Epidemiological Dynamics of Urogenital Congenital Anomalies: A Temporal and Regional Analysis from the Global Burden of Disease Study 2021

Epidemiological Dynamics of Urogenital Congenital Anomalies: A Temporal and Regional Analysis from 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 Epidemiological Dynamics of Urogenital Congenital Anomalies: A Temporal and Regional Analysis from the Global Burden of Disease Study 2021 城玮 范, 治春 董, 公平 伍, 冬暖 姚, 伟涛 于, 学明 马, 俊强 田 This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6330031/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background： Urogenital congenital anomalies (UCAs) are significant contributors to morbidity and disability worldwide, disproportionately affecting regions with limited healthcare resources [1] . These conditions impose a substantial burden on individuals and healthcare systems, yet their global trends and disparities remain insufficiently understood [2] . This study aimed to analyze temporal trends in incidence, prevalence, and Disability-Adjusted Life Years (DALYs) of UCAs across five Socio-Demographic Index (SDI) regions from 1990 to 2021, alongside a detailed assessment of disparities among 204 countries and territories. Methods: Using data from the Global Burden of Disease (GBD) Study 2021, this study extrapolated age-standardized incidence, prevalence, deaths, and DALYs for UCAs. Temporal trends were evaluated using Joinpoint regression analysis to identify salient changes. The relationship between SDI and UCA burden was analyzed through regression and frontier analysis, while ARIMA modeling was used to project future trends. Results were stratified by SDI, region, and gender, with statistical significance set at P < 0.05. Results: Between 1990 and 2021, the global epidemiological patterns of urogenital congenital anomalies (UCAs) displayed pronounced temporal and regional heterogeneity across varying SDI levels. In High SDI regions, the total mortality burden markedly declined from 1,020 deaths (95% UI: 791–1433) in 1990 to 498 deaths (95% UI: 335–652) by 2021. Concurrently, the age-standardized death rate (ASDR) decreased from 0.17 per 100,000 population (95% UI: 0.13–0.23) to 0.09 per 100,000 (95% UI: 0.06–0.12).In contrast, Low-middle SDI regions observed a reduction in deaths from 3,429 (95% UI: 1262–6649) to 2,817 (95% UI: 1442–5307) over the same period; however, ASDR values remained relatively steady, registering 0.20 (95% UI: 0.08–0.37) in 1990 and 0.19 (95% UI: 0.09–0.38) in 2021.Analysis employing Joinpoint regression identified significant trend shifts. In High SDI regions, ASIR demonstrated a significant downward trajectory between 1990 and 2003 (APC = -0.37%, P < 0.05) and further reduction from 2003 to 2014 (APC = -0.23%, P < 0.05). Notably, an inflection occurred post-2016, with ASIR increasing from 2016 to 2019 (APC = +1.53%, P < 0.05) and accelerating between 2019 and 2021 (APC = +5.94%, P < 0.001).Similarly, Low-middle SDI regions evidenced a significant ASIR decline from 1990 to 1993 (APC = -1.52%, P < 0.05) and from 1993 to 1998 (APC = -0.61%, P < 0.05), succeeded by a positive trend after 2016 (APC = +1.14%, P < 0.05).Clear sex-based discrepancies in UCA-associated mortality were observed across all SDI strata. In High SDI regions, male mortality decreased from 691 cases (95% UI: 505–1068) in 1990 to 335 cases (95% UI: 200–451) in 2021, whereas female deaths declined from 329 (95% UI: 183–536) to 163 (95% UI: 90–268) during the same interval. Correspondingly, the ASDR for males declined from 0.22 per 100,000 (95% UI: 0.16–0.34) to 0.12 per 100,000 (95% UI: 0.07–0.17), and for females from 0.11 (95% UI: 0.06–0.18) to 0.06 (95% UI: 0.03–0.10).In Low-middle SDI settings, male ASDR remained at 0.19 per 100,000 (95% UI: 0.09–0.38) by 2021, while the female ASDR was comparatively lower at 0.10 (95% UI: 0.05–0.18), suggesting persistent sex-related survival advantages.Age-specific analysis indicated that disease prevalence predominantly concentrated in the under-five age group, particularly in High SDI regions, where prevalence rates reached approximately 360 per 100,000 for females and 300 per 100,000 for males.Autoregressive integrated moving average (ARIMA) models projected differentiated trajectories of ASIR and ASDR. In High SDI areas, the ASIR is anticipated to increase from 14.4 per 100,000 (95% PI: 12.1–16.7) in 2021 to 16.2 per 100,000 (95% PI: 13.5–19.3) by 2035, predominantly driven by rising male incidence. Simultaneously, the ASDR is projected to decrease further, attaining 0.07 per 100,000 (95% PI: 0.05–0.10) by 2035 (P trend < 0.001).For Low and Low-middle SDI regions, male ASIR is forecasted to ascend from 19.8 per 100,000 (95% PI: 14.8–26.3) to 22.7 per 100,000 (95% PI: 17.2–28.4) by 2035, whereas ASDR values are expected to stabilize between 0.11–0.13 per 100,000 (95% PI: 0.08–0.16).Frontier analysis underscored significant discrepancies in Disability-Adjusted Life Years (DALYs) attributable to UCAs across 204 nations. High SDI countries, exemplified by Japan and Germany, aligned closely with the efficiency frontier, maintaining DALYs below 10 per 100,000. Conversely, Low SDI countries such as Somalia and Chad recorded DALYs exceeding 150 per 100,000, reflecting substantial deviation from optimal efficiency benchmarks (P < 0.001). Conclusion: This study presents a comprehensive evaluation of the global epidemiology and temporal trajectories of urogenital congenital anomalies (UCAs) from 1990 to 2021, utilizing data from the Global Burden of Disease Study 2021. Through Joinpoint regression, ARIMA-based projections, and frontier benchmarking, we identified substantial heterogeneity in incidence, mortality, and DALYs across development levels and sexes. Sustained mortality reductions were evident in high-SDI contexts, whereas lower-SDI settings exhibited persistent and widening differentials. These findings reflect the interplay of health system maturity, early detection, and policy responsiveness in determining UCA outcomes. Theoretically, the study offers contextualized insight into a neglected congenital subgroup; practically, it supports forecast-informed prioritization and policy targeting. Future research should address current data sparsity, integrate socioeconomic determinants, and enhance model validation in underrepresented settings to guide equitable and effective responses. Our results reinforce the urgency of bridging structural inequities in congenital anomaly control at the global scale. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction UCAs constitute a critical public health concern, particularly affecting neonates and infants, and represent a significant contributor to global pediatric morbidity and mortality [ 3 , 4 ] . UCAs are defined as structural malformations of the urinary tract or genital system present at birth, arising from disruptions during embryonic development (GBD 2019 Diseases and Injuries Collaborators, 2020; Liu et al., 2023) [ 5 , 6 ] . They encompass a broad range of conditions, including congenital anomalies of the kidney and urinary tract (CAKUT), hypospadias, cryptorchidism, bladder exstrophy, and ambiguous genitalia [ 7 ] . The classification of UCAs is primarily anatomical, involving renal anomalies (e.g., renal agenesis, hypoplasia), ureteral anomalies, bladder anomalies, and genital malformations [ 8 ] . The etiology of UCAs is multifactorial, involving genetic mutations, chromosomal abnormalities, epigenetic factors, and environmental exposures such as maternal infections, nutritional deficiencies, teratogenic agents, and socioeconomic conditions (Sun et al., 2023) [ 9 ] .The pathogenesis of UCAs is linked to aberrant expression of critical developmental genes and signaling pathways regulating urogenital differentiation [ 10 , 11 ] . Key mechanisms include disruptions in the renin-angiotensin system, Wnt/β-catenin signaling, and Pax gene family dysregulation [ 11 , 12 ] . Such molecular perturbations impair normal morphogenesis, resulting in structural defects. Clinically, UCAs present with a wide range of severity, from minor anomalies diagnosed incidentally to severe life-threatening conditions such as obstructive uropathy, end-stage renal disease, and impaired fertility [ 13 ] . Treatment strategies are tailored to the anomaly type and severity, typically encompassing surgical correction, hormonal therapies, and long-term medical management [ 14 ] . Early diagnosis and intervention are crucial in improving prognosis; however, in resource-limited settings, lack of access to neonatal screening and specialized care often leads to poor outcomes, increased disability-adjusted life years (DALYs), and heightened psychosocial and economic burdens on affected families [ 15 ] .Globally, the epidemiology of UCAs exhibits significant regional and socioeconomic disparities [ 2 , 6 ] . Data from prior Global Burden of Disease (GBD) studies indicate that the highest prevalence and mortality rates are concentrated in low- and middle-Socio-Demographic Index (SDI) regions, including sub-Saharan Africa and South Asia, where healthcare infrastructures and prenatal diagnostic services are often inadequate (GBD 2019 Risk Factors Collaborators, 2020) [ 16 ] . High SDI countries, benefiting from advanced surgical interventions, comprehensive antenatal care, and systematic neonatal screening programs, have reported declining mortality and DALY rates associated with UCAs [ 2 , 9 ] . Conversely, low-SDI countries continue to bear a disproportionate burden, reflecting inequities in healthcare access, maternal care quality, and socioeconomic development [ 2 , 9 ] . Gender disparities further complicate the burden, with male neonates exhibiting higher incidence rates of certain anomalies, such as hypospadias and cryptorchidism, while females are more affected by specific genital anomalies [ 9 , 17 ] .Despite the recognition of UCAs’ clinical significance, prior epidemiological investigations, particularly those based on GBD frameworks, have largely analyzed congenital anomalies as an aggregate category, often failing to provide disaggregated, detailed assessments specific to UCAs [ 18 ] . Previous studies predominantly focused on overall congenital malformations without accounting for the heterogeneity inherent in urogenital anomalies [ 19 , 20 ] . Moreover, analyses lacked stratification by key variables such as age, sex, SDI quintiles, and regional disparities, limiting the ability to inform targeted healthcare policies [ 21 ] . Additionally, predictive modeling approaches, including autoregressive integrated moving average (ARIMA) forecasting, and healthcare frontier efficiency analyses have not been systematically employed, further restricting insights into future disease trajectories and healthcare system performance [ 22 ] .Addressing these critical gaps, our study offers a comprehensive evaluation of the global, regional, and national burden of UCAs over a 31-year period from 1990 to 2021. Utilizing data from the GBD 2021 study, we systematically analyze incidence, prevalence, mortality, and DALYs, stratified by age, sex, and SDI quintiles. To elucidate temporal trends, we employ Joinpoint regression analysis, while ARIMA modeling is incorporated to predict future trajectories up to 2035. Furthermore, frontier efficiency analyses are applied to benchmark healthcare performance and identify regions with the greatest need for intervention. Through this integrative approach, our study aims to fill longstanding gaps in UCA-specific epidemiological data, provide actionable insights into global disparities, and advocate for evidence-based public health strategies designed to reduce the persistent and disproportionate burden of UCAs worldwide. Materials and methods 1. Overview This study aimed to analyze the global, regional, and national burden of urogenital congenital anomalies (UCAs) from 1990 to 2021. Data were obtained from the Global Burden of Disease (GBD) Study 2021, which provides a standardized framework for assessing the burden of diseases and injuries [23] . Key metrics including incidence, prevalence, deaths, and disability-adjusted life years (DALYs) were examined. Analyses were stratified by age, gender, region, and Socio-Demographic Index (SDI). Temporal trends were evaluated using Joinpoint regression analysis, healthcare efficiencies were assessed with frontier analysis, and future projections were performed using ARIMA modeling [24,] [25] . Reporting adhered to international standards, including the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) [26] . 2. Data Sources Data for this analysis were extracted from the GBD 2021 database, which integrates data from multiple sources, including vital registration systems, hospital records, disease registries, surveys, and academic publications [27] . The GBD framework applies robust data processing techniques, such as bias adjustment and modeling using DisMod-MR 2.1, to ensure consistency across geographies and time periods [28] . Data stratification was performed by five SDI quintiles, 21 GBD regions, and 204 countries or territories. The availability of age- and gender-specific data facilitated detailed exploration of disease burden across different populations. 3. Disease Burdens: Measures and Metrics Disease burden was quantified using four core metrics: Incidence: The number of new UCA cases per 100,000 population annually was calculated to measure the risk of disease occurrence; Prevalence: Age-standardized prevalence rates (ASPRs) were expressed as the number of individuals living with UCAs per 100,000 population. Deaths: Age-standardized death rates (ASDRs) represented the number of deaths attributable to UCAs per 100,000 population annually. DALYs: DALYs, a composite measure of premature mortality and disability, were calculated as the sum of years of life lost (YLLs) and years lived with disability (YLDs). DALYs were age-standardized to allow comparisons across different demographic and geographic contexts. 4. Distribution and Trends of Disease Burdens This study systematically quantified the global, regional, and national burden of UCAs over the period from 1990 to 2021, utilizing comprehensive data extracted from the Global Burden of Disease Study 2021. Key epidemiological indicators including age-standardized incidence rates (ASIR), prevalence rates (ASPR), death rates (ASDR), and disability-adjusted life years (DALYs) were employed to ensure standardized comparisons across countries, socio-demographic strata, and time periods.To capture temporal variations and identify statistically meaningful inflection points, Joinpoint regression analysis was conducted [29] . This technique enabled detection of significant shifts in burden trends by segmenting the time series into discrete intervals, with calculation of annual percentage change (APC) and average annual percentage change (AAPC), each accompanied by 95% confidence intervals to determine statistical significance [30] .Furthermore, frontier efficiency analysis was applied to assess the relative efficiency of countries and regions in mitigating UCA-related DALYs in relation to their Socio-Demographic Index (SDI). The deviation from the optimal efficiency frontier was calculated to reveal disparities between actual and theoretically achievable health system performances [31] .For future burden forecasting, we adopted the Autoregressive Integrated Moving Average (ARIMA) model, which was parameterized based on historical trend data. This model provided short-term projections of UCA burden indicators, contributing to evidence-based health policy planning and resource allocation [32] .Through this integrative approach, the study comprehensively elucidated the historical burden trajectories, geographic heterogeneity, and potential future patterns of UCAs, thereby informing targeted public health interventions. 5. Socio-Demographic Index (SDI) SDI is a composite indicator that captures development levels based on income per capita, educational attainment, and total fertility rates under age 25. SDI values range from 0 (low development) to 1 (high development) [33] . Countries were categorized into five SDI quintiles (low, low-middle, middle, high-middle, and high) for analysis. This stratification facilitated the examination of disparities in disease burden and their association with socio-economic development levels. Previous studies have established the relevance of SDI in explaining variations in health outcomes globally [34] . 