Global trends and socio-demographic inequalities in the burden of genital neoplasms: a 30-year comprehensive analysis of the Global Burden of Disease Study 2021

Global trends and socio-demographic inequalities in the burden of genital neoplasms: a 30-year comprehensive analysis of the Global Burden of Disease Study 2021 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global trends and socio-demographic inequalities in the burden of genital neoplasms: a 30-year comprehensive analysis of the Global Burden of Disease Study 2021 Binbin Luo, Zuo Shiquan, Rui Zhang, Shenyi Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6699229/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background While global longevity increases, economic disparities drive unequal burdens of genital neoplasms. This first comprehensive study evaluates how Socio-demographic Index (SDI) shapes the epidemiology of six major genital neoplasms (uterine fibroids [UFs], prostate [PC], cervical [CC], uterine [UC], testicular [TC], and ovarian cancer [OC]), providing evidence for equitable resource allocation. Methods Using 2021 Global Burden of Disease data (1990-2021), we analyzed age-standardized rates (ASRs) of incidence (ASIR), prevalence (ASPR), mortality (ASMR), and disability-adjusted life years (DALYs) (ASDR) across 204 countries, stratified by SDI quintiles, age, and region. Trend analysis employed estimated annual percentage changes (EAPCs). Inequality was quantified via slope/concentration indices (SII/CI). Age-period-cohort modeling identified risk transitions. Results There are notable disparities in the burden of genital neoplasms by cancer type. UFs showed the highest global prevalence (ASPR 2,841.07/100,000), while PC dominated mortality (ASMR 12.63/100,000). Divergent trends emerged: CC burden declined (DALYs -31.45%, 1990-2021) but rose for TC (ASPR EAPC 1.80%). High-SDI regions had 3.2-fold higher PC incidence yet 67% lower CC mortality than low-SDI areas. SDI-driven inequalities narrowed for UC (SII Δ-12.46) but persisted for PC (CI crossed zero). Projections suggest rising UFs cases (+15.98% by 2035) despite stable ASRs, highlighting demographic pressures. Conclusions SDI-mediated disparities require targeted interventions, particularly CC screening in low-resource settings and TC/PC prevention in high-income regions. Limitations include underdiagnosis in low-SDI areas. These findings establish a framework for global cancer control prioritization. Health equity Socio-demographic Index Genital neoplasms Cancer disparities Global Burden of Disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background The worldwide disease spectrum has shifted dramatically as a result of socioeconomic progress. Infectious diseases that once raged have been effectively controlled in some regions. Meanwhile, chronic non-communicable diseases, especially various genital neoplasms such as uterine fibroids (UFs), uterine cancer (UC), cervical cancer (CC), ovarian cancer (OC), testicular cancer (TC), and prostate cancer (PC), have gradually become the key factors threatening human health [ 1 ]. The vast number of patients, the protracted nature of the disease, and the difficulty of therapy make these neoplasms a significant burden on the healthcare system and the socioeconomic sector [ 2 , 3 ]. In addition, there are disparities in genital neoplasms across countries due to differences in economic development, health awareness, educational attainment, and medical standards [ 2 , 3 ]. Therefore, an accurate assessment of the burden of genital neoplasms and cross-country inequalities is essential. Tumor statistics were once scattered across nations' sectors. Inconsistent formats and scopes hindered understanding of the global tumor burden and inequalities, limiting effective support for policymakers. The Global Burden of Disease Study 2021 (GBD) integrates extensive health data, transforming fragmented information into comprehensive resources through standardization processing [ 4 ]. Governments and organizations can create effective tumor prevention policies using reliable data analysis, optimize resources, and address gaps in prevention control [ 5 ]. However, a comprehensive and in-depth analysis of the latest epidemiological data on genital neoplasms remains insufficient. Socio-demographic index (SDI) profoundly influences tumorigeneses and progression. On the one hand, the aging of the population and the high incidence of cancer among the elderly have raised the total burden of cancer, and the prevalence of cancers such as PC and OC has continued to climb among the old [ 2 , 3 ]. On the other hand, lifestyle changes, such as sedentary behavior, a high-calorie diet, smoking, alcohol misuse, and other poor habits, are expanding among younger people, contributing to an earlier age of onset and a higher prevalence of TC and CC [ 6 ]. Gender differences also influence the distribution of cancers, with women predisposed to gynecological neoplasms and men to PC and TC [ 7 ]. Conducting a comprehensive analysis based on the GBD database is essential, given the dire and unequal state of genital neoplasms prevention and treatment. The precise quantification of the disease burden of genital neoplasms enables the visual presentation of differences in incidence, prevalence, mortality, and disability-adjusted life years (DALYs) in different countries and regions [ 8 ]. Furthermore, delving deeply into the root causes of transnational inequalities, whether related to economic development, medical resource distribution, or social and cultural factors, is conducive to promoting international exchanges and cooperation and advocating resource sharing and technical assistance [ 9 ]. This allows global collaboration to address genital neoplasms, gradually closing the gap created by geographical, economic, and other factors, ultimately reducing the overall cancer burden for humanity. This study focuses on the inequalities in six genital neoplasms across various countries and regions, analyzing their evolution from 1990 to 2021. Utilizing SDI and factors like age, period, and cohort, we aim to guide global tumor prevention strategies by predicting trends and burdens of these diseases until 2035 at global and regional levels. Materials and methods Data and ethical considerations The GBD database is exceptionally comprehensive, integrating multivariate data from 204 countries and regions worldwide. The disease burden data effectively evaluates the impact of various diseases and injuries on human health, highlighting key variables such as incidence, mortality, prevalence, and DALYs [ 10 – 12 ]. Health risk factors encompass influences such as lifestyle choices like smoking and drinking, as well as environmental and socioeconomic aspects. Geographic and demographic data detail the population structure across regions. Medical resource data shows the distribution and usage of resources in various locations. Census data, derived from a thorough population survey, provides a solid demographic basis for evaluating disease burden and supports global health research. The University of Washington's Institutional Review Board has approved waiving informed consent requirements for de-identified data in Global Burden of Disease research. Trend analysis To comprehensively assess long-term trends in disease burden, we analyzed all data points from 1990 to 2021 from the Global Burden of Disease (GBD) database rather than relying solely on the initial and final years. This approach minimizes bias and provides a more accurate representation of temporal patterns. We estimated the annual percentage change (EAPC) for key indicators, including incidence, mortality, and disability-adjusted life year (DALY) rates, using a log-linear regression model: ln( Y ) = a + b · t + ϵ , where Y represents the target metric, t denotes time, a is the intercept, b is the regression coefficient indicating the rate of change over time, and ϵ is the error term [ 13 ]. The EAPC is calculated as: EAPC = ( e b − 1) × 100%, the 95% confidence interval (CI) for EAPC is derived from the standard error (SE) of b : CI = [ b − 1.96 ⋅ SE b ​, b + 1.96 ⋅ SE b ​]. By focusing on age-standardized indicators, we mitigated the influence of age-related factors, ensuring a more accurate representation of underlying disease trends. Socio-demographic Index (SDI) The Socio-demographic Index (SDI) is used to assess the socio-demographic development level of countries or regions and to explore its relationship with health outcomes. SDI integrates three core indicators - income, education, and fertility - each scaled from 0 to 1. The final SDI value is calculated as the average of these indicators: SDI = (income index + educational index + fertility index) / 3. The income index measures growth based on GDP per capita; the educational index considers average schooling years or completion rates; and the fertility index reflects population fertility and societal development. Countries and regions are classified into five development-level groups based on SDI values: Low SDI, Low-middle SDI, Middle SDI, High-middle SDI, and High SDI. A higher SDI indicates a more advanced socio-demographic development level [ 11 ]. Age-period-cohort (APC) analysis We employed the intrinsic estimator (IE) model to address the identifiability issue in APC analysis. The IE model leverages inherent constraints among age, period, and cohort variables, avoiding non-unique solutions caused by overparameterization [ 14 , 15 ]. The model is expressed as: ln ( Y apc ​ ) = µ + α a ​ + β p ​+ γ c ​ + ϵ apc ​ , where Y apc ​ represents the target metric, µ is the overall mean, α a ​ denotes the age effect, β p ​ denotes the period effect, γ c denotes the cohort effect, and ϵ apc ​ is the error term. Disease burden and population data from 1990 to 2021 were gathered from the GBD database. Age groups were categorized into 5-year intervals: 0–4, 5–9, 10–14, ..., 80–84, and 85+. Periods were also split into 5-year ranges, including 1990–1994, 1995–1999, 2000–2004, ..., 2015–2019, and the final range of 2020–2021 (spanning 2 years). Cohorts followed the same 5-year intervals, such as 1895–1899, 1900–1904, ..., 2015–2019. Data were organized into age groups, periods, and cohorts for APC analysis, ensuring each combination had cohort information. The organized data were input into the IE model for log-linear regression. Model parameters were estimated using singular value decomposition (SVD). The estimated age effect ( α a ​ ), period effect ( β p ​), cohort effect ( γ c ​), and standard errors were extracted. Results were visualized using R to illustrate trends in age, period, and cohort effects. Cross-country inequality analysis In this study, we employed two key metrics - the slope index of inequality (SII) and the concentration index (CI) - to assess the relationship between health outcomes and socioeconomic status (SES), uncovering the magnitude and patterns of health inequality [ 16 ]. The SII measures absolute inequality by analyzing the linear relationship between health outcomes and SES. It is calculated using a regression model: Y i ​ = β 0​ + β 1​ · R i ​ + ϵ i ​ , where Y i ​ represents the health outcome, R i denotes the rank of socioeconomic status (sorted by SDI values), β 1​ (SII) indicates the slope of the health outcome concerning socioeconomic status, and ϵ i ​ is the error term. The CI measures relative inequality by assessing the distribution of health outcomes across countries ranked by SES. It is calculated as: CI \(\:=\frac{2}{n*\stackrel{-}{Y}}{\sum\:}_{i=0}^{n}{Y}_{i}·{R}_{i}-1,\) where Y i ​ represents the health outcome, R i​ denotes the rank of socioeconomic status, and \(\:\stackrel{-}{Y}\) is the mean of the health outcome. Together, these metrics provide a comprehensive health inequality assessment, informing targeted interventions to reduce disparities. Predictive analysis To proactively identify future health threats and strengthen public