Analyzing the global burden of 11 subtypes of congenital birth defects: trends, sociodemographic correlates, and outcomes from 1990 to 2021
E. Yang et al.
Department of Child Healthcare, Wenzhou People’s Hospital, Wenzhou, China
Children’s Heart Center, Institute of Cardiovascular Development and Translational Medicine, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
Zhejiang Provincial Clinical Research Center for Pediatric Disease, Wenzhou, China
Department of Traditional Chinese Medicine, Wenzhou Yebo Proctology Hospital, Wenzhou, China
Department of Gastroenterology, Pingyang Hospital of Wenzhou Medical University, Wenzhou, China
Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, China
Zhejiang Provincial Clinical Research Center for Pediatric Disease, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
Department of Pediatric Rheumatology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
CONTACT Maoping Chu chmping@hotmail.comXing Rong rstar1978mail@wmu.edu.cn Children’s Heart Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Zhejiang Province, ChinaAbstract
Background
Congenital birth defects (CBDs) significantly affect child mortality and disability worldwide, yet regional understandings of their burden and contributing factors remain poorly defined. This study examines global trends in prevalence, disability-adjusted life years (DALYs), and mortality related to CBDs and their 11 subtypes from 1990 to 2021.
Methods
Employing data from the Global Burden of Disease Study 2021, this cross-sectional analysis covers CBD prevalence, mortality, and DALYs among children aged 0–14 years. It also investigates relationships between these trends and the Socio-Demographic Index (SDI), employing advanced statistics like Joinpoint regression and heatmap analyses to elucidate geographic variations.
Results
From 1990 to 2021, global trends in CBD prevalence, mortality, and DALYs generally decreased. The neonatal period (<28 days) presented the highest risk, with notable prevalence, mortality, and DALY rates. Among the 11 subtypes, congenital musculoskeletal and limb anomalies were most prevalent, while Turner syndrome was least common. Congenital heart anomalies recorded the highest mortality and DALYs. There was a negative correlation between CBD burden and SDI.
Conclusion
The study underscores the global impact of CBDs on child health, pointing to significant regional disparities linked to socioeconomic factors. The findings advocate for enhanced prevention and management strategies specifically designed for regions with lower SDIs.
KEY MESSAGES
- The prevalence, mortality, and DALYs attributable to congenital birth defects (CBDs) significantly decreased globally from 1990 to 2021, though notable regional disparities persist.
- A negative correlation was observed between the burden of CBDs and the Socio-Demographic Index (SDI), highlighting the role of socioeconomic development in reducing disease burden.
- The neonatal period (<28 days) remains the critical window for addressing CBD burden, emphasizing the need for enhanced early screening and intervention programs globally.
Introduction
Congenital birth defects (CBDs) refer to structural, functional, or metabolic abnormalities that occur during fetal development in the womb and potentially affect the health functions of multiple systems [1]. The Global Burden of Disease (GBD) database categorizes CBDs into 11 main types, including congenital heart anomalies, congenital musculoskeletal and limb anomalies, digestive congenital anomalies, urogenital congenital anomalies, neural tube defects, Down syndrome, orofacial clefts, Turner syndrome, Klinefelter syndrome, other chromosomal abnormalities and other CBDs. These birth defects have a significant impact on child health globally and are among the leading causes of neonatal death and long-term disability [2]. CBDs not only affect the quality of life of individuals and increase the burden of caregiving on families but also impose a significant economic burden on public health systems [3]. Congenital heart defects are among the most common types of birth defects worldwide and a major cause of infant mortality in many countries. Each year, around 10 million infants are impacted by structural heart defects, with some requiring surgical intervention shortly after birth [4].
Neural tube defects, such as anencephaly and spina bifida, are among the types of birth defects with relatively high mortality rates, particularly in low-income countries. Owing to the lack of resources for early intervention and prenatal screening, the mortality rate from neural tube defects is even more pronounced [5]. Research indicates that folate deficiency is a leading cause of neural tube defects. Folate supplementation programs targeting pregnant women have significantly reduced the incidence of such defects in some regions [6]. Down syndrome is a genetic disorder caused by trisomy of chromosome 21, with a global incidence rate that is consistently estimated at approximately 1 in every 800 live births [7]. Individuals with Down syndrome not only have distinctive facial features, developmental delays and intellectual disability but also frequently experience various health issues, such as congenital heart defects, immune system dysfunction, and an increased risk of leukemia [8]. These health issues increase the medical burden on and complexity of caregiving for children with Down syndrome. Orofacial clefts have a relatively uniform incidence rate globally and primarily affect the facial structure of infants and normal functions such as feeding and speech [9]. Owing to the need for multidisciplinary plastic surgery and rehabilitative support, cleft lip and palate impose significant economic pressures on families, particularly in low- and middle-income countries, where many patients do not receive effective treatment [10]. Among all birth defects, sex chromosome abnormalities such as Klinefelter syndrome and Turner syndrome are relatively rare, but their impact is significant. Individuals with Klinefelter syndrome typically have testicular dysfunction and increased height and may experience learning difficulties [11]. Turner syndrome, which affects only females, is often associated with short stature, cardiovascular abnormalities, and other health issues that affect lifespan and quality of life [12]. In addition to these common birth defects, other chromosomal abnormalities and various unclassified CBDs also pose a significant threat to global child health. Overall, the etiology of CBDs is complex and involves interactions among genetic, environmental, and maternal health factors [13,14]. These factors affect not only the incidence of defects but also their severity. It is estimated that approximately 6% of infants worldwide are affected by these defects. Approximately 240,000 newborns die within the first month of life each year due to birth defects, and an additional 170,000 children aged 1 to 59 months die from these defects [15]. Birth defects have become the fourth leading cause of death in children under five, accounting for nearly 10% of all deaths in this age group [3].