6. Joinpoint Regression Analysis Joinpoint regression analysis was conducted to identify significant changes in temporal trends of UCA burden. This method segments the study period into distinct intervals, each characterized by an annual percentage change (APC). Joinpoints, or points of inflection, were identified where statistically significant changes in trend direction occurred [35] . Analyses were stratified by gender and SDI quintile to capture variations across populations. Average annual percentage changes (AAPCs) were calculated for each segment to summarize overall trends during the study period. Joinpoint regression is widely used in disease burden studies for detecting temporal shifts . 7. Frontier Analysis Healthcare system efficiency in managing UCAs was evaluated using frontier analysis. The frontier represents the optimal outcomes achievable at a given SDI level, based on the lowest observed DALYs. Countries were assessed for deviations from this frontier, with larger deviations indicating inefficiencies in healthcare delivery. Results were stratified by SDI quintile to highlight regions with the greatest potential for improvement.This method has been applied in previous studies to assess healthcare system performance and inform policy recommendations [36] . 8. Prediction Analysis Future trends in UCA burden were projected using Autoregressive Integrated Moving Average (ARIMA) modeling. Historical data from 1990 to 2021 were used to forecast incidence, prevalence, and DALYs through 2035. ARIMA models were validated using out-of-sample data and were adjusted for seasonality, autocorrelation, and demographic transitions. Predictions were presented with 95% prediction intervals, providing a robust basis for long-term planning and resource allocation [37] . Projections were stratified by SDI and gender to identify high-risk populations and regions. 9. Statistical Software and Statistical Significance All statistical analyses were conducted using R software (version 4.2.2) and Joinpoint Regression Program (version 4.9.1.0). Data visualization was performed using the ggplot2 package in R. Statistical significance was set at P < 0.05, with all estimates presented alongside 95% confidence intervals. Cross-validation of results with GBD outputs ensured consistency and reliability. 10. Ethical Approval The study utilized publicly available, de-identified data from the GBD Study 2021. Ethical approval was obtained from the Institutional Review Board of the Second Hospital of Lanzhou University. Additional ethical approval was not required, as no identifiable personal data or direct human participants were involved in the analysis. 11. Reporting Standards This study adhered to the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER), ensuring transparency and reproducibility in data collection, analysis, and reporting. All data sources, statistical methods, and analytical codes are available upon request, facilitating replication and secondary analyses (Stevens, 2016). Results Temporal Trends in UCAs Across Five SDI Regions (1990-2021). Table 1 provides a comprehensive comparison of key metrics (deaths, disability-adjusted life years [DALYs], years lived with disability [YLDs], years of life lost [YLLs], prevalence, and incidence) related to urogenital congenital anomalies across five SDI regions (High SDI, High-Middle SDI, Middle SDI, Low-Middle SDI, and Low SDI) for the years 1990 and 2021. The data reveals significant regional differences in the burden of disease, as well as substantial temporal shifts in both incidence and prevalence over the 31-year period. In the High SDI region , the total number of deaths due to urogenital congenital anomalies decreased from 1,020 (0.17 per 100,000) in 1990 to 498 (0.09 per 100,000) in 2021, reflecting a statistically significant reduction in mortality (P < 0.05). Likewise, the total DALYs experienced a marked decline from 108,352 (17.35 per 100,000) in 1990 to 60,842 (10.88 per 100,000) in 2021. A corresponding decrease in YLLs was observed, with a reduction from 89,373 (14.66 per 100,000) to 42,208 (8.17 per 100,000), suggesting improvements in survival outcomes. However, the prevalence of urogenital congenital anomalies remained largely unchanged, from 547,268 cases (78.01 per 100,000) in 1990 to 531,934 cases (78.44 per 100,000) in 2021, indicating a stable disease burden despite reductions in mortality and DALYs. Similarly, the incidence rate decreased slightly from 92,805 cases (15.44 per 100,000) to 71,235 cases (14.4 per 100,000). In the High-Middle SDI region , mortality also showed a significant decline, from 1,333 deaths (0.15 per 100,000) in 1990 to 201,238 deaths (8.99 per 100,000) in 2021, with DALYs reducing from 145,490 (15.95 per 100,000) to 23,864 (2.72 per 100,000). YLLs in this region decreased substantially from 116,702 (13.08 per 100,000) to 677,368 (78.53 per 100,000), while the prevalence rate remained almost unchanged, with 833,380 cases (83.76 per 100,000) in 1990 compared to 82,520 cases (76.46 per 100,000) in 2021. The incidence rate, however, showed a slight decline from 145,347 cases (16.56 per 100,000) to 71,235 cases (14.4 per 100,000), indicating a general reduction in the number of new cases. The Middle SDI region exhibited similar trends, with mortality decreasing from 2,444 deaths (0.12 per 100,000) in 1990 to 1,745 deaths (0.1 per 100,000) in 2021. DALYs in this region decreased from 265,771 (13.33 per 100,000) in 1990 to 201,238 (11.63 per 100,000) in 2021, alongside a reduction in YLLs from 214,090 (10.75 per 100,000) to 144,378 (8.84 per 100,000). While YLDs increased slightly from 2.58 per 100,000 in 1990 to 2.79 per 100,000 in 2021, the prevalence increased marginally from 1,464,798 cases (73.05 per 100,000) to 1,540,119 cases (76.46 per 100,000). The incidence rate also saw a small decline, from 323,762 cases (16.16 per 100,000) to 238,029 cases (15.55 per 100,000). In the Low-Middle SDI region , the number of deaths decreased from 3,429 (0.19 per 100,000) in 1990 to 2,816 (0.15 per 100,000) in 2021, while DALYs dropped from 363,558 (20.60 per 100,000) to 324,373 (17.03 per 100,000). The prevalence increased significantly, from 1,543,625 cases (98.13 per 100,000) in 1990 to 2,006,134 cases (76.46 per 100,000) in 2021. The incidence rate remained relatively stable, with 371,688 cases (20.00 per 100,000) in 1990 and 359,652 cases (19.28 per 100,000) in 2019, indicating a reduction in the number of new cases. Finally, in the Low SDI region , mortality decreased from 1,447 deaths (0.15 per 100,000) in 1990 to 1,968 deaths (0.12 per 100,000) in 2021, and DALYs dropped from 159,376 (16.88 per 100,000) in 1990 to 233,055 (14.26 per 100,000) in 2021. Prevalence in the Low SDI region increased significantly, from 828,395 cases (108.34 per 100,000) in 1990 to 1,583,659 cases (105.02 per 100,000) in 2021, with the incidence rate decreasing slightly from 232,774 cases (21.92 per 100,000) in 1990 to 342,073 cases (19.80 per 100,000) in 2021. Overall, these findings illustrate a general decline in mortality and DALYs across all regions, particularly in the high and high-middle SDI regions. However, the prevalence rates either remained stable or increased, reflecting a persistent disease burden despite the improvements in survival and reductions in disability. These trends highlight the complex nature of urogenital congenital anomalies, with improvements in mortality and DALYs often not translating into reductions in disease prevalence, especially in regions with lower SDI.Abbreviations: DALYs : Disability-Adjusted Life Years. Discussion This study presents a detailed and multifaceted investigation into the global burden and epidemiological characteristics of UCAs, utilizing data from the Global Burden of Disease Study 2021 spanning the years 1990 to 2021. By leveraging advanced statistical techniques—including Joinpoint regression for identifying trend shifts, ARIMA forecasting to predict future disease trajectories, and frontier efficiency analysis to evaluate health system performance—this research dissects the temporal and spatial evolution of UCAs across countries with varying socio-demographic contexts. Stratified by age, sex, and the SDI, the findings illuminate significant heterogeneity in incidence, prevalence, mortality, and DALYs. In particular, the burden of UCAs is markedly uneven across regions and sexes: while higher diagnostic rates are observed among females in high-SDI countries, males in low- and middle-SDI settings consistently exhibit elevated DALYs, indicative of more severe disease progression or reduced access to timely interventions [38] . Although trends in more developed nations show signs of stabilization, sustained or increasing burdens are evident in under-resourced regions, especially sub-Saharan Africa and Southeast Asia, where healthcare infrastructure and access remain limited [39] . The divergent burden patterns observed across SDI levels and between sexes can be attributed to a complex interplay of biomedical, healthcare system, and socio-environmental determinants [38,] [40] . In affluent settings, increased detection of UCAs may be largely explained by widespread access to sophisticated prenatal diagnostic technologies, such as high-resolution ultrasonography and fetal MRI, which facilitate the early identification of both major and minor anomalies during gestation [41] . Additionally, the availability of specialized neonatal and pediatric healthcare services ensures prompt postnatal screening, diagnosis, and therapeutic intervention. These factors collectively contribute to improved outcomes and higher recorded prevalence due to better case capture [42] . In contrast, healthcare systems in many low- and lower-middle-income countries face persistent structural challenges, including inadequate antenatal care coverage, shortages of trained medical personnel, limited public health outreach, and insufficient diagnostic infrastructure [43] . These constraints often lead to delayed recognition of UCAs and hinder effective clinical management, thereby increasing both the duration and severity of disease impact, as reflected in higher DALYs and mortality [38,] [44] . The application of frontier analysis in this study reinforces these findings, revealing substantial inefficiencies in health system performance relative to national development status, which may exacerbate the observed disparities [45] . Furthermore, ARIMA-based time series modeling suggests that incidence rates in high-SDI countries may be reaching a plateau, likely due to a saturation of diagnostic capacity and stabilization of maternal risk profiles, including maternal age and environmental teratogen exposure [46] . However, projections for low-SDI countries indicate that the burden of UCAs is likely to continue rising in the absence of targeted policy interventions and sustained investment in healthcare infrastructure. The gender-based discrepancies uncovered in this analysis, particularly the higher DALY burden among males, may be driven by a range of biological and social factors. Sex-linked genetic susceptibility, hormonal influences on fetal development, and cultural norms affecting healthcare access and decision-making all likely contribute to the observed differences [38] . For instance, in some regions, male children may be prioritized for treatment, while in others, systemic neglect or delays in diagnosis for one gender may lead to disproportionate disease progression and disability. Comparisons with earlier research highlight both consistencies and advancements in understanding UCA burden. Previous GBD reports, including those from 2017 and 2019, aggregated congenital anomalies into broad categories, which limited their capacity to reveal trends specific to urogenital subtypes. The present study addresses this gap by isolating UCAs as an independent focus, uncovering unique burden trajectories that differ across SDI gradients. While previous literature identified growing congenital anomaly burdens in low-income settings, they rarely disaggregated data to the level of specific organ system involvement or provided robust forecasts [47] . By offering more refined estimates and projecting future disease trends through ARIMA modeling, this study enhances the granularity and practical relevance of epidemiological insight, particularly for global health planning and intervention design [48] . Moreover, the gender-specific patterns observed in UCAs burden present a compelling contrast to established findings in other non-communicable diseases. In conditions such as cardiovascular disease or chronic respiratory disorders, males often experience higher mortality and overall burden. In the context of UCAs, however, the pattern is bifurcated: females tend to have higher incidence and prevalence, likely due to more frequent detection in high-SDI regions, while males face a higher DALY burden, suggesting more severe long-term outcomes. This divergence underscores the importance of incorporating gender-sensitive analyses into congenital anomaly research, as well as the need to explore the underlying biological, behavioral, and structural factors contributing to these outcomes [49] . Understanding how sex intersects with healthcare access, survival probabilities, and treatment prioritization is essential for developing equitable and effective intervention strategies. From a health systems standpoint, the study highlights the central role of early detection, referral pathways, and surgical infrastructure in mitigating the consequences of congenital anomalies. In well-resourced settings, integrated maternal and child health services, along with prenatal counseling and timely interventions, have proven instrumental in reducing both mortality and disability associated with UCAs [50] . Conversely, the lack of these services in many low-SDI countries perpetuates cycles of delayed care, untreated conditions, and poor long-term outcomes. Previous studies have acknowledged the growing burden of congenital conditions in underfunded healthcare systems [51] . The present study expands upon this perspective by focusing specifically on urogenital anomalies, offering targeted evidence that can inform specialized resource allocation and program development in global child health initiatives. In conclusion, this study contributes a comprehensive and forward-looking analysis of the global burden of UCAs, elucidating disparities rooted in regional development, healthcare capacity, and gender. By applying advanced modeling approaches and disaggregated data analyses, it provides valuable evidence for health system evaluation and public health policy formation. The persistence of inequities in both disease detection and management highlights the urgency of developing gender-responsive, region-specific, and system-level interventions [18,] [42,] [47] . Future research should prioritize improving surveillance mechanisms in data-scarce regions, advancing our understanding of biological sex differences in congenital anomaly manifestation, and interrogating the social determinants that shape disparities in access to care and outcomes. Addressing these challenges will be critical to achieving global health equity and reducing the preventable burden of UCAs across diverse populations. Abbreviations AAPC Average Annual Percentage Change APC Annual Percentage Change ARIMA Autoregressive Integrated Moving Average ASDR Age-Standardized Death Rate ASIR Age-Standardized Incidence Rate ASPR Age-Standardized Prevalence Rate CI Confidence Interval DALYs Disability-Adjusted Life Years GBD Global Burden of Disease GATHER Guidelines for Accurate and Transparent Health Estimates Reporting IHME Institute for Health Metrics and Evaluation LOESS Locally Estimated Scatterplot Smoothing SDI Socio-Demographic Index UCAs Urogenital Congenital Anomalies UI Uncertainty Interval WHO World Health Organization YLDs Years Lived with Disability YLLs Years of Life Lost Declarations Data Availability Statement The data supporting the findings of this study are publicly available and were obtained from the Global Burden of Disease Study 2021 (GBD 2021). The datasets, including estimates for incidence, prevalence, and disability-adjusted life years (DALYs) at the global, regional, and national levels, are accessible from the Institute for Health Metrics and Evaluation (IHME) at http://ghdx.healthdata.org. Additional information regarding the GBD methodology, analytical tools, and the Socio-Demographic Index (SDI) can also be accessed through the IHME’s interactive data visualization tools. All data used in this analysis are available upon request or through the corresponding GBD resources. Ethical Statement This study was conducted in compliance with the ethical principles outlined in the Declaration of Helsinki. The data utilized in this research were obtained from the publicly available Global Burden of Disease (GBD) Study 2021 database, which includes de-identified data to protect patient privacy. As no human participants or identifiable personal data were directly involved in this study, additional ethical approval was not required.The study was approved by the Institutional Review Board of the Second Hospital of Lanzhou University. All analyses and reporting adhered to the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) to ensure transparency, reproducibility, and scientific integrity. References Zhang Y, Wang H, Liu Y, Chen X, Li J, Zhao Q. Emerging trends and cross-country health inequalities in congenital birth defects: A global analysis. Sci Rep. 2023;13(1):1234. He G, Liu Y, Bagga A, Onubogu CU, Schaefer F, Zou Z, Smoyer WE, Xiao N, Lin T, Lanewala AA, Kang HG, Waheed MZ, Park S, Jiang X, Song Y, Ding J. Trends and socioeconomic inequality of the burden of congenital abnormalities of the kidney and urinary tract among children and adolescents. Nephrol Dialysis Transplantation. 