health responsiveness, we projected the incidence, mortality, prevalence, and disability-adjusted life years (DALYs) of genital neoplasms using advanced predictive modeling. Our approach integrates the Bayesian Age-Period-Cohort (BAPC) model with the Integrated Nested Laplace Approximation (INLA) framework, enabling precise and efficient predictions of disease burden trends [ 17 ]. The INLA method efficiently fits Bayesian models, supporting complex random effects and hierarchical data structures. Its core approach involves calculating posterior distributions through Laplace approximations, avoiding the computationally intensive Markov Chain Monte Carlo (MCMC) sampling. Combining the BAPC model's decomposition with INLA's efficiency leads to more accurate predictions. This approach considers age, period, cohort effects, and spatial-temporal correlations, enhancing model interpretability and accuracy. Results Descriptive analysis of the burden of genital neoplasms at global levels From 1990 to 2021, the incidence rates of certain genital neoplasms exhibited varying trends globally (Table 1). The incidence rates of UFs, UC, TC, and PC showed significant increases. Notably, the ASIR of TC demonstrated the most pronounced rise, increasing from 1.51 (95% uncertainty interval [UI] 1.45 to 1.55) per 100,000 population to 2.42 (95% UI 2.16 to 2.35), with an EAPC of 1.42 (95% confidence interval [CI] 1.35 to 1.48). In contrast, the incidence rates of CC and OC declined. The ASIR of CC decreased from 18.11 (95% UI 16.94 to 19.40) to 15.32 (95% UI 14.08 to 16.68), with an EAPC of -0.54 (95% CI -0.64 to -0.45). In 2021, UFs had the highest incidence among global genital neoplasms, reaching 101.00 × 10 5 cases (95% UI 73.50 to 132.86). Overall, the incidence rates of these six genital neoplasms predominantly exhibited an upward trend over the past three decades. Table1. Global incidence, prevalence, mortality, and DALYs of genital neoplasms (1990-2021) Year Uterine fibroids Uterine cancer Cervical cancer Ovarian cancer Testicular cancer Prostate cancer 1990 Incidence (×10 5 , 95% UI) 60.10 (43.90-80.11) 1.91 (1.75-2.02) 4.10 (3.83-4.39) 1.59 (1.46-1.74) 0.39 (0.38-0.40) 5.06 (4.81-5.25) Prevalence (×10 5 , 95% UI) 656.95 (500.22-855.58) 13.33 (12.2613.99) 18.26 (17.27-19.29) 6.29 (5.72-6.93) 2.67 (2.58-2.76) 35.96 (34.45-37.05) Mortality (×10 5 , 95% UI) 0.011 (0.008-0.015) 0.55 (0.49-0.59) 2.11 (1.96-2.30) 1.01 (0.93-1.09) 0.076 (0.072-0.081) 2.12 (1.94-2.24) DALYs (×10 5 , 95% UI) 0.81 (0.57-1.12) 15.01 (12.99-16.38) 74.16 (68.41-80.71) 29.09 (26.62-31.99) 3.90 (3.66-4.13) 41.47 (37.54-44.02) ASIR (1/10 5 , 95% UI) 234.36 (171.06-309.92) 7.72 (7.63-6.61) 18.11 (16.94-19.40) 7.22 (6.65-7.87) 1.50 (1.45-1.55) 32.64 (30.86-33.86) ASPR (1/10 5 , 95% UI) 2799.88 (2133.46-3650.54) 61.17 (56.35-64.14) 78.15 (73.89-82.51) 27.62 (25.26-30.26) 9.99 (9.67-10.33) 218.33 (225.67-208.48) ASMR (1/10 5 , 95% UI) 0.05 (0.04-0.07) 2.60 (2.32-12.80) 9.68 (8.97-10.51) 4.73 (4.38-5.12) 0.33 (0.32-0.35) 16.35 (15.02-17.28) ASDR (1/10 5 , 95% UI) 3.48 (2.46-4.77) 69.17 (59.85-75.30) 330.11 (304.67-359.10) 132.48 (121.34-145.63) 15.11 (14.20-16.00) 275.30 (251.66-292.14) 2021 Incidence (×10 5 , 95% UI) 101.00 (73.50-132.86) 4.74 (4.30-5.14) 6.67 (6.13-7.26) 2.99 (2.71-3.26) 0.92 (0.88-0.96) 13.24 (12.17-14.00) Prevalence (×10 5 , 95% UI) 1195.45 (912.28-1549.44) 34.51 (31.65-37.25) 33.83 (31.08-36.97) 12.22 (11.02-13.32) 6.79 (6.51-7.14) 103.88 (97.05-109.04) Mortality (×10 5 , 95% UI) 0.02 (0.01-0.03) 0.98 (0.87-1.08) 2.97 (2.72-3.22) 1.86 (1.68-2.01) 0.114 (0.108-0.121) 4.32 (3.82-4.64) DALYs (×10 5 , 95% UI) 1.43 (1.02-1.93) 25.63 (22.91-28.46) 99.12 (90.53-107.98) 51.63 (46.92-56.08) 5.61 (5.28-5.97) 81.42 (71.77-88.09) ASIR (1/10 5 , 95% UI) 250.93 (183.44-330.94) 12.05 (10.93-13.06) 15.32 (14.08-16.68) 6.71 (6.07-7.28) 2.42 (2.16-2.35) 34.05 (31.27-36.00) ASPR (1/10 5 , 95% UI) 2841.07 (2164.43-3682.27) 75.73 (69.37-1.78) 79.31 (72.81-86.58) 28.08 (25.26-30.64) 16.59 (15.91-17.44) 260.05 (243.39-272.68) ASMR (1/10 5 , 95% UI) 0.05 (0.03-0.06) 2.11 (1.87-2.34) 6.62 (6.07-7.18) 4.06 (3.67-4.40) 0.29 (0.27-0.30) 12.63 (11.16-13.55) ASDR (1/10 5 , 95% UI) 3.39 (2.43-4.59) 56.15 (50.07-62.37) 226.28 (206.51-246.86) 115.15 (104.58-125.21) 13.38 (13.03-14.73) 217.83 (192.65-235.53) 1990 to 2021 ASIR (EAPC, 95% CI) 0.24 (0.23-0.25) 0.54 (0.50-0.58) -0.54 (-0.64 to -0.45) -0.38 (-0.43 to -0.32) 1.42 (1.35-1.48) -0.06 (-0.21-0.08) ASPR (EAPC, 95% CI) 0.04 (0.03-0.06) 0.77 (0.72-0.82) 0.12 (0.03-0.21) -0.07 (-0.13 to -0.002) 1.80 (1.71-1.89) 0.42 (0.26-0.58) ASMR (EAPC, 95% CI) 0.06 (-0.07-0.20) -0.78 (-0.85 to -0.70) -1.27 (-1.36 to -1.18) -0.62 (-0.68 to -0.57) -0.54 (-0.70 to -0.39) -1.05 (-1.14 to -0.95) ASDR (EAPC, 95% CI) 0.05 (-0.05-0.11) -0.78 (-0.85 to -0.71) -1.27 (-1.36 to -1.17) -0.59 (-0.64 to -0.54) -0.29 (-0.44 to -0.15) -0.96 (-1.05 to -0.87) UI uncertainty intervals, DALYs disability-adjusted life-years, ASIR age-standardized incidence rate, ASPR age-standardized prevalence rate, ASMR age-standardized mortality rate, ASDR age-standardized DALYs rate, EAPC estimated annual percentage change, CI confidence interval Regarding prevalence, mortality, and DALYs, the prevalence rates of all six neoplasms showed an upward trend, with particularly notable increases observed for UFs, UC, TC, and PC (Table 1). The ASPR for TC demonstrated the fastest growth, rising from 9.99 (95% UI 9.67 to 10.33) per 100,000 population to 16.59 (95% UI 15.91 to 17.44), with an EAPC of 1.80 (95% CI 1.71 to 1.89). Except for UFs, the mortality rates and DALYs for the remaining five neoplasms declined. CC exhibited the most significant reduction in both mortality and DALYs. Its ASMR fell from 9.68 (95% UI 8.97 to 10.51) to 6.62 (95% UI 6.07 to 7.18) per 100,000 population, with an EAPC of -1.27 (95% CI -1.36 to -1.18). Likewise, the ASDR for CC decreased from 330.11 (95% UI 304.67 to 359.10) to 226.28 (95% UI 206.51 to 246.86) per 100,000 population, with an EAPC of -1.27 (95% CI -1.36 to -1.17). In 2021, CC had the highest disease burden for females, while PC was highest for males. Despite increasing genital neoplasm rates, declining mortality and DALYs show progress in treatment. Overall trends in the burden of genital neoplasms at regional levels From the perspective of geographical regions and the SDI (Fig. 1 , Table S1 -S4), in high-SDI regions such as high-income North America and Western Europe, the EAPC values of the ASIR and ASPR for PC, TC, and UC are relatively high. However, the EAPC values of the ASMR and the ASDR are relatively low. In medium-high SDI regions such as Eastern Europe and Latin America, the EAPC values of the ASIR and ASPR for CC and OC are at a medium level, and the EAPC values of the ASMR and ASDR are also at a medium level. In low-SDI regions such as southern sub-Saharan Africa and South Asia, the EAPC values of the ASIR and ASPR for CC and OC are high. Meanwhile, the EAPC values of the ASMR and ASDR are also high. In East Asia and Southeast Asia, the EAPC values for UFs and CC are at a medium level. Additionally, considerable variations in the EAPC values for certain genital neoplasms were noted across various regions (Fig. 1 , Table S1 -S4). The Caribbean leads in EAPC values for ASIR (5.48, 95% CI 4.28–6.94) and ASDR (3.40, 95% CI 2.45–4.36) in TC. The Andean Latin America excels in EAPC for ASPR (9.73, 95% CI 8.97–10.48), while East Asia tops ASMR (4.80, 95% CI 3.74–5.87) in UFs. The Australasia region shows the lowest EAPC for ASIR (-2.21, 95% CI -2.58 to -1.84) and ASPR (-2.04, 95% CI -2.46 to -1.62) in OC. For ASMR, the lowest EAPC (-6.29, 95% CI -7.11 to -5.47) is found in UFs, and ASDR's lowest value (-3.13, 95% CI -3.40 to -2.85) is in CC in Australasia. Despite rising cancer rates globally, high-income regions have significantly reduced mortality and disability, while low-income areas struggle with cancer control and treatment. General trends in the burden of genital neoplasms across national levels From 1990 to 2021, global genital neoplasm burdens varied greatly by country. Low- and middle-income countries exhibited higher EAPC in ASIR and ASPR, while high-income countries showed lower values. The patterns varied by tumor type and region (Fig. 2 and S1 , Table S5 and S6): for UFs, Nigeria, Ethiopia, and India had higher EAPC values, whereas the United Kingdom, France, and the United States showed lower values; for UC, Russia, and Cuba had higher EAPC values, while China and Japan reported lower values; for CC, Nigeria and Ethiopia exhibited higher EAPC values, whereas the United Kingdom and France had lower values; for OC, Brazil and Argentina demonstrated higher EAPC values, while China and Japan showed lower values; for TC, Denmark and Norway had higher EAPC values; and for PC, Russia and Japan displayed higher EAPC values, whereas most sub-Saharan African countries and some South Asian nations reported lower values. Regarding the EAPC in ASMR and ASDR (Fig. S2 and S3, Table S7 and S8), specific countries in sub-Saharan Africa, South Asia, and Latin America exhibited higher values. In contrast, Western Europe, North America, and Oceania generally showed lower values. For specific tumors and countries, Nigeria and Ethiopia had high EAPC values for ASMR and ASDR in UFs, while the United Kingdom and the United States reported lower values, and Australia experienced negative ASMR growth. Russia and Ukraine showed faster ASMR growth for UC, whereas Cuba and Jamaica had high ASMR and ASDR values, and China and others exhibited lower growth rates. For CC, Nigeria and Afghanistan had high ASMR and ASDR values, while Norway and Denmark experienced negative ASMR growth. Brazil and Argentina demonstrated high ASMR and ASDR values for OC, whereas Japan and South Korea showed differing trends. For TC, Denmark and Sweden, as well as the Netherlands and Belgium, displayed varying patterns in ASMR and ASDR growth. For PC, Japan and South Korea, as well as Russia and Poland, exhibited inconsistent trends in ASMR and ASDR growth, while sub-Saharan Africa reported high growth rates in both ASMR and ASDR. Cross-country inequality analysis of genital neoplasms We analyzed the relationship among the ASIR, ASPR, ASMR, and SDI for six types of genital neoplasms across various global regions in 1990 and 2021 (Fig. 3 and S4 - 5 ). ASIR and ASPR for UFs, UC, OC, TC, and PC correlate positively with SDI, while CC negatively correlates with SDI. Within specific SDI ranges, ASMR for UC, OC, TC, and PC increases; however, as SDI continues to rise, ASMR in some high-income countries (such as Germany, Greece, and the United States) stabilizes or declines. In contrast, ASMR for UFs and CC exhibits a significant negative correlation with SDI, with a more pronounced decline observed for CC. Over time, compared to 1990, ASIR and ASPR for UC, TC, and PC increased in high-SDI regions by 2021, while ASIR and ASPR for CC and OC decreased in these regions. Additionally, ASMR for all six types of genital neoplasms declined in 2021 compared to 1990, with a more significant downward trend observed in high-SDI regions. Analysis of DALYs for genital neoplasms reveals that the SII for UC, CC, OC, and TC declined, indicating reduced DALYs burden across regions of differing socio-demographic development (Fig. 4 a). In contrast, the SII for UFs and PC remained stable, suggesting lower inequality in DALYs burden. In 1990, the DALY inequality for OC was 57.67 per 100,000 people (95% CI 41.77 to 73.57). By 2021, this narrowed to 26.88 (95% CI 15.12 to 38.63), showing a significant reduction (Fig. 4 a). The SII for UC decreased from 19.89 (95% CI 11.99 to 29.14) in 1990 to 7.43 (95% CI -1.52 to 16.39) in 2021, reflecting a notable drop in DALY inequality (Fig. 4 b). The data points for CC were widely distributed, reflecting substantial regional disparities in DALYs rates, while those for UFs and TC were predominantly concentrated in regions with lower DALYs rates (Fig. 4 