Although epidemiological descriptions of CBDs are crucial, comprehensive data on the prevalence of, mortality due to, and DALYs attributable to CBDs are still lacking. Therefore, to understand the global and regional trends of CBDs and to gain a deeper understanding of the impacts of these defects on global child health, this study aimed to analyze the trends in the prevalence of, DALYs attributable to, and mortality due to CBDs and their 11 subtypes globally from 1990 to 2021. The study will first analyze the overall burden of CBDs, followed by a discussion of the burden of each subtype, clearly illustrating the differences and relationships between the overall burden and the subtypes. This information will not only help policymakers identify high-risk groups but also support improvements in screening, prevention, and intervention policies for birth defects.
Methods
Data sources and definitions
The data for this study were sourced from the GBD 2021 database, which provides comprehensive information on 369 diseases and injuries and 88 risk factors across 204 countries and regions from 1990 to 2021. Specifically, data related to CBDs and their 11 subtypes, including data on the prevalence of, mortality due to, and DALYs attributable to CBDs, were extracted. The data were accessed and downloaded through the Global Health Data Exchange platform (GHDx) (http://ghdx.healthdata.org/gbd-results-tool). The GBD classification system creates a detailed and nonoverlapping catalog of health conditions, which are systematically divided into four levels. In this classification, birth defects are listed under noncommunicable diseases at the first level and further categorized as other noncommunicable diseases at the second level. The birth defects subgroup (B12.1) at the fourth level includes eleven conditions: congenital heart anomalies, congenital musculoskeletal and limb anomalies, digestive congenital anomalies, urogenital congenital anomalies, neural tube defects, Down syndrome, orofacial clefts, Klinefelter syndrome, Turner syndrome, other chromosomal abnormalities and other CBDs. This study utilized a deidentified aggregate dataset, for which individual informed consent was not required. This provision was reviewed and approved by the University of Washington’s ethics review board, which waived the informed consent requirement for this study. As this study utilized publicly available, deidentified aggregate data from secondary sources, no direct involvement of human participants occurred. The study methods comply with the principles of the Declaration of Helsinki. The data sources for the Global Burden of Disease (GBD) database include vital registration, verbal autopsies, censuses, surveys, and disease registries. In the modeling process, GBD handles missing data with Bayesian models and meta-regressions, using data from neighboring regions for estimation. For non-fatal health outcomes, GBD applies microsimulation methods to calculate disability weights and prevalence, generating global health metrics [16].
The purpose of this study was to analyze the trends in the prevalence of, DALYs attributable to, and mortality due to CBDs and their 11 subtypes globally from 1990 to 2021. A range of metrics, including the estimated annual percentage change (EAPC) and average annual percentage change (AAPC), were employed in the analysis. The 95% uncertainty intervals (UIs) were defined by the 2.5th and 97.5th percentiles, representing the 25th and 975th values of 1000 sorted estimates, respectively. These intervals were calculated via the algorithmic methods employed in the GBD study.
Global and regional disease burden analyses
To analyze the global distribution and regional differences of CBDs and their subtypes, we generated global maps and conducted regional comparative analyses. The data were aggregated according to the geographic regions defined by the GBD study, and maps were created via the ggplot2 and sf packages in R software (version 4.4.1) to visualize the distribution of disease burden.
Time trend analysis
Joinpoint regression analysis was used to evaluate the time trends in the prevalence of, mortality due to, and DALYs attributable to birth defects from 1990 to 2021. The analysis utilized the ‘segment’ and ‘broom’ packages in R to identify significant changes in time trends.
Population analysis
The distribution of birth defects across different population groups, including age, sex, and specific subgroups, was analyzed. The data were stratified into eight age groups (0–28 days, 1–5 months, 6–11 months, 12–23 months, 2–4 years, 5–9 years, and 10–14 years) and analyzed separately for males and females. Statistical analysis was performed in R, with the results visualized via the ggplot2 package.
Heatmap analysis
Heatmaps were generated to visualize the correlations between different variables and the burden of CBDs and their 11 subtypes. These heatmaps were created via the ggplot2 package in R, allowing us to identify patterns and relationships between different factors, such as age, sex, and regional differences.
AAPC analysis
The AAPC for the major categories of CBDs was calculated to measure the average rate of change in disease burden over time. This analysis was performed via the ‘segment’ package in R, with the results expressed as annual percentage changes and 95% confidence intervals (95% CIs).
Sociodemographic index (SDI) analysis
The relationship between the SDI and the disease burden of CBDs was examined by calculating SDI-specific prevalence rates. The disease burden under different levels of socioeconomic development was compared using SDI categories (low, low-middle, middle, high-middle, and high SDIs). Data processing and visualization were carried out via the dplyr and ggplot2 packages in R 4.4.1.
Statistical analysis
All the statistical analyses and data visualizations were conducted via R (version 4.4.1) and the Free Statistical analysis platform (version 1.9, Beijing, China). Descriptive statistics were generated for all key variables, and the results are presented as the means with 95% UIs. For trend analyses, a p value <0.05 was considered to indicate statistical significance.
Results
Global overview of the burden of CBDs
Analysis of the prevalence of, mortality due to, and disability-adjusted life years (DALYs) attributable to CBDs for the population aged 0–14 years (1990–2021)
Regional variation analysis
From 1990–2021, the prevalence of birth defects among children aged 0–14 years decreased from 1705.17 cases per 100,000 people (95% UI, 1535.59–1891.43) in 1990 to 1572.51 cases per 100,000 people (95% UI, 1413.66–1747.51) in 2021, representing a 7.78% decrease. The DALY rate decreased from 4798.45 years of life lost per 100,000 population (95% UI, 2976.77–6202.37) in 1990 to 2271.43 years of life lost per 100,000 population (95% UI, 1946.60–2729.33) in 2021. Mortality rates also decreased from 51.91 deaths per 100,000 people (95% UI, 31.49–67.70) in 1990 to 23.65 deaths per 100,000 people (95% UI, 20.03–28.95) in 2021 (Table 1 and Tables S1–S2).