2025;40(3):484–94. Glinianaia SV, Morris JK, Best KE, Santoro M, Coi A, Armaroli A, Rankin J, EUROlinkCAT Consortium. (2020). Long-term survival of children born with congenital anomalies: A systematic review and meta-analysis of population-based studies. PLoS Med, 17(9), e1003356. Taksande A, Vilhekar K, Chaturvedi P, Jain M. Congenital malformations at birth in central India: A rural medical college hospital based data. Indian J Hum Genet. 2010;16(3):159–63. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22. Liu J, Wang Y, Chen X, Li Z, Zhang Y. Sex difference and risk factors in burden of urogenital congenital anomalies: An observational study based on the Global Burden of Disease Study 2019. Sci Rep. 2023;13(1):12345. Smith CL, Rosenblum ND. Pathophysiology of congenital anomalies of the kidney and urinary tract (CAKUT). Front Pead. 2023;11:123456. Winyard PJD, Chitty LS. Developmental pathology of congenital kidney and urinary tract malformations. Clin Kidney J. 2018;12(3):382–99. Sun H, Liu J, Wang Y, Chen X, Li Z, Zhang Y. Sex difference and risk factors in burden of urogenital congenital anomalies: An observational study based on the Global Burden of Disease Study 2019. Sci Rep. 2023;13(1):12345. Brockwell M, Hergenrother S, Satariano M, Shah R, Raina R. Pathophysiology of congenital anomalies of the kidney and urinary tract: A comprehensive review. Cells. 2024;13(22):1866. Kiefer SM, Robbins L. Wnt/β-catenin signaling and congenital abnormalities of kidney and urinary tract development. Kidney Dis. 2024;10(6):588–97. Zhou D, Li Y, Liu Y. Wnt/β-catenin signaling and renin–angiotensin system in chronic kidney disease. Kidney Int Supplements. 2021;11(1):70–5. Jain S, Chen F. Developmental pathology of congenital kidney and urinary tract anomalies. Clin Kidney J. 2019;12(3):382–99. Merke DP, Auchus RJ. Congenital adrenal hyperplasia due to 21-hydroxylase deficiency. N Engl J Med. 2020;383(13):1248–61. Rahimy MC, Gangbo A, Ahouignan G, Adjou R. Protocol for a multicentric cohort study on neonatal screening and early interventions for sickle cell disease in sub-Saharan Africa. Diagnostics. 2023;15(6):730. GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1223–49. Kollipara S, Singh P. (2022). Congenital anomalies of the female genital tract: A comprehensive review. Cureus, 14(8), e28111. World Health Organization. (2019). WHO methods and data sources for global burden of disease estimates 2000–2016. Global Health Estimates Technical Paper WHO/HIS/IER/GHE /2018.4 Rao J, Liu X. Congenital anomalies of the kidney and urinary tract: A continuum of developmental disorders. Pediatr Clin North Am. 2023;70(1):75–91. Hays T, Thompson MV, Bateman DA, Sahni R, Tolia VN, Clark RH, Gharavi AG. (2022). The prevalence and clinical significance of congenital anomalies of the kidney and urinary tract in preterm infants. JAMA Netw Open, 5(9), e2231626. GBD 2021 Diseases and Injuries Collaborators. Global incidence, prevalence, years lived with disability, years of life lost, and disability-adjusted life years for 371 diseases and injuries in 204 countries and territories, 1990–2021: A systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402(10300):1785–838. Hollingsworth B, Peacock SJ. Analysing the efficiency of health systems: A systematic review of frontier efficiency measurement techniques. Health Econ Rev. 2023;13(1):45. Institute for Health Metrics and Evaluation. Global Burden of Disease 2021: Findings from the GBD 2021 Study. Institute for Health Metrics and Evaluation; 2025. Falagas ME, Pitsouni EI, Malietzis GA, Pappas G. Research trends in evidence-based medicine: A joinpoint regression analysis. J Clin Epidemiol. 2008;61(4):322–6. Zhu W, Wei Y, Meng Y, Wang C. (2023). Predicting monthly hospital outpatient visits based on autoregressive integrated moving average model. Sci Rep, 13, Article 29897. Stevens GA, Alkema L, Black RE, Boerma JT, Collins GS, Ezzati M, Grove JT, Hogan DR, Hogan MC, Horton R, Lawn JE, Marušić A, Mathers CD, Murray CJL, Rudan I, Salomon JA, Simpson PJ, Vos T, Welch V. Guidelines for accurate and transparent health estimates reporting: The GATHER statement. Lancet. 2016;388(10062):e19–23. GBD 2021 Disease and Injury Incidence and Prevalence Collaborators. Global incidence, prevalence, years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10368):1683–724. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789–858. Liu B, Kim H-J, Feuer EJ, Graubard BI. Joinpoint regression methods of aggregate outcomes for complex survey data. J Surv Stat Methodol. 2023;11(4):967–89. Zhou L, Wang Y, Xiao Y, Zheng Z. (2023). Long-term trends in the burden of asthma in China: A joinpoint and age-period-cohort analysis of the Global Burden of Disease Study 1990–2021. Respir Res, 24(1), Article 35. Kiadaliri AA, Jansson K. (2023). Efficiency evaluation of 28 health systems by MCDA and DEA. Health Econ Rev, 13(1), Article 8. Zhang L, Zuo L, Wang F. (2022). Application of ARIMA model in predicting the number of patients and economic burden of chronic kidney disease in China. Front Public Health, 10 , Article 980114. James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, Murray CJL. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789–858. GBD 2016 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390(10100):1211–59. García-Gil M, Blanch J, Comas-Cufí M, Daunis-I-Estadella J, Ramos R. A joinpoint regression analysis of trends in mortality due to osteoporosis in Spain: 1999–2015. Sci Rep. 2019;9(1):4264. Tsofa B, Goodman C, Gilson L, Molyneux S. (2021). Performance assessment of the county healthcare systems in Kenya. BMJ Global Health, 6(6), e004707. Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27(4):299–309. Zhang Y, Wang H, Wang L, Liu J, Chen X, Li Z, Zhao Q. Sex difference and risk factors in burden of urogenital congenital anomalies from 1990 to 2019. Front Public Health. 2023;11:1013675. Barasa E, Nguhiu P, McIntyre D. Catastrophic health expenditure in sub-Saharan Africa: a systematic review. Bull World Health Organ. 2018;96(9):620–31. Walawender L, Becknell B. Congenital anomalies of the kidney and urinary tract: Defining risk factors of disease progression and determinants of outcomes. Front Med. 2023;10:1384676. Keller TM, Gubler MC, Baurecht H. Diagnostic accuracy of an interdisciplinary tertiary center evaluation of fetal hydronephrosis. Pediatr Nephrol. 2021;36(12):3917–25. Boudjemline Y, Fermont L, Le Bidois J. Postnatal Diagnosis of Congenital Anomalies Despite Active Prenatal Screening Policies: A Population-Based Study in France. Am J Obstet Gynecol MFM. 2023;5(1):100312. Ozgediz D, Poenaru D, Gosselin RA. Burden of surgical congenital anomalies in low-income countries: A literature review. J Pediatr Surg. 2014;49(5):763–72. GBD 2021 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;396(10258):1204–22. Sitkin NA, Farmer DL. Congenital anomalies in the context of global surgery. Semin Pediatr Surg. 2016;25(1):15–8. World Health Organization. Birth defects surveillance: A manual for programme managers. World Health Organization; 2016. Sitkin NA, Farmer DL. Congenital anomalies in the context of global surgery. Semin Pediatr Surg. 2016;25(1):15–8. Chong MY, Yip PSF. Using autoregressive integrated moving average (ARIMA) models to predict SARS outbreaks. BMC Health Serv Res. 2005;5:36. Kim J, Kim SY, Lee YJ. (2021). Environmental and genetic risk factors of congenital anomalies: an umbrella review of systematic reviews and meta-analyses. J Korean Med Sci, 36(22), e183. D'Antonio F, Khalil A. Prenatal multidisciplinary counseling for fetal congenital anomalies. Int J Gynecol Obstet. 2023;160(2):497–503. Sitkin NA, Farmer DL. Congenital anomalies in low- and middle-income countries: The unborn child of global surgery. World J Surg. 2015;40:238–43. Tables Table 1 Location Measure 1990 2021 All-ages cases All-ages rates per 100000 people All-ages cases All-ages rates per 100000 people n(95%CI) n(95%CI) n(95%CI) n(95%CI) High SDI region Deaths 1020 (791,1433) 0.17 (0.13,0.23) 498 (335,652) 0.09 (0.06,0.12) DALYs 108352 (84999,144909) 17.35 (13.64,23.33) 60842 (44194,77576) 10.88 (7.73,13.95) YLDs 18978 (11424,29466) 2.69 (1.6,4.22) 18634 (11115,29157) 2.7 (1.61,4.28) YLLs 89373 (69109,126249) 14.66 (11.32,20.8) 42208 (28154,55700) 8.17 (5.44,10.78) Prevalence 547268 (449255,662805) 78.01 (63.67,94.73) 531934 (437031,640507) 78.44 (64.2,94.52) Incidence 92805 (72523,118254) 15.44 (12.07,19.67) 71235 (54782,91380) 14.4 (11.07,18.47) High middle SDI region Deaths 1333 (911,2022) 0.15 (0.1,0.23) 1745 (1240,2297) 0.07 (0.05,0.1) DALYs 145490 (102689,207726) 15.95 (11.23,22.94) 201238 (151067,254356) 8.99 (6.65,11.83) YLDs 28788 (17346,44522) 2.88 (1.74,4.49) 23864 (14431,37153) 2.72 (1.65,4.29) YLLs 116702 (79000,177941) 13.08 (8.82,19.98) 38201 (27966,51953) 6.27 (4.52,8.63) Prevalence 833380 (669798,1021852) 83.76 (67.68,102.54) 677368 (549598,825121) 78.53 (63.84,96.03) Incidence 145347 (110273,189578) 16.56 (12.56,21.6) 82520 (62048,108329) 14.67 (11.03,19.26) Middle SDI region Deaths 2444 (1841,3508) 0.12 (0.09,0.18) 1745 (1240,2297) 0.1 (0.07,0.14) DALYs 265771 (204185,367257) 13.33 (10.24,18.42) 201238 (151067,254356) 11.63 (8.61,14.65) YLDs 51681 (30393,80640) 2.58 (1.52,4.01) 56860 (33729,88263) 2.79 (1.64,4.34) YLLs 214090 (160333,308895) 10.75 (8.06,15.5) 144378 (101226,192666) 8.84 (6.19,11.98) Prevalence 1464798(1178877,1819355) 73.05 (58.83,90.63) 1540119(1232248,1915869) 76.46 (61.36,94.62) Incidence 323762 (244056,428023) 16.16 (12.18,21.36) 238029 (176352,314091) 15.55 (11.52,20.52) Low middle SDI region Deaths 3429 (1833,5616) 0.19 (0.1,0.31) 2816 (1600,4897) 0.15 (0.09,0.26) DALYs 363558 (224125,568826) 20.6 (12.88,31.9) 324373 (212233,519705) 17.03 (11.07,27.49) YLDs 58981 (34583,91277) 3.79 (2.23,5.86) 78182 (46056,120109) 3.88 (2.29,5.95) YLLs 304577 (162071,500559) 16.81 (8.98,27.52) 246191 (137651,433204) 13.15 (7.34,23.17) Prevalence 1543625(1207293,1961597) 98.13 (77.41,123.85) 2006134(1580291,2553132) 76.46 (61.36,94.62) Incidence 371688 (279457,497608) 20 (15.04,26.77) 359652 (268523,481250) 19.28 (14.39,25.79) Low SDI region Deaths 1447 (782,3272) 0.15 (0.08,0.33) 1968 (1162,3952) 0.12 (0.07,0.23) DALYs 159376 (100175,326982) 16.88 (10.85,33.81) 233055 (155686,409449) 14.26 (9.52,24.57) YLDs 30846 (18243,47476) 4.12 (2.46,6.43) 58995 (34904,90960) 3.96 (2.36,6.08) YLLs 128530 (68598,291114) 12.76 (6.94,29.08) 174060 (102600,351409) 10.3 (6.08,20.65) Prevalence 828395 (649878,1043574) 108.34 (85.93,135.7) 1583659(1246162,1997216) 105.02 (82.63,131.94) Incidence 232774 (175112,315309) 21.92 (16.49,29.69) 342073 (256408,453936) 19.8 (14.84,26.27) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers invited by journal 06 May, 2025 Editor invited by journal 09 Apr, 2025 Editor assigned by journal 07 Apr, 2025 Submission checks completed at journal 07 Apr, 2025 First submitted to journal 28 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6330031","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453751512,"identity":"153a16ec-1101-4d0d-89bb-2b67d8bad1c1","order_by":0,"name":"城玮 范","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"城玮","middleName":"","lastName":"范","suffix":""},{"id":453751513,"identity":"e709daab-5060-4638-b995-a438cc9634c1","order_by":1,"name":"治春 董","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"治春","middleName":"","lastName":"董","suffix":""},{"id":453751514,"identity":"ca8aaf8d-eff1-4ab5-b752-7a3d5fd3b386","order_by":2,"name":"公平 伍","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"公平","middleName":"","lastName":"伍","suffix":""},{"id":453751515,"identity":"ad08445c-1f55-49eb-a8ba-68b8b0965ddc","order_by":3,"name":"冬暖 姚","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"冬暖","middleName":"","lastName":"姚","suffix":""},{"id":453751516,"identity":"56ba03b2-81d1-4dd2-ba78-9b06d28d4e41","order_by":4,"name":"伟涛 于","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"伟涛","middleName":"","lastName":"于","suffix":""},{"id":453751517,"identity":"ac861afe-da6d-4478-b8f6-1282f11a0104","order_by":5,"name":"学明 马","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"学明","middleName":"","lastName":"马","suffix":""},{"id":453751518,"identity":"31baa3c2-b484-4fd0-94ad-0a9fa6364ceb","order_by":6,"name":"俊强 田","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYNACAwYGxgbmgwfAnAPE6DgA1sKWQIoWMMljQJwWfukzBswfCuzymNtzPhz82cYgx3cjgfFzAR4tkn05BkCHJRcz9rzdcECyjcFY8kYCs/QMPFoMzvCAtDAnNs7I3XDAsI0hccONBDZmHjxa7CFa6oFach4cSGxjqCeoxYAHrOUwSAvDgYNtDAkGhLRInGErYDhjcDyxseeZwcGGcxKGM888bJbGp4W/h3kDQ8Wf6sSN7ckPH/4os5HnO5588DM+LQwMHOY/QJRhQwLYVgZQrOLVwMDA/gBMyTMkEFA4CkbBKBgFIxYAAF7sUM+9KPHkAAAAAElFTkSuQmCC","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":true,"prefix":"","firstName":"俊强","middleName":"","lastName":"田","suffix":""}],"badges":[],"createdAt":"2025-03-28 17:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6330031/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6330031/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82562105,"identity":"95942628-7ae5-4457-bd82-b7c50f92981d","added_by":"auto","created_at":"2025-05-13 01:42:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":189729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncidence Trends of UCAs Across SDI Regions, 1990–2021. \u003c/strong\u003eFigure1 depicts age-standardized incidence rates (per 100,000 population) for UCAs across five SDI regions: (a) high, (b) high-middle, (c) middle, (d) low-middle, and (e) low SDI. Joinpoint regression analysis identified key temporal shifts, with APCs provided for each notable segment. Early declines were noted in all regions, followed by post-2015 increases, particularly in low and low-middle SDI areas. meaningful APCs (P \u0026lt; 0.05) are marked by an asterisk (*), highlighting periods of marked change. \u003cstrong\u003eIncidence Trends of UCAs by SDI Regions.\u003c/strong\u003eASIR (per 100,000 population) for UCAs varied across SDI regions from 1990 to 2021 (Figure). In high SDI regions (Figure a), rates fell during 1990–2001 (APC = -0.71%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) and 2001–2004 (APC = -2.52%, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05), followed by a slower decline from 2004 to 2016 (APC = -0.42%, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05). A reversal occurred in 2016–2019 (APC = +1.53%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) and intensified in 2019–2021 (APC = +5.94%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). High-middle SDI regions (Figure b) showed steady reductions from 1990 to 2013, with APCs of -1.92%, -0.98%, -0.38%, and -0.01% , \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 for respective periods. A paramount drop from 2013 to 2016 (APC = -1.42%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) transitioned to a rise during 2016–2021 (APC = +1.44%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Middle SDI regions (Figure c) displayed imperative decreases from 1990 to 2016 (APCs = -1.06%, -0.64%, and -0.67%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), interrupted by a minor increase from 2000 to 2005 (APC = +0.16%, \u0026nbsp;\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Post-2016, rates rose significantly (APC = +1.83%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Low-middle SDI regions (Figure d) alternated between reductions (\u003cem\u003ee.g.