b). Overall, the global burden of DALYs for genital neoplasms in 2021 decreased compared to 1990, particularly in regions with higher socio-demographic development levels (Fig. 4 b). The CI for PC shifted from negative (-0.03, 95% CI -0.11 to 0.05) to positive (0.04, 95% CI -0.04 to 0.12), indicating a transition in DALYs burden from low-SDI to high-SDI populations. Meanwhile, the CI for OC decreased from − 0.19 (95% CI -0.27 to -0.11) in 1990 to -0.06 (95% CI -0.14 to 0.02) in 2021, demonstrating a significant reduction in inequality in DALYs burden among populations with different SDI levels (Fig. 4 b). Age-period-cohort analysis of the burden of genital neoplasms The incidence and prevalence rates of UFs have changed significantly, with the highest ASIR in 37.5-year-olds and the highest ASPR in 42.5-year-olds. ASIR and ASPR decline in the 40–60 age group, while period and cohort risks trend downward and stabilize (Fig. 5 and S6 ). For UC, ASIR (0.89%, 95% CI 0.83 to 0.96) and ASPR (1.05, 95% CI 0.98 to 1.13) show upward trends, with a significant increase in the 57.5-year-old population; however, cohort risks for those born after 1985 have declined (Fig. 5 and S6 ). CC shows an annual decline in ASIR (-0.60, 95% CI -0.64 to -0.56), peaking in the 57.5-year-old group. Both period and cohort risks trend downward, while ASPR remains stable across age groups, with cohort risks stabilizing for those born after 1970 (Fig. 5 and S6 ). OC shows minor local drifts in ASIR (-0.25, 95% CI -0.28 to -0.22) and ASPR (-0.04, 95% CI -0.07 to -0.01), with rates increasing with age. However, cohort risks for those born after 1980 are rising (Fig. 5 and S6 ). TC shows the largest annual changes, with the highest ASIR and ASPR in the 30-year-olds, while period and cohort risks have worsened recently (Fig. 5 and S6 ). For PC, the age distribution of ASIR and ASPR has changed, with annual local variations exceeding 0.82% in the 22.5–57.5 age group. Although ASIR and ASPR have shown some alleviation since 2007, cohort risks have increased (Fig. 5 and S6 ). UFs show rising ASMR (0.32, 95% CI 0.03 to 0.60) and ASDR of (0.21, 95% CI 0.10 to 0.32), with ASMR significantly increased in the 77.5-year-old population and severe ASDR in the 47.5-year-old group. Both period and cohort risks show an upward trend (Fig. S7 and S8). For UC, ASMR and ASDR decline across all age groups, with mortality increasing with age. The 67.5-year-old population bears the heaviest disability burden, while period and cohort risks demonstrate a downward trend (Fig. S7 and S8). CC shows the largest decline in ASMR (1.3, 95% CI -1.36 to -1.25)) and ASDR (1.28, 95% CI -1.34 to -1.22), with stable mortality in the 57.5-year-old population but the highest disability burden in this group. Period and cohort risks have improved (Fig. S7 and S8). OC exhibits an overall downward trend in ASMR and ASDR, with the 67.5-year-old population experiencing the most significant disability burden, while risks for populations born after 1980 are rising (Fig. S7 and S8). TC shows a declining trend, with the highest disability burden in the 27.5-year-old population, and both cohort and period risks are improving (Fig. S7 and S8). For PC, ASMR and ASDR remain low and stable in the 0-62.5 age group, with significant risk declines across different periods and birth cohorts (Fig. S7 and S8). Predictive analysis of the burden of genital neoplasms by 2035 We forecasted the case numbers and age-standardized rates (ASR) for incidence, prevalence, and DALYs related to six genital neoplasms until 2035. Globally, the incidence and prevalence of genital tumors are anticipated to rise (Fig. 6 and S9 ). By 2035, the incidence and prevalence of UFs, UC, OC, TC, and PC will increase (Fig. 6 and S9 ). Conversely, CC cases may fluctuate initially but exhibit a stable trend over time (Fig. 6 and S9 ). The ASIR shows different trends. The ASIRs for UFs and OC are expected to rise over time, while the ASIRs for UC, CC, TC, and PC show a downward trend (Fig. 6 ). Regarding the ASPR, except for the ASPR of OC, which is expected to increase over time, the ASPR for the other tumors all show a decreasing trend (Fig. 6 ). Additionally, the number of DALY cases related to UFs, UC, OC, and TC is expected to show an upward trend (Fig. S10). In contrast, the overall trends for the number of DALY cases of CC and PC remain relatively stable (Fig. S10). The ASDR for UC, CC, and TC are forecasted to decline; the ASDR for PC is subject to inevitable fluctuations, and the ASDR for UFs and OC is relatively stable (Fig. S10). Discussion This study offers a comprehensive analysis of genital neoplasms using 1990–2021 GBD data, applying EAPC, age-period-cohort, inequality, and predictive analyses. Findings show increasing global rates of UFs, UC, TC, and PC, while CC and OC rates declined. Prevalence rose for all tumors, particularly TC. Mortality and DALYs fell for all except UFs, with CC seeing the largest decrease. Low- and middle-income countries exhibited higher EAPC for ASIR and ASPR, whereas high-income countries had lower EAPC. Mortality and DALYs EAPC were elevated in sub-Saharan Africa, South Asia, and Latin America, but lower in Western Europe, North America, and Oceania. The study emphasizes improving prevention and screening for rising tumor incidences like TC. It suggests expanding successful strategies for declining mortality and DALYs in CC. Clinicians can adapt their diagnostic and treatment approaches to enhance patient care outcomes. APC analysis is crucial for understanding disease patterns and formulating prevention strategies [ 14 ]. This study used APC analysis to discern the impacts of age, period, and cohort on genital neoplasm burden changes. For UFs, ASIR and ASPR peak at ages 37.5 and 42.5, then decline due to reproductive and hormonal changes; hormone fluctuations stimulate fibroid development, while stable levels between 40 and 60 restrict growth [ 18 , 19 ]. In UC, ASIR and ASPR rose for those aged 57.5 but fell for those born after 1985, as menopausal estrogen fluctuations increased risk in older women, while better health management lowered it in younger women [ 20 , 21 ]. For CC, the decline in ASIR, ASMR, and ASDR indicates the effectiveness of CC vaccines and screening programs like smear tests and HPV detection, which should be further promoted [ 22 ]. OC risk rises with age as tissue aging and reduced cell repair and immune functions increase susceptibility, and the cohort risk for those born after 1980 has increased due to factors like environmental pollution and changing fertility patterns [ 23 – 25 ]. Although OC mortality and disability rates have generally decreased, late diagnosis due to lack of early symptoms burdens the 67.5-year-old population, but improved medical treatments have enhanced prognosis and lowered these rates [ 2 ]. For PC, annual changes in the 22.5–57.5 age group may relate to androgen levels, high-fat diets, lack of exercise, and occupational exposure [ 26 ]. The low ASMR and ASDR in the 0-62.5 age group are likely due to the low incidence of PC and effective early screening and treatment [ 27 ]. Declining risks across periods and birth cohorts may benefit from advancements in screening technologies and improved treatment methods [ 28 ]. This study found that the ASIR and ASPR of TC significantly increased from 1990 to 2021, with worsening period and cohort risks. The trend of global population aging was evident, leading to a growing elderly population [ 29 ]. Although TC is more common in young people, the number of patients will increase due to changing age demographics [ 30 ]. The 27.5-year-old population bears the heaviest DALYs burden, with the high-incidence age group being 15–35. This group may be more vulnerable to environmental, lifestyle, and reproductive health issues [ 31 ]. Analyzing these causes can provide a scientific basis for targeted prevention and control strategies for TC and PC. High-SDI regions boast abundant medical resources and advanced technologies, enabling accurate disease detection and diagnosing previously undetected tumors, leading to increased ASIR and ASPR [ 28 , 32 ]. For instance, advanced screening can identify early-stage micro-tumors. Additionally, lifestyle and environmental factors in these areas, such as high-calorie diets, inactivity, and pollution, may raise certain tumor risks. In low SDI regions, as economies grow and healthcare improves, tumor diagnoses and treatments rise, increasing patient diagnoses [ 32 ]. This initially elevates ASMR. However, with further development in high-SDI regions, their superior medical technology and public health systems enhance cancer prevention and treatment, leading to better mortality control [ 33 ]. Consequently, ASMR in some high-SDI countries in Europe and America has stabilized or declined thanks to advanced cancer treatments and personalized plans [ 34 ]. UFs are non-fatal diseases [ 35 ]. In high-SDI regions with adequate medical resources, fibroid-related issues can be addressed quickly, reducing death risk [ 36 ]. Thus, there's a negative correlation between ASMR and SDI. In high-SDI regions, prevention and treatment of CC are more effective [ 37 ]. Vaccination and screening have significantly reduced CC mortality, and as SDI increases, ASMR for CC declines [ 38 ]. The global dissemination of medical technologies has allowed low-SDI regions to improve cancer treatment and control. The gap between low-SDI and high-SDI regions has narrowed, reducing DALYs inequality for UC, CC, OC, and TC [ 4 ]. International aid and technical exchanges have enhanced medical standards in the low-SDI regions. UFs typically have a mild impact on DALYs, and their effects are stable across regions. PC pathogenesis and treatment effects are consistent across SDI regions, leading to low DALYs burden inequality and stable SII [ 39 ]. PC is ordinary in older men, especially in high-SDI regions where population aging is pronounced, leading to more patients [ 40 ]. Diagnosis and treatment are better standardized in these areas. While survival time has improved, the number of patients living with the disease has risen, shifting the DALYs burden to high-SDI regions [ 3 , 41 ]. Formulate prevention and control strategies for genital tumors based on their features, incidence, prevalence, mortality, and factors like SDI [ 42 ]. For UFs, health management for women aged 37.5 to 42.5 should be improved, regular check-ups encouraged, health promotion focused on 40-60-year-olds, a healthy lifestyle adopted, and medical services for older women enhanced. UC should increase screening for women aged 57.5, utilize advanced techniques for early diagnosis, promote successful strategies for those born after 1985, and strengthen rehabilitation for elderly patients. For CC, treatment and rehabilitation for 57.5-year-old patients should be enhanced to improve their quality of life. For OC, monitoring of high-risk groups like those with family history and nulliparous women should be strengthened, genetic testing and preventive measures carried out, and early-diagnosis technologies invested in. For TC, research investment is crucial, and treatment and rehabilitation for 27.5-year-old young patients should be optimized. For PC, research on high-risk factors like lifestyle and genetics is essential to develop targeted prevention strategies given the gradually increasing cohort risk despite low and stable ASMR and ASDR in the 0 to 62.5 age group. This study uses the GBD database to investigate genital neoplasms, noting data quality, disease definition, and the influence of external factors limitations. Caution is warranted when applying findings, and various approaches should address these issues [ 11 , 43 ]. Low- and middle-income countries' lack of dependable epidemiological data can jeopardize accuracy. Diagnostic biases in initial studies may impact the GBD database, diminishing the reliability of the research. Differences in diagnostic