substantial allocation of resources to enhance these critical health indicators. In sum, Central Asia continues to grapple with a relatively higher burden of CHD, indicating that it confronts considerable obstacles in its efforts to manage and control the condition (Fig. 1 and supplementary S1). Table 2 AAPC analysis of the congenital heart disease burden temporal trends in 5 regions (East Asia, High-income Asia, South Asia, Southeast Asia, Central Asia), 1990–2021 East Asia High-income Asia South Asia Southeast Asia Central Asia Deaths -4.49(-4.71, -4.27) -4.91(-5.36, -4.45) -2.36(-2.57, -2.14) -2.18(-2.27, -2.1) 0.03*(-0.43, 0.49) Incidence -0.61(-0.72, -0.51) -0.28(-0.39, -0.179) 0.11(0.05, 0.16) -0.13(-0.16, -0.10) 0.15(0.09, 0.21) Prevalence 0.11(0.10, 0.11) -0.06(-0.07, -0.04) 0.14(0.09, 0.20) 0.04(0.04, 0.04) 0.12(0.10, 0.14) DALYs -4.45 (-4.67, -4.23) -4.49 (-4.96, -4.01) -2.30 (-2.51, -2.10) -2.16(-2.25, -2.07) 0.03* (-0.39,0.45) YLDS 0.46(0.44, 0.48) 0*(-0.01, 0.02) 0.28(0.24, 0.31) 0.07(0.06, 0.08) 0.26(0.23, 0.28) YLLs -4.60(-4.83, -4.37) -5.16(-5.74, -4.57) -2.38(-2.59, -2.16) -2.21(-2.30, -2.12) 0.02*(-0.41, 0.45) Table 2 . Data in parentheses estimate with a 95% CI, * mark the p value of AAPC > 0.05 which represent no significant difference between 1990 and 2021 In East Asia, the ASIR of congenital heart disease experienced a notable rise from 1990 to 1993, with an APC of 1.62% (95% CI: 1.33–1.91%; p < 0.001) (supplementary Table S1 ). This period of increase was followed by a relatively stable phase from 1993 to 1996. Subsequently, the ASIR embarked on a downward trajectory from 1996 to 2021. From 1990 to 2021, the overall AAPC amounted to -0.61% (95% CI: -0.72% to -0.51%; p < 0.001), indicating a downward trend (Fig. 1 A and Table 2 ). Conversely, the overall AAPC in South Asia exhibited an upward trend during the same period (AAPC = 0.11%; 95% CI: 0.05% -0.16%; P < 0.001), (Table 2 ). However, Central Asia did not display a declining tendency from 1990 to 2021 (AAPC = 0.15%; 95% CI: 0.09–0.21%; P < 0.001), (Table 2 ). Between 1990 and 2021, East Asia, South Asia, Southeast Asia, and the high-income Asia Pacific region exhibited a consistent downward trend in ASMR. Specifically, the overall AAPC was − 4.49% in East Asia (95% CI: -4.71% to -4.27%; p < 0.001), -2.36% in South Asia (95% CI: -2.57% to -2.14%; p < 0.001), -2.18% in Southeast Asia (95% CI: -2.27% to -2.10%; p < 0.001), and − 4.91% in the high-income Asia Pacific region (95% CI: -5.36% to -4.45%; p < 0.001),(Table 2 ). Conversely, Central Asia did not demonstrate a significant decline during this period. However, Central Asia did not exhibit an overall change from 1990 to 2021 (AAPC = 0.03%; 95% CI:-0.43–0.49%; P = 0.904), primarily due to a significant increase from 1997 to 2002 (APC = 5.15%; 95% CI: 4.04–6.26%; P < 0.001), (Fig. 1 C),and from 2014 to 2018 (APC = 4.56%; 95% CI: 2.13–7.04%; P = 0.001) (supplementary Table S1 ). The trends in DALYs and YLLs in five regions closely mirror the trends observed in ASMR(Fig. 1 ). 3.1.3. Regional temporal trends in gender and age structures The prevalence of CHD has predominantly been higher among the male population (Fig. 2 ), with the exception of countries within the high-income Asia region and South Asia (Fig. 2 B and C). Over the observed timeframe, mortality number associated with CHD in five regions has also exhibited a preponderance in males compared to females (Fig. 2 ). In Southeast Asia, although both the ASMR and the number of deaths due to CHD have declined in both males and females, recent trends indicate a convergence in mortality figures between the two sexes (Fig. 2 D).Apart from Central Asia, the other four regions also reveal that male patients exceed female patients in terms of DALYs and YLLs, aligning with the trend observed in mortality figures(supplementary Fig. S1 ). 3.2. Country level 3.2.1. Country burden and trends Table 3 Thirty-four countries and territories of the Congenital heart disease burden in 2021 Countries and Territories Deaths ASMR Prevalence ASPR Incidence ASIR DALYs YLDs YLLs Armenia 64 (50,82) 3.36 (2.6,4.32) 10428 (9166,11646) 393.3 (346.56,438.42) 794 (622,1046) 47.88 (37.49,63.11) 311.25 (243.22,398.16) 25.66 (14.51,39.54) 285.59 (218.17,370.01) Azerbaijan 307 (192,476) 4.43 (2.76,6.99) 27577 (24360,30860) 294 (259.45,329.91) 3332 (2622,4208) 51.73 (40.7,65.32) 405.83 (259.87,635.46) 17.99 (10.07,28.49) 387.84 (240.27,615.72) Bangladesh 4722 (2269,8333) 3.28 (1.57,5.91) 287803 (254349,322201) 181.65 (160.5,203.57) 48676 (37251,64651) 36.27 (27.76,48.17) 291.23 (142.49,516.44) 12.32 (7.07,19.07) 278.91 (132.5,504.46) Bhutan 22 (11,36) 3.53 (1.71,5.82) 1282 (1139,1426) 180.49 (160.06,201.62) 213 (165,284) 35.56 (27.61,47.46) 316.49 (159.03,523.05) 12.42 (7.13,19.15) 304.07 (146.58,510.3) Brunei Darussalam 8 (7,11) 2.56 (1.96,3.21) 1033 (909,1159) 248.24 (220.38,278.25) 94 (78,117) 31.95 (26.35,39.67) 230.04 (177.13,287.47) 15.11 (8.39,23.68) 214.93 (163.44,273.21) Cambodia 1418 (990,1922) 8.19 (5.69,11.11) 31252 (27712,35107) 179.45 (159.26,201.28) 6927 (5443,8820) 40.47 (31.8,51.53) 713.9 (499.85,963.1) 12.49 (7.23,19.53) 701.41 (489.12,949.28) China 20071 (16216,24857) 2.72 (2.14,3.44) 2344480 (2065383,2611300) 196.03 (173.73,216.93) 173609 (132165,234712) 32.76 (24.94,44.28) 241.68 (191.38,304.84) 14.44 (8.47,21.89) 227.24 (175.89,290.32) Democratic People’s Republic of Korea 612 (411,906) 3.42 (2.26,5.12) 42386 (37032,47288) 185.55 (164.22,206.58) 4382 (3243,5929) 31.03 (22.97,41.98) 291.98 (195.53,438.23) 13.12 (7.43,20.09) 278.86 (182.31,424.12) Georgia 63 (48,79) 2.59 (1.95,3.3) 8478 (7603,9318) 270.6 (241.36,297.46) 747 (614,927) 34.76 (28.59,43.18) 238.31 (181.92,299.56) 19.82 (11.52,29.58) 218.49 (162.57,281.32) India 39983 (30039,54628) 3.6 (2.66,4.99) 2504266 (2229279,2767961) 189.41 (168.83,209.79) 413505 (325155,537339) 39.4 (30.98,51.2) 321.95 (239.51,447.86) 13.27 (7.96,19.88) 308.68 (225.47,436.2) Indonesia 7799 (5547,10516) 3.52 (2.49,4.75) 458757 (402699,511759) 175.35 (154.16,195.69) 77604 (60259,101204) 36.64 (28.45,47.78) 317.12 (227.69,423.92) 11.34 (6.27,17.68) 305.78 (215.78,414.88) Japan 568 (438,710) 0.83 (0.6,1.08) 377172 (331413,421293) 336.98 (302.1,371.45) 12186 (9958,15228) 29.47 (24.08,36.83) 85.99 (65.86,107.35) 21.37 (12.02,32.95) 64.62 (45.79,86) Kazakhstan 749 (609,920) 3.8 (3.09,4.67) 58462 (51592,65615) 307.03 (271.06,344.38) 8887 (7037,11316) 45.29 (35.86,57.66) 336.23 (273.61,408.94) 20.38 (11.48,31.23) 315.85 (254.87,390.75) Kyrgyzstan 295 (241,355) 3.91 (3.19,4.7) 22310 (19822,24841) 306.36 (271.61,340.96) 3566 (2791,4540) 47.46 (37.15,60.44) 355.11 (289.76,425.62) 20.83 (12.06,31.63) 334.29 (269.94,405.26) Lao People’s Democratic Republic 803 (509,1151) 9.65 (6.13,13.81) 13951 (12226,15599) 178.25 (156.54,198.97) 3735 (2957,4771) 44.33 (35.1,56.63) 845.73 (535.03,1211.07) 11.73 (6.52,18.56) 834.01 (522.56,1198.58) Malaysia 450 (365,555) 1.71 (1.37,2.15) 58106 (51139,64910) 192.46 (169.94,214.99) 5292 (3805,7342) 23.06 (16.58,31.99) 153.03 (121.21,192.36) 14.25 (8.29,21.34) 138.78 (108.8,176.2) Maldives 8 (6,11) 2.5 (1.84,3.45) 822 (722,922) 175.78 (155.45,196.16) 79 (58,109) 27.28 (20.14,37.85) 228.45 (170.49,313.23) 12.47 (7.13,19.37) 215.98 (157.73,300.35) Mauritius 28 (24,37) 3.75 (3.08,4.93) 2128 (1873,2382) 195.23 (172.94,217.46) 137 (99,188) 22.52 (16.29,30.8) 328.05 (269.85,427.63) 14.59 (8.43,22.19) 313.46 (255.23,414.33) Myanmar 5234 (3243,7267) 10 (6.16,13.91) 96707 (85649,108614) 174.86 (154.76,196.15) 21283 (16623,27447) 41.38 (32.32,53.36) 880.79 (545.06,1226.29) 11.41 (6.24,18.23) 869.39 (529.43,1213.78) Mongolia 116 (81,151) 3.16 (2.21,4.13) 10826 (9509,12118) 304.45 (268.8,340.52) 2151 (1728,2713) 58.67 (47.14,73.99) 286.17 (206.28,369.31) 19.25 (10.77,30.05) 266.91 (185.44,350.52) Nepal 580 (317,1257) 1.86 (1.02,4.04) 51255 (45564,57207) 162.88 (144.75,182.01) 10570 (8111,14108) 34.32 (26.33,45.8) 169.69 (96,358.14) 10.62 (5.95,16.35) 159.07 (85.79,345.9) Pakistan 11953 (7226,17842) 4.09 (2.48,6.09) 486204 (425422,544812) 185.36 (163.08,207.22) 124043 (97864,160140) 41.52 (32.76,53.6) 364.84 (225.32,534.24) 11.51 (6.44,18.07) 353.33 (212.99,524.54) Philippines 4833 (3910,6158) 4.29 (3.45,5.5) 208585 (183956,231485) 181.51 (160.13,201.3) 37160 (28598,49085) 34.06 (26.22,45) 374.05 (301.48,480.51) 12.36 (7.05,19.08) 361.69 (288.47,470.49) Republic of Korea 162 (127,231) 0.71 (0.53,1.03) 113149 (99469,128128) 265.32 (236.22,294.99) 3186 (2596,3943) 24.8 (20.21,30.69) 76.09 (59.63,106.49) 17.97 (10.21,27.73) 58.12 (42.8,84.66) Seychelles 3 (3,4) 4.07 (3.09,5.24) 190 (168,212) 196.13 (173.16,217.82) 18 (13,26) 24.33 (17.68,33.86) 357.11 (270.64,458.72) 14.97 (8.96,23.03) 342.14 (256.44,444.6) Singapore 22 (15,29) 0.59 (0.39,0.84) 11318 (9979,12611) 231.82 (205.7,257.46) 475 (388,601) 17.96 (14.65,22.7) 63.94 (47.09,83.08) 17.35 (10.51,26) 46.6 (30.69,67.71) Sri Lanka 459 (332,631) 2.7 (1.95,3.71) 39273 (34614,43871) 192.84 (169.9,215.68) 3344 (2446,4674) 23.08 (16.88,32.26) 235.52 (172.66,324.25) 14.81 (8.72,22.52) 220.71 (159.24,306.28) Taiwan (Province of China) 155 (135,172) 1.33 (1.12,1.52) 37436 (33338,41319) 190.49 (170.85,209.27) 1645 (1158,2364) 21.72 (15.29,31.23) 122.21 (102.26,139.32) 13.56 (7.71,20.84) 108.64 (90.54,126.32) Thailand 724 (558,901) 2.18 (1.58,2.7) 101051 (89093,113524) 186.76 (165.35,209.39) 6800 (5102,9464) 26.37 (19.78,36.7) 197.21 (144.15,245.88) 13.96 (8.02,21.01) 183.24 (130.26,228.91) Timor-Leste 132 (96,185) 7.04 (5.22,9.75) 2752 (2433,3106) 172.91 (152.48,194.49) 772 (611,1006) 39.41 (31.22,51.38) 617.7 (446.77,859.1) 11.28 (6.21,17.97) 606.42 (436.55,848.39) Tajikistan 384 (225,797) 2.87 (1.69,5.93) 34235 (29755,38395) 299.01 (261.22,335.13) 7849 (6223,9768) 57.73 (45.77,71.84) 270.09 (164.37,544.42) 18.33 (9.89,28.63) 251.77 (147.28,523.72) Turkmenistan 341 (248,445) 6.4 (4.65,8.35) 15778 (13828,17686) 300.07 (263.28,335.78) 2731 (2158,3455) 51.6 (40.77,65.27) 570.58 (413.9,740.07) 18.66 (10.34,29.68) 551.92 (396.56,723.5) Uzbekistan 2311 (1681,3008) 6.07 (4.43,7.89) 108768 (96113,122017) 306.05 (270.65,342.98) 19401 (15302,24301) 50.69 (39.98,63.49) 548.53 (400.19,704.41) 20.1 (11.04,31.21) 528.42 (382.86,689.35) Viet Nam 1302 (857,1907) 1.63 (1.07,2.41) 170347 (149870,190138) 178.6 (157.46,199.6) 19333 (14420,27118) 25.96 (19.36,36.41) 151.31 (104.44,215.92) 12.18 (6.7,18.74) 139.13 (91.3,206.03) Table 3 . Data in parentheses represent the 95% uncertainty intervals In 2021, among the 34 countries and regions assessed for the burden of CHD(Fig. 3 ). For ASIR, Mongolia had the highest rate, quantified at 58.67 (95% UI: 47.14–73.99), followed by Tajikistan at 57.73 (95% UI: 45.77–71.84) and Azerbaijan at 51.73 (95% UI: 40.7–65.32), (Fig. 3 A). Singapore had the lowest ASIR, with a value of 17.96 (95% UI: 14.65–22.7), (Table 3 ).Myanmar had the highest ASMR, quantified at 10 (95% UI: 6.16–13.91), followed by the Lao People’s Democratic Republic at 9.65 (95% UI: 6.13–13.81) and Cambodia at 8.19 (95% UI: 5.69–11.11),(Fig. 3 B). In stark contrast, Japan exhibited the lowest ASMR, with a value of 0.83 (95% UI: 0.6–1.08), (Table 3 ).When it came to ASPR, Armenia led the list with a value of 393.3 (95% UI: 346.56–438.42), followed by Japan at 336.98 (95% UI: 302.1–371.45) and Kazakhstan at 307.03 (95% UI: 271.06–344.38),(Fig. 3 C). Nepal had the lowest ASPR, quantified at 162.88 (95% UI: 144.75–182.01),(Table 3 ).In terms of DALYs, Myanmar had the highest burden, quantified at 880.79 (95% UI: 545.06–1226.29), followed by the Lao People’s Democratic Republic at 845.73 (95% UI: 535.03–1211.07) and Cambodia at 713.9 (95% UI: 499.85–963.1),(Fig. 3 D). Singapore had the lowest DALYs, with a value of 63.94 (95% UI: 47.09–83.08),(Table 3 ).Regarding YLDs, Armenia had the highest rate, quantified at 25.66 (95% UI: 14.51–39.54), followed by Japan at 21.37 (95% UI: 12.02–32.95) and Kyrgyzstan at 20.83 (95% UI: 12.06–31.63). Nepal had the lowest YLDs, with a value of 10.62 (95% UI: 5.95–16.35), (Table 3 ). Lastly, for YLLs, Myanmar had the highest value, quantified at 869.39 (95% UI: 529.43–1213.78), followed by the Lao People’s Democratic Republic at 834.01 (95% UI: 522.56–1198.58) and Cambodia at 701.41 (95% UI: 489.12–949.28). Singapore had the lowest YLLs, with a value of 46.6 (95% UI: 30.69–67.71), (Table 3 ). 