| Location | Rate per 100 000 (95% UI), percent change (%) | ||||||
|---|---|---|---|---|---|---|---|
| 1990 | 2021 | 1990–2021 | |||||
| Prevalence number | Prevalence Rate | Prevalence number | Prevalence Rate | Percent change | EAPC | AAPC | |
| Global | 29655482.29(26706229.74,32894804.58) | 1705.17(1535.59,1891.43) | 31636759.02(28440817.08,35157389.69) | 1572.51(1413.66,1747.51) | −7.78(-9.27,-6.30) | −0.17(-0.21,-0.13) | −4.20(-4.33,-4.08) |
| High SDI | 3101400.62(2842296.64,3399234.72) | 1669.14(1529.70,1829.44) | 2623497.47(2408444.04,2864268.57) | 1520.55(1395.90,1660.09) | −8.90(-10.35,-7.44) | −0.23(-0.26,-0.19) | −4.80(-4.94,-4.67) |
| High-middle SDI | 4667855.61(4232232.21,5130754.52) | 1705.94(1546.74,1875.12) | 3489088.95(3174130.74,3805648.62) | 1511.15(1374.74,1648.25) | −11.42(-13.47,-9.34) | −0.12(-0.22,-0.02) | −6.36(-6.51,-6.21) |
| Middle SDI | 9187648.46(8269613.63,10219054.66) | 1591.72(1432.68,1770.41) | 8214106.06(7401467.68,9070575.32) | 1449.05(1305.69,1600.14) | −8.96(-10.78,-6.98) | −0.15(-0.21,-0.10) | −4.72(-4.82,-4.61) |
| Low-middle SDI | 8410191.39(7541709.94,9406253.79) | 1781.40(1597.44,1992.38) | 9495469.46(8509708.00,10635960.24) | 1637.60(1467.59,1834.29) | −8.07(-9.57,-6.56) | −0.24(-0.26,-0.21) | −4.65(-4.75,-4.55) |
| Low SDI | 4265884.07(3814937.04,4768848.67) | 1863.54(1666.54,2083.25) | 7791719.27(6937731.73,8682606.86) | 1693.02(1507.46,1886.60) | −9.15(-10.72,-7.52) | −0.33(-0.37,-0.30) | −5.36(-5.54,-5.18) |
| Andean Latin America | 209116.48(186352.40,233807.98) | 1408.00(1254.73,1574.25) | 253454.93(229504.61,278461.42) | 1400.71(1268.35,1538.90) | −0.52(-3.86,3.14) | −0.00(-0.04,0.03) | −0.08(-0.22,0.07) |
| Australasia | 72164.02(65948.73,79052.22) | 1573.59(1438.06,1723.79) | 82147.78(74917.56,89761.88) | 1433.36(1307.20,1566.21) | −8.91(-12.56,-5.22) | −0.26(-0.30,-0.23) | −4.93(-5.39,-4.47) |
| Caribbean | 177637.67(159937.38,196311.42) | 1556.53(1401.43,1720.16) | 179205.57(160769.32,199228.94) | 1557.62(1397.37,1731.66) | 0.07(-2.52,2.45) | 0.03(0.01,0.05) | 0.03(-0.13,0.18) |
| Central Asia | 477802.21(434256.92,522404.67) | 1911.88(1737.64,2090.35) | 531551.87(483756.61,583430.46) | 1920.64(1747.94,2108.09) | 0.46(-1.55,2.36) | 0.24(0.11,0.36) | 0.09(-0.21,0.38) |
| Central Europe | 433700.10(397233.85,475431.63) | 1470.99(1347.30,1612.53) | 255067.99(233391.78,280593.84) | 1440.96(1318.51,1585.17) | −2.04(-3.92,-0.24) | 0.04(-0.01,0.09) | −0.91(-1.05,-0.77) |
| Central Latin America | 1374061.17(1174995.96,1601052.36) | 2134.26(1825.06,2486.83) | 1235280.25(1071767.14,1417672.83) | 1945.78(1688.22,2233.08) | −8.83(-11.50,-6.12) | −0.32(-0.34,-0.30) | −6.17(-6.32,-6.01) |
| Central Sub-Saharan Africa | 480552.91(429514.75,534143.38) | 1899.52(1697.78,2111.35) | 980981.71(877223.58,1100080.97) | 1671.70(1494.89,1874.66) | −11.99(-15.10,-8.97) | −0.40(-0.46,-0.33) | −7.11(-7.33,-6.89) |
| East Asia | 4899250.54(4420609.08,5419042.17) | 1485.37(1340.25,1642.96) | 3264359.00(2970053.14,3569065.18) | 1221.00(1110.91,1334.97) | −17.80(-20.93,-14.52) | −0.28(-0.40,-0.16) | −8.84(-9.26,-8.41) |
| Eastern Europe | 1018481.14(924301.20,1116394.25) | 1979.09(1796.09,2169.36) | 622934.80(566379.69,682382.86) | 1757.52(1597.95,1925.24) | −11.20(-12.79,-9.57) | −0.01(-0.17,0.15) | −7.07(-7.48,-6.66) |
| Eastern Sub-Saharan Africa | 1627728.53(1460890.52,1818379.74) | 1797.19(1612.98,2007.69) | 2837515.10(2528821.72,3167769.44) | 1590.25(1417.25,1775.34) | −11.51(-13.49,-9.55) | −0.41(-0.45,-0.38) | −6.51(-6.78,-6.25) |
| High-income Asia Pacific | 829646.00(723781.43,946799.69) | 2356.98(2056.22,2689.81) | 500742.26(437971.57,570313.87) | 2232.91(1953.00,2543.14) | −5.26(-8.38,-1.96) | −0.19(-0.22,-0.15) | −4.07(-4.31,-3.84) |
| High-income North America | 862272.71(786239.59,947421.93) | 1398.03(1274.76,1536.09) | 818144.52(752480.01,884316.10) | 1246.81(1146.74,1347.65) | −10.82(-13.58,-8.24) | −0.29(-0.35,-0.23) | −4.56(-4.98,-4.14) |
| North Africa and Middle East | 2611692.88(2363533.46,2896769.09) | 1859.05(1682.40,2061.97) | 3114927.37(2814423.58,3443403.63) | 1699.16(1535.23,1878.34) | −8.60(-10.36,-6.79) | −0.18(-0.24,-0.12) | −5.04(-5.34,-4.75) |