criteria and technology could cause misdiagnosis or underdiagnosis, leading to data that might not accurately represent the true prevalence of the disease burden. Secondly, the GBD database definitions of genital tumors may not encompass all relevant disease conditions, potentially leading to an underestimation of the disease burden. Some rare subtypes or early-stage genital neoplasms might be inaccurately defined or excluded from the statistics, thus limiting the comprehensiveness of the findings. Additionally, the lack of consideration for exceptional circumstances like the COVID-19 pandemic can skew mortality estimates. In severely affected regions, mortality data may be inaccurate, and the GBD database might not reflect these changes, leading to discrepancies between research findings and reality. Conclusions In summary, this study analyzed the GBD database to predict the global burden of genital neoplasms, revealing distinct trends and regional disparities by SDI levels. High-SDI regions showed higher incidence rates but achieved effective prevention, whereas low-SDI regions faced greater challenges. Tailored strategies are essential: high-SDI areas should advance screening and treatment technologies, while low-SDI areas need improved healthcare infrastructure and health education. Global collaboration is vital for enhanced data collection and international efforts to strengthen the prevention and control of genital neoplasms. Declarations Acknowledgements We thank the collaborators of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. Authors’ contributions B.B. Luo researched and analyzed the data and wrote the paper. S.Q. Zuo oversaw data collation. R. Zhang assisted in the data analysis and the layout of the paper. S.Y. Wang designed, revised and directed the manuscript. All authors reviewed the manuscript. Funding This research was funded by the National Natural Science Foundation of China (82305021), the Natural Science Research Project of the Anhui Provincial Department of Education (2024AH030036), the Traditional Chinese Medicine Inheritance and Innovation Project of Anhui Province (2024CCCX266), the Research Project of the Second People's Hospital of Hefei (2023yjc008). Availability of data and materials All data about this study is accessible at no cost via the GBD 2021 portal (http://ghdx.healthdata.org/gbd-2021). R scripts to read in R environment and the RDS files of the datasets analyzed here are available from the authors upon reasonable request. Ethics approval and consent to participate Not applicable. 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Endocr Rev. 2022;43(4):678-719. Ali, M, Ciebiera, M, Wlodarczyk, M, et al., Current and emerging treatment options for uterine fibroids . Drugs. 2023;83(18):1649-1675. Amboree, T L, Paguio, J, and Sonawane, K, HPV vaccine: the key to eliminating cervical cancer inequities . BMJ. 2024;385:q996. Huang, Z, Wang, J, Liu, H, et al., Global trends in adolescent and young adult female cancer burden, 1990-2021: insights from the Global Burden of Disease Study . ESMO Open. 2024;9(11):103958. Liu, D, Zhou, L, Li, C, et al., Endocrine cancer trends 1990-2021: global disparities and health inequalities . Endocr Relat Cancer. 2024;31(11). Robin, T P, Geiger, C L, Callihan, E B, et al., Prostate cancer in older adults: risk of clinically meaningful disease, the role of screening and special considerations . Curr Oncol Rep. 2021;23(11):130. Huang, M, Narita, S, Koizumi, A, et al., Macrophage inhibitory cytokine-1 induced by a high-fat diet promotes prostate cancer progression by stimulating tumor-promoting cytokine production from tumor stromal cells . Cancer Commun. 2021;41(5):389-403. Collaborators, G V, Five insights from the Global Burden of Disease Study 2019 . Lancet. 2020;396(10258):1135-1159. Collaborators, G D, Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021 . Lancet. 2024;403(10440):1989-2056. Additional Declarations No competing interests reported. Supplementary Files Supmet05191914.docx Figure S1. The EPAC trend of ASPR for six types of genital neoplasms from 1990 to 2021. EAPC estimated annual percentage change, ASPR age-standardized prevalence rate. Figure S2. The EPAC trend of ASMR for six types of genital neoplasms from 1990 to 2021. EAPC estimated annual percentage change, ASMR age-standardized mortality rate. Figure S3. The EPAC trend of ASDR for six types of genital neoplasms from 1990 to 2021. EAPC estimated annual percentage change, ASDR age-standardized disability-adjusted life years rate. Figure S4. The relationship between the ASPR (per 100,000 population) of six types of genital neoplasms and SDI in different regions in 1990 and 2021. ASPR age-standardized prevalence rate, SDI socio-demographic index. Figure S5. The relationship between the ASMR (per 100,000 population) of six types of genital neoplasms and SDI in different regions in 1990 and 2021. ASMR age-standardized mortality rate, SDI socio-demographic index. Figure S6. The impact of age, period, and birth cohort on the relative risk of prevalence for six types of genital neoplasms. The changes in the prevalence rates for all age groups and age-standardized prevalence rates between 1990 and 2021 are presented. Local drifts indicate the annual percentage change (%/year) in prevalence rates for five-year age groups (from 10-14 to 85+ years). The figure annotates the net drift index and its 95% CI, which can be used to measure the rate of change in prevalence over time. The age effect represents the prevalence rates of tumors across different age groups. The period effect is expressed by the relative risk of prevalence (prevalence rate ratio), calculated as the age-specific rate ratio for each period compared to the reference period of 2000-2005. The cohort effect illustrates the changes in the prevalence rate ratios for different birth cohorts. The shaded areas represent the corresponding 95% CI for each point estimate. CI confidence intervals. Figure S7. The impact of age, period, and birth cohort on the relative risk of mortality for six types of genital neoplasms. The changes in the mortality rates for all age groups and age-standardized mortality rates between 1990 and 2021 are presented. Local drifts indicate the annual percentage change (%/year) in mortality rates for five-year age groups (from 10-14 to 85+ years). The figure annotates the net drift index and its 95% CI, which can be used to measure the rate of change in mortality over time. The age effect represents the mortality rates of tumors across different age groups. The period effect is expressed by the relative risk of mortality (mortality rate ratio), calculated as the age-specific rate ratio for each period compared to the reference period of 2000-2005. The cohort effect illustrates the changes in the mortality rate ratios for different birth cohorts. The shaded areas represent the corresponding 95% CI for each point estimate. CI confidence intervals. Figure S8. The impact of age, period, and birth cohort on the relative risk of DALYs for six types of genital neoplasms. The changes in the DALYs rates for all age groups and age-standardized DALYs rates between 1990 and 2021 are presented. Local drifts indicate the annual percentage change (%/year) in DALYs rates for five-year age groups (from 10-14 to 85+ years). The figure annotates the net drift index and its 95% CI, which can be used to measure the rate of change in DALYs over time. The age effect represents the DALYs rates of tumors across different age groups. The period effect is expressed by the relative risk of DALYs (DALYs rate ratio), calculated as the age-specific rate ratio for each period compared to the reference period of 2000-2005. The cohort effect illustrates the changes in the DALYs rate ratios for different birth cohorts. The shaded areas represent the corresponding 95% CI for each point estimate. DALYs disability-adjusted life years, CI confidence intervals. Figure S9. The changing trends and predictions of the prevalence numbers and ASPR of six types of genital neoplasms during the period from 1990 to 2035. ASPR age-standardized prevalence rate. Figure S10. The changing trends and predictions of the DALYs numbers and ASDR of six types of genital neoplasms during the period from 1990 to 2035. DALYs disability-adjusted life years, ASDR age-standardized disability-adjusted life years rate. Supplementary Information Table S1. EAPC of the ASIR of genital neoplasms in global, SDI, and 21 regions Table S2. EAPC of the ASPR of genital neoplasms in global, SDI, and 21 regions Table S3. EAPC of the ASMR of genital neoplasms in global, SDI, and 21 regions Table S4. EAPC of the ASDR of genital neoplasms in global, SDI, and 21 regions Table S5. EAPC of ASIR for genital neoplasms in 204 countries and territories Table S6. EAPC of ASPR for genital neoplasms in 204 countries and territories Table S7. EAPC of ASMR for genital neoplasms in 204 countries and territories Table S8. EAPC of ASDR for genital neoplasms in 204 countries and territories Figure S1. The EPAC trend of ASPR of genital neoplasms from 1990 to 2021 Figure S2. The EPAC trend of ASMR of genital neoplasms from 1990 to 2021 Figure S3. The EPAC trend of ASDR of genital neoplasms from 1990 to 2021 Figure S4. The ASPR of genital neoplasms and SDI across regions in 1990 and 2021 Figure S5. The ASMR of genital neoplasms and SDI across regions in 1990 and 2021 Figure S6. Age-period-cohort analysis of the prevalence of genital neoplasms Figure S7. Age-period-cohort analysis of the mortality of genital neoplasms Figure S8. Age-period-cohort analysis of the DALYs of genital neoplasms Figure S9. Prevalence trends of genital neoplasms from 1990 to 2035 Figure S10. DALYs trends of genital neoplasms from 1990 to 2035 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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-6699229","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470491357,"identity":"e2bace71-ccff-490e-ac28-4b2f9cb996e0","order_by":0,"name":"Binbin Luo","email":"","orcid":"","institution":"The Second People's Hospital of Hefei, Hefei Hospital, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Binbin","middleName":"","lastName":"Luo","suffix":""},{"id":470491358,"identity":"9ef72d19-b3bc-4cf5-a0c0-42dbe52f1b28","order_by":1,"name":"Zuo Shiquan","email":"","orcid":"","institution":"Anhui University of 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13:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6699229/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6699229/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84819300,"identity":"24c4f798-b5cd-4ac3-87ea-4d3b3a258b06","added_by":"auto","created_at":"2025-06-17 15:58:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEAPC of ASIR, ASPR, ASMR, and ASDR for six types of genital neoplasms in global and regions. \u003c/strong\u003eEAPC estimated annual percentage change, ASIR age-standardized incidence rate, ASPR age-standardized prevalence rate, ASMR age-standardized mortality rate, ASDR age-standardized disability-adjusted life years rate, SDI, socio-demographic index, CI confidence intervals, UFs uterine fibroids, UC uterine cancer, CC cervical cancer, OC ovarian cancer, TC testicular cancer, PC prostate cancer.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6699229/v1/6929149a443e3bd2af57888b.png"},{"id":84819301,"identity":"c9057023-1d61-4e30-b335-b9871cadb69f","added_by":"auto","created_at":"2025-06-17 15:58:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":116751,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe EPAC trend of ASIR for six types of genital neoplasms from 1990 to 2021. \u003c/strong\u003eEAPC estimated annual percentage change, ASIR age-standardized incidence rate.