3.2.2. Temporal joinpoint analysis in country level From1990 to 2021, a comparative analysis showed that the ASMR generally decreased in most countries and territories. However, Uzbekistan was an exception, with an increasing ASMR trend (AAPC = 1.61%; 95% CI: 0.46–2.77%; P = 0.06) (supplementary Table S2). This pattern was also observed in the age-standardized YLLs rate (AAPC = 1.61%; 95% CI: 0.44–2.8%; P = 0.007) (supplementary Table S3).A similar upward trend was also observed in the DALYs in Uzbekistan (AAPC = 1.57%; 95% CI: 0.49–2.65%; P = 0.004) (supplementary Table S4).The ASPR has been on the rise in most countries, while the declines were observed in Lao People’s Democratic Republic(AAPC = -0.02%; 95% CI: -0.03% to -0.01%; P = 0.002), the Republic of Korea(AAPC = -0.03%; 95% CI: -0.04% to -0.03%; P < 0.001), and Singapore(AAPC = -0.03%; 95% CI: -0.04% to -0.01%; P < 0.001) (supplementary Table S5).The YLDs (Years Lived with Disability) have shown an increasing trend in the majority of countries as well. However, declines were observed in the Republic of Korea (AAPC = -0.12%; 95% CI: -0.16% to -0.09%; P < 0.001) and Taiwan (Province of China) (AAPC = -0.09%; 95% CI: -0.12% to -0.06%; P < 0.001), (supplementary Table S6). Between 1990 and 2021, we have conducted a comprehensive analysis of data from mainland China. The results show that the ASPR has been increasing (AAPC = 0.11%; 95% CI: 0.1% -0.11%; P < 0.001), (supplementary Table S5), as have the YLDs (AAPC = 0.47%; 95% CI: 0.45%-0.5%; P < 0.001), (supplementary Table S6). However, other indicators, including ASIR, ASMR, DALYs, and YLLs, have all shown a downward trend during this period. In China's Taiwan region, the ASPR has also increased (AAPC = 0.017%; 95% CI: 0.012–0.02%; P < 0.001), (supplementary Table S5), but the other indicators have decreased. When examining the ASMR and ASPR, it is evident that the proportion of males remains relatively high. This is also reflected in the under five years mortality rate, which is consistent with the regional analysis, primarily due to the higher proportion of male (Fig. 4 , supplementary S3and S4). 3.3. Variation in CHD burden by SDI In Asia, the ASMR is correlated with the SDI across 34 countries in five regions. In most countries, the ASMR decreases as the SDI value increases. Higher mortality rates are observed in countries with low and low–middle SDI, such as Myanmar (10), Lao People’s Democratic Republic (9.65), and Cambodia (8.19). Conversely, mortality rates are significantly lower in countries with high SDI. 4. Discussion Congenital Heart Disease (CHD) is one of the most common congenital disorders globally and accounts for a substantial proportion of the disease burden affecting both pediatric and adult populations [ 13 ] . In addition to impairing the quality of life and survival rates of affected individuals, CHD imposes a considerable economic and psychological burden on families and society. This study provides valuable insights by analyzing data from 34 countries across five subregions in Asia. It clarifies the trends in the CHD disease burden from 1990 to 2021 and investigates its association with the Socio-Demographic Index (SDI). The findings serve as critical evidence for informing the development of targeted public health policies and interventions [ 14 ] . The findings indicate a significant decline in mortality rates and case numbers in Asia from 1990 to 2021. Additionally, there has been a notable reduction in YLLs and DALYs. China, as one of the countries in East Asia, bears a substantial burden of CHD due to its large patient population. However, the study demonstrates that the ASIR, ASMR, DALYs, and YLLs have all exhibited a downward trend. This is attributed to advancements in medical technology and the Chinese government's prioritization of CHD management [ 15 ] . Similarly, in the Southeast Asia region, the ASMR has shown a decreasing trend from 1990 to 2021, with Vietnam serving as an example. The Vietnamese government's emphasis on CHD, along with the establishment of the Alain Carpentier Foundation, has played a crucial role. The foundation has provided surgical and treatment services to underprivileged patients [ 16 ] . However, Central Asia stands out with contrasting data, indicating that this region confronts considerable obstacles in the prevention and treatment of CHD. In 2021, Central Asia exhibited significantly higher ASMR, ASIR, age-standardized YLLs rates, and age-standardized DALYs rates compared to the other four Asian regions under study. This disparity may be associated with a variety of factors. Many low-income and middle-income countries struggle to provide the health services investment required for life-saving CHD surgery [ 17 ] . For instance, in terms of socioeconomic factors, the level of economic development in Central Asia is relatively low, with limited medical resources and weak public health infrastructure, which results in insufficient capacity for the screening, diagnosis, and treatment of CHD. The study further demonstrated that in 2021, within the five Asian subregions, the ASMR and ASIR for children under five years of age were at their highest levels. This underscores that CHD in children continues to be a pressing public health concern warranting dedicated attention. The impact of CHD on children extends beyond individual health and survival, contributing to significant long-term medical burdens and broader socioeconomic consequences. Consequently, enhancing screening, diagnosis, and treatment for pediatric CHD [ 18 ] , coupled with increased public awareness of children's heart health, is essential to effectively reduce mortality rates associated with this condition. Among all age groups, the ASPR and ASMR of CHD in males are generally higher than those in females. This characteristic has also been observed in other studies [ 19 ] . From 1990 to 2021, the ASMR due to CHD in high SDI countries has significantly decreased. This demonstrates that improvements in socioeconomic development and advancements in medical technology have played a positive role in the prevention and control of CHD. High SDI countries typically have better medical resources, more robust public health systems, and higher levels of health awareness, all of which contribute to the early diagnosis and effective treatment of CHD. The most majority of children with CHD in high-income countries survive into adulthood. Further, pediatric cardiac services have expanded in middle-income countries. Both evolutions have resulted in an increasing number of CHD survivors [ 20 ] . However, in low and low-middle SDI regions, such as countries like Myanmar, Laos, and Cambodia, the ASMR remain high and require serious attention as well as increased investment in health [ 21 , 22 ] . In summary, although progress has been made in the prevention and control of CHD in the Asian region, regional disparities and the high burden in specific populations still require serious attention [ 23 ] . Through comprehensive public health strategies and targeted interventions, it is hoped that the disease burden of CHD can be further reduced and the health level of the population improved. Declarations Ethics approval and consent to participate Due to the public nature of the GBD database, this study was granted an ethical exemption. Consent for publication Not applicable. Availability of data and material Publicly available datasets were analyzed in this study. The data can be found here: http://ghdx.healthdata.org/gbd-results-tool . Funding This study was sponsored by Fujian provincial health technology project(grant No.2024CXA036), Joint Funds for the Innovation of Science and Technology, Fujian Province (grant No. 2020Y9159), Joint Funds for the Innovation of Science and Technology, Fujian Province(grant No. 2024Y9583), Key Project on the Integration of Industry, Education and Research Collaborative Innovation of Fujian Province (grant No. 2021YZ034011), Key Project on Science and Technology Program of Fujian Health Commission (grant No. 2021ZD01002). Author contributions: H.Z. ,S.C., and Y.Q.conceived the idea, provided critical data curation and analysis, validated the findings, and drafted the initial manuscript. N.L., M.C., and Y.D. orchestrated the study’s design and coordination, directed the study, and examined and reviewed the manuscript. All authors contributed to the article and approved the submitted version. 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Cardiac surgical missions: what works, what does not, where we need to go from here [J]. Curr Opin Cardiol, 2020, 35(1): 76-9. MUHARRAM F R, MULTAZAM C, MUSTOFA A, et al. The 30 Years of Shifting in The Indonesian Cardiovascular Burden-Analysis of The Global Burden of Disease Study [J]. J Epidemiol Glob Health, 2024, 14(1): 193-212. WEBB G, MULDER B J, ABOULHOSN J, et al. The care of adults with congenital heart disease across the globe: Current assessment and future perspective: A position statement from the International Society for Adult Congenital Heart Disease (ISACHD) [J]. Int J Cardiol, 2015, 195: 326-33. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.zip 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-7026873","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501293931,"identity":"68e1101e-b166-4f77-a646-ff30705819ed","order_by":0,"name":"Haimei Zhang","email":"","orcid":"","institution":"College of Clinical Medicine for Obstetrics \u0026 Gynecology and Pediatrics, Medical Genetic Diagnosis and Therapy Center of Fujian Maternity and Child Health Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haimei","middleName":"","lastName":"Zhang","suffix":""},{"id":501293932,"identity":"86276b1a-ee86-41e7-b01e-e245b81a025c","order_by":1,"name":"Siqi Cao","email":"","orcid":"","institution":"College of Clinical Medicine for Obstetrics \u0026 Gynecology and Pediatrics, Medical Genetic Diagnosis and Therapy Center of Fujian Maternity and Child Health Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Cao","suffix":""},{"id":501293933,"identity":"3443eaf3-4cf2-42de-9841-f7a686a5fd2b","order_by":2,"name":"Yanting Que","email":"","orcid":"","institution":"Medical Genetic Diagnosis and Therapy Center of Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics \u0026 Gynecology and Pediatrics, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanting","middleName":"","lastName":"Que","suffix":""},{"id":501293934,"identity":"9c560cdc-e159-43da-9191-c22b14c9cea1","order_by":3,"name":"Yifang Dai","email":"","orcid":"","institution":"Medical Genetic Diagnosis and Therapy Center of Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics \u0026 Gynecology and Pediatrics, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yifang","middleName":"","lastName":"Dai","suffix":""},{"id":501293935,"identity":"247b70a1-8dc7-4c30-b7f0-2b3e06793430","order_by":4,"name":"Meiying Cai","email":"","orcid":"","institution":"Medical Genetic Diagnosis and Therapy Center of Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics \u0026 Gynecology and Pediatrics, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meiying","middleName":"","lastName":"Cai","suffix":""},{"id":501293936,"identity":"658bf6a0-f768-44f6-ba9e-f5393793ca14","order_by":5,"name":"Na Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACfobDxz8kVLDJ2bc3EKlFsvFYGsODM3zGBjwHiNRicPiMGuPDNrlEA4kEYm1pO8P2ILHNLMFc8vHGGww1NtEEtfDznD1ukHAuLc9ydlqxBcOxtNwGgrbMOJcgkVB2rJjhdo6ZBGPDYcJaDO6/AfqC7X9iw80zxGo5AFSZ0MaWuOEGD5FaJBuOJRsknGEzluwB+iWBGL8Ao/Lgwx/AqORnP7zxxocaG8JaUBxJdNQgaSFVxygYBaNgFIwMAADLUUbD/BU2KgAAAABJRU5ErkJggg==","orcid":"","institution":"Medical Genetic Diagnosis and Therapy Center of Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics \u0026 Gynecology and Pediatrics, Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Na","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-07-02 08:09:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7026873/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7026873/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89384860,"identity":"abedfc27-a2ce-4435-b036-1a702a188890","added_by":"auto","created_at":"2025-08-19 12:26:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1845606,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint regression analysis of the congenital heart disease burden temporal trends in 5 regions (East Asia, High-income Asia Pacific, South Asia, Southeast Asia，Central Asia ), 1990–2021.\u003c/p\u003e\n\u003cp\u003e(A) Age-standardized Incidence rates; (B) Age-standardized prevalence rates; (C)Age-standardized mortality rates; (D) Age-standardized YLDs rates; (E) Age-standardized YLLs rates; (F) Age-standardized DALYs rates\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7026873/v1/1ca5be7e23db4f28bb85561b.png"},{"id":89386208,"identity":"a24a1802-00a8-4625-ab20-eba4ff9c5648","added_by":"auto","created_at":"2025-08-19 12:34:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1686445,"visible":true,"origin":"","legend":"\u003cp\u003eSex- and age- structured analysis of the Congenital heart disease burden of ASPR;ASMR;Prevalence and Deaths in 2021.\u003c/p\u003e\n\u003cp\u003e(A) Central Asia; (B) South Asia; (C) High-income Asia Pacific; (D) Southeast Asia;(E) East Asia;(F) East Asia\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7026873/v1/98e041256ccc646aa8d8c3f7.png"},{"id":89384862,"identity":"32ed5e49-b6c3-4cbc-b13f-b4e1c6054fb9","added_by":"auto","created_at":"2025-08-19 12:26:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":886807,"visible":true,"origin":"","legend":"\u003cp\u003eThirty-four countries and territories’ distribution of congenital heart disease burden in 2021\u003c/p\u003e\n\u003cp\u003e(A) Age-standardized Incidence rates; (B) Age-standardized mortality rates; (C) Age-standardized prevalence rates; (D) Age-standardized DALYs rates\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7026873/v1/c9f007ea0e21ea2e6c81e04c.png"},{"id":89384861,"identity":"eced633f-75d8-4ac7-8cc0-3f6158746586","added_by":"auto","created_at":"2025-08-19 12:26:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":852627,"visible":true,"origin":"","legend":"\u003cp\u003eSex- and age-structured analysis of the congenital heart disease burden in China, focusing on Prevalence and Deaths in 2021.