| Oceania | 36568.09(33070.48,40388.19) | 1364.55(1234.04,1507.10) | 74727.39(67587.21,83158.76) | 1470.76(1330.23,1636.70) | 7.78(4.17,11.13) | 0.27(0.25,0.29) | 3.36(3.25,3.47) |
| South Asia | 7862221.56(7019469.58,8873882.89) | 1814.24(1619.77,2047.68) | 8292168.03(7404632.26,9431558.89) | 1635.47(1460.42,1860.19) | −9.85(-11.48,-8.11) | −0.31(-0.33,-0.28) | −5.79(-5.92,-5.66) |
| Southeast Asia | 2460290.37(2187223.67,2776613.73) | 1440.89(1280.97,1626.15) | 2372963.75(2111292.86,2668711.43) | 1374.41(1222.85,1545.70) | −4.61(-6.12,-2.92) | −0.13(-0.15,-0.11) | −2.19(-2.24,-2.13) |
| Southern Latin America | 249631.94(222831.78,278496.02) | 1672.42(1492.87,1865.79) | 241944.60(216758.08,268647.77) | 1669.10(1495.34,1853.31) | −0.20(-4.37,4.20) | 0.16(0.08,0.24) | −0.10(-0.27,0.07) |
| Southern Sub-Saharan Africa | 335590.44(304913.60,367933.25) | 1622.05(1473.77,1778.37) | 384297.58(348085.27,423085.87) | 1596.86(1446.39,1758.03) | −1.55(-3.66,0.64) | 0.07(-0.00,0.13) | −1.11(-1.30,-0.92) |
| Tropical Latin America | 799568.09(732859.30,865954.40) | 1491.34(1366.92,1615.17) | 725190.26(658133.24,798048.66) | 1444.80(1311.20,1589.96) | −3.12(-6.86,0.82) | −0.07(-0.09,-0.05) | −1.45(-1.80,-1.10) |
| Western Europe | 1194459.75(1102917.88,1290811.80) | 1681.90(1553.01,1817.58) | 1124837.82(1031660.11,1231368.96) | 1651.30(1514.51,1807.69) | −1.82(-4.44,0.58) | 0.01(-0.03,0.06) | −1.13(-1.25,-1.01) |
| Western Sub-Saharan Africa | 1643045.70(1469390.12,1834623.30) | 1869.65(1672.04,2087.65) | 3744316.47(3343370.45,4181135.47) | 1743.46(1556.77,1946.85) | −6.75(-8.12,-5.25) | −0.26(-0.32,-0.20) | −4.05(-4.26,-3.85) |
| Subtypes/age | <28 days | 1–5 months | 6–11 months | 12–23 months | 2–4 years | 5–9 years | 10–14 years | <28 days | 1–5 months | 6–11 months | 12–23 months | 2–4 years | 5–9 years | 10–14 years |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1990 prevalence rate | 2021 prevalence rate | |||||||||||||
| Congenital musculoskeletal and limb anomalies | 1875.97(1332.70,2620.44) | 1680.68(1198.52,2350.16) | 1373.25(974.97,1916.58) | 984.21(719.59,1358.85) | 484.71(372.24,618.15) | 343.49(261.98,441.10) | 329.48(252.44,419.02) | 1853.56(1321.53,2551.90) | 1661.60(1183.35,2290.68) | 1359.09(972.53,1894.76) | 964.73(709.54,1329.10) | 437.50(339.06,552.60) | 297.52(227.60,382.59) | 284.32(218.27,362.80) |
| Congenital heart anomalies | 1551.43(1326.20,1807.44) | 1082.63(930.52,1259.22) | 841.92(717.93,994.78) | 722.87(617.50,855.83) | 507.54(440.73,580.12) | 265.94(226.88,319.44) | 180.07(158.40,200.76) | 1539.77(1309.62,1805.88) | 1065.71(918.86,1239.67) | 819.89(702.02,969.68) | 705.50(603.23,834.17) | 505.19(437.73,575.14) | 272.57(229.53,329.51) | 181.22(159.62,202.57) |
| Digestive congenital anomalies | 373.38(286.41,475.16) | 336.19(255.23,429.16) | 292.14(221.09,372.31) | 249.27(190.84,315.49) | 175.66(133.98,220.01) | 98.94(74.11,125.03) | 56.05(41.66,71.82) | 339.47(266.97,434.57) | 308.63(240.74,396.77) | 268.67(209.81,342.24) | 226.20(177.08,285.36) | 154.87(120.10,195.17) | 85.84(65.46,109.97) | 50.04(37.97,63.79) |
| Down syndrome | 59.24(49.34,71.91) | 54.93(45.62,66.74) | 51.03(42.24,62.11) | 49.38(40.53,60.22) | 47.71(39.25,58.26) | 46.67(38.53,57.23) | 47.07(38.81,57.85) | 44.38(37.53,51.62) | 41.96(35.13,49.30) | 39.07(32.50,46.26) | 37.76(31.20,45.05) | 36.86(30.37,44.16) | 36.06(29.77,43.39) | 35.29(29.11,42.44) |
| Klinefelter syndrome | 50.98(36.13,67.63) | 49.32(34.97,65.33) | 46.69(33.05,61.72) | 42.76(30.35,56.67) | 34.94(25.43,46.23) | 25.87(19.26,34.17) | 20.29(15.09,26.50) | 52.38(37.23,69.10) | 50.74(36.07,66.77) | 48.00(34.15,63.08) | 43.90(31.61,57.92) | 35.45(25.80,46.41) | 26.22(19.62,34.41) | 20.88(15.62,27.20) |
| Neural tube defects | 105.63(86.81,125.41) | 73.21(60.02,86.37) | 53.77(43.94,63.83) | 46.68(37.95,55.92) | 39.36(31.94,47.43) | 30.57(24.79,36.92) | 25.79(20.98,30.85) | 70.09(60.63,79.33) | 54.51(46.92,62.41) | 44.17(37.67,51.12) | 39.26(33.26,45.91) | 32.54(27.40,38.47) | 25.13(20.98,29.56) | 21.91(18.43,25.91) |