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6699229/v1/1b40acc40ac09c243f672984.png"},{"id":84820117,"identity":"73a2b386-c956-4027-895f-376996bcb1fe","added_by":"auto","created_at":"2025-06-17 16:06:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":105377,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between the ASIR (per 100,000 population) of six types of genital neoplasms and SDI in different regions in 1990 and 2021.\u003c/strong\u003e ASIR age-standardized incidence rate, SDI socio-demographic index.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6699229/v1/dbc515184871dd396b171bb4.png"},{"id":84819303,"identity":"ff5ce38c-7069-4ce3-b1eb-57c6b7e4dfe0","added_by":"auto","created_at":"2025-06-17 15:58:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":101593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSDI-related health inequality regression (A) and concentration (B) curves for the DALYs of six types of genital neoplasms global, 1990 and 2021.\u003c/strong\u003e SDI socio-demographic index, DALYs disability-adjusted life years.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6699229/v1/2c56641ad54cfcb7c96ccbb9.png"},{"id":84819306,"identity":"75676209-1486-49b9-9316-403561bfdbd5","added_by":"auto","created_at":"2025-06-17 15:58:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":93349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe impact of age, period, and birth cohort on the relative risk of incidence for six types of genital neoplasms. \u003c/strong\u003eThe changes in the incidence rates for all age groups and age-standardized incidence rates between 1990 and 2021 are presented. Local drifts indicate the annual percentage change (%/year) in incidence rates for five-year age groups (from 10-14 to 85+ years). The figure annotates the net drift index and its 95% CI, which can be used to measure the rate of change in incidence over time. The age effect represents the incidence rates of tumors across different age groups. The period effect is expressed by the relative risk of incidence (incidence rate ratio), calculated as the age-specific rate ratio for each period compared to the reference period of 2000-2005. The cohort effect illustrates the changes in the incidence rate ratios for different birth cohorts. The shaded areas represent the corresponding 95% CI for each point estimate. CI confidence intervals.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6699229/v1/dceb75436212a910509349c9.png"},{"id":84819315,"identity":"59762daf-ecad-4acd-81f7-0139fdfe1cf4","added_by":"auto","created_at":"2025-06-17 15:58:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":50155,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe changing trends and predictions of the incidence numbers and ASIR of six types of genital neoplasms during the period from 1990 to 2035.\u003c/strong\u003e ASIR age-standardized incidence rate.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6699229/v1/01a7ebe5ac74e363a8f15e20.png"},{"id":99798651,"identity":"f412343d-65e2-4e88-af3e-6f99ddce186d","added_by":"auto","created_at":"2026-01-08 13:48:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1747128,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6699229/v1/78bf30dd-4cee-4d6a-8bb9-995caeb06893.pdf"},{"id":84819314,"identity":"69737ee8-d613-4187-b50c-222198a15729","added_by":"auto","created_at":"2025-06-17 15:58:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16504888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. The EPAC trend of ASPR for six types of genital neoplasms from 1990 to 2021. \u003c/strong\u003eEAPC estimated annual percentage change, ASPR age-standardized prevalence rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S2. The EPAC trend of ASMR for six types of genital neoplasms from 1990 to 2021. \u003c/strong\u003eEAPC estimated annual percentage change, ASMR age-standardized mortality rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S3. The EPAC trend of ASDR for six types of genital neoplasms from 1990 to 2021. \u003c/strong\u003eEAPC estimated annual percentage change, ASDR age-standardized disability-adjusted life years rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S4. The relationship between the ASPR (per 100,000 population) of six types of genital neoplasms and SDI in different regions in 1990 and 2021.\u003c/strong\u003e ASPR age-standardized prevalence rate, SDI socio-demographic index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S5. The relationship between the ASMR (per 100,000 population) of six types of genital neoplasms and SDI in different regions in 1990 and 2021.\u003c/strong\u003e ASMR age-standardized mortality rate, SDI socio-demographic index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S6. The impact of age, period, and birth cohort on the relative risk of prevalence for six types of genital neoplasms. \u003c/strong\u003eThe changes in the prevalence rates for all age groups and age-standardized prevalence rates between 1990 and 2021 are presented. Local drifts indicate the annual percentage change (%/year) in prevalence rates for five-year age groups (from 10-14 to 85+ years). The figure annotates the net drift index and its 95% CI, which can be used to measure the rate of change in prevalence over time. The age effect represents the prevalence rates of tumors across different age groups. The period effect is expressed by the relative risk of prevalence (prevalence rate ratio), calculated as the age-specific rate ratio for each period compared to the reference period of 2000-2005. The cohort effect illustrates the changes in the prevalence rate ratios for different birth cohorts. The shaded areas represent the corresponding 95% CI for each point estimate. CI confidence intervals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S7. The impact of age, period, and birth cohort on the relative risk of mortality for six types of genital neoplasms. \u003c/strong\u003eThe changes in the mortality rates for all age groups and age-standardized mortality rates between 1990 and 2021 are presented. Local drifts indicate the annual percentage change (%/year) in mortality rates for five-year age groups (from 10-14 to 85+ years). The figure annotates the net drift index and its 95% CI, which can be used to measure the rate of change in mortality over time. The age effect represents the mortality rates of tumors across different age groups. The period effect is expressed by the relative risk of mortality (mortality rate ratio), calculated as the age-specific rate ratio for each period compared to the reference period of 2000-2005. The cohort effect illustrates the changes in the mortality rate ratios for different birth cohorts. The shaded areas represent the corresponding 95% CI for each point estimate. CI confidence intervals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S8. The impact of age, period, and birth cohort on the relative risk of DALYs for six types of genital neoplasms. \u003c/strong\u003eThe changes in the DALYs rates for all age groups and age-standardized DALYs rates between 1990 and 2021 are presented. Local drifts indicate the annual percentage change (%/year) in DALYs rates for five-year age groups (from 10-14 to 85+ years). The figure annotates the net drift index and its 95% CI, which can be used to measure the rate of change in DALYs over time. The age effect represents the DALYs rates of tumors across different age groups. The period effect is expressed by the relative risk of DALYs (DALYs rate ratio), calculated as the age-specific rate ratio for each period compared to the reference period of 2000-2005. The cohort effect illustrates the changes in the DALYs rate ratios for different birth cohorts. The shaded areas represent the corresponding 95% CI for each point estimate. DALYs disability-adjusted life years, CI confidence intervals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S9. The changing trends and predictions of the prevalence numbers and ASPR of six types of genital neoplasms during the period from 1990 to 2035.\u003c/strong\u003e ASPR age-standardized prevalence rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S10. The changing trends and predictions of the DALYs numbers and ASDR of six types of genital neoplasms during the period from 1990 to 2035.\u003c/strong\u003e DALYs disability-adjusted life years, ASDR age-standardized disability-adjusted life years rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable S1. EAPC of the ASIR of genital neoplasms in global, SDI, and 21 regions\u003c/p\u003e\n\u003cp\u003eTable S2. EAPC of the ASPR of genital neoplasms in global, SDI, and 21 regions\u003c/p\u003e\n\u003cp\u003eTable S3. EAPC of the ASMR of genital neoplasms in global, SDI, and 21 regions\u003c/p\u003e\n\u003cp\u003eTable S4. EAPC of the ASDR of genital neoplasms in global, SDI, and 21 regions\u003c/p\u003e\n\u003cp\u003eTable S5. EAPC of ASIR for genital neoplasms in 204 countries and territories\u003c/p\u003e\n\u003cp\u003eTable S6. EAPC of ASPR for genital neoplasms in 204 countries and territories\u003c/p\u003e\n\u003cp\u003eTable S7. EAPC of ASMR for genital neoplasms in 204 countries and territories\u003c/p\u003e\n\u003cp\u003eTable S8. EAPC of ASDR for genital neoplasms in 204 countries and territories\u003c/p\u003e\n\u003cp\u003eFigure S1. The EPAC trend of ASPR of genital neoplasms from 1990 to 2021\u003c/p\u003e\n\u003cp\u003eFigure S2. The EPAC trend of ASMR of genital neoplasms from 1990 to 2021\u003c/p\u003e\n\u003cp\u003eFigure S3. The EPAC trend of ASDR of genital neoplasms from 1990 to 2021\u003c/p\u003e\n\u003cp\u003eFigure S4. The ASPR of genital neoplasms and SDI across regions in 1990 and 2021\u003c/p\u003e\n\u003cp\u003eFigure S5. The ASMR of genital neoplasms and SDI across regions in 1990 and 2021\u003c/p\u003e\n\u003cp\u003eFigure S6. Age-period-cohort analysis of the prevalence of genital neoplasms\u003c/p\u003e\n\u003cp\u003eFigure S7. Age-period-cohort analysis of the mortality of genital neoplasms\u003c/p\u003e\n\u003cp\u003eFigure S8. Age-period-cohort analysis of the DALYs of genital neoplasms\u003c/p\u003e\n\u003cp\u003eFigure S9. Prevalence trends of genital neoplasms from 1990 to 2035\u003c/p\u003e\n\u003cp\u003eFigure S10. DALYs trends of genital neoplasms from 1990 to 2035\u003c/p\u003e","description":"","filename":"Supmet05191914.docx","url":"https://assets-eu.researchsquare.com/files/rs-6699229/v1/eb23d20f115d6789238931b0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global trends and socio-demographic inequalities in the burden of genital neoplasms: a 30-year comprehensive analysis of the Global Burden of Disease Study 2021","fulltext":[{"header":"Background","content":"\u003cp\u003eThe worldwide disease spectrum has shifted dramatically as a result of socioeconomic progress. Infectious diseases that once raged have been effectively controlled in some regions. Meanwhile, chronic non-communicable diseases, especially various genital neoplasms such as uterine fibroids (UFs), uterine cancer (UC), cervical cancer (CC), ovarian cancer (OC), testicular cancer (TC), and prostate cancer (PC), have gradually become the key factors threatening human health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The vast number of patients, the protracted nature of the disease, and the difficulty of therapy make these neoplasms a significant burden on the healthcare system and the socioeconomic sector [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, there are disparities in genital neoplasms across countries due to differences in economic development, health awareness, educational attainment, and medical standards [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, an accurate assessment of the burden of genital neoplasms and cross-country inequalities is essential.