\u003c/p\u003e\n\u003cp\u003e(A) The number of Prevalence; (B) The Rates of Prevalence; (C) The number of Deaths; (D) The Rates of Deaths\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7026873/v1/2924a9c6266cec29f5a3c9fe.png"},{"id":89386209,"identity":"96fafe77-bc22-4f50-aed2-9efae02f38bd","added_by":"auto","created_at":"2025-08-19 12:34:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":276455,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual change of socio-demographic index with the ASMR in thirty-four countries.\u003c/p\u003e\n\u003cp\u003eSouth Korea=Republic of Korea；North Korea=Democratic People's Republic of Korea；Laos=Lao People's Democratic Republic; For each country, points from left to right depict estimates from each year from 1990 to 2021.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7026873/v1/af916b187c4abc1d6f58be8d.png"},{"id":95653907,"identity":"728a410c-22ce-46d5-baab-fc1d39320d4b","added_by":"auto","created_at":"2025-11-11 16:05:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6547970,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7026873/v1/e751daea-f130-49a0-9e99-98eec4689d39.pdf"},{"id":89384867,"identity":"ed50470c-7073-4816-9de0-492807f2fe5c","added_by":"auto","created_at":"2025-08-19 12:26:56","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1677144,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.zip","url":"https://assets-eu.researchsquare.com/files/rs-7026873/v1/2c076dde6f663089acc03e67.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Burden of Congenital Heart Disease in Asia and 34 Countries and Territories from 1990 to 2021: A Systematic Analysis Based on the Global Burden of Disease Study 2021","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCongenital Heart Disease (CHD) refers to structural and functional abnormalities of the heart that arise during embryonic development and represents one of the most prevalent birth defects globally\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Although there have been substantial improvements in survival rates for CHD patients due to advancements in medical technology, it continues to pose a significant threat to child health, particularly in low- and middle-income countries\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. CHD not only severely compromises the physical health of patients but also imposes a considerable economic burden on families and society\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Therefore,early diagnosis and comprehensive care are crucial for reducing the incidence of CHD and improving patient outcomes.\u003c/p\u003e\u003cp\u003eIn recent years, driven by the continuous advancement of medical technology, the diagnosis and treatment of CHD have been markedly improved\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. For instance, the development of prenatal screening technologies has enabled the identification of many complex cardiac defects prior to birth, thereby facilitating early intervention. Moreover, minimally invasive surgical techniques and advanced interventional treatments have substantially enhanced both surgical success rates and patients\u0026rsquo;quality of life\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, while high-income countries have achieved significant progress in CHD management and treatment, low- and middle-income countries continue to encounter numerous challenges in diagnosing and treating CHD, such as constrained medical resources, a shortage of specialized medical personnel, and limited awareness of CHD\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe GBD study presents a comprehensive framework for assessing the global burden of diseases\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. This analysis employed CHD data from the GBD 2021, which is openly accessible to the public. It encompasses CHD related data across different age groups in the Asian region impacted by CHD from 1990 to 2021. The study detailed estimates of CHD incidence, mortality, and disability. These data not only help to understand the global epidemiological trends of CHD but also provide a scientific basis for formulating public health policies and resource allocation. This research aimed to explore the temporal, economic, and gender trends in the burden of congenital heart disease in 34 countries in the Asian region, thereby offering an evidence-based foundation for CHD prevention and treatment strategies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data acquisition and download\u003c/h2\u003e\u003cp\u003eThe 2021 Global Burden of Disease (GBD) study extensively assessed health damages related to 369 specific diseases, injuries, and disabilities, as well as 88 risk factors. It covered 204 countries and regions, using the latest epidemiological data and standardized methods\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Burden description\u003c/h2\u003e\u003cp\u003eWe have been chosen East Asia region includes China, Taiwan (Province of China) and Democratic People\u0026rsquo;s Republic of Korea. South Asia region includes Bangladesh, Bhutan, India, Nepal, Pakistan. Southeast Asia region includes Cambodia, Indonesia, Lao People\u0026rsquo;s Democratic Republic, Malaysia, Maldives, Mauritius, Myanmar, Philippines, Seychelles, Sri Lanka, Thailand, Timor-Leste, Viet Nam. High-income Asia-Pacific region includes Brunei Darussalam, Japan, Republic of Korea and Singapore. Central Asia region includes Armenia, Azerbaijan, Uzbekistan, Georgia, Kazakhstan, Mongolia, Kyrgyzstan, Tajikistan, Turkmenistan.\u003c/p\u003e\u003cp\u003eWe conducted a comprehensive assessment of CHD prevalence, incidence, mortality, years of life lost (YLL), years lived with disability (YLD), and disability-adjusted life years (DALY) for five Asian regions from 1990 to 2021, extracted from the GBD 2021 dataset. DALYs are calculated by summing two components: YLL from premature mortality and YLD associated with the health condition. The computational formulas were applied as follows: (1) YLL\u0026thinsp;=\u0026thinsp;Number of deaths \u0026times; Standard life expectancy at the age of death; (2) YLD\u0026thinsp;=\u0026thinsp;Prevalence of the condition \u0026times; Disability weight; Where disability weights, derived through systematic expert consensus in the GBD study framework, quantify health loss on a scale from 0 (representing optimal health) to 1 (equivalent to fatal outcomes).The Socio-demographic Index (SDI) \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003emeasures a country's or region's development level using indicators such as fertility rates, education levels, and per capita income. The SDI ranges from 0 to 1, with higher values indicating greater socioeconomic development\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Joinpoint regression analysis\u003c/h2\u003e\u003cp\u003eJoinpoint regression analysis, performed using the Joinpoint regression program version 5.3.0, is a widely used statistical method in epidemiological studies to assess changes in disease prevalence, incidence or mortality rates over time. This method effectively identified and quantified critical points of change in the time series data for CHD burden. It allowed for the calculation of the annual percent change (APC) along with its 95% confidence interval (CI), providing a clear depiction of disease burden trends within specific time segments. Additionally, the average annual percent change (AAPC) was computed to offer a comprehensive overview of the overall trend throughout the study period from 1990 to 2021\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eStatistically, the interpretation of APC and AAPC estimates is as follows: an APC or AAPC estimate with a 95% CI lower bound above zero indicates an increasing trend; an APC or AAPC estimate with a 95% CI upper bound below zero suggests a decreasing trend; and a 95% CI that includes zero for the APC or AAPC implies a stable trend\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Statistics analysis\u003c/h2\u003e\u003cp\u003eThe prevalence, incidence, mortality, YLLs, YLDs, and DALYs was represented as a projection for every 100,000 persons individuals in the populace, inclusive of its 95% UI. Their age-standard rate was used to eliminate the impact of differences in age structure among various populations on rates, thereby making data comparable across different time points or populations. All data analysis and visualizations were performed via R language programming (version 4.4.2, R Development Core Team). p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Regional level\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1. Regional burden and trends\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrevalent cases, incident cases, death cases, YLDs, YLL, DALYs for congenital heart disease in 1990 and 2021 for both sexes and their age-standardized rates in east Asia, high-income Asia Pacific, south Asia, Southeast Asia region, Central Asia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eCentral Asia\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCounts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge-standard adjust\u003c/p\u003e\u003cp\u003erate per 100,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCounts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAge-standard adjust\u003c/p\u003e\u003cp\u003erate per 100,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCounts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAge-standard adjust\u003c/p\u003e\u003cp\u003erate per 100,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCounts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAge-standard adjust\u003c/p\u003e\u003cp\u003erate per 100,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCounts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eAge-standard adjust\u003c/p\u003e\u003cp\u003erate per 100,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127882 (82117,176229)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.98 (7.01,15.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4302 (3538,4795)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.86 (3.14,4.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e115427 (72640,154224)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.41 (4.67,9.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e46652 (23424,62790)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.03 (4.06,10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4237 (3679,4788)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.61 (4.02,5.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11180599 (7172266,15360042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e962.04 (614.3,1322.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e374999 (307491,424327)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e344.78 (279.67,392.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10254281 (6526703,13707205)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e652.5 (414.83,870.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4133016 (2062756,5556044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e708.51 (354.57,951.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e385254 (335781,435341)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e417.57 (363.7,471.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYLDs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e151290 (84912,237579)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.47 (7.09,19.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29887 (16491,46781)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.16 (11.39,31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e160851 (96884,243971)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.82 (6.91,18.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e63581 (36995,98438)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11.86 (6.77,18.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14893 (8617,23209)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e18.34 (10.38,28.76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYLL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11029309 (7015899,15231588)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e949.57 (601.34,1312.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e345112 (280554,387833)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e324.62 (262.04,367.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10093429 (6355779,13514636)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e640.68 (402.22,856.