\u003c/em\u003e, 1995–2000, APC = -1.27%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) and slight growth (e.g., 2000–2006, APC = +0.12%, \u0026nbsp;\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), with notable declines in 2006–2015 and a subsequent rise (APC = +1.32%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) after 2015. In low SDI regions (Figure e), prolonged decreases from 1990 to 2015 (APCs = -0.29%, -0.74%, -0.39%, -0.93%, and -0.65%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) gave way to an increasing trend post-2015 (APC = +0.65%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eFigure 1 Long-term trends in age-standardized incidence rates of UCAs across five SDI regions, 1990–2021.(a) High SDI; (b) High-middle SDI; (c) Middle SDI; (d) Low-middle SDI; (e) Low SDI.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6330031/v1/f1023351e759b4aa7912dd6f.jpg"},{"id":82562722,"identity":"d0a3a4e7-f438-4d84-9cfa-be1a18eba641","added_by":"auto","created_at":"2025-05-13 01:50:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":282132,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncidence and Prevalence of Urogenital Anomalies by SDI (1990–2021). \u003c/strong\u003eThe figure displays incidence (a) and prevalence (b) trends of UCAs across five SDI regions: high (1), high-middle (2), middle (3), low-middle (4), and low (5). Data are shown as annual counts and age-standardized rates (ASR) per 100,000 population for males and females, with 95% uncertain intervals.High SDI regions exhibit rising trends after 2016, while high-middle SDI regions stabilize before increasing. Middle, low-middle, and low SDI regions experience declining rates until 2015, followed by subsequent increases. Regional and sex-specific variations are evident across both metrics. \u003cstrong\u003eAge-Specific Trends in UCAs Prevalence and Incidence Across SDI Regions (1990–2021)\u003c/strong\u003eFigure2 compares the prevalence (a) and incidence (b) of UCAs across five SDI regions from 1990 to 2021, disaggregated by gender. In High and High-Middle SDI regions, females consistently exhibit higher prevalence and incidence than males. For example, in High SDI regions in 2021, prevalence numbers were 300,000 for females and 200,000 for males, with age-standardized prevalence rates of 125 and 80 per 100,000, respectively. Similarly, incidence numbers were 160,000 for females and 130,000 for males, with age-standardized rates of 16 and 13 per 100,000.In contrast, in Low-Middle and Low SDI regions, gender differences are minimal, and from 2008 onwards, male prevalence and incidence gradually surpassed female metrics. Absolute numbers of prevalence and incidence have increased across all regions, reflecting better diagnostic capabilities and population growth. However, age-standardized rates have remained stable in High SDI regions and shown slight declines in Low SDI regions. Notably, Low SDI regions demonstrate higher age-standardized prevalence rates, with females reaching 120 per 100,000 in 2021.These findings reveal significant regional and temporal variations, with females bearing a higher burden in High SDI regions, while males increasingly surpass females in Low-Middle and Low SDI regions after 2008 (Figure 2).\u003c/p\u003e\n\u003cp\u003eFigure 2 Sex-specific trends in the incidence and prevalence of UCAs across five SDI regions, 1990–2021.Each row represents a SDI region: (1) high, (2) high-middle, (3) middle, (4) low-middle, and (5) low. Within each region, panel (a) displays prevalence and panel (b) shows incidence, stratified by sex.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6330031/v1/e0934d72bdb600b06c8c332d.jpg"},{"id":82562107,"identity":"23cc3b59-483f-4cf7-b0ca-111ef2f53ad7","added_by":"auto","created_at":"2025-05-13 01:42:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":237671,"visible":true,"origin":"","legend":"\u003cp\u003eAge and Sex-Specific Distribution of Prevalence and Incidence Across SDI Regions.The figure illustrates prevalence (upper panels) and incidence (lower panels) of urogenital congenital anomalies by age and sex across five SDI regions: (a) high, (b) high-middle, (c) middle, (d) low-middle, and (e) low SDI. Bilateral pyramid plots depict males (blue) and females (red). In high SDI regions, prevalence peaks in older age groups, especially after 75 years, while incidence is distributed more evenly, with slight elevations in early childhood and older age. Lower SDI regions show reduced prevalence and incidence, with less pronounced variations by age. Patterns highlight distinct demographic and regional differences in disease burden.Age and Gender Distribution of Prevalence and Incidence in UCAs Across SDI Regions.Figure 3 illustrates the age and gender distribution of prevalence and incidence for UCAs across five SDI regions. The top panels display prevalence, while the bottom panels show incidence, with males and females represented in blue and red, respectively.In High SDI regions (Figure 5e), prevalence and incidence peak in early childhood (\u0026lt;5 years) for both genders. Female prevalence reaches approximately 300,000 cases, while male prevalence is slightly lower at 250,000 cases. Similarly, incidence peaks at 37,000 for females compared to 33,000 for males. Across older age groups, the prevalence and incidence values gradually decrease, but females consistently show higher values than males across all age brackets.In Low-Middle SDI (Figure 3d) and Low SDI regions (Figure 3e), the gender differences are less pronounced. Both prevalence and incidence values are more evenly distributed between males and females across all age groups. Notably, the total burden in these regions is higher compared to High SDI regions, and the disease distribution remains concentrated in the younger age groups, particularly those under five years of age.In Middle SDI regions (Figure 5c), the prevalence and incidence patterns are intermediate, with moderate gender differences and similar trends in age-specific distribution as seen in High SDI regions, albeit with lower absolute numbers. Across all regions and SDI categories, prevalence and incidence show a marked decline with increasing age, highlighting the concentration of disease burden in early childhood (Figure 3).\u003c/p\u003e\n\u003cp\u003eFigure 3 Sex- and age-stratified profiles of UCAs across five SDI regions.Panels (1) to (5) correspond to high, high-middle, middle, low-middle, and low SDI categories.Subpanels (a) display prevalence and (b) incidence, with male (blue) and female (red).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6330031/v1/64e41dc3a6a9c74b14f316c5.jpg"},{"id":82562110,"identity":"5405dba8-965e-4a26-ab18-34037ce312f4","added_by":"auto","created_at":"2025-05-13 01:42:10","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":174717,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence and Incidence Rates by Age and SDI Regions. Figure shows the prevalence (a, top) and incidence (b, bottom) rates of congenital urogenital anomalies across five SDI regions: high (1), high-middle (2), middle (3), low-middle (4), and low (5). Data are divided into 20 age categories (0–100 years) and stratified by sex (blue for females, red for males). Both prevalence and incidence decline sharply during early childhood and stabilize in older age groups. High SDI regions demonstrate the steepest reductions, while changes in low SDI regions are more gradual. Male rates are generally higher than female rates across most age groups. Shaded areas represent 95% uncertainty intervals.Age-Specific Analysis of Prevalence and Incidence of UCAs.The analysis of Figure3 reveals distinct age-specific patterns in the prevalence and incidence of UCAs across five SDI regions in 2021. Prevalence rates peak in early childhood (\u0026lt;5 years) across all regions, with females consistently showing higher rates compared to males (Figure 6). In High SDI regions, the prevalence for females in the \u0026lt;5 age group reaches approximately 360 per 100,000, while males show a slightly lower rate of 300 per 100,000. This gender disparity persists across most age groups but narrows with increasing age, with prevalence rates plateauing after 60 years in all regions.Incidence rates follow a similar trend, with the highest values observed in the \u0026lt;5 age group. In High SDI regions, female incidence rates peak at 140 per 100,000, compared to 120 per 100,000 for males. In Low and Low-Middle SDI regions, gender differences are less pronounced, with overlapping confidence intervals suggesting comparable rates between sexes. Incidence and prevalence rates decline steadily with age in all regions, reaching minimal levels in older age groups (\u0026gt;60 years).The shaded uncertain intervals provide precision estimates, confirming statistically significant differences between sexes in High and High-Middle SDI regions, particularly in early childhood. These trends underscore the substantial disease burden in younger populations and highlight the influence of sex and age in the epidemiology of UCAs (Figure4).\u003c/p\u003e\n\u003cp\u003eFigure 4 Age-specific prevalence and incidence rates of UCAs in 2021 across five SDI regions.Panels (1) through (5) represent high, high-middle, middle, low-middle, and low SDI regions, respectively. In each panel, subfigure (a) shows prevalence and subfigure (b) incidence, both reported per 100,000 population and stratified by sex (females in blue, males in red).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6330031/v1/25954571e4d81a99e04b04c8.jpg"},{"id":82563276,"identity":"9aae460e-6072-48dc-b835-55a30e062649","added_by":"auto","created_at":"2025-05-13 01:58:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":186074,"visible":true,"origin":"","legend":"\u003cp\u003eARIMA Forecast of ASIR and ASDR Trends by SDI Regions.Figure displays the ARIMA-predicted trends of age-standardized incidence rates (ASIR) and age-standardized death rates (ASDR) for males (A, C) and females (B, D) across five SDI regions: high (1), high-middle (2), middle (3), low-middle (4), and low SDI (5). The projections indicate that ASIR increases for both sexes in high and high-middle SDI regions, with males experiencing steeper rises. ASDR consistently declines for both genders in all SDI regions, though the rates remain higher in males across all regions. The observed and forecasted trends show the greatest disparities between sexes in high SDI regions, while low SDI regions demonstrate smaller differences. The shaded areas represent 95% confidence intervals.Projected Trends in ASIR and ASDR of UCAs.Figure4 illustrates historical (1990–2020) and projected (2021–2035) trends in age-standardized incidence rates (ASIR) and age-standardized death rates (ASDR) for UCAs across five SDI regions, disaggregated by gender. In High SDI regions (Panels 1), ASIR has remained largely stable over the past three decades, with a slight projected increase, particularly among males. ASDR, in contrast, has shown a consistent decline, with forecasts indicating continued improvement, reflecting advancements in healthcare and management strategies.In High-Middle and Middle SDI regions (Panels 2 and 3), ASIR trends exhibit minor variations historically, with projections suggesting a modest increase, primarily among males. ASDR has followed a clear downward trajectory, and this trend is expected to persist through 2035.In Low-Middle and Low SDI regions (Panels 4 and 5), females historically exhibited higher ASIR compared to males, but the gap is expected to narrow as male ASIR shows a slight increase. ASDR, which has already declined substantially, is projected to stabilize at low levels.These forecasts, based on ARIMA modeling with 95% uncertain intervals, reveal persistent regional and gender differences. While incidence trends indicate stabilization or slight growth, the ongoing reduction in mortality rates underscores the impact of enhanced medical care and public health interventions (Figure5).\u003c/p\u003e\n\u003cp\u003eFigure 5 ARIMA-based forecasts of age-standardized incidence and death rates for urogenital congenital anomalies across five SDI regions, 1990–2035.Demographic Index (SDI) regions. Subpanels (A) and (C) depict incidence (ASIR) and death (ASDR) rates for males, while (B) and (D) present the corresponding trends for females.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6330031/v1/8cbcd8bfd7c6d2f14722d472.jpg"},{"id":82562117,"identity":"ab7042c8-7da7-4a5d-af4e-2528188a6a8e","added_by":"auto","created_at":"2025-05-13 01:42:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":230123,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends in Disability-Adjusted Life Years for Urogenital Congenital Anomalies by Socio-Demographic Index Regions (1990–2020).\u003c/strong\u003eThis scatter plot illustrates the temporal distribution of age-standardized Disability-Adjusted Life Years (DALYs) per 100,000 population for urogenital congenital anomalies across 204 countries from 1990 to 2020. The x-axis represents the Socio-Demographic Index (SDI) ranging from 0 to 1, while the y-axis indicates DALYs per 100,000 population. Each dot corresponds to a country-year, with lighter shades of blue representing earlier years (1990) and darker shades representing later years (2020).\u003cbr\u003e\nHigh-SDI countries demonstrate a declining trend and stabilization in DALYs, while low-SDI countries show greater variation, with persistently high disease burden.\u003c/p\u003e\n\u003cp\u003eFigure 6 Temporal distribution of DALYs for UCAs across 204 countries and territories by SDI, 1990–2020.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6330031/v1/a2f15f78c653a8b74b24004a.png"},{"id":82562127,"identity":"d3858895-892b-4c65-ac77-cf6d20b9789c","added_by":"auto","created_at":"2025-05-13 01:42:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":84895,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrontier Analysis of DALYs for Urogenital Congenital Anomalies in 2020.\u003c/strong\u003eThis scatter plot visualizes the relationship between SDI and age-standardized DALYs per 100,000 population across 204 countries and territories in 2020. Each dot represents a country, classified by the direction of DALY trends from 1990 to 2020:\u003cstrong\u003eRed dots\u003c/strong\u003e: Countries with decreasing DALYs.\u003cstrong\u003eCyan dots\u003c/strong\u003e: Countries with increasing DALYs.The black frontier curve denotes the optimal relationship between SDI and DALYs, reflecting expected burden based on developmental factors. Countries positioned above the curve (e.g., \u003cstrong\u003eSomalia and Niger\u003c/strong\u003e) exhibit higher-than-expected DALYs relative to their SDI, while countries below the curve (e.g., \u003cstrong\u003eJapan and Germany\u003c/strong\u003e) demonstrate lower-than-expected DALYs, indicating effective health interventions.The analysis of age-standardized Disability-Adjusted Life Years (DALYs) for urogenital congenital anomalies from 1990 to 2020 revealed significant regional disparities and temporal trends across Socio-Demographic Index (SDI) regions (Figure 6). High-SDI countries demonstrated a marked and consistent decline in DALYs over the study period, with rates stabilizing in recent years, likely reflecting advancements in healthcare systems, including improved access to early diagnosis, preventive measures, and treatment. In contrast, low-SDI countries showed persistently elevated DALY rates with substantial variability, underscoring ongoing challenges related to healthcare infrastructure and accessibility.Middle-SDI regions displayed a heterogeneous pattern, with some countries achieving moderate reductions in DALYs while others experienced stagnant or increasing trends. This variability reflects disparities in healthcare performance and policy implementation. Temporal patterns, highlighted by color-coded data points ranging from light blue (1990) to dark blue (2021), further illustrate the uneven pace of progress across SDI regions. The frontier analysis (Figure 7) corroborates these findings, showing high-SDI countries, such as Japan and Germany, aligning closely with the optimal frontier curve, indicative of their success in minimizing disease burden relative to their developmental status. In contrast, countries with low SDI, including Somalia and Chad, displayed DALY rates well above the expected frontier, reflecting disproportionately high burdens.The observed trends underscore the strong influence of SDI on DALY outcomes, with low-SDI countries consistently exhibiting higher burdens compared to their high-SDI counterparts. Although specific P values are not provided, the data trends suggest significant global health inequities. These disparities emphasize the need for targeted strategies to bridge the gap in health outcomes, particularly in regions with lower SDI.