| Orofacial clefts | 182.79(134.86,232.48) | 177.88(130.43,226.74) | 161.39(118.76,206.78) | 118.41(92.87,147.00) | 72.95(57.89,88.87) | 60.51(48.02,74.10) | 54.52(43.39,66.80) | 140.68(103.83,185.54) | 139.01(102.13,183.87) | 131.42(97.27,171.99) | 104.39(81.59,130.21) | 69.89(55.55,85.74) | 59.04(46.68,73.15) | 55.37(43.98,68.58) |
| Turner syndrome | 18.17(13.64,24.46) | 17.83(13.37,23.98) | 17.22(12.89,23.13) | 16.29(12.31,21.84) | 14.25(10.85,19.20) | 11.53(8.83,15.41) | 9.49(7.30,12.60) | 18.43(13.82,24.67) | 18.05(13.55,24.24) | 17.39(13.00,23.35) | 16.38(12.32,21.95) | 14.12(10.72,19.03) | 11.31(8.68,15.18) | 9.37(7.19,12.42) |
| Urogenital congenital anomalies | 865.14(657.51,1147.20) | 757.19(580.92,973.43) | 611.66(476.70,781.96) | 469.32(366.00,592.45) | 287.03(216.79,369.21) | 161.81(118.38,214.87) | 104.78(74.46,140.61) | 822.63(616.90,1075.07) | 723.73(546.69,934.20) | 588.09(455.24,763.19) | 454.27(356.08,584.25) | 280.89(212.15,359.97) | 164.43(119.29,218.76) | 112.16(80.76,153.30) |
| Other chromosomal abnormalities | 354.22(300.57,422.90) | 314.99(270.42,369.94) | 271.63(235.62,317.60) | 152.91(133.06,177.23) | 128.02(110.63,148.35) | 92.99(80.40,107.73) | 74.57(64.16,86.59) | 336.37(281.53,404.85) | 297.95(252.80,355.26) | 253.32(218.07,297.65) | 140.37(120.49,163.85) | 114.90(99.72,133.62) | 81.67(71.65,94.39) | 63.80(55.54,73.88) |
| Other congenital birth defects | 173.95(92.56,280.67) | 173.10(92.10,279.31) | 172.52(91.78,278.37) | 171.25(91.09,276.31) | 170.63(90.82,275.18) | 170.42(90.85,274.66) | 163.39(87.06,262.53) | 187.07(99.31,300.58) | 186.51(99.01,299.59) | 185.75(98.61,298.24) | 184.22(97.82,295.44) | 179.95(95.62,287.78) | 179.65(95.63,286.93) | 183.79(98.12,293.44) |
An analysis of long-term trends over 30 years revealed a clear downward trajectory for the prevalence of, mortality due to, and DALYs attributable to CBDs. The Average Annual Percentage Change (AAPC) in the prevalence, mortality, and DALYs were −4.204 (95% CI, −4.33 to −4.08), −0.91 (95% CI, −0.91 to −0.90), and −81.00 (95% CI, −81.78 to −80.24), respectively. Notably, while the decreases in the mortality and DALY rates were consistent, the prevalence slightly increased between 1998 and 2009, potentially associated with advancements in prenatal screening worldwide (Table 1, Tables S1–S2 and Figure 1). At the regional level across 21 world regions, a slight increase in the EAPC in prevalence was observed in Central Asia (EAPC: 0.24; 95% UI, 0.11–0.36) and Oceania (EAPC: 0.27; 95% UI, 0.25–0.29). Globally, however, the prevalence, mortality, and DALY trends predominantly decreased over the last 30 years (Table 1 and Tables S1–S2).
At the country level, among 204 nations, 55 presented a slight increase in the EAPC in prevalence, with Georgia showing the most significant increase (EAPC: 0.83; 95% UI, 0.63–1.03). Over the long term, discrepancies were noted between the AAPCs and EAPCs in prevalence, with only 41 countries demonstrating increasing prevalence trends, with Georgia again showing the most significant increase (AAPC: 6.34; 95% UI, 5.66–7.02). For DALYs and mortality, EAPC increases were identified in 14 countries, with Turkmenistan showing the highest increase in DALYs (EAPC: 2.24; 95% UI, 1.50–3.00) and mortality (EAPC: 2.34; 95% UI, 1.56–3.14). The long-term trends revealed slight inconsistencies between the AAPC and EAPC, with only 9 countries exhibiting increasing trends, such as Niue (mortality: AAPC: 1.51; 95% UI, 1.29–1.73) (DALYS: AAPC: 132.01; 95% UI, 112.48–151.55) (Table S3, Figure 2).
In certain countries, such as Georgia, there is a significant difference between EAPC and AAPC. The smaller EAPC reflects the long-term trend, minimizing the impact of short-term fluctuations and providing a more accurate depiction of long-term changes. In contrast, the higher AAPC is influenced by specific events in certain years, such as advancements in prenatal screening (1998–2009), which caused significant short-term fluctuations and led to a higher annual change.