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4069435 (2010586,5488366)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e696.65 (344.87,939.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e370361 (320496,419157)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e399.23 (346.3,451.31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevalence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2297423 (2016404,2547966)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e189.43 (166.35,210.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e508797 (451039,564854)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e317.65 (284.45,351.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2320460 (2040136,2595536)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e179.43 (159.06,199.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e917081 (805694,1019274)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e177.6 (156.19,197.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e230075 (201956,258401)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e294.09 (258.24,329.31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e452156 (348091,599753)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.42 (30.35,52.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28831 (23823,35778)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.36 (25.09,37.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e629918 (493661,816392)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38.21 (29.95,49.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e209335 (163445,272853)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e35.32 (27.58,46.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e45189 (36153,56330)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e47.88 (38.31,59.69)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20838 (16840,25797)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.71 (2.15,3.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e760 (618,964)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.79 (0.62,1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57260 (41723,78497)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.63 (2.62,5.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23227 (19107,28530)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.09 (3.36,5.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4629 (3673,5683)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.68 (3.72,5.74)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1782703 (1440585,2196783)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e241.17 (191.48,305.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76963 (61679,96224)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82.88 (68.11,103.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5102106 (3703401,7010944)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e324.04 (234.07,448.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2053890 (1683426,2518299)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e364.01 (298.07,447.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e418325 (332291,513640)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e423.52 (336.81,519.76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYLDs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164415 (92153,253356)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.41 (8.46,21.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27237 (14235,43355)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.21 (11.44,30.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e225720 (131421,342403)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.85 (7.58,19.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e77762 (42889,120893)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12.13 (6.84,18.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e19299 (10759,30014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e19.85 (11.07,30.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYLLs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1618288 (1289564,2025532)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e226.76 (176.37,291.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49726 (39632,64660)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62.67 (48.79,83.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4876386 (3513945,6805460)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e311.19 (222.88,436.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1976127 (1622750,2436158)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e351.88 (288.23,435.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e399025 (314011,493138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e403.67 (317.88,498.44)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevalence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2424302 (2134904,2699200)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e195.8 (173.55,216.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e502672 (442568,560253)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e312.02 (280.79,343.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3330810 (2964168,3697815)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e187.82 (167.12,209.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1185575 (1041838,1313767)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e179.93 (158.29,199.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e296863 (262771,330687)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e306 (271.06,340.42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e179635 (136692,242919)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.56 (24.78,44.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15941 (13114,19793)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.9 (22.95,34.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e597007 (469727,777530)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e39.43 (31.03,51.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e182739 (141503,238879)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e33.89 (26.24,44.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e49458 (39873,61684)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e50.36 (40.6,62.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Data in parentheses represent the 95% uncertainty intervals\u003c/p\u003e\u003cp\u003eIn 2021, research on CHD in East Asia, high-income Asia Pacific, South Asia, Southeast Asia, and Central Asia revealed that South Asia had the highest number of deaths, at 57,260 (95% UI: 41,723\u0026thinsp;\u0026minus;\u0026thinsp;78,497), with an age-standardized mortality rate (ASMR) of 3.63 (95% UI: 2.62\u0026ndash;5.02). Southeast Asia had the second-highest number of deaths, at 23,227 (95% UI: 19,107\u0026thinsp;\u0026minus;\u0026thinsp;28,530), but its ASMR was higher at 4.09 (95% UI: 3.36\u0026ndash;5.04). Despite having a lower ASMR compared to Southeast Asia, South Asia's large population base resulted in a higher number of death cases. East Asia ranked third in terms of death cases, with 20,838 (95% UI: 16,840\u0026thinsp;\u0026minus;\u0026thinsp;25,797) and an ASMR of 2.71 (95% UI: 2.15\u0026ndash;3.45). In contrast, both Central Asia and the high-income Asia Pacific region saw a significant reduction in death cases (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom 1990 to 2021, there was a marked decline in both the number of death cases and the ASMR for CHD in East Asia, the high-income Asia Pacific region, South Asia, and Southeast Asia. Specifically, East Asia saw a reduction of approximately 84.5% in death cases, from 127,882 to 20,838, and a decrease of about 75.3% in ASMR, from 10.98 to 2.71. The high-income Asia Pacific region experienced a decrease of around 82.3% in death cases, from 4,302 to 760, and a reduction of about 79.5% in ASMR, from 3.86 to 0.79. South Asia witnessed a decline of approximately 50.4% in death cases, from 115,427 to 57,260, and a decrease of about 50.9% in ASMR, from 7.41 to 3.63. Southeast Asia had a reduction of around 49.8% in death cases, from 46,652 to 23,227, and a decrease of about 49.1% in ASMR, from 8.03 to 4.09(Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, Central Asia showed a different trend. The number of death cases increased by about 9.2%, from 4,237 to 4,629, and the ASMR rose by approximately 1.5%, from 4.61 to 4.68(Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn 2021, the five Asian regions showed significant differences in disability-adjusted life years(DALYs) for CHD. South Asia had the highest DALYs at 5,102,106 (95% UI: 3,703,401-7,010,944), with an age-standardized DALYs rate of 324.04 (95% UI: 234.07-448.15). Southeast Asia had the second-highest DALYs at 2,053,890 (95% UI: 1,683,426-2,518,299), and the highest age-standardized DALYs rate of 364.01 (95% UI: 298.07-447.05). East Asia had the third-highest DALYs at 1,782,703 (95% UI: 1,440,585-2,196,783), with a relatively low age-standardized DALYs rate of 241.17 (95% UI: 191.48-305.04). The high-income Asia Pacific region had the lowest DALYs and age-standardized DALYs rate, with 76,963 DALYs (95% UI: 61,679\u0026thinsp;\u0026minus;\u0026thinsp;96,224) and a rate of 82.88 (95% UI: 68.11-103.25). Central Asia had the fourth-highest DALYs at 418,325 (95% UI: 332,291\u0026ndash;513,640), with a high age-standardized DALYs rate of 423.52 (95% UI: 336.81-519.76), (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the High-income Asia Pacific region, the age-standardized prevalence rate (ASPR) of CHD saw a modest decline. Specifically, the ASPR decreased from 317.65 per 100,000 in 1990 (UI95%: 284.45\u0026ndash;351.28) to 312.02 per 100,000 in 2021 (95% UI: 280.79\u0026ndash;343.6), representing a reduction of 1.77%. Conversely, the remaining four regions experienced increases in their respective ASPR. Notably, East Asia witnessed a 3.36% rise, South Asia saw a 4.68% increase, Central Asia had a 4.05% climb, and Southeast Asia registered a 1.31% upturn (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBetween 1990 and 2021, the age-standardized incidence rates (ASIR) of CHD exhibited diverse trajectories across five Asian regions. In East Asia, the ASIR experienced the most substantial decline, plummeting from 39.42 (95%UI: 30.35, 52.29) per 100,000 to 32.56 (95%UI: 24.78, 44.03) per 100,000, a reduction of approximately 17.35%. The High-income Asia Pacific region witnessed a moderate decline, with the ASIR dropping from 30.36 (95%UI: 25.09, 37.68) per 100,000 to 27.9 (95%UI: 22.95, 34.64) per 100,000, representing a decrease of about 7.44%. In Southeast Asia, the ASIR experienced a slight decline, falling from 35.32 (95%UI: 27.58, 46.04) per 100,000 to 33.89 (95%UI: 26.24, 44.3) per 100,000, a decline of approximately 3.48%. Conversely, the ASIR in Central Asia and South Asia witnessed an increase, rising by about 5.18% and 3.19%, respectively (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2. Temporal joinpoint analysis in regional level\u003c/h2\u003e\u003cp\u003eBetween 1990 and 2021, East Asia and the High-income Asia Pacific region have demonstrated a notable decrease in ASMR, ASIR and DALYs for congenital heart disease. This trend likely signifies advancements in medical technology and the effectiveness of public health initiatives within these regions. The marked reduction in ASMR and DALYs in East Asia. Conversely, while South Asia, Southeast Asia, and Central Asia have also experienced a downward trend in mortality rates and DALYs, the improvements are less significant(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and supplementary S1).\u003c/p\u003e\u003cp\u003eFurthermore, the trajectories of YLDs and YLLs underscore the varying degrees of challenge that different regions encounter in disease management and health promotion efforts. The declining YLDs and YLLs in East Asia suggest successful strides in mitigating the disease's impact on quality of life and in prolonging healthy lifespans. In contrast, the rise in YLDs and YLLs observed in South Asia, Southeast Asia, and Central Asia could signal shortcomings in the realms of disease prevention, treatment, and rehabilitation. These regions require more intensive public health interventions and a more substantial allocation of resources to enhance these critical health indicators. In sum, Central Asia continues to grapple with a relatively higher burden of CHD, indicating that it confronts considerable obstacles in its efforts to manage and control the condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and supplementary S1).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAAPC analysis of the congenital heart disease burden temporal trends in 5 regions (East Asia, High-income Asia, South Asia, Southeast Asia, Central Asia), 1990\u0026ndash;2021\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh-income Asia\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCentral Asia\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.49(-4.71, -4.