\u003c/p\u003e \u003cp\u003eTumor statistics were once scattered across nations' sectors. Inconsistent formats and scopes hindered understanding of the global tumor burden and inequalities, limiting effective support for policymakers. The Global Burden of Disease Study 2021 (GBD) integrates extensive health data, transforming fragmented information into comprehensive resources through standardization processing [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Governments and organizations can create effective tumor prevention policies using reliable data analysis, optimize resources, and address gaps in prevention control [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, a comprehensive and in-depth analysis of the latest epidemiological data on genital neoplasms remains insufficient.\u003c/p\u003e \u003cp\u003eSocio-demographic index (SDI) profoundly influences tumorigeneses and progression. On the one hand, the aging of the population and the high incidence of cancer among the elderly have raised the total burden of cancer, and the prevalence of cancers such as PC and OC has continued to climb among the old [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. On the other hand, lifestyle changes, such as sedentary behavior, a high-calorie diet, smoking, alcohol misuse, and other poor habits, are expanding among younger people, contributing to an earlier age of onset and a higher prevalence of TC and CC [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Gender differences also influence the distribution of cancers, with women predisposed to gynecological neoplasms and men to PC and TC [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConducting a comprehensive analysis based on the GBD database is essential, given the dire and unequal state of genital neoplasms prevention and treatment. The precise quantification of the disease burden of genital neoplasms enables the visual presentation of differences in incidence, prevalence, mortality, and disability-adjusted life years (DALYs) in different countries and regions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, delving deeply into the root causes of transnational inequalities, whether related to economic development, medical resource distribution, or social and cultural factors, is conducive to promoting international exchanges and cooperation and advocating resource sharing and technical assistance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This allows global collaboration to address genital neoplasms, gradually closing the gap created by geographical, economic, and other factors, ultimately reducing the overall cancer burden for humanity.\u003c/p\u003e \u003cp\u003eThis study focuses on the inequalities in six genital neoplasms across various countries and regions, analyzing their evolution from 1990 to 2021. Utilizing SDI and factors like age, period, and cohort, we aim to guide global tumor prevention strategies by predicting trends and burdens of these diseases until 2035 at global and regional levels.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData and ethical considerations\u003c/h2\u003e \u003cp\u003eThe GBD database is exceptionally comprehensive, integrating multivariate data from 204 countries and regions worldwide. The disease burden data effectively evaluates the impact of various diseases and injuries on human health, highlighting key variables such as incidence, mortality, prevalence, and DALYs [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Health risk factors encompass influences such as lifestyle choices like smoking and drinking, as well as environmental and socioeconomic aspects. Geographic and demographic data detail the population structure across regions. Medical resource data shows the distribution and usage of resources in various locations. Census data, derived from a thorough population survey, provides a solid demographic basis for evaluating disease burden and supports global health research. The University of Washington's Institutional Review Board has approved waiving informed consent requirements for de-identified data in Global Burden of Disease research.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTrend analysis\u003c/h3\u003e\n\u003cp\u003eTo comprehensively assess long-term trends in disease burden, we analyzed all data points from 1990 to 2021 from the Global Burden of Disease (GBD) database rather than relying solely on the initial and final years. This approach minimizes bias and provides a more accurate representation of temporal patterns. We estimated the annual percentage change (EAPC) for key indicators, including incidence, mortality, and disability-adjusted life year (DALY) rates, using a log-linear regression model: ln(\u003cem\u003eY\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;\u003cem\u003ea\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eb\u003c/em\u003e \u0026middot; \u003cem\u003et\u003c/em\u003e + \u003cem\u003eϵ\u003c/em\u003e, where \u003cem\u003eY\u003c/em\u003e represents the target metric, \u003cem\u003et\u003c/em\u003e denotes time, \u003cem\u003ea\u003c/em\u003e is the intercept, \u003cem\u003eb\u003c/em\u003e is the regression coefficient indicating the rate of change over time, and \u003cem\u003eϵ\u003c/em\u003e is the error term [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The EAPC is calculated as: EAPC = (\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e \u0026minus; 1) \u0026times; 100%, the 95% confidence interval (CI) for EAPC is derived from the standard error (SE) of \u003cem\u003eb\u003c/em\u003e: CI = [\u003cem\u003eb\u003c/em\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1.96 \u0026sdot; SE\u003csub\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sub\u003e​, \u003cem\u003eb\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1.96 \u0026sdot; SE\u003csub\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sub\u003e​]. By focusing on age-standardized indicators, we mitigated the influence of age-related factors, ensuring a more accurate representation of underlying disease trends.\u003c/p\u003e\n\u003ch3\u003eSocio-demographic Index (SDI)\u003c/h3\u003e\n\u003cp\u003eThe Socio-demographic Index (SDI) is used to assess the socio-demographic development level of countries or regions and to explore its relationship with health outcomes. SDI integrates three core indicators - income, education, and fertility - each scaled from 0 to 1. The final SDI value is calculated as the average of these indicators: SDI = (income index\u0026thinsp;+\u0026thinsp;educational index\u0026thinsp;+\u0026thinsp;fertility index) / 3. The income index measures growth based on GDP per capita; the educational index considers average schooling years or completion rates; and the fertility index reflects population fertility and societal development. Countries and regions are classified into five development-level groups based on SDI values: Low SDI, Low-middle SDI, Middle SDI, High-middle SDI, and High SDI. A higher SDI indicates a more advanced socio-demographic development level [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAge-period-cohort (APC) analysis\u003c/h3\u003e\n\u003cp\u003eWe employed the intrinsic estimator (IE) model to address the identifiability issue in APC analysis. The IE model leverages inherent constraints among age, period, and cohort variables, avoiding non-unique solutions caused by overparameterization [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The model is expressed as: ln (\u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003eapc\u003c/em\u003e​\u003c/sub\u003e) = \u003cem\u003e\u0026micro;\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e ​\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e ​+ \u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e ​\u003c/sub\u003e+ \u003cem\u003eϵ\u003c/em\u003e\u003csub\u003e\u003cem\u003eapc\u003c/em\u003e​\u003c/sub\u003e, where \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003eapc\u003c/em\u003e​\u003c/sub\u003e represents the target metric, \u003cem\u003e\u0026micro;\u003c/em\u003e is the overall mean, \u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e​\u003c/sub\u003e denotes the age effect, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e​ denotes the period effect, \u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e denotes the cohort effect, and \u003cem\u003eϵ\u003c/em\u003e\u003csub\u003e\u003cem\u003eapc\u003c/em\u003e​\u003c/sub\u003e is the error term. Disease burden and population data from 1990 to 2021 were gathered from the GBD database. Age groups were categorized into 5-year intervals: 0\u0026ndash;4, 5\u0026ndash;9, 10\u0026ndash;14, ..., 80\u0026ndash;84, and 85+. Periods were also split into 5-year ranges, including 1990\u0026ndash;1994, 1995\u0026ndash;1999, 2000\u0026ndash;2004, ..., 2015\u0026ndash;2019, and the final range of 2020\u0026ndash;2021 (spanning 2 years). Cohorts followed the same 5-year intervals, such as 1895\u0026ndash;1899, 1900\u0026ndash;1904, ..., 2015\u0026ndash;2019. Data were organized into age groups, periods, and cohorts for APC analysis, ensuring each combination had cohort information. The organized data were input into the IE model for log-linear regression. Model parameters were estimated using singular value decomposition (SVD). The estimated age effect (\u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e​\u003c/em\u003e), period effect (\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e​), cohort effect (\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e​), and standard errors were extracted. Results were visualized using R to illustrate trends in age, period, and cohort effects.\u003c/p\u003e\n\u003ch3\u003eCross-country inequality analysis\u003c/h3\u003e\n\u003cp\u003eIn this study, we employed two key metrics - the slope index of inequality (SII) and the concentration index (CI) - to assess the relationship between health outcomes and socioeconomic status (SES), uncovering the magnitude and patterns of health inequality [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The SII measures absolute inequality by analyzing the linear relationship between health outcomes and SES. It is calculated using a regression model: \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e​\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e0​\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1​\u003c/sub\u003e \u0026middot; \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e ​\u003c/sub\u003e+ \u003cem\u003eϵ\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e​\u003c/sub\u003e, where \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e​\u003c/sub\u003e represents the health outcome, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e denotes the rank of socioeconomic status (sorted by SDI values), \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1​\u003c/sub\u003e (SII) indicates the slope of the health outcome concerning socioeconomic status, and \u003cem\u003eϵ\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e​\u003c/sub\u003e is the error term. The CI measures relative inequality by assessing the distribution of health outcomes across countries ranked by SES. It is calculated as: CI\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:=\\frac{2}{n*\\stackrel{-}{Y}}{\\sum\\:}_{i=0}^{n}{Y}_{i}\u0026middot;{R}_{i}-1,\\)\u003c/span\u003e\u003c/span\u003ewhere \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e​\u003c/sub\u003e represents the health outcome, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ei​\u003c/em\u003e\u003c/sub\u003e denotes the rank of socioeconomic status, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{Y}\\)\u003c/span\u003e\u003c/span\u003e is the mean of the health outcome. Together, these metrics provide a comprehensive health inequality assessment, informing targeted interventions to reduce disparities.