Age and sex differences in the burden of CBDs
From 1990 to 2021, the burden of CBDs in children aged 0–14 years, including prevalence, mortality, and DALYs, showed an overall downward trend. While the prevalence across age groups remained relatively stable, the neonatal period (<28 days), as a critical window for identifying and monitoring birth defects, exhibited the highest prevalence, mortality, and DALY rates. The DALY and mortality rates in neonates have significantly decreased over the past three decades (DALYs EAPC: −1.70; 95% UI, −1.75 to −1.64; mortality EAPC: −1.70; 95% UI, −1.75 to −1.65) (Table S4, Figures S1–S2). However, in the trend analysis of prevalence, there was a short-term increase in birth defect prevalence among children aged 0–14 years between 1998 and 2009. Notably, the prevalence of birth defects in infants under 2 years old rebounded in 2015, while other age groups continued to show a steady decline. It is also worth noting that throughout the entire observation period, no significant differences were observed in prevalence, DALYs, or mortality rates based on gender. (Figure 2, Figure S3).
National level
Changes in prevalence
The prevalence of congenital heart anomalies was highest in Mongolia in 1990 but highest in Armenia in 2021. Congenital musculoskeletal and limb anomalies had the highest prevalence in Mexico in 1990 and in Japan in 2021. Digestive congenital anomalies were most prevalent in Cuba in 1990 but most prevalent in Brazil in 2021. Urogenital congenital anomalies were most severe in South Africa in 1990 but most severe in Singapore in 2021. Orofacial clefts consistently had the highest prevalence in Palestine over the 30 years. Klinefelter syndrome was most common CBD in Greenland in 1990 and in Portugal by 2021. Down syndrome had the highest prevalence in Ghana in 1990 and in Brunei by 2021. Neural tube defects had the highest prevalence in Sierra Leone in 1990, with Burkina Faso having the highest prevalence in 2021. Other chromosomal abnormalities were most prevalent in Ireland in 1990 and in Brunei in 2021. Turner syndrome had the highest prevalence in Greenland in 1990 but to the highest prevalence in New Zealand in 2021. Other CBDs consistently had the highest prevalence in Madagascar (Figure 4).
Changes in DALYs and mortality
At the national level, changes in DALYs and mortality over the 30 years showed consistent trends. Afghanistan had the highest DALY and mortality rates for congenital heart anomalies and congenital musculoskeletal and limb anomalies in both 1990 and 2021. The highest rates of digestive congenital anomalies were reported in Sierra Leone in 1990 and Burkina Faso in 2021. Urogenital congenital anomalies were most severe in Turkey in 1990 and in Afghanistan in 2021. Orofacial clefts had the lowest DALY and mortality rates in Laos in 1990 and the highest DALY and mortality rates in Papua New Guinea in 2021. Down syndrome had the highest rates in Afghanistan in 1990 and in Tokelau in 2021. Neural tube defects consistently presented the highest burden in Sudan over the 30 years. Other chromosomal abnormalities were most common in Palestine in 1990 and in Brunei in 2021. Other CBDs consistently had the highest prevalence in Afghanistan (Figure 4).
Sex and age differences in CBD subtypes
Sex differences
Klinefelter syndrome is exclusively observed in males, whereas Turner syndrome occurs only in females. Neither syndrome showed significant differences in DALY or mortality rates. For other CBDs, no significant sex differences were found in the prevalence, mortality, or DALY rates. When trends in the prevalence of, mortality due to, and DALYs attributable to CBDs in males and females in 1990 and 2021 were analyzed, the following patterns were observed:
Congenital musculoskeletal and limb anomalies consistently had the highest prevalence among females, whereas congenital heart anomalies had the highest mortality and DALY rates. Turner syndrome had the lowest prevalence, while the mortality and DALY rates were lowest for urogenital congenital anomalies in 1990 and for orofacial clefts in 2021.
Congenital musculoskeletal and limb anomalies also had the highest prevalence among males, with neural tube defects having the lowest prevalence. Congenital heart anomalies had the highest DALY and mortality rates among males, whereas urogenital congenital anomalies had the lowest DALY and mortality rates in 1990, with orofacial clefts having the lowest rates by 2021 (Figures S7–S8).
Age differences
From 1990 to 2021, the neonatal period (<28 days) emerged as the critical window for CBD burden, with the highest mortality and DALY rates observed during this stage. Conversely, children aged 10–14 years had the lowest prevalence of CBDs, which aligns with the early detection of CBDs. Among all age groups, congenital musculoskeletal and limb anomalies consistently had the highest prevalence, whereas Turner syndrome had the lowest.
Congenital heart anomalies had the highest mortality rate across all age groups. Among the specific age groups (<28 days, 1–5 months, 6–11 months, and 12–23 months), urogenital congenital anomalies had the lowest DALY and mortality rates in 1990, with orofacial clefts having the lowest rates by 2021. In the older age groups (2–4 years, 5–9 years, and 10–14 years), orofacial clefts consistently had the lowest DALY rate, whereas the rate of mortality due to orofacial clefts was lowest in 2–4-year-olds. In the group aged 5–14 years, orofacial clefts were not associated with mortality due to their clinical characteristics. Among the 5–9-year-olds and 10–14-year-olds, those with urogenital congenital anomalies had the lowest mortality rate among all the subgroups (Table 2 and Tables S5–S6, Figure S9).
Discussion
Analysis of data from the GBD database revealed significant changes in the disease burden of CBDs among children aged 0–14 years globally from 1990 to 2021. This study comprehensively examined the prevalence of, mortality due to, and DALYs attributable to CBDs worldwide, revealing several key epidemiological findings and trends.
Overall trend in the disease burden of birth defects over the past 30 years
With the advancement of medical technology, the strengthening of public health interventions, and the widespread use of prenatal screening and early diagnosis, the global burden of birth defects has generally shown a declining trend. Analysis of the time trends from 1990 to 2021 indicates that the mortality and DALYs associated with birth defects have decreased overall. However, the prevalence of birth defects experienced a short-term rebound between 1998 and 2009. Although there is currently no literature supporting this data perspective, it may be related to the interaction of multiple factors, including environmental exposure, improvements in diagnostic capabilities, and changes in regional epidemiological characteristics. Specifically, for age groups, the prevalence of birth defects in infants under 2 years old rebounded in 2015, which may be associated with significant advancements in birth defect screening that year, including factors such as regional conflicts, scarcity of medical resources, inadequate public health measures, increased detection rates, the importance of genetic testing, and the development of non-invasive prenatal testing technologies [17–19].