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-4.91(-5.36, -4.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.36(-2.57, -2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.18(-2.27, -2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03*(-0.43, 0.49)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.61(-0.72, -0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.28(-0.39, -0.179)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11(0.05, 0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.13(-0.16, -0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.15(0.09, 0.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevalence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11(0.10, 0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-0.06(-0.07, -0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14(0.09, 0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04(0.04, 0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.12(0.10, 0.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.45\u003c/p\u003e\u003cp\u003e(-4.67, -4.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4.49\u003c/p\u003e\u003cp\u003e(-4.96, -4.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.30\u003c/p\u003e\u003cp\u003e(-2.51, -2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.16(-2.25, -2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03*\u003c/p\u003e\u003cp\u003e(-0.39,0.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYLDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.46(0.44, 0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e0*(-0.01, 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28(0.24, 0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07(0.06, 0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.26(0.23, 0.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYLLs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.60(-4.83, -4.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e-5.16(-5.74, -4.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.38(-2.59, -2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.21(-2.30, -2.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02*(-0.41, 0.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Data in parentheses estimate with a 95% CI, * mark the p value of AAPC\u0026thinsp;\u0026gt;\u0026thinsp;0.05 which represent no significant difference between 1990 and 2021\u003c/p\u003e\u003cp\u003eIn East Asia, the ASIR of congenital heart disease experienced a notable rise from 1990 to 1993, with an APC of 1.62% (95% CI: 1.33\u0026ndash;1.91%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This period of increase was followed by a relatively stable phase from 1993 to 1996. Subsequently, the ASIR embarked on a downward trajectory from 1996 to 2021. From 1990 to 2021, the overall AAPC amounted to -0.61% (95% CI: -0.72% to -0.51%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a downward trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Conversely, the overall AAPC in South Asia exhibited an upward trend during the same period (AAPC\u0026thinsp;=\u0026thinsp;0.11%; 95% CI: 0.05% -0.16%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, Central Asia did not display a declining tendency from 1990 to 2021 (AAPC\u0026thinsp;=\u0026thinsp;0.15%; 95% CI: 0.09\u0026ndash;0.21%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBetween 1990 and 2021, East Asia, South Asia, Southeast Asia, and the high-income Asia Pacific region exhibited a consistent downward trend in ASMR. Specifically, the overall AAPC was \u0026minus;\u0026thinsp;4.49% in East Asia (95% CI: -4.71% to -4.27%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), -2.36% in South Asia (95% CI: -2.57% to -2.14%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), -2.18% in Southeast Asia (95% CI: -2.27% to -2.10%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and \u0026minus;\u0026thinsp;4.91% in the high-income Asia Pacific region (95% CI: -5.36% to -4.45%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001),(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ). Conversely, Central Asia did not demonstrate a significant decline during this period. However, Central Asia did not exhibit an overall change from 1990 to 2021 (AAPC\u0026thinsp;=\u0026thinsp;0.03%; 95% CI:-0.43\u0026ndash;0.49%; P\u0026thinsp;=\u0026thinsp;0.904), primarily due to a significant increase from 1997 to 2002 (APC\u0026thinsp;=\u0026thinsp;5.15%; 95% CI: 4.04\u0026ndash;6.26%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC),and from 2014 to 2018 (APC\u0026thinsp;=\u0026thinsp;4.56%; 95% CI: 2.13\u0026ndash;7.04%; P\u0026thinsp;=\u0026thinsp;0.001) (supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The trends in DALYs and YLLs in five regions closely mirror the trends observed in ASMR(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3. Regional temporal trends in gender and age structures\u003c/h2\u003e\u003cp\u003eThe prevalence of CHD has predominantly been higher among the male population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with the exception of countries within the high-income Asia region and South Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and C). Over the observed timeframe, mortality number associated with CHD in five regions has also exhibited a preponderance in males compared to females (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In Southeast Asia, although both the ASMR and the number of deaths due to CHD have declined in both males and females, recent trends indicate a convergence in mortality figures between the two sexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).Apart from Central Asia, the other four regions also reveal that male patients exceed female patients in terms of DALYs and YLLs, aligning with the trend observed in mortality figures(supplementary Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Country level\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1. Country burden and trends\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThirty-four countries and territories of the Congenital heart disease burden in 2021\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountries and Territories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eASMR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePrevalence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eASPR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIncidence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eASIR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYLDs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYLLs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArmenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (50,82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.36 (2.6,4.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10428 (9166,11646)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e393.3 (346.56,438.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e794 (622,1046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e47.88 (37.49,63.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e311.25 (243.22,398.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e25.66 (14.51,39.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e285.59 (218.17,370.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAzerbaijan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e307 (192,476)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.43 (2.76,6.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27577 (24360,30860)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e294 (259.45,329.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3332 (2622,4208)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e51.73 (40.7,65.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e405.83 (259.87,635.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e17.99 (10.07,28.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e387.84 (240.27,615.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4722 (2269,8333)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.28 (1.57,5.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e287803 (254349,322201)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e181.65 (160.5,203.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48676 (37251,64651)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e36.27 (27.76,48.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e291.23 (142.49,516.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12.32 (7.07,19.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e278.91 (132.5,504.46)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBhutan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (11,36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.53 (1.71,5.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1282 (1139,1426)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e180.49 (160.06,201.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e213 (165,284)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e35.56 (27.61,47.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e316.49 (159.03,523.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12.42 (7.13,19.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e304.07 (146.58,510.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrunei Darussalam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (7,11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.56 (1.96,3.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1033 (909,1159)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e248.24 (220.38,278.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94 (78,117)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e31.95 (26.35,39.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e230.04 (177.13,287.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e15.11 (8.39,23.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e214.93 (163.44,273.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCambodia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1418 (990,1922)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.19 (5.69,11.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31252 (27712,35107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e179.45 (159.26,201.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6927 (5443,8820)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e40.47 (31.8,51.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e713.9 (499.85,963.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12.49 (7.23,19.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e701.41 (489.12,949.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20071 (16216,24857)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.72 (2.14,3.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2344480 (2065383,2611300)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e196.03 (173.73,216.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e173609 (132165,234712)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e32.76 (24.94,44.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e241.68 (191.38,304.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e14.44 (8.47,21.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e227.24 (175.89,290.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemocratic People\u0026rsquo;s Republic of Korea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e612 (411,906)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.42 (2.26,5.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42386 (37032,47288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e185.55 (164.22,206.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4382 (3243,5929)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e31.03 (22.97,41.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e291.98 (195.53,438.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e13.12 (7.43,20.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e278.86 (182.31,424.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeorgia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (48,79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.59 (1.95,3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8478 (7603,9318)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e270.6 (241.36,297.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e747 (614,927)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e34.76 (28.59,43.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e238.31 (181.92,299.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e19.82 (11.52,29.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e218.49 (162.57,281.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39983 (30039,54628)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.6 (2.66,4.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2504266 (2229279,2767961)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e189.41 (168.83,209.