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePredictive analysis\u003c/h2\u003e \u003cp\u003eTo proactively identify future health threats and strengthen public health responsiveness, we projected the incidence, mortality, prevalence, and disability-adjusted life years (DALYs) of genital neoplasms using advanced predictive modeling. Our approach integrates the Bayesian Age-Period-Cohort (BAPC) model with the Integrated Nested Laplace Approximation (INLA) framework, enabling precise and efficient predictions of disease burden trends [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The INLA method efficiently fits Bayesian models, supporting complex random effects and hierarchical data structures. Its core approach involves calculating posterior distributions through Laplace approximations, avoiding the computationally intensive Markov Chain Monte Carlo (MCMC) sampling. Combining the BAPC model's decomposition with INLA's efficiency leads to more accurate predictions. This approach considers age, period, cohort effects, and spatial-temporal correlations, enhancing model interpretability and accuracy.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eDescriptive analysis of the burden of genital neoplasms at global levels\u003c/h2\u003e\n \u003cp\u003eFrom 1990 to 2021, the incidence rates of certain genital neoplasms exhibited varying trends globally (Table\u0026nbsp;1). The incidence rates of UFs, UC, TC, and PC showed significant increases. Notably, the ASIR of TC demonstrated the most pronounced rise, increasing from 1.51 (95% uncertainty interval [UI] 1.45 to 1.55) per 100,000 population to 2.42 (95% UI 2.16 to 2.35), with an EAPC of 1.42 (95% confidence interval [CI] 1.35 to 1.48). In contrast, the incidence rates of CC and OC declined. The ASIR of CC decreased from 18.11 (95% UI 16.94 to 19.40) to 15.32 (95% UI 14.08 to 16.68), with an EAPC of -0.54 (95% CI -0.64 to -0.45). In 2021, UFs had the highest incidence among global genital neoplasms, reaching 101.00 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cases (95% UI 73.50 to 132.86). Overall, the incidence rates of these six genital neoplasms predominantly exhibited an upward trend over the past three decades.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable1. Global incidence, prevalence, mortality, and DALYs of genital neoplasms (1990-2021)\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003eUterine\u003c/p\u003e\n \u003cp\u003efibroids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eUterine\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCervical\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eOvarian\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eTesticular cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eProstate\u003c/p\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eIncidence\u003c/p\u003e\n \u003cp\u003e(\u0026times;10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e60.10\u003c/p\u003e\n \u003cp\u003e(43.90-80.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003cp\u003e(1.75-2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4.10\u003c/p\u003e\n \u003cp\u003e(3.83-4.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003cp\u003e(1.46-1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003cp\u003e(0.38-0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5.06\u003c/p\u003e\n \u003cp\u003e(4.81-5.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ePrevalence\u003c/p\u003e\n \u003cp\u003e(\u0026times;10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e656.95\u003c/p\u003e\n \u003cp\u003e(500.22-855.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e13.33\u003c/p\u003e\n \u003cp\u003e(12.2613.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e18.26\u003c/p\u003e\n \u003cp\u003e(17.27-19.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6.29\u003c/p\u003e\n \u003cp\u003e(5.72-6.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003cp\u003e(2.58-2.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e35.96\u003c/p\u003e\n \u003cp\u003e(34.45-37.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMortality\u003c/p\u003e\n \u003cp\u003e(\u0026times;10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003cp\u003e(0.008-0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003cp\u003e(0.49-0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003cp\u003e(1.96-2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003cp\u003e(0.93-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003cp\u003e(0.072-0.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003cp\u003e(1.94-2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eDALYs\u003c/p\u003e\n \u003cp\u003e(\u0026times;10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003cp\u003e(0.57-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e15.01\u003c/p\u003e\n \u003cp\u003e(12.99-16.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e74.16\u003c/p\u003e\n \u003cp\u003e(68.41-80.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e29.09\u003c/p\u003e\n \u003cp\u003e(26.62-31.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3.90\u003c/p\u003e\n \u003cp\u003e(3.66-4.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e41.47\u003c/p\u003e\n \u003cp\u003e(37.54-44.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eASIR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1/10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e234.36\u003c/p\u003e\n \u003cp\u003e(171.06-309.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7.72\u003c/p\u003e\n \u003cp\u003e(7.63-6.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e18.11\u003c/p\u003e\n \u003cp\u003e(16.94-19.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7.22\u003c/p\u003e\n \u003cp\u003e(6.65-7.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003cp\u003e(1.45-1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e32.64\u003c/p\u003e\n \u003cp\u003e(30.86-33.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eASPR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1/10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2799.88\u003c/p\u003e\n \u003cp\u003e(2133.46-3650.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e61.17\u003c/p\u003e\n \u003cp\u003e(56.35-64.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e78.15\u003c/p\u003e\n \u003cp\u003e(73.89-82.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e27.62\u003c/p\u003e\n \u003cp\u003e(25.26-30.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e9.99\u003c/p\u003e\n \u003cp\u003e(9.67-10.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e218.33\u003c/p\u003e\n \u003cp\u003e(225.67-208.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eASMR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1/10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003cp\u003e(0.04-0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003cp\u003e(2.32-12.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e9.68\u003c/p\u003e\n \u003cp\u003e(8.97-10.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4.73\u003c/p\u003e\n \u003cp\u003e(4.38-5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003cp\u003e(0.32-0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e16.35\u003c/p\u003e\n \u003cp\u003e(15.02-17.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eASDR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1/10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003cp\u003e(2.46-4.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e69.17\u003c/p\u003e\n \u003cp\u003e(59.85-75.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e330.11\u003c/p\u003e\n \u003cp\u003e(304.67-359.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e132.48\u003c/p\u003e\n \u003cp\u003e(121.34-145.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e15.11\u003c/p\u003e\n \u003cp\u003e(14.20-16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e275.30\u003c/p\u003e\n \u003cp\u003e(251.66-292.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eIncidence\u003c/p\u003e\n \u003cp\u003e(\u0026times;10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e101.00\u003c/p\u003e\n \u003cp\u003e(73.50-132.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.74\u003c/p\u003e\n \u003cp\u003e(4.30-5.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003cp\u003e(6.13-7.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003cp\u003e(2.71-3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003cp\u003e(0.88-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e13.24\u003c/p\u003e\n \u003cp\u003e(12.17-14.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ePrevalence\u003c/p\u003e\n \u003cp\u003e(\u0026times;10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1195.45\u003c/p\u003e\n \u003cp\u003e(912.28-1549.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e34.51\u003c/p\u003e\n \u003cp\u003e(31.65-37.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e33.83\u003c/p\u003e\n \u003cp\u003e(31.08-36.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e12.22\u003c/p\u003e\n \u003cp\u003e(11.02-13.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6.79\u003c/p\u003e\n \u003cp\u003e(6.51-7.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e103.88\u003c/p\u003e\n \u003cp\u003e(97.05-109.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMortality\u003c/p\u003e\n \u003cp\u003e(\u0026times;10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003cp\u003e(0.01-0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.87-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003cp\u003e(2.72-3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003cp\u003e(1.68-2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003cp\u003e(0.108-0.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003cp\u003e(3.82-4.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eDALYs\u003c/p\u003e\n \u003cp\u003e(\u0026times;10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003cp\u003e(1.02-1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e25.63\u003c/p\u003e\n \u003cp\u003e(22.91-28.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e99.12\u003c/p\u003e\n \u003cp\u003e(90.53-107.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e51.63\u003c/p\u003e\n \u003cp\u003e(46.92-56.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5.61\u003c/p\u003e\n \u003cp\u003e(5.28-5.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e81.42\u003c/p\u003e\n \u003cp\u003e(71.77-88.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eASIR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1/10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e250.93\u003c/p\u003e\n \u003cp\u003e(183.44-330.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e12.05\u003c/p\u003e\n \u003cp\u003e(10.93-13.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e15.32\u003c/p\u003e\n \u003cp\u003e(14.08-16.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6.71\u003c/p\u003e\n \u003cp\u003e(6.07-7.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003cp\u003e(2.16-2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e34.05\u003c/p\u003e\n \u003cp\u003e(31.27-36.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eASPR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1/10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2841.07\u003c/p\u003e\n \u003cp\u003e(2164.43-3682.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e75.73\u003c/p\u003e\n \u003cp\u003e(69.37-1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e79.31\u003c/p\u003e\n \u003cp\u003e(72.81-86.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e28.08\u003c/p\u003e\n \u003cp\u003e(25.26-30.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e16.59\u003c/p\u003e\n \u003cp\u003e(15.91-17.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e260.05\u003c/p\u003e\n \u003cp\u003e(243.39-272.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eASMR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1/10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003cp\u003e(0.03-0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003cp\u003e(1.87-2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6.62\u003c/p\u003e\n \u003cp\u003e(6.07-7.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003cp\u003e(3.67-4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003cp\u003e(0.27-0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e12.63\u003c/p\u003e\n \u003cp\u003e(11.16-13.