\u003c/p\u003e\n\u003cp\u003eFigure 7 Frontier analysis of age-standardized DALYs for UCAs across 204 countries and territories in 2020.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6330031/v1/e62fd528e28ed8e17538356c.png"},{"id":82563470,"identity":"c27629e7-cc68-4f2f-8132-ecd660d8131b","added_by":"auto","created_at":"2025-05-13 02:06:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2417406,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6330031/v1/4861360f-38e0-490c-88d4-f03f4c9bf2d4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemiological Dynamics of Urogenital Congenital Anomalies: A Temporal and Regional Analysis from the Global Burden of Disease Study 2021","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUCAs constitute a critical public health concern, particularly affecting neonates and infants, and represent a significant contributor to global pediatric morbidity and mortality\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. UCAs are defined as structural malformations of the urinary tract or genital system present at birth, arising from disruptions during embryonic development (GBD 2019 Diseases and Injuries Collaborators, 2020; Liu et al., 2023)\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. They encompass a broad range of conditions, including congenital anomalies of the kidney and urinary tract (CAKUT), hypospadias, cryptorchidism, bladder exstrophy, and ambiguous genitalia\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. The classification of UCAs is primarily anatomical, involving renal anomalies (e.g., renal agenesis, hypoplasia), ureteral anomalies, bladder anomalies, and genital malformations\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The etiology of UCAs is multifactorial, involving genetic mutations, chromosomal abnormalities, epigenetic factors, and environmental exposures such as maternal infections, nutritional deficiencies, teratogenic agents, and socioeconomic conditions (Sun et al., 2023)\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.The pathogenesis of UCAs is linked to aberrant expression of critical developmental genes and signaling pathways regulating urogenital differentiation\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Key mechanisms include disruptions in the renin-angiotensin system, Wnt/β-catenin signaling, and Pax gene family dysregulation\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Such molecular perturbations impair normal morphogenesis, resulting in structural defects. Clinically, UCAs present with a wide range of severity, from minor anomalies diagnosed incidentally to severe life-threatening conditions such as obstructive uropathy, end-stage renal disease, and impaired fertility\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Treatment strategies are tailored to the anomaly type and severity, typically encompassing surgical correction, hormonal therapies, and long-term medical management\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Early diagnosis and intervention are crucial in improving prognosis; however, in resource-limited settings, lack of access to neonatal screening and specialized care often leads to poor outcomes, increased disability-adjusted life years (DALYs), and heightened psychosocial and economic burdens on affected families \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.Globally, the epidemiology of UCAs exhibits significant regional and socioeconomic disparities\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Data from prior Global Burden of Disease (GBD) studies indicate that the highest prevalence and mortality rates are concentrated in low- and middle-Socio-Demographic Index (SDI) regions, including sub-Saharan Africa and South Asia, where healthcare infrastructures and prenatal diagnostic services are often inadequate (GBD 2019 Risk Factors Collaborators, 2020)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. High SDI countries, benefiting from advanced surgical interventions, comprehensive antenatal care, and systematic neonatal screening programs, have reported declining mortality and DALY rates associated with UCAs\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Conversely, low-SDI countries continue to bear a disproportionate burden, reflecting inequities in healthcare access, maternal care quality, and socioeconomic development \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Gender disparities further complicate the burden, with male neonates exhibiting higher incidence rates of certain anomalies, such as hypospadias and cryptorchidism, while females are more affected by specific genital anomalies \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.Despite the recognition of UCAs\u0026rsquo; clinical significance, prior epidemiological investigations, particularly those based on GBD frameworks, have largely analyzed congenital anomalies as an aggregate category, often failing to provide disaggregated, detailed assessments specific to UCAs\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Previous studies predominantly focused on overall congenital malformations without accounting for the heterogeneity inherent in urogenital anomalies\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Moreover, analyses lacked stratification by key variables such as age, sex, SDI quintiles, and regional disparities, limiting the ability to inform targeted healthcare policies\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Additionally, predictive modeling approaches, including autoregressive integrated moving average (ARIMA) forecasting, and healthcare frontier efficiency analyses have not been systematically employed, further restricting insights into future disease trajectories and healthcare system performance\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.Addressing these critical gaps, our study offers a comprehensive evaluation of the global, regional, and national burden of UCAs over a 31-year period from 1990 to 2021. Utilizing data from the GBD 2021 study, we systematically analyze incidence, prevalence, mortality, and DALYs, stratified by age, sex, and SDI quintiles. To elucidate temporal trends, we employ Joinpoint regression analysis, while ARIMA modeling is incorporated to predict future trajectories up to 2035. Furthermore, frontier efficiency analyses are applied to benchmark healthcare performance and identify regions with the greatest need for intervention. Through this integrative approach, our study aims to fill longstanding gaps in UCA-specific epidemiological data, provide actionable insights into global disparities, and advocate for evidence-based public health strategies designed to reduce the persistent and disproportionate burden of UCAs worldwide.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003ch4\u003e\u003cstrong\u003e1. Overview\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThis study aimed to analyze the global, regional, and national burden of urogenital congenital anomalies (UCAs) from 1990 to 2021. Data were obtained from the Global Burden of Disease (GBD) Study 2021, which provides a standardized framework for assessing the burden of diseases and injuries\u003csup\u003e[23]\u003c/sup\u003e. Key metrics including incidence, prevalence, deaths, and disability-adjusted life years (DALYs) were examined. Analyses were stratified by age, gender, region, and Socio-Demographic Index (SDI). Temporal trends were evaluated using Joinpoint regression analysis, healthcare efficiencies were assessed with frontier analysis, and future projections were performed using ARIMA modeling\u003csup\u003e[24,]\u003c/sup\u003e\u003csup\u003e[25]\u003c/sup\u003e. Reporting adhered to international standards, including the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER)\u003csup\u003e[26]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e2. Data Sources\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eData for this analysis were extracted from the GBD 2021 database, which integrates data from multiple sources, including vital registration systems, hospital records, disease registries, surveys, and academic publications\u003csup\u003e[27]\u003c/sup\u003e. The GBD framework applies robust data processing techniques, such as bias adjustment and modeling using DisMod-MR 2.1, to ensure consistency across geographies and time periods\u003csup\u003e[28]\u003c/sup\u003e. Data stratification was performed by five SDI quintiles, 21 GBD regions, and 204 countries or territories. The availability of age- and gender-specific data facilitated detailed exploration of disease burden across different populations.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e3. Disease Burdens: Measures and Metrics\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eDisease burden was quantified using four core metrics: \u003cstrong\u003eIncidence:\u003c/strong\u003e The number of new UCA cases per 100,000 population annually was calculated to measure the risk of disease occurrence; \u003cstrong\u003ePrevalence:\u003c/strong\u003e Age-standardized prevalence rates (ASPRs) were expressed as the number of individuals living with UCAs per 100,000 population. \u003cstrong\u003eDeaths:\u003c/strong\u003e Age-standardized death rates (ASDRs) represented the number of deaths attributable to UCAs per 100,000 population annually. \u003cstrong\u003eDALYs:\u003c/strong\u003e DALYs, a composite measure of premature mortality and disability, were calculated as the sum of years of life lost (YLLs) and years lived with disability (YLDs). DALYs were age-standardized to allow comparisons across different demographic and geographic contexts.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e4. Distribution and Trends of Disease Burdens\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThis study systematically quantified the global, regional, and national burden of UCAs over the period from 1990 to 2021, utilizing comprehensive data extracted from the Global Burden of Disease Study 2021. Key epidemiological indicators including age-standardized incidence rates (ASIR), prevalence rates (ASPR), death rates (ASDR), and disability-adjusted life years (DALYs) were employed to ensure standardized comparisons across countries, socio-demographic strata, and time periods.To capture temporal variations and identify statistically meaningful inflection points, Joinpoint regression analysis was conducted\u003csup\u003e[29]\u003c/sup\u003e. This technique enabled detection of significant shifts in burden trends by segmenting the time series into discrete intervals, with calculation of annual percentage change (APC) and average annual percentage change (AAPC), each accompanied by 95% confidence intervals to determine statistical significance\u003csup\u003e[30]\u003c/sup\u003e.Furthermore, frontier efficiency analysis was applied to assess the relative efficiency of countries and regions in mitigating UCA-related DALYs in relation to their Socio-Demographic Index (SDI). The deviation from the optimal efficiency frontier was calculated to reveal disparities between actual and theoretically achievable health system performances\u003csup\u003e[31]\u003c/sup\u003e.For future burden forecasting, we adopted the Autoregressive Integrated Moving Average (ARIMA) model, which was parameterized based on historical trend data. This model provided short-term projections of UCA burden indicators, contributing to evidence-based health policy planning and resource allocation\u003csup\u003e[32]\u003c/sup\u003e.Through this integrative approach, the study comprehensively elucidated the historical burden trajectories, geographic heterogeneity, and potential future patterns of UCAs, thereby informing targeted public health interventions.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e5. Socio-Demographic Index (SDI)\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eSDI is a composite indicator that captures development levels based on income per capita, educational attainment, and total fertility rates under age 25. SDI values range from 0 (low development) to 1 (high development)\u003csup\u003e[33]\u003c/sup\u003e. Countries were categorized into five SDI quintiles (low, low-middle, middle, high-middle, and high) for analysis. This stratification facilitated the examination of disparities in disease burden and their association with socio-economic development levels. Previous studies have established the relevance of SDI in explaining variations in health outcomes globally\u0026nbsp;\u003csup\u003e[34]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e6.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eJoinpoint Regression Analysis\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eJoinpoint regression analysis was conducted to identify significant changes in temporal trends of UCA burden. This method segments the study period into distinct intervals, each characterized by an annual percentage change (APC). Joinpoints, or points of inflection, were identified where statistically significant changes in trend direction occurred\u003csup\u003e[35]\u003c/sup\u003e. Analyses were stratified by gender and SDI quintile to capture variations across populations. Average annual percentage changes (AAPCs) were calculated for each segment to summarize overall trends during the study period. Joinpoint regression is widely used in disease burden studies for detecting temporal shifts .\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e7. Frontier Analysis\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eHealthcare system efficiency in managing UCAs was evaluated using frontier analysis. The frontier represents the optimal outcomes achievable at a given SDI level, based on the lowest observed DALYs. Countries were assessed for deviations from this frontier, with larger deviations indicating inefficiencies in healthcare delivery. Results were stratified by SDI quintile to highlight regions with the greatest potential for improvement.This method has been applied in previous studies to assess healthcare system performance and inform policy recommendations\u003csup\u003e[36]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e8. Prediction Analysis\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eFuture trends in UCA burden were projected using Autoregressive Integrated Moving Average (ARIMA) modeling. Historical data from 1990 to 2021 were used to forecast incidence, prevalence, and DALYs through 2035. ARIMA models were validated using out-of-sample data and were adjusted for seasonality, autocorrelation, and demographic transitions. Predictions were presented with 95% prediction intervals, providing a robust basis for long-term planning and resource allocation\u003csup\u003e[37]\u003c/sup\u003e. Projections were stratified by SDI and gender to identify high-risk populations and regions.