Regional variations and geographic distribution of specific CBD subtypes
Significant differences were observed in the prevalence and mortality rates of certain CBD subtypes across different geographic regions. The prevalence of, mortality due to, and DALYs attributable to CBDs generally showed a decreasing trend globally, indicating improvements in child health conditions worldwide, particularly in the management and prevention of CBDs. However, there was a slight increase in the prevalence in parts of Central Asia and Oceania, which may be due to multiple factors. First, in some areas of Central Asia and Oceania where mining and industrial activities are concentrated, pregnant women may be exposed to environmental pollution and adverse socioeconomic conditions, thereby increasing the risk of CBDs [20]. Second, genetic factors and changes in population structure, such as an increase in consanguineous marriages, may also lead to an increased incidence of certain hereditary birth defects [21]. Furthermore, with advancements in medical diagnostic technologies, more cases, which may have been previously undiagnosed, are being detected, potentially increasing the prevalence rate [22].
First, in the analysis of CBD subtypes revealed that from 1990 to 2021, congenital musculoskeletal and limb anomalies consistently maintained a high prevalence globally. This may be attributed to the fact that congenital musculoskeletal and limb deformities are often associated with a variety of genetic and environmental factors, such as maternal malnutrition, infections, and exposure to drugs and chemicals, which are prevalent in many regions worldwide, leading to a high incidence of these deformities [23,24]. Previous studies have indicated that malnutrition, diabetes, and exposure to certain environmental toxins are directly linked to the high incidence of musculoskeletal deformities in children [25]. Second, the diagnosis of congenital musculoskeletal and limb anomalies is usually straightforward, and these anomalies are often detected shortly after birth through physical examinations and imaging studies. The high prevalence of heart anomalies as the leading birth defects in regions such as sub-Saharan Africa and certain Asian countries may be related to specific genetic characteristics of certain ethnicities or regions [26]. The prevalence of Turner syndrome is relatively low; this may be because this chromosomal abnormality is usually a random event that primarily affects females, and the occurrence of the condition is not influenced by external environmental factors. The diagnosis of Turner syndrome may require further genetic analysis on the basis of clinical symptoms. One study reported that the global incidence of this syndrome is low, but the rate of diagnosis may be limited by the availability of medical resources and genetic counseling [27]. Despite significant improvements in treatment and management methods worldwide, congenital heart anomalies remain the leading cause of high mortality and DALY rates. This finding is related to the complex structural abnormalities involved in congenital heart anomalies, as well as the extreme diversity in the severity and types of heart anomalies [28]. Even with advances in diagnostic and treatment technologies, some heart anomalies remain difficult to diagnose immediately at birth or are challenging to manage owing to their complex treatment requirements [29]. For example, regions such as sub-Saharan Africa, Central Europe, and Central Asia may lack sufficient medical resources and healthcare facilities, leading to higher mortality and DALY rates for children with heart anomalies in these areas. Moreover, global health inequalities result in significant disparities within the same regions in terms of access to medical resources, medical technology, and healthcare services. These inequalities affect the opportunities for children with heart anomalies to receive early and effective treatment, thereby impacting their risk of mortality and quality of life [30]. While East Asia region may have more abundant medical resources, the area still has high mortality and DALY rates for heart defects due to high population density and genetic factors. From 1990 to 2021, countries such as Georgia (located in the Caucasus region) and Turkmenistan (located in Central Asia) presented increasing prevalence, DALY, and mortality rates for certain specific birth defects, such as congenital heart disease, or other significant subtypes. However, it should be noted that these trends are based on GBD data, and due to the lack of independent studies or recent systematic reviews, there may be biases in the data. Therefore, while the data suggests an increased burden of disease in these countries, the results should be interpreted with caution, considering the influence of factors such as medical facilities, population structure, health behaviors, and environmental and socioeconomic factors [31,32]. Previous studies have shown that socioeconomic status significantly shapes the epidemiological burden of congenital birth defects through its impact on healthcare accessibility, environmental exposures, prevention measures, and long-term management capabilities [33]. Moreover, the analysis in this study further demonstrates a significant correlation between the Social Development Index (SDI) and the burden of congenital birth defects (CBDs). As SDI increases, mortality and disability-adjusted life years (DALY) rates associated with birth defects decrease significantly, which may reflect the improved screening and diagnostic capabilities in higher-SDI regions, leading to the identification of more cases. High-SDI regions typically have better healthcare resources, technological advancements, and public health systems, enabling more effective diagnosis, treatment, and prevention of birth defects, thus reducing the disease burden. In contrast, low-SDI regions often face a heavier burden due to insufficient resources, limited healthcare services, and inadequate health education. These regions have weaker public health infrastructure and limited capacity for early diagnosis and treatment of birth defects, leading to a higher burden of congenital defects. Therefore, to reduce the burden in low-SDI regions, we recommend strengthening public health infrastructure, improving maternal care, and enhancing birth defect prevention efforts. These findings suggest that improvements in socioeconomic development not only reduce the disease burden through better availability and quality of medical resources but also lead to more comprehensive detection and recording of disease burdens, further improving the accuracy of burden assessments.