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e413505 (325155,537339)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e39.4 (30.98,51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e321.95 (239.51,447.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e13.27 (7.96,19.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e308.68 (225.47,436.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndonesia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7799 (5547,10516)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.52 (2.49,4.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e458757 (402699,511759)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e175.35 (154.16,195.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77604 (60259,101204)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e36.64 (28.45,47.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e317.12 (227.69,423.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11.34 (6.27,17.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e305.78 (215.78,414.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJapan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e568 (438,710)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.83 (0.6,1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e377172 (331413,421293)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e336.98 (302.1,371.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12186 (9958,15228)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e29.47 (24.08,36.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e85.99 (65.86,107.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e21.37 (12.02,32.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e64.62 (45.79,86)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKazakhstan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e749 (609,920)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.8 (3.09,4.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58462 (51592,65615)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e307.03 (271.06,344.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8887 (7037,11316)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e45.29 (35.86,57.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e336.23 (273.61,408.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e20.38 (11.48,31.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e315.85 (254.87,390.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKyrgyzstan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e295 (241,355)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.91 (3.19,4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22310 (19822,24841)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e306.36 (271.61,340.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3566 (2791,4540)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e47.46 (37.15,60.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e355.11 (289.76,425.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e20.83 (12.06,31.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e334.29 (269.94,405.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLao People\u0026rsquo;s Democratic Republic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e803 (509,1151)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.65 (6.13,13.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13951 (12226,15599)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e178.25 (156.54,198.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3735 (2957,4771)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e44.33 (35.1,56.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e845.73 (535.03,1211.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11.73 (6.52,18.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e834.01 (522.56,1198.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalaysia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e450 (365,555)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.71 (1.37,2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58106 (51139,64910)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e192.46 (169.94,214.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5292 (3805,7342)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e23.06 (16.58,31.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e153.03 (121.21,192.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e14.25 (8.29,21.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e138.78 (108.8,176.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaldives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (6,11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.5 (1.84,3.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e822 (722,922)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e175.78 (155.45,196.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79 (58,109)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e27.28 (20.14,37.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e228.45 (170.49,313.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12.47 (7.13,19.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e215.98 (157.73,300.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMauritius\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (24,37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.75 (3.08,4.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2128 (1873,2382)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e195.23 (172.94,217.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e137 (99,188)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22.52 (16.29,30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e328.05 (269.85,427.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e14.59 (8.43,22.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e313.46 (255.23,414.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyanmar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5234 (3243,7267)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (6.16,13.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96707 (85649,108614)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e174.86 (154.76,196.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21283 (16623,27447)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e41.38 (32.32,53.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e880.79 (545.06,1226.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11.41 (6.24,18.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e869.39 (529.43,1213.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMongolia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (81,151)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.16 (2.21,4.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10826 (9509,12118)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e304.45 (268.8,340.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2151 (1728,2713)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e58.67 (47.14,73.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e286.17 (206.28,369.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e19.25 (10.77,30.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e266.91 (185.44,350.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNepal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e580 (317,1257)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.86 (1.02,4.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51255 (45564,57207)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e162.88 (144.75,182.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10570 (8111,14108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e34.32 (26.33,45.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e169.69 (96,358.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e10.62 (5.95,16.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e159.07 (85.79,345.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11953 (7226,17842)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.09 (2.48,6.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e486204 (425422,544812)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e185.36 (163.08,207.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e124043 (97864,160140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e41.52 (32.76,53.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e364.84 (225.32,534.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11.51 (6.44,18.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e353.33 (212.99,524.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhilippines\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4833 (3910,6158)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.29 (3.45,5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e208585 (183956,231485)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e181.51 (160.13,201.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37160 (28598,49085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e34.06 (26.22,45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e374.05 (301.48,480.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12.36 (7.05,19.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e361.69 (288.47,470.49)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRepublic of Korea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e162 (127,231)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.71 (0.53,1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113149 (99469,128128)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e265.32 (236.22,294.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3186 (2596,3943)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e24.8 (20.21,30.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e76.09 (59.63,106.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e17.97 (10.21,27.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e58.12 (42.8,84.66)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeychelles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (3,4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.07 (3.09,5.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e190 (168,212)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e196.13 (173.16,217.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18 (13,26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e24.33 (17.68,33.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e357.11 (270.64,458.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e14.97 (8.96,23.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e342.14 (256.44,444.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingapore\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (15,29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.59 (0.39,0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11318 (9979,12611)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e231.82 (205.7,257.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e475 (388,601)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17.96 (14.65,22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e63.94 (47.09,83.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e17.35 (10.51,26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e46.6 (30.69,67.71)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSri Lanka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e459 (332,631)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.7 (1.95,3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39273 (34614,43871)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e192.84 (169.9,215.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3344 (2446,4674)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e23.08 (16.88,32.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e235.52 (172.66,324.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e14.81 (8.72,22.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e220.71 (159.24,306.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTaiwan (Province of China)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155 (135,172)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.33 (1.12,1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37436 (33338,41319)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e190.49 (170.85,209.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1645 (1158,2364)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e21.72 (15.29,31.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e122.21 (102.26,139.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e13.56 (7.71,20.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e108.64 (90.54,126.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThailand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e724 (558,901)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.18 (1.58,2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101051 (89093,113524)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e186.76 (165.35,209.