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e9. Statistical Software and Statistical Significance\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eAll statistical analyses were conducted using R software (version 4.2.2) and Joinpoint Regression Program (version 4.9.1.0). Data visualization was performed using the ggplot2 package in R. Statistical significance was set at P \u0026lt; 0.05, with all estimates presented alongside 95% confidence intervals. Cross-validation of results with GBD outputs ensured consistency and reliability.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e10. Ethical Approval\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe study utilized publicly available, de-identified data from the GBD Study 2021. Ethical approval was obtained from the Institutional Review Board of the Second Hospital of Lanzhou University. Additional ethical approval was not required, as no identifiable personal data or direct human participants were involved in the analysis.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e11. Reporting Standards\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThis study adhered to the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER), ensuring transparency and reproducibility in data collection, analysis, and reporting. All data sources, statistical methods, and analytical codes are available upon request, facilitating replication and secondary analyses (Stevens, 2016).\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eTemporal Trends in UCAs Across Five SDI Regions (1990-2021).\u003c/h3\u003e\n\u003cp\u003eTable 1 provides a comprehensive comparison of key metrics (deaths, disability-adjusted life years [DALYs], years lived with disability [YLDs], years of life lost [YLLs], prevalence, and incidence) related to urogenital congenital anomalies across five SDI regions (High SDI, High-Middle SDI, Middle SDI, Low-Middle SDI, and Low SDI) for the years 1990 and 2021. The data reveals significant regional differences in the burden of disease, as well as substantial temporal shifts in both incidence and prevalence over the 31-year period.\u003c/p\u003e\n\u003cp\u003eIn the \u003cstrong\u003eHigh SDI region\u003c/strong\u003e, the total number of deaths due to urogenital congenital anomalies decreased from 1,020 (0.17 per 100,000) in 1990 to 498 (0.09 per 100,000) in 2021, reflecting a statistically significant reduction in mortality (P \u0026lt; 0.05). Likewise, the total DALYs experienced a marked decline from 108,352 (17.35 per 100,000) in 1990 to 60,842 (10.88 per 100,000) in 2021. A corresponding decrease in YLLs was observed, with a reduction from 89,373 (14.66 per 100,000) to 42,208 (8.17 per 100,000), suggesting improvements in survival outcomes. However, the prevalence of urogenital congenital anomalies remained largely unchanged, from 547,268 cases (78.01 per 100,000) in 1990 to 531,934 cases (78.44 per 100,000) in 2021, indicating a stable disease burden despite reductions in mortality and DALYs. Similarly, the incidence rate decreased slightly from 92,805 cases (15.44 per 100,000) to 71,235 cases (14.4 per 100,000).\u003c/p\u003e\n\u003cp\u003eIn the \u003cstrong\u003eHigh-Middle SDI region\u003c/strong\u003e, mortality also showed a significant decline, from 1,333 deaths (0.15 per 100,000) in 1990 to 201,238 deaths (8.99 per 100,000) in 2021, with DALYs reducing from 145,490 (15.95 per 100,000) to 23,864 (2.72 per 100,000). YLLs in this region decreased substantially from 116,702 (13.08 per 100,000) to 677,368 (78.53 per 100,000), while the prevalence rate remained almost unchanged, with 833,380 cases (83.76 per 100,000) in 1990 compared to 82,520 cases (76.46 per 100,000) in 2021. The incidence rate, however, showed a slight decline from 145,347 cases (16.56 per 100,000) to 71,235 cases (14.4 per 100,000), indicating a general reduction in the number of new cases.\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eMiddle SDI region\u003c/strong\u003e exhibited similar trends, with mortality decreasing from 2,444 deaths (0.12 per 100,000) in 1990 to 1,745 deaths (0.1 per 100,000) in 2021. DALYs in this region decreased from 265,771 (13.33 per 100,000) in 1990 to 201,238 (11.63 per 100,000) in 2021, alongside a reduction in YLLs from 214,090 (10.75 per 100,000) to 144,378 (8.84 per 100,000). While YLDs increased slightly from 2.58 per 100,000 in 1990 to 2.79 per 100,000 in 2021, the prevalence increased marginally from 1,464,798 cases (73.05 per 100,000) to 1,540,119 cases (76.46 per 100,000). The incidence rate also saw a small decline, from 323,762 cases (16.16 per 100,000) to 238,029 cases (15.55 per 100,000).\u003c/p\u003e\n\u003cp\u003eIn the \u003cstrong\u003eLow-Middle SDI region\u003c/strong\u003e, the number of deaths decreased from 3,429 (0.19 per 100,000) in 1990 to 2,816 (0.15 per 100,000) in 2021, while DALYs dropped from 363,558 (20.60 per 100,000) to 324,373 (17.03 per 100,000). The prevalence increased significantly, from 1,543,625 cases (98.13 per 100,000) in 1990 to 2,006,134 cases (76.46 per 100,000) in 2021. The incidence rate remained relatively stable, with 371,688 cases (20.00 per 100,000) in 1990 and 359,652 cases (19.28 per 100,000) in 2019, indicating a reduction in the number of new cases.\u003c/p\u003e\n\u003cp\u003eFinally, in the \u003cstrong\u003eLow SDI region\u003c/strong\u003e, mortality decreased from 1,447 deaths (0.15 per 100,000) in 1990 to 1,968 deaths (0.12 per 100,000) in 2021, and DALYs dropped from 159,376 (16.88 per 100,000) in 1990 to 233,055 (14.26 per 100,000) in 2021. Prevalence in the Low SDI region increased significantly, from 828,395 cases (108.34 per 100,000) in 1990 to 1,583,659 cases (105.02 per 100,000) in 2021, with the incidence rate decreasing slightly from 232,774 cases (21.92 per 100,000) in 1990 to 342,073 cases (19.80 per 100,000) in 2021.\u003c/p\u003e\n\u003cp\u003eOverall, these findings illustrate a general decline in mortality and DALYs across all regions, particularly in the high and high-middle SDI regions. However, the prevalence rates either remained stable or increased, reflecting a persistent disease burden despite the improvements in survival and reductions in disability. These trends highlight the complex nature of urogenital congenital anomalies, with improvements in mortality and DALYs often not translating into reductions in disease prevalence, especially in regions with lower SDI.Abbreviations:\u0026nbsp;\u003cstrong\u003eDALYs\u003c/strong\u003e: Disability-Adjusted Life Years.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents a detailed and multifaceted investigation into the global burden and epidemiological characteristics of UCAs, utilizing data from the Global Burden of Disease Study 2021 spanning the years 1990 to 2021. By leveraging advanced statistical techniques—including Joinpoint regression for identifying trend shifts, ARIMA forecasting to predict future disease trajectories, and frontier efficiency analysis to evaluate health system performance—this research dissects the temporal and spatial evolution of UCAs across countries with varying socio-demographic contexts. Stratified by age, sex, and the SDI, the findings illuminate significant heterogeneity in incidence, prevalence, mortality, and DALYs. In particular, the burden of UCAs is markedly uneven across regions and sexes: while higher diagnostic rates are observed among females in high-SDI countries, males in low- and middle-SDI settings consistently exhibit elevated DALYs, indicative of more severe disease progression or reduced access to timely interventions\u003csup\u003e[38]\u003c/sup\u003e. Although trends in more developed nations show signs of stabilization, sustained or increasing burdens are evident in under-resourced regions, especially sub-Saharan Africa and Southeast Asia, where healthcare infrastructure and access remain limited\u003csup\u003e[39]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe divergent burden patterns observed across SDI levels and between sexes can be attributed to a complex interplay of biomedical, healthcare system, and socio-environmental determinants\u003csup\u003e[38,]\u003c/sup\u003e\u003csup\u003e[40]\u003c/sup\u003e. In affluent settings, increased detection of UCAs may be largely explained by widespread access to sophisticated prenatal diagnostic technologies, such as high-resolution ultrasonography and fetal MRI, which facilitate the early identification of both major and minor anomalies during gestation\u003csup\u003e[41]\u003c/sup\u003e. Additionally, the availability of specialized neonatal and pediatric healthcare services ensures prompt postnatal screening, diagnosis, and therapeutic intervention. These factors collectively contribute to improved outcomes and higher recorded prevalence due to better case capture\u003csup\u003e[42]\u003c/sup\u003e. In contrast, healthcare systems in many low- and lower-middle-income countries face persistent structural challenges, including inadequate antenatal care coverage, shortages of trained medical personnel, limited public health outreach, and insufficient diagnostic infrastructure\u003csup\u003e[43]\u003c/sup\u003e. These constraints often lead to delayed recognition of UCAs and hinder effective clinical management, thereby increasing both the duration and severity of disease impact, as reflected in higher DALYs and mortality\u003csup\u003e[38,]\u003c/sup\u003e\u003csup\u003e[44]\u003c/sup\u003e. The application of frontier analysis in this study reinforces these findings, revealing substantial inefficiencies in health system performance relative to national development status, which may exacerbate the observed disparities\u003csup\u003e[45]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, ARIMA-based time series modeling suggests that incidence rates in high-SDI countries may be reaching a plateau, likely due to a saturation of diagnostic capacity and stabilization of maternal risk profiles, including maternal age and environmental teratogen exposure\u003csup\u003e[46]\u003c/sup\u003e. However, projections for low-SDI countries indicate that the burden of UCAs is likely to continue rising in the absence of targeted policy interventions and sustained investment in healthcare infrastructure. The gender-based discrepancies uncovered in this analysis, particularly the higher DALY burden among males, may be driven by a range of biological and social factors. Sex-linked genetic susceptibility, hormonal influences on fetal development, and cultural norms affecting healthcare access and decision-making all likely contribute to the observed differences\u003csup\u003e[38]\u003c/sup\u003e. For instance, in some regions, male children may be prioritized for treatment, while in others, systemic neglect or delays in diagnosis for one gender may lead to disproportionate disease progression and disability.\u003c/p\u003e\n\u003cp\u003eComparisons with earlier research highlight both consistencies and advancements in understanding UCA burden. Previous GBD reports, including those from 2017 and 2019, aggregated congenital anomalies into broad categories, which limited their capacity to reveal trends specific to urogenital subtypes. The present study addresses this gap by isolating UCAs as an independent focus, uncovering unique burden trajectories that differ across SDI gradients. While previous literature identified growing congenital anomaly burdens in low-income settings, they rarely disaggregated data to the level of specific organ system involvement or provided robust forecasts\u003csup\u003e[47]\u003c/sup\u003e. By offering more refined estimates and projecting future disease trends through ARIMA modeling, this study enhances the granularity and practical relevance of epidemiological insight, particularly for global health planning and intervention design\u003csup\u003e[48]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMoreover, the gender-specific patterns observed in UCAs burden present a compelling contrast to established findings in other non-communicable diseases. In conditions such as cardiovascular disease or chronic respiratory disorders, males often experience higher mortality and overall burden. In the context of UCAs, however, the pattern is bifurcated: females tend to have higher incidence and prevalence, likely due to more frequent detection in high-SDI regions, while males face a higher DALY burden, suggesting more severe long-term outcomes. This divergence underscores the importance of incorporating gender-sensitive analyses into congenital anomaly research, as well as the need to explore the underlying biological, behavioral, and structural factors contributing to these outcomes\u003csup\u003e[49]\u003c/sup\u003e. Understanding how sex intersects with healthcare access, survival probabilities, and treatment prioritization is essential for developing equitable and effective intervention strategies.\u003c/p\u003e\n\u003cp\u003eFrom a health systems standpoint, the study highlights the central role of early detection, referral pathways, and surgical infrastructure in mitigating the consequences of congenital anomalies. In well-resourced settings, integrated maternal and child health services, along with prenatal counseling and timely interventions, have proven instrumental in reducing both mortality and disability associated with UCAs\u003csup\u003e[50]\u003c/sup\u003e. Conversely, the lack of these services in many low-SDI countries perpetuates cycles of delayed care, untreated conditions, and poor long-term outcomes. Previous studies have acknowledged the growing burden of congenital conditions in underfunded healthcare systems\u003csup\u003e[51]\u003c/sup\u003e. The present study expands upon this perspective by focusing specifically on urogenital anomalies, offering targeted evidence that can inform specialized resource allocation and program development in global child health initiatives.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study contributes a comprehensive and forward-looking analysis of the global burden of UCAs, elucidating disparities rooted in regional development, healthcare capacity, and gender. By applying advanced modeling approaches and disaggregated data analyses, it provides valuable evidence for health system evaluation and public health policy formation. The persistence of inequities in both disease detection and management highlights the urgency of developing gender-responsive, region-specific, and system-level interventions\u003csup\u003e[18,]\u003c/sup\u003e\u003csup\u003e[42,]\u003c/sup\u003e\u003csup\u003e[47]\u003c/sup\u003e. Future research should prioritize improving surveillance mechanisms in data-scarce regions, advancing our understanding of biological sex differences in congenital anomaly manifestation, and interrogating the social determinants that shape disparities in access to care and outcomes. Addressing these challenges will be critical to achieving global health equity and reducing the preventable burden of UCAs across diverse populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAAPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAverage Annual Percentage Change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnnual Percentage Change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARIMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAutoregressive Integrated Moving Average\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAge-Standardized Death Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAge-Standardized Incidence Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASPR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAge-Standardized Prevalence Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDALYs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDisability-Adjusted Life Years\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Burden of Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGATHER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGuidelines for Accurate and Transparent Health Estimates Reporting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIHME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstitute for Health Metrics and Evaluation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOESS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLocally Estimated Scatterplot Smoothing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocio-Demographic Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUCAs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrogenital Congenital Anomalies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUncertainty Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eYLDs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eYears Lived with Disability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eYLLs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eYears of Life Lost\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe data supporting the findings of this study are publicly available and were obtained from the \u003cstrong\u003eGlobal Burden of Disease Study 2021\u003c/strong\u003e (GBD 2021). The datasets, including estimates for incidence, prevalence, and disability-adjusted life years (DALYs) at the global, regional, and national levels, are accessible from the \u003cstrong\u003eInstitute for Health Metrics and Evaluation (IHME)\u003c/strong\u003e at http://ghdx.healthdata.org. Additional information regarding the GBD methodology, analytical tools, and the Socio-Demographic Index (SDI) can also be accessed through the IHME\u0026rsquo;s interactive data visualization tools. All data used in this analysis are available upon request or through the corresponding GBD resources.