Sex and age differences
Although there were no significant differences in the CBD prevalence, mortality, or DALY rates between sexes, the neonatal period (<28 days) represented a critical window for disease burden. This finding highlights the crucial role of early screening and intervention. Early screening for neonatal diseases is prioritized globally because it significantly reduces infant mortality and morbidity by detecting treatable severe conditions early in life. Various policies have been established in different regions to ensure the effective implementation of newborn screening programs (Universal newborn screening: Implementation guidance). For example, in the United States, each state has a mandatory newborn screening program that includes tests for metabolic disorders, endocrine diseases, cystic fibrosis, sickle cell disease, and other serious conditions. While the list of conditions screened may vary by state, there is a recommended uniform screening panel to standardize medical services across all states [34]. The World Health Organization (WHO) also emphasizes the importance of newborn screening in improving maternal and infant care worldwide. The WHO provides guidelines and support to countries that wish to develop or enhance newborn screening programs, highlighting the importance of early detection and intervention. Recent discussions and roundtable meetings have underscored the need to incorporate modern genomic technologies into newborn screening systems and address inequalities in the implementation of newborn screening programs across different regions [35]. These discussions involve a wide range of stakeholders, including patient advocates, healthcare providers, and policy experts, who collaborate to develop feasible policy solutions aimed at enhancing the effectiveness of newborn screening programs. It is recommended that federal and state agencies increase coordination and transparency to streamline the process of adding new conditions to the screening panel. Strengthening improvements and establishing clear guidelines within federal regulation are crucial for maintaining the efficacy of newborn screening systems and for responding to new scientific discoveries and public health needs [34]. Overall, integrating newborn screening into public health policies is crucial for providing early interventions that significantly improve long-term health outcomes for children worldwide. Enhanced support and updated guidelines from international and national agencies can help optimize these programs, leading to the coverage of more conditions and improving diagnostic techniques to ensure that all children, regardless of their place of birth, undergo life-saving screenings. In contrast, the prevalence of birth defects is lowest among children aged 10–14 years, which may be related to natural physiological development and the natural course of diseases during childhood [3].
An in-depth understanding of the characteristics of the global epidemiological distribution and trends of different CBD subtypes provides a scientific basis for future prevention strategies and intervention measures. These results also emphasize the importance of continuous monitoring, international cooperation, and resource optimization in improving global child health. This study has several limitations, mainly related to data sources and data quality. First, while the Global Burden of Disease (GBD) database provides valuable data on a global scale, over-reliance on this database can lead to incomplete or inaccurate data, particularly in low- and middle-income countries (LMICs). These countries often lack comprehensive birth defect registries, and many regions rely on limited hospital records or local data, which can be unrepresentative, potentially underestimating the burden of congenital defects in certain areas. For instance, in resource-limited countries like Myanmar, the lack of data on high-burden diseases such as neural tube defects and congenital heart defects further exacerbates errors in global burden estimates. Additionally, the public health infrastructure in LMICs is generally underdeveloped, with inadequate infrastructure and technology compromising the accuracy and consistency of data, thus affecting the estimation of the birth defect burden. The incidence of birth defects is also influenced by multiple factors, such as the availability of prenatal and neonatal screening, diagnostic capabilities, and biases in sample selection. In regions with limited screening or diagnostic capacity, many birth defects may go undiagnosed or misdiagnosed, leading to an underestimation of the burden. Furthermore, biases in random sample selection may also affect the representativeness of the data, further impacting the accuracy of global burden estimates. Although this study employed data weighting and multi-source calibration methods to mitigate these biases and validated the data by comparing it with data from high-income countries, the impact of missing or low-quality data cannot be fully eliminated. These biases may result in the underestimation of the birth defect burden in LMICs and affect the accuracy of global disease burden estimates. To improve future accuracy, future research should focus on strengthening birth defect screening systems in LMICs, improving diagnostic capabilities, and enhancing data collection and long-term monitoring to reduce data bias and provide a more scientific and accurate basis for the development of public health policies and interventions.
Conclusion
This study comprehensively analyzed the prevalence, mortality, and DALYs attributed to 11 subtypes of CBDs globally from 1990 to 2021, revealing key epidemiological findings and trends regarding these health burdens. The results indicate a general downward trend in the prevalence, mortality, and DALYs associated with CBDs worldwide, though significant regional disparities exist. Moreover, a negative correlation was found between the SDI and disease burden, suggesting that as socioeconomic development improves, the mortality and DALYs associated with CBDs significantly decrease. These findings underscore the importance of early screening and intervention, particularly neonatal screenings, which can substantially reduce infant mortality and disease burden. Thus, it is recommended that health policymakers strengthen newborn screening programs, expand the scope of screenings, and enhance the systems with modern genomic technologies to improve coverage and diagnostic techniques, ensuring that all children receive life-saving screenings. Future research should delve deeper into the environmental and genetic factors influencing CBDs to devise more effective prevention strategies.
Supplementary Material
Acknowledgments
EY and FC had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. EY, FC, XH, YY, RX, and MC conceptualized and designed the study. EY, FC, XH, WZ, Yuansi Z, HL, Yu Z, YY, and MC contributed to the acquisition, analysis, or interpretation of data. EY and FC drafted the manuscript. EY, FC, XH, WZ, Yuansi Z, HL, Yu Z, YY, RX, and MC revised the manuscript critically for important intellectual content. EY, FC, YY, and Yuansi Z conducted statistical analysis. EY, FC, XH, YY, and MC provided administrative, technical, or material support. EY, FC, XH, WZ, Yuansi Z, HL, RX, and Yu Z contributed to validation. EY and FC worked on visualization. Yu Z and MC supervised the work. All authors have directly accessed and verified the underlying data reported in the manuscript. RX, and MC were responsible for the decision to submit the manuscript. All authors read and approved the final manuscript.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Ethics statement
This study was reviewed and approved by the Ethics Committee of Wenzhou People’s Hospital (approval number: KY-202501-015). Additionally, the Ethics Committee of The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University determined the study to be exempt from full review, as it utilized publicly available, de-identified aggregate data from the Global Burden of Disease database and other secondary sources. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). No direct human participation occurred, and no identifiable personal data were used. The study was also reviewed by the University of Washington Institutional Review Board (IRB), which waived the requirement for informed consent, as the research involved publicly available, de-identified data and fell under the exempt category according to U.S. Department of Health and Human Services regulations (45 CFR 46.101(b)).
Data availability statement
The data that support the findings of this study are available from the corresponding author, XR, upon reasonable request.