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6800 (5102,9464)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e26.37 (19.78,36.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e197.21 (144.15,245.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e13.96 (8.02,21.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e183.24 (130.26,228.91)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTimor-Leste\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (96,185)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.04 (5.22,9.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2752 (2433,3106)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e172.91 (152.48,194.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e772 (611,1006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e39.41 (31.22,51.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e617.7 (446.77,859.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11.28 (6.21,17.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e606.42 (436.55,848.39)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTajikistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e384 (225,797)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.87 (1.69,5.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34235 (29755,38395)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e299.01 (261.22,335.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7849 (6223,9768)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e57.73 (45.77,71.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e270.09 (164.37,544.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e18.33 (9.89,28.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e251.77 (147.28,523.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurkmenistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e341 (248,445)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.4 (4.65,8.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15778 (13828,17686)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e300.07 (263.28,335.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2731 (2158,3455)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e51.6 (40.77,65.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e570.58 (413.9,740.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e18.66 (10.34,29.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e551.92 (396.56,723.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUzbekistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2311 (1681,3008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.07 (4.43,7.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108768 (96113,122017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e306.05 (270.65,342.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19401 (15302,24301)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e50.69 (39.98,63.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e548.53 (400.19,704.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e20.1 (11.04,31.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e528.42 (382.86,689.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViet Nam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1302 (857,1907)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.63 (1.07,2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e170347 (149870,190138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e178.6 (157.46,199.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19333 (14420,27118)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25.96 (19.36,36.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e151.31 (104.44,215.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12.18 (6.7,18.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e139.13 (91.3,206.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Data in parentheses represent the 95% uncertainty intervals\u003c/p\u003e\u003cp\u003eIn 2021, among the 34 countries and regions assessed for the burden of CHD(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For ASIR, Mongolia had the highest rate, quantified at 58.67 (95% UI: 47.14\u0026ndash;73.99), followed by Tajikistan at 57.73 (95% UI: 45.77\u0026ndash;71.84) and Azerbaijan at 51.73 (95% UI: 40.7\u0026ndash;65.32), (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Singapore had the lowest ASIR, with a value of 17.96 (95% UI: 14.65\u0026ndash;22.7), (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).Myanmar had the highest ASMR, quantified at 10 (95% UI: 6.16\u0026ndash;13.91), followed by the Lao People\u0026rsquo;s Democratic Republic at 9.65 (95% UI: 6.13\u0026ndash;13.81) and Cambodia at 8.19 (95% UI: 5.69\u0026ndash;11.11),(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In stark contrast, Japan exhibited the lowest ASMR, with a value of 0.83 (95% UI: 0.6\u0026ndash;1.08), (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).When it came to ASPR, Armenia led the list with a value of 393.3 (95% UI: 346.56\u0026ndash;438.42), followed by Japan at 336.98 (95% UI: 302.1\u0026ndash;371.45) and Kazakhstan at 307.03 (95% UI: 271.06\u0026ndash;344.38),(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Nepal had the lowest ASPR, quantified at 162.88 (95% UI: 144.75\u0026ndash;182.01),(Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).In terms of DALYs, Myanmar had the highest burden, quantified at 880.79 (95% UI: 545.06\u0026ndash;1226.29), followed by the Lao People\u0026rsquo;s Democratic Republic at 845.73 (95% UI: 535.03\u0026ndash;1211.07) and Cambodia at 713.9 (95% UI: 499.85\u0026ndash;963.1),(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Singapore had the lowest DALYs, with a value of 63.94 (95% UI: 47.09\u0026ndash;83.08),(Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).Regarding YLDs, Armenia had the highest rate, quantified at 25.66 (95% UI: 14.51\u0026ndash;39.54), followed by Japan at 21.37 (95% UI: 12.02\u0026ndash;32.95) and Kyrgyzstan at 20.83 (95% UI: 12.06\u0026ndash;31.63). Nepal had the lowest YLDs, with a value of 10.62 (95% UI: 5.95\u0026ndash;16.35), (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Lastly, for YLLs, Myanmar had the highest value, quantified at 869.39 (95% UI: 529.43\u0026ndash;1213.78), followed by the Lao People\u0026rsquo;s Democratic Republic at 834.01 (95% UI: 522.56\u0026ndash;1198.58) and Cambodia at 701.41 (95% UI: 489.12\u0026ndash;949.28). Singapore had the lowest YLLs, with a value of 46.6 (95% UI: 30.69\u0026ndash;67.71), (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2. Temporal joinpoint analysis in country level\u003c/h2\u003e\u003cp\u003eFrom1990 to 2021, a comparative analysis showed that the ASMR generally decreased in most countries and territories. However, Uzbekistan was an exception, with an increasing ASMR trend (AAPC\u0026thinsp;=\u0026thinsp;1.61%; 95% CI: 0.46\u0026ndash;2.77%; P\u0026thinsp;=\u0026thinsp;0.06) (supplementary Table S2). This pattern was also observed in the age-standardized YLLs rate (AAPC\u0026thinsp;=\u0026thinsp;1.61%; 95% CI: 0.44\u0026ndash;2.8%; P\u0026thinsp;=\u0026thinsp;0.007) (supplementary Table S3).A similar upward trend was also observed in the DALYs in Uzbekistan (AAPC\u0026thinsp;=\u0026thinsp;1.57%; 95% CI: 0.49\u0026ndash;2.65%; P\u0026thinsp;=\u0026thinsp;0.004) (supplementary Table S4).The ASPR has been on the rise in most countries, while the declines were observed in Lao People\u0026rsquo;s Democratic Republic(AAPC = -0.02%; 95% CI: -0.03% to -0.01%; P\u0026thinsp;=\u0026thinsp;0.002), the Republic of Korea(AAPC = -0.03%; 95% CI: -0.04% to -0.03%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Singapore(AAPC = -0.03%; 95% CI: -0.04% to -0.01%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (supplementary Table S5).The YLDs (Years Lived with Disability) have shown an increasing trend in the majority of countries as well. However, declines were observed in the Republic of Korea (AAPC = -0.12%; 95% CI: -0.16% to -0.09%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Taiwan (Province of China) (AAPC = -0.09%; 95% CI: -0.12% to -0.06%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (supplementary Table S6).\u003c/p\u003e\u003cp\u003eBetween 1990 and 2021, we have conducted a comprehensive analysis of data from mainland China. The results show that the ASPR has been increasing (AAPC\u0026thinsp;=\u0026thinsp;0.11%; 95% CI: 0.1% -0.11%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (supplementary Table S5), as have the YLDs (AAPC\u0026thinsp;=\u0026thinsp;0.47%; 95% CI: 0.45%-0.5%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (supplementary Table S6). However, other indicators, including ASIR, ASMR, DALYs, and YLLs, have all shown a downward trend during this period. In China's Taiwan region, the ASPR has also increased (AAPC\u0026thinsp;=\u0026thinsp;0.017%; 95% CI: 0.012\u0026ndash;0.02%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (supplementary Table S5), but the other indicators have decreased. When examining the ASMR and ASPR, it is evident that the proportion of males remains relatively high. This is also reflected in the under five years mortality rate, which is consistent with the regional analysis, primarily due to the higher proportion of male (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, supplementary S3and S4).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Variation in CHD burden by SDI\u003c/h2\u003e\u003cp\u003eIn Asia, the ASMR is correlated with the SDI across 34 countries in five regions. In most countries, the ASMR decreases as the SDI value increases. Higher mortality rates are observed in countries with low and low\u0026ndash;middle SDI, such as Myanmar (10), Lao People\u0026rsquo;s Democratic Republic (9.65), and Cambodia (8.19). Conversely, mortality rates are significantly lower in countries with high SDI.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCongenital Heart Disease (CHD) is one of the most common congenital disorders globally and accounts for a substantial proportion of the disease burden affecting both pediatric and adult populations\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. In addition to impairing the quality of life and survival rates of affected individuals, CHD imposes a considerable economic and psychological burden on families and society. This study provides valuable insights by analyzing data from 34 countries across five subregions in Asia. It clarifies the trends in the CHD disease burden from 1990 to 2021 and investigates its association with the Socio-Demographic Index (SDI). The findings serve as critical evidence for informing the development of targeted public health policies and interventions\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe findings indicate a significant decline in mortality rates and case numbers in Asia from 1990 to 2021. Additionally, there has been a notable reduction in YLLs and DALYs. China, as one of the countries in East Asia, bears a substantial burden of CHD due to its large patient population. However, the study demonstrates that the ASIR, ASMR, DALYs, and YLLs have all exhibited a downward trend. This is attributed to advancements in medical technology and the Chinese government's prioritization of CHD management\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Similarly, in the Southeast Asia region, the ASMR has shown a decreasing trend from 1990 to 2021, with Vietnam serving as an example. The Vietnamese government's emphasis on CHD, along with the establishment of the Alain Carpentier Foundation, has played a crucial role. The foundation has provided surgical and treatment services to underprivileged patients\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHowever, Central Asia stands out with contrasting data, indicating that this region confronts considerable obstacles in the prevention and treatment of CHD. In 2021, Central Asia exhibited significantly higher ASMR, ASIR, age-standardized YLLs rates, and age-standardized DALYs rates compared to the other four Asian regions under study. This disparity may be associated with a variety of factors. Many low-income and middle-income countries struggle to provide the health services investment required for life-saving CHD surgery\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. For instance, in terms of socioeconomic factors, the level of economic development in Central Asia is relatively low, with limited medical resources and weak public health infrastructure, which results in insufficient capacity for the screening, diagnosis, and treatment of CHD.