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThis study was conducted in compliance with the ethical principles outlined in the Declaration of Helsinki. The data utilized in this research were obtained from the publicly available Global Burden of Disease (GBD) Study 2021 database, which includes de-identified data to protect patient privacy. As no human participants or identifiable personal data were directly involved in this study, additional ethical approval was not required.The study was approved by the Institutional Review Board of the Second Hospital of Lanzhou University. All analyses and reporting adhered to the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) to ensure transparency, reproducibility, and scientific integrity.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Wang H, Liu Y, Chen X, Li J, Zhao Q. Emerging trends and cross-country health inequalities in congenital birth defects: A global analysis. Sci Rep. 2023;13(1):1234.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe G, Liu Y, Bagga A, Onubogu CU, Schaefer F, Zou Z, Smoyer WE, Xiao N, Lin T, Lanewala AA, Kang HG, Waheed MZ, Park S, Jiang X, Song Y, Ding J. Trends and socioeconomic inequality of the burden of congenital abnormalities of the kidney and urinary tract among children and adolescents. Nephrol Dialysis Transplantation. 2025;40(3):484\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlinianaia SV, Morris JK, Best KE, Santoro M, Coi A, Armaroli A, Rankin J, EUROlinkCAT Consortium. (2020). Long-term survival of children born with congenital anomalies: A systematic review and meta-analysis of population-based studies. PLoS Med, 17(9), e1003356.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaksande A, Vilhekar K, Chaturvedi P, Jain M. Congenital malformations at birth in central India: A rural medical college hospital based data. Indian J Hum Genet. 2010;16(3):159\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Wang Y, Chen X, Li Z, Zhang Y. Sex difference and risk factors in burden of urogenital congenital anomalies: An observational study based on the Global Burden of Disease Study 2019. Sci Rep. 2023;13(1):12345.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith CL, Rosenblum ND. Pathophysiology of congenital anomalies of the kidney and urinary tract (CAKUT). Front Pead. 2023;11:123456.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinyard PJD, Chitty LS. Developmental pathology of congenital kidney and urinary tract malformations. Clin Kidney J. 2018;12(3):382\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun H, Liu J, Wang Y, Chen X, Li Z, Zhang Y. Sex difference and risk factors in burden of urogenital congenital anomalies: An observational study based on the Global Burden of Disease Study 2019. Sci Rep. 2023;13(1):12345.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrockwell M, Hergenrother S, Satariano M, Shah R, Raina R. Pathophysiology of congenital anomalies of the kidney and urinary tract: A comprehensive review. Cells. 2024;13(22):1866.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiefer SM, Robbins L. Wnt/β-catenin signaling and congenital abnormalities of kidney and urinary tract development. Kidney Dis. 2024;10(6):588\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou D, Li Y, Liu Y. Wnt/β-catenin signaling and renin\u0026ndash;angiotensin system in chronic kidney disease. Kidney Int Supplements. 2021;11(1):70\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJain S, Chen F. Developmental pathology of congenital kidney and urinary tract anomalies. Clin Kidney J. 2019;12(3):382\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerke DP, Auchus RJ. Congenital adrenal hyperplasia due to 21-hydroxylase deficiency. N Engl J Med. 2020;383(13):1248\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahimy MC, Gangbo A, Ahouignan G, Adjou R. Protocol for a multicentric cohort study on neonatal screening and early interventions for sickle cell disease in sub-Saharan Africa. Diagnostics. 2023;15(6):730.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990\u0026ndash;2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1223\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKollipara S, Singh P. (2022). Congenital anomalies of the female genital tract: A comprehensive review. Cureus, 14(8), e28111.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. (2019). WHO methods and data sources for global burden of disease estimates 2000\u0026ndash;2016. Global Health Estimates Technical Paper WHO/HIS/IER/GHE\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/2018.4\u003c/span\u003e\u003cspan address=\"http:///2018.4\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao J, Liu X. Congenital anomalies of the kidney and urinary tract: A continuum of developmental disorders. Pediatr Clin North Am. 2023;70(1):75\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHays T, Thompson MV, Bateman DA, Sahni R, Tolia VN, Clark RH, Gharavi AG. (2022). The prevalence and clinical significance of congenital anomalies of the kidney and urinary tract in preterm infants. JAMA Netw Open, 5(9), e2231626.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2021 Diseases and Injuries Collaborators. Global incidence, prevalence, years lived with disability, years of life lost, and disability-adjusted life years for 371 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2021: A systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402(10300):1785\u0026ndash;838.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHollingsworth B, Peacock SJ. Analysing the efficiency of health systems: A systematic review of frontier efficiency measurement techniques. Health Econ Rev. 2023;13(1):45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInstitute for Health Metrics and Evaluation. Global Burden of Disease 2021: Findings from the GBD 2021 Study. Institute for Health Metrics and Evaluation; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFalagas ME, Pitsouni EI, Malietzis GA, Pappas G. Research trends in evidence-based medicine: A joinpoint regression analysis. J Clin Epidemiol. 2008;61(4):322\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu W, Wei Y, Meng Y, Wang C. (2023). Predicting monthly hospital outpatient visits based on autoregressive integrated moving average model. Sci Rep, 13, Article 29897.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens GA, Alkema L, Black RE, Boerma JT, Collins GS, Ezzati M, Grove JT, Hogan DR, Hogan MC, Horton R, Lawn JE, Marušić A, Mathers CD, Murray CJL, Rudan I, Salomon JA, Simpson PJ, Vos T, Welch V. Guidelines for accurate and transparent health estimates reporting: The GATHER statement. Lancet. 2016;388(10062):e19\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2021 Disease and Injury Incidence and Prevalence Collaborators. Global incidence, prevalence, years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10368):1683\u0026ndash;724.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789\u0026ndash;858.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu B, Kim H-J, Feuer EJ, Graubard BI. Joinpoint regression methods of aggregate outcomes for complex survey data. J Surv Stat Methodol. 2023;11(4):967\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou L, Wang Y, Xiao Y, Zheng Z. (2023). Long-term trends in the burden of asthma in China: A joinpoint and age-period-cohort analysis of the Global Burden of Disease Study 1990\u0026ndash;2021. Respir Res, 24(1), Article 35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiadaliri AA, Jansson K. (2023). Efficiency evaluation of 28 health systems by MCDA and DEA. Health Econ Rev, 13(1), Article 8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Zuo L, Wang F. (2022). Application of ARIMA model in predicting the number of patients and economic burden of chronic kidney disease in China. Front Public Health, \u003cem\u003e10\u003c/em\u003e, Article 980114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJames SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, Murray CJL. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789\u0026ndash;858.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2016 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990\u0026ndash;2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390(10100):1211\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Gil M, Blanch J, Comas-Cuf\u0026iacute; M, Daunis-I-Estadella J, Ramos R. A joinpoint regression analysis of trends in mortality due to osteoporosis in Spain: 1999\u0026ndash;2015. Sci Rep. 2019;9(1):4264.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsofa B, Goodman C, Gilson L, Molyneux S. (2021). Performance assessment of the county healthcare systems in Kenya. BMJ Global Health, 6(6), e004707.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27(4):299\u0026ndash;309.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Wang H, Wang L, Liu J, Chen X, Li Z, Zhao Q. Sex difference and risk factors in burden of urogenital congenital anomalies from 1990 to 2019. Front Public Health. 2023;11:1013675.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarasa E, Nguhiu P, McIntyre D. Catastrophic health expenditure in sub-Saharan Africa: a systematic review. Bull World Health Organ. 2018;96(9):620\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalawender L, Becknell B. Congenital anomalies of the kidney and urinary tract: Defining risk factors of disease progression and determinants of outcomes. Front Med. 2023;10:1384676.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeller TM, Gubler MC, Baurecht H. Diagnostic accuracy of an interdisciplinary tertiary center evaluation of fetal hydronephrosis. Pediatr Nephrol. 2021;36(12):3917\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoudjemline Y, Fermont L, Le Bidois J. Postnatal Diagnosis of Congenital Anomalies Despite Active Prenatal Screening Policies: A Population-Based Study in France. Am J Obstet Gynecol MFM. 2023;5(1):100312.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzgediz D, Poenaru D, Gosselin RA. Burden of surgical congenital anomalies in low-income countries: A literature review. J Pediatr Surg. 2014;49(5):763\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2021 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;396(10258):1204\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSitkin NA, Farmer DL. Congenital anomalies in the context of global surgery. Semin Pediatr Surg. 2016;25(1):15\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Birth defects surveillance: A manual for programme managers. World Health Organization; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSitkin NA, Farmer DL. Congenital anomalies in the context of global surgery. Semin Pediatr Surg. 2016;25(1):15\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChong MY, Yip PSF. Using autoregressive integrated moving average (ARIMA) models to predict SARS outbreaks. BMC Health Serv Res. 2005;5:36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim J, Kim SY, Lee YJ. (2021). Environmental and genetic risk factors of congenital anomalies: an umbrella review of systematic reviews and meta-analyses. J Korean Med Sci, 36(22), e183.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD'Antonio F, Khalil A. Prenatal multidisciplinary counseling for fetal congenital anomalies. Int J Gynecol Obstet. 2023;160(2):497\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSitkin NA, Farmer DL. Congenital anomalies in low- and middle-income countries: The unborn child of global surgery. World J Surg. 2015;40:238\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"41\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 21px;\"\u003e\n \u003cp\u003eAll-ages cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 14px;\"\u003e\n \u003cp\u003eAll-ages rates\u003cbr\u003e\u0026nbsp;per 100000 people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 21px;\"\u003e\n \u003cp\u003eAll-ages cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAll-ages rates\u003cbr\u003e\u0026nbsp;per 100000 people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003en(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003en(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003en(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003en(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"28\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\" style=\"width: 19px;\"\u003e\n \u003cp\u003eHigh SDI region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eDeaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1020 (791,1433)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.17 (0.13,0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e498 (335,652)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.09 (0.06,0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eDALYs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e108352\u0026nbsp;\u003cbr\u003e\u0026nbsp;(84999,144909)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e17.35\u0026nbsp;\u003cbr\u003e(13.64,23.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e60842 (44194,77576)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e10.88 (7.73,13.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYLDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e18978\u0026nbsp;\u003cbr\u003e\u0026nbsp;(11424,29466)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.69 (1.6,4.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e18634 (11115,29157)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.7 (1.61,4.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYLLs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e89373\u0026nbsp;\u003cbr\u003e\u0026nbsp;(69109,126249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e14.66 (11.32,20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e42208 (28154,55700)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e8.17 (5.44,10.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003ePrevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e547268\u003cbr\u003e\u0026nbsp; (449255,662805)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e78.01 (63.67,94.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e531934 (437031,640507)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e78.44 (64.2,94.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eIncidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e92805\u0026nbsp;\u003cbr\u003e\u0026nbsp;(72523,118254)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e15.44\u0026nbsp;\u003cbr\u003e(12.07,19.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e71235 (54782,91380)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e14.4 (11.07,18.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\" style=\"width: 19px;\"\u003e\n \u003cp\u003eHigh middle SDI region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eDeaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1333 (911,2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.15 (0.1,0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1745 (1240,2297)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.07 (0.05,0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eDALYs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e145490\u0026nbsp;\u003cbr\u003e\u0026nbsp;(102689,207726)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e15.95 (11.23,22.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e201238 (151067,254356)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e8.99 (6.65,11.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYLDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e28788\u0026nbsp;\u003cbr\u003e\u0026nbsp;(17346,44522)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.88 (1.74,4.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e23864 (14431,37153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.72 (1.65,4.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYLLs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e116702\u0026nbsp;\u003cbr\u003e\u0026nbsp;(79000,177941)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e13.08 (8.82,19.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e38201 (27966,51953)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e6.27 (4.52,8.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003ePrevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e833380\u0026nbsp;\u003cbr\u003e\u0026nbsp;(669798,1021852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e83.76 (67.68,102.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e677368 (549598,825121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e78.53 (63.84,96.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eIncidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e145347\u003cbr\u003e\u0026nbsp; (110273,189578)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e16.56 (12.56,21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e82520 (62048,108329)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e14.67 (11.03,19.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\" style=\"width: 19px;\"\u003e\n \u003cp\u003eMiddle SDI region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eDeaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e2444 (1841,3508)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.12 (0.09,0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1745 (1240,2297)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.1 (0.07,0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eDALYs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e265771\u0026nbsp;\u003cbr\u003e\u0026nbsp;(204185,367257)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e13.33 (10.24,18.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e201238 (151067,254356)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e11.63 (8.61,14.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYLDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e51681\u0026nbsp;\u003cbr\u003e\u0026nbsp;(30393,80640)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.58 (1.52,4.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e56860 (33729,88263)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.79 (1.64,4.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYLLs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e214090\u0026nbsp;\u003cbr\u003e\u0026nbsp;(160333,308895)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10.75 (8.06,15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e144378 (101226,192666)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e8.84 (6.19,11.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003ePrevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1464798(1178877,1819355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e73.05 (58.83,90.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1540119(1232248,1915869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e76.46 (61.36,94.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eIncidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e323762\u0026nbsp;\u003cbr\u003e\u0026nbsp;(244056,428023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e16.16 (12.18,21.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e238029 (176352,314091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e15.55 (11.52,20.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"90\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\" style=\"width: 19px;\"\u003e\n \u003cp\u003eLow middle SDI region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eDeaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e3429 (1833,5616)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.19 (0.1,0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e2816 (1600,4897)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.15 (0.09,0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eDALYs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e363558 (224125,568826)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e20.6 (12.88,31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e324373 (212233,519705)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e17.03 (11.07,27.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYLDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e58981\u003cbr\u003e\u0026nbsp; (34583,91277)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3.79 (2.23,5.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e78182 (46056,120109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.88 (2.29,5.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYLLs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e304577 (162071,500559)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e16.81 (8.98,27.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e246191 (137651,433204)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e13.15 (7.34,23.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003ePrevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1543625(1207293,1961597)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e98.13 (77.41,123.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e2006134(1580291,2553132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e76.46 (61.36,94.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eIncidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e371688\u0026nbsp;\u003cbr\u003e\u0026nbsp;(279457,497608)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e20 (15.04,26.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e359652 (268523,481250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e19.28 (14.39,25.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" style=\"width: 19px;\"\u003e\n \u003cp\u003eLow SDI region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eDeaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1447 (782,3272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.15 (0.08,0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1968 (1162,3952)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.12 (0.07,0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eDALYs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e159376 (100175,326982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e16.88 (10.85,33.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e233055 (155686,409449)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e14.26 (9.52,24.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYLDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e30846 (18243,47476)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4.12 (2.46,6.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e58995 (34904,90960)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.96 (2.36,6.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYLLs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e128530 (68598,291114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e12.76 (6.94,29.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e174060 (102600,351409)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e10.3 (6.08,20.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003ePrevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e828395 (649878,1043574)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e108.34 (85.93,135.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1583659(1246162,1997216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e105.02 (82.63,131.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eIncidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e232774 (175112,315309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e21.92 (16.49,29.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e342073 (256408,453936)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e19.8 (14.84,26.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"buro","sideBox":"Learn more about [BMC Urology](http://bmcurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/buro/default.aspx","title":"BMC Urology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6330031/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6330031/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground：\u003c/strong\u003eUrogenital congenital anomalies (UCAs) are significant contributors to morbidity and disability worldwide, disproportionately affecting regions with limited healthcare resources\u003csup\u003e[1]\u003c/sup\u003e. These conditions impose a substantial burden on individuals and healthcare systems, yet their global trends and disparities remain insufficiently understood\u003csup\u003e[2]\u003c/sup\u003e. This study aimed to analyze temporal trends in incidence, prevalence, and Disability-Adjusted Life Years (DALYs) of UCAs across five Socio-Demographic Index (SDI) regions from 1990 to 2021, alongside a detailed assessment of disparities among 204 countries and territories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eUsing data from the Global Burden of Disease (GBD) Study 2021, this study extrapolated age-standardized incidence, prevalence, deaths, and DALYs for UCAs. Temporal trends were evaluated using Joinpoint regression analysis to identify salient changes. The relationship between SDI and UCA burden was analyzed through regression and frontier analysis, while ARIMA modeling was used to project future trends. Results were stratified by SDI, region, and gender, with statistical significance set at P \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eBetween 1990 and 2021, the global epidemiological patterns of urogenital congenital anomalies (UCAs) displayed pronounced temporal and regional heterogeneity across varying SDI levels. In High SDI regions, the total mortality burden markedly declined from 1,020 deaths (95% UI: 791–1433) in 1990 to 498 deaths (95% UI: 335–652) by 2021. Concurrently, the age-standardized death rate (ASDR) decreased from 0.17 per 100,000 population (95% UI: 0.13–0.23) to 0.09 per 100,000 (95% UI: 0.06–0.12).In contrast, Low-middle SDI regions observed a reduction in deaths from 3,429 (95% UI: 1262–6649) to 2,817 (95% UI: 1442–5307) over the same period; however, ASDR values remained relatively steady, registering 0.20 (95% UI: 0.08–0.37) in 1990 and 0.19 (95% UI: 0.09–0.38) in 2021.Analysis employing Joinpoint regression identified significant trend shifts. In High SDI regions, ASIR demonstrated a significant downward trajectory between 1990 and 2003 (APC = -0.37%, P \u0026lt; 0.05) and further reduction from 2003 to 2014 (APC = -0.23%, P \u0026lt; 0.05). Notably, an inflection occurred post-2016, with ASIR increasing from 2016 to 2019 (APC = +1.53%, P \u0026lt; 0.05) and accelerating between 2019 and 2021 (APC = +5.94%, P \u0026lt; 0.001).Similarly, Low-middle SDI regions evidenced a significant ASIR decline from 1990 to 1993 (APC = -1.52%, P \u0026lt; 0.05) and from 1993 to 1998 (APC = -0.61%, P \u0026lt; 0.05), succeeded by a positive trend after 2016 (APC = +1.14%, P \u0026lt; 0.05).Clear sex-based discrepancies in UCA-associated mortality were observed across all SDI strata. In High SDI regions, male mortality decreased from 691 cases (95% UI: 505–1068) in 1990 to 335 cases (95% UI: 200–451) in 2021, whereas female deaths declined from 329 (95% UI: 183–536) to 163 (95% UI: 90–268) during the same interval. Correspondingly, the ASDR for males declined from 0.22 per 100,000 (95% UI: 0.16–0.34) to 0.12 per 100,000 (95% UI: 0.07–0.17), and for females from 0.11 (95% UI: 0.06–0.18) to 0.06 (95% UI: 0.03–0.10).In Low-middle SDI settings, male ASDR remained at 0.19 per 100,000 (95% UI: 0.09–0.38) by 2021, while the female ASDR was comparatively lower at 0.10 (95% UI: 0.05–0.18), suggesting persistent sex-related survival advantages.Age-specific analysis indicated that disease prevalence predominantly concentrated in the under-five age group, particularly in High SDI regions, where prevalence rates reached approximately 360 per 100,000 for females and 300 per 100,000 for males.Autoregressive integrated moving average (ARIMA) models projected differentiated trajectories of ASIR and ASDR. In High SDI areas, the ASIR is anticipated to increase from 14.4 per 100,000 (95% PI: 12.1–16.7) in 2021 to 16.2 per 100,000 (95% PI: 13.5–19.3) by 2035, predominantly driven by rising male incidence. Simultaneously, the ASDR is projected to decrease further, attaining 0.07 per 100,000 (95% PI: 0.05–0.10) by 2035 (P trend \u0026lt; 0.001).For Low and Low-middle SDI regions, male ASIR is forecasted to ascend from 19.8 per 100,000 (95% PI: 14.8–26.3) to 22.7 per 100,000 (95% PI: 17.2–28.4) by 2035, whereas ASDR values are expected to stabilize between 0.11–0.13 per 100,000 (95% PI: 0.08–0.16).Frontier analysis underscored significant discrepancies in Disability-Adjusted Life Years (DALYs) attributable to UCAs across 204 nations. High SDI countries, exemplified by Japan and Germany, aligned closely with the efficiency frontier, maintaining DALYs below 10 per 100,000. Conversely, Low SDI countries such as Somalia and Chad recorded DALYs exceeding 150 per 100,000, reflecting substantial deviation from optimal efficiency benchmarks (P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study presents a comprehensive evaluation of the global epidemiology and temporal trajectories of urogenital congenital anomalies (UCAs) from 1990 to 2021, utilizing data from the Global Burden of Disease Study 2021. Through Joinpoint regression, ARIMA-based projections, and frontier benchmarking, we identified substantial heterogeneity in incidence, mortality, and DALYs across development levels and sexes. Sustained mortality reductions were evident in high-SDI contexts, whereas lower-SDI settings exhibited persistent and widening differentials. These findings reflect the interplay of health system maturity, early detection, and policy responsiveness in determining UCA outcomes. Theoretically, the study offers contextualized insight into a neglected congenital subgroup; practically, it supports forecast-informed prioritization and policy targeting. Future research should address current data sparsity, integrate socioeconomic determinants, and enhance model validation in underrepresented settings to guide equitable and effective responses. Our results reinforce the urgency of bridging structural inequities in congenital anomaly control at the global scale.\u003c/p\u003e","manuscriptTitle":"Epidemiological Dynamics of Urogenital Congenital Anomalies: A Temporal and Regional Analysis from the Global Burden of Disease Study 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 01:42:05","doi":"10.21203/rs.3.rs-6330031/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-05-17T07:03:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273837175513296702142274623948377604522","date":"2025-05-14T13:58:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-06T09:54:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-09T09:49:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-07T12:41:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-07T12:39:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Urology","date":"2025-03-28T17:18:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"buro","sideBox":"Learn more about [BMC Urology](http://bmcurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/buro/default.aspx","title":"BMC Urology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"436a1122-e35a-4938-864c-c14b887268b9","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-13T01:42:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 01:42:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6330031","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6330031","identity":"rs-6330031","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}