\u003c/p\u003e\u003cp\u003eThe study further demonstrated that in 2021, within the five Asian subregions, the ASMR and ASIR for children under five years of age were at their highest levels. This underscores that CHD in children continues to be a pressing public health concern warranting dedicated attention. The impact of CHD on children extends beyond individual health and survival, contributing to significant long-term medical burdens and broader socioeconomic consequences. Consequently, enhancing screening, diagnosis, and treatment for pediatric CHD\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, coupled with increased public awareness of children's heart health, is essential to effectively reduce mortality rates associated with this condition. Among all age groups, the ASPR and ASMR of CHD in males are generally higher than those in females. This characteristic has also been observed in other studies\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFrom 1990 to 2021, the ASMR due to CHD in high SDI countries has significantly decreased. This demonstrates that improvements in socioeconomic development and advancements in medical technology have played a positive role in the prevention and control of CHD. High SDI countries typically have better medical resources, more robust public health systems, and higher levels of health awareness, all of which contribute to the early diagnosis and effective treatment of CHD. The most majority of children with CHD in high-income countries survive into adulthood. Further, pediatric cardiac services have expanded in middle-income countries. Both evolutions have resulted in an increasing number of CHD survivors\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. However, in low and low-middle SDI regions, such as countries like Myanmar, Laos, and Cambodia, the ASMR remain high and require serious attention as well as increased investment in health\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn summary, although progress has been made in the prevention and control of CHD in the Asian region, regional disparities and the high burden in specific populations still require serious attention\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Through comprehensive public health strategies and targeted interventions, it is hoped that the disease burden of CHD can be further reduced and the health level of the population improved.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eDue to the public nature of the GBD database, this study was granted an ethical exemption.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and material\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. The data can be found here: http://ghdx.healthdata.org/gbd-results-tool .\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was sponsored by Fujian provincial health technology project(grant No.2024CXA036), Joint Funds for the Innovation of Science and Technology, Fujian Province (grant No. 2020Y9159), Joint Funds for the Innovation of Science and Technology, Fujian Province(grant No. 2024Y9583), Key Project on the Integration of Industry, Education and Research Collaborative Innovation of Fujian Province (grant No. 2021YZ034011), Key Project on Science and Technology Program of Fujian Health Commission (grant No. 2021ZD01002).\u003c/p\u003e\n\u003cp\u003eAuthor contributions:\u003c/p\u003e\n\u003cp\u003eH.Z. ,S.C., and Y.Q.conceived the idea, provided critical data curation and analysis, validated the findings, and drafted the initial manuscript. N.L., M.C., and Y.D. orchestrated the study\u0026rsquo;s design and coordination, directed the study, and examined and reviewed the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to all researchers and participants involved in the GBD program.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMENG X, SONG M, ZHANG K, et al. Congenital heart disease: types, pathophysiology, diagnosis, and treatment options [J]. MedComm (2020), 2024, 5(7): e631.\u003c/li\u003e\n\u003cli\u003eHASAN B S, BHATTI A, MOHSIN S, et al. Recommendations for developing effective and safe paediatric and congenital heart disease services in low-income and middle-income countries: a public health framework [J]. BMJ Glob Health, 2023, 8(5).\u003c/li\u003e\n\u003cli\u003eLUDOMIRSKY A B, BUCHOLZ E M, NEWBURGER J W. Association of Financial Hardship Because of Medical Bills With Adverse Outcomes Among Families of Children With Congenital Heart Disease [J]. JAMA Cardiol, 2021, 6(6): 713-7.\u003c/li\u003e\n\u003cli\u003eBESSI\u0026egrave;RE F, WALDMANN V, COMBES N, et al. Ventricular Arrhythmias in Adults With Congenital Heart Disease, Part I: JACC State-of-the-Art Review [J]. J Am Coll Cardiol, 2023, 82(11): 1108-20.\u003c/li\u003e\n\u003cli\u003eMERY C M, WELL A. Congenital Heart Surgery Outcomes: Looking Beyond the Hospital Walls [J]. J Am Coll Cardiol, 2023, 82(9): 814-6.\u003c/li\u003e\n\u003cli\u003eTAYLOR R S, DALAL H M, MCDONAGH S T J. The role of cardiac rehabilitation in improving cardiovascular outcomes [J]. Nat Rev Cardiol, 2022, 19(3): 180-94.\u003c/li\u003e\n\u003cli\u003eMURRAY C J L. The Global Burden of Disease Study at 30 years [J]. Nat Med, 2022, 28(10): 2019-26.\u003c/li\u003e\n\u003cli\u003eGlobal burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019 [J]. Lancet, 2020, 396(10258): 1223-49.\u003c/li\u003e\n\u003cli\u003eLI Y, CAO G Y, JING W Z, et al. Global trends and regional differences in incidence and mortality of cardiovascular disease, 1990-2019: findings from 2019 global burden of disease study [J]. Eur J Prev Cardiol, 2023, 30(3): 276-86.\u003c/li\u003e\n\u003cli\u003eGlobal incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021 [J]. Lancet, 2024, 403(10440): 2133-61.\u003c/li\u003e\n\u003cli\u003eKIM H J, CHEN H S, MIDTHUNE D, et al. Data-driven choice of a model selection method in joinpoint regression [J]. J Appl Stat, 2023, 50(9): 1992-2013.\u003c/li\u003e\n\u003cli\u003eKIM H J, FAY M P, FEUER E J, et al. Permutation tests for joinpoint regression with applications to cancer rates [J]. Stat Med, 2000, 19(3): 335-51.\u003c/li\u003e\n\u003cli\u003eVAN DER BOM T, ZOMER A C, ZWINDERMAN A H, et al. The changing epidemiology of congenital heart disease [J]. Nat Rev Cardiol, 2011, 8(1): 50-60.\u003c/li\u003e\n\u003cli\u003eOSTER M E, RIEHLE-COLARUSSO T, SIMEONE R M, et al. Public health science agenda for congenital heart defects: report from a Centers for Disease Control and Prevention experts meeting [J]. J Am Heart Assoc, 2013, 2(5): e000256.\u003c/li\u003e\n\u003cli\u003eMA K, HE Q, DOU Z, et al. Current treatment outcomes of congenital heart disease and future perspectives [J]. Lancet Child Adolesc Health, 2023, 7(7): 490-501.\u003c/li\u003e\n\u003cli\u003eLAJOS P S, CARPENTIER A F. Viện Tim Institut du Coeur: Success of a Congenital Heart Disease Center in a Developing Country [J]. Ann Glob Health, 2016, 82(4): 621-4.\u003c/li\u003e\n\u003cli\u003eRAHMAN S, ZHELEVA B, CHERIAN K M, et al. Linking world bank development indicators and outcomes of congenital heart surgery in low-income and middle-income countries: retrospective analysis of quality improvement data [J]. BMJ Open, 2019, 9(6): e028307.\u003c/li\u003e\n\u003cli\u003eCHOWDHURY D, ELLIOTT P A, ASAKI S Y, et al. Addressing Disparities in Pediatric Congenital Heart Disease: A Call for Equitable Health Care [J]. J Am Heart Assoc, 2024, 13(13): e032415.\u003c/li\u003e\n\u003cli\u003eMARELLI A, GAUVREAU K, LANDZBERG M, et al. Sex differences in mortality in children undergoing congenital heart disease surgery: a United States population-based study [J]. Circulation, 2010, 122(11 Suppl): S234-40.\u003c/li\u003e\n\u003cli\u003eMOONS P, BRATT E L, DE BACKER J, et al. Transition to adulthood and transfer to adult care of adolescents with congenital heart disease: a global consensus statement of the ESC Association of Cardiovascular Nursing and Allied Professions (ACNAP), the ESC Working Group on Adult Congenital Heart Disease (WG ACHD), the Association for European Paediatric and Congenital Cardiology (AEPC), the Pan-African Society of Cardiology (PASCAR), the Asia-Pacific Pediatric Cardiac Society (APPCS), the Inter-American Society of Cardiology (IASC), the Cardiac Society of Australia and New Zealand (CSANZ), the International Society for Adult Congenital Heart Disease (ISACHD), the World Heart Federation (WHF), the European Congenital Heart Disease Organisation (ECHDO), and the Global Alliance for Rheumatic and Congenital Hearts (Global ARCH) [J]. Eur Heart J, 2021, 42(41): 4213-23.\u003c/li\u003e\n\u003cli\u003ePOLIVENOK I, GELATT M, CARDARELLI M. Cardiac surgical missions: what works, what does not, where we need to go from here [J]. Curr Opin Cardiol, 2020, 35(1): 76-9.\u003c/li\u003e\n\u003cli\u003eMUHARRAM F R, MULTAZAM C, MUSTOFA A, et al. The 30 Years of Shifting in The Indonesian Cardiovascular Burden-Analysis of The Global Burden of Disease Study [J]. J Epidemiol Glob Health, 2024, 14(1): 193-212.\u003c/li\u003e\n\u003cli\u003eWEBB G, MULDER B J, ABOULHOSN J, et al. The care of adults with congenital heart disease across the globe: Current assessment and future perspective: A position statement from the International Society for Adult Congenital Heart Disease (ISACHD) [J]. Int J Cardiol, 2015, 195: 326-33.\u003c/li\u003e\n\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":"congenital heart disease, GBD (global burden of disease), prevalence, mortality","lastPublishedDoi":"10.21203/rs.3.rs-7026873/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7026873/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne of the most prevalent birth defects worldwide, congenital heart disease (CHD) presents a serious risk to a child’s health. Although the survival rate of CHD has increased recently due to advancements in medical technology, the disease burden and incidence are still significant. Based on data from the Global Burden of Disease (GBD) study, this article examines the distribution characteristics and trends of CHD in regions with varying Socio-Demographic Index (SDI) levels. It does this by analyzing the disease burden indices of CHD in five Asian regions and the thirty-four countries and territories that are included from 1990 to 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GBD database covers 204 countries and regions and provides data on the incidence, mortality, and disability rates of 369 diseases and injuries from 1990 to 2021. Data on the prevalence, incidence, mortality, Disability-Adjusted Life Years (DALYs), years of life lost (YLL) and years lived with disability (YLD) of CHD\u003c/p\u003e\n\u003cp\u003ewere extracted from the GBD 2021 study for South, East, Southeast, and high-income Asia regions, encompassing the relevant countries and territories from 1990 to 2021. The Average Annual Percent Change (AAPC) in age-standardized rates of congenital heart disease was determined to assess temporal trends. Cohort proportions by age and gender were taken into account. The various SDI in five regions were used to assess the illness burden.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur research encompasses 34 countries and regions across five Asian subregions, conducting a detailed analysis of the disease burden related to CHD. The results indicate a significant reduction in mortality rates and case numbers between 1990 and 2021. Additionally, there has been a substantial decrease in YLLs and DALYs. However, Central Asia presents contrasting trends, suggesting that this region faces considerable challenges in the prevention and management of CHD. In 2021, Central Asia demonstrated markedly higher ASMR, ASIR, age-standardized YLLs rates, and age-standardized DALYs rates compared to the other four Asian subregions under investigation. In the same year, data from the five Asian subregions revealed that ASMR and ASPR were highest among children under the age of five. Moreover, across all age groups, the prevalence rate was consistently higher among males. From 1990 to 2021, high SDI countries experienced a notable decline in the proportion of childhood deaths attributable to CHD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 1990 to 2021, while the mortality rate of congenital heart disease (CHD) exhibited a downward trend in most regions and countries, significant disparities in CHD incidence and mortality rates persist across different countries. Many countries and regions still face substantial challenges in further reducing the disease burden. Consequently, it is imperative to enhance public health interventions and allocate additional resources to medical infrastructure, with the aim of improving patient prognosis and alleviating the burden of CHD. Notably, for countries with mid-to-low SDI, prioritizing policies that promote early diagnosis and comprehensive care is essential.\u003c/p\u003e","manuscriptTitle":"The Burden of Congenital Heart Disease in Asia and 34 Countries and Territories from 1990 to 2021: A Systematic Analysis Based on the Global Burden of Disease Study 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 12:26:51","doi":"10.21203/rs.3.rs-7026873/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":"b63456bf-6587-4679-a75e-76ca5de3d904","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-06T17:23:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 12:26:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7026873","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7026873","identity":"rs-7026873","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}