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eASDR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1/10\u003csup\u003e5\u003c/sup\u003e, 95% UI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003cp\u003e(2.43-4.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e56.15\u003c/p\u003e\n \u003cp\u003e(50.07-62.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e226.28\u003c/p\u003e\n \u003cp\u003e(206.51-246.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e115.15\u003c/p\u003e\n \u003cp\u003e(104.58-125.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e13.38\u003c/p\u003e\n \u003cp\u003e(13.03-14.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e217.83\u003c/p\u003e\n \u003cp\u003e(192.65-235.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1990 to 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eASIR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(EAPC, 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003cp\u003e(0.23-0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003cp\u003e(0.50-0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.54\u003c/p\u003e\n \u003cp\u003e(-0.64 to -0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003cp\u003e(-0.43 to -0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003cp\u003e(1.35-1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003cp\u003e(-0.21-0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eASPR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(EAPC, 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003cp\u003e(0.03-0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003cp\u003e(0.72-0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003cp\u003e(0.03-0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003cp\u003e(-0.13 to -0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003cp\u003e(1.71-1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003cp\u003e(0.26-0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eASMR\u003c/p\u003e\n \u003cp\u003e(EAPC, 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003cp\u003e(-0.07-0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.78\u003c/p\u003e\n \u003cp\u003e(-0.85 to -0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-1.27\u003c/p\u003e\n \u003cp\u003e(-1.36 to -1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.62\u003c/p\u003e\n \u003cp\u003e(-0.68 to -0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.54\u003c/p\u003e\n \u003cp\u003e(-0.70 to -0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-1.05\u003c/p\u003e\n \u003cp\u003e(-1.14 to -0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eASDR\u003c/p\u003e\n \u003cp\u003e(EAPC, 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003cp\u003e(-0.05-0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.78\u003c/p\u003e\n \u003cp\u003e(-0.85 to -0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-1.27\u003c/p\u003e\n \u003cp\u003e(-1.36 to -1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003cp\u003e(-0.64 to -0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003cp\u003e(-0.44 to -0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.96\u003c/p\u003e\n \u003cp\u003e(-1.05 to -0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eUI uncertainty intervals, DALYs disability-adjusted life-years, ASIR age-standardized incidence rate, ASPR age-standardized prevalence rate, ASMR age-standardized mortality rate, ASDR age-standardized DALYs rate, EAPC estimated annual percentage change, CI confidence interval\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRegarding prevalence, mortality, and DALYs, the prevalence rates of all six neoplasms showed an upward trend, with particularly notable increases observed for UFs, UC, TC, and PC (Table\u0026nbsp;1). The ASPR for TC demonstrated the fastest growth, rising from 9.99 (95% UI 9.67 to 10.33) per 100,000 population to 16.59 (95% UI 15.91 to 17.44), with an EAPC of 1.80 (95% CI 1.71 to 1.89). Except for UFs, the mortality rates and DALYs for the remaining five neoplasms declined. CC exhibited the most significant reduction in both mortality and DALYs. Its ASMR fell from 9.68 (95% UI 8.97 to 10.51) to 6.62 (95% UI 6.07 to 7.18) per 100,000 population, with an EAPC of -1.27 (95% CI -1.36 to -1.18). Likewise, the ASDR for CC decreased from 330.11 (95% UI 304.67 to 359.10) to 226.28 (95% UI 206.51 to 246.86) per 100,000 population, with an EAPC of -1.27 (95% CI -1.36 to -1.17). In 2021, CC had the highest disease burden for females, while PC was highest for males. Despite increasing genital neoplasm rates, declining mortality and DALYs show progress in treatment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eOverall trends in the burden of genital neoplasms at regional levels\u003c/h2\u003e\n \u003cp\u003eFrom the perspective of geographical regions and the SDI (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e-S4), in high-SDI regions such as high-income North America and Western Europe, the EAPC values of the ASIR and ASPR for PC, TC, and UC are relatively high. However, the EAPC values of the ASMR and the ASDR are relatively low. In medium-high SDI regions such as Eastern Europe and Latin America, the EAPC values of the ASIR and ASPR for CC and OC are at a medium level, and the EAPC values of the ASMR and ASDR are also at a medium level. In low-SDI regions such as southern sub-Saharan Africa and South Asia, the EAPC values of the ASIR and ASPR for CC and OC are high. Meanwhile, the EAPC values of the ASMR and ASDR are also high. In East Asia and Southeast Asia, the EAPC values for UFs and CC are at a medium level.\u003c/p\u003e\n \u003cp\u003eAdditionally, considerable variations in the EAPC values for certain genital neoplasms were noted across various regions (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e-S4). The Caribbean leads in EAPC values for ASIR (5.48, 95% CI 4.28\u0026ndash;6.94) and ASDR (3.40, 95% CI 2.45\u0026ndash;4.36) in TC. The Andean Latin America excels in EAPC for ASPR (9.73, 95% CI 8.97\u0026ndash;10.48), while East Asia tops ASMR (4.80, 95% CI 3.74\u0026ndash;5.87) in UFs. The Australasia region shows the lowest EAPC for ASIR (-2.21, 95% CI -2.58 to -1.84) and ASPR (-2.04, 95% CI -2.46 to -1.62) in OC. For ASMR, the lowest EAPC (-6.29, 95% CI -7.11 to -5.47) is found in UFs, and ASDR\u0026apos;s lowest value (-3.13, 95% CI -3.40 to -2.85) is in CC in Australasia. Despite rising cancer rates globally, high-income regions have significantly reduced mortality and disability, while low-income areas struggle with cancer control and treatment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eGeneral trends in the burden of genital neoplasms across national levels\u003c/h2\u003e\n \u003cp\u003eFrom 1990 to 2021, global genital neoplasm burdens varied greatly by country. Low- and middle-income countries exhibited higher EAPC in ASIR and ASPR, while high-income countries showed lower values. The patterns varied by tumor type and region (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table S5 and S6): for UFs, Nigeria, Ethiopia, and India had higher EAPC values, whereas the United Kingdom, France, and the United States showed lower values; for UC, Russia, and Cuba had higher EAPC values, while China and Japan reported lower values; for CC, Nigeria and Ethiopia exhibited higher EAPC values, whereas the United Kingdom and France had lower values; for OC, Brazil and Argentina demonstrated higher EAPC values, while China and Japan showed lower values; for TC, Denmark and Norway had higher EAPC values; and for PC, Russia and Japan displayed higher EAPC values, whereas most sub-Saharan African countries and some South Asian nations reported lower values.\u003c/p\u003e\n \u003cp\u003eRegarding the EAPC in ASMR and ASDR (Fig. S2 and S3, Table S7 and S8), specific countries in sub-Saharan Africa, South Asia, and Latin America exhibited higher values. In contrast, Western Europe, North America, and Oceania generally showed lower values. For specific tumors and countries, Nigeria and Ethiopia had high EAPC values for ASMR and ASDR in UFs, while the United Kingdom and the United States reported lower values, and Australia experienced negative ASMR growth. Russia and Ukraine showed faster ASMR growth for UC, whereas Cuba and Jamaica had high ASMR and ASDR values, and China and others exhibited lower growth rates. For CC, Nigeria and Afghanistan had high ASMR and ASDR values, while Norway and Denmark experienced negative ASMR growth. Brazil and Argentina demonstrated high ASMR and ASDR values for OC, whereas Japan and South Korea showed differing trends. For TC, Denmark and Sweden, as well as the Netherlands and Belgium, displayed varying patterns in ASMR and ASDR growth. For PC, Japan and South Korea, as well as Russia and Poland, exhibited inconsistent trends in ASMR and ASDR growth, while sub-Saharan Africa reported high growth rates in both ASMR and ASDR.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eCross-country inequality analysis of genital neoplasms\u003c/h2\u003e\n \u003cp\u003eWe analyzed the relationship among the ASIR, ASPR, ASMR, and SDI for six types of genital neoplasms across various global regions in 1990 and 2021 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e-\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). ASIR and ASPR for UFs, UC, OC, TC, and PC correlate positively with SDI, while CC negatively correlates with SDI. Within specific SDI ranges, ASMR for UC, OC, TC, and PC increases; however, as SDI continues to rise, ASMR in some high-income countries (such as Germany, Greece, and the United States) stabilizes or declines. In contrast, ASMR for UFs and CC exhibits a significant negative correlation with SDI, with a more pronounced decline observed for CC. Over time, compared to 1990, ASIR and ASPR for UC, TC, and PC increased in high-SDI regions by 2021, while ASIR and ASPR for CC and OC decreased in these regions. Additionally, ASMR for all six types of genital neoplasms declined in 2021 compared to 1990, with a more significant downward trend observed in high-SDI regions.\u003c/p\u003e\n \u003cp\u003eAnalysis of DALYs for genital neoplasms reveals that the SII for UC, CC, OC, and TC declined, indicating reduced DALYs burden across regions of differing socio-demographic development (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). In contrast, the SII for UFs and PC remained stable, suggesting lower inequality in DALYs burden. In 1990, the DALY inequality for OC was 57.67 per 100,000 people (95% CI 41.77 to 73.57). By 2021, this narrowed to 26.88 (95% CI 15.12 to 38.63), showing a significant reduction (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). The SII for UC decreased from 19.89 (95% CI 11.99 to 29.14) in 1990 to 7.43 (95% CI -1.52 to 16.39) in 2021, reflecting a notable drop in DALY inequality (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). The data points for CC were widely distributed, reflecting substantial regional disparities in DALYs rates, while those for UFs and TC were predominantly concentrated in regions with lower DALYs rates (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). Overall, the global burden of DALYs for genital neoplasms in 2021 decreased compared to 1990, particularly in regions with higher socio-demographic development levels (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). The CI for PC shifted from negative (-0.03, 95% CI -0.11 to 0.05) to positive (0.04, 95% CI -0.04 to 0.12), indicating a transition in DALYs burden from low-SDI to high-SDI populations. Meanwhile, the CI for OC decreased from \u0026minus;\u0026thinsp;0.19 (95% CI -0.27 to -0.11) in 1990 to -0.06 (95% CI -0.14 to 0.02) in 2021, demonstrating a significant reduction in inequality in DALYs burden among populations with different SDI levels (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eAge-period-cohort analysis of the burden of genital neoplasms\u003c/h2\u003e\n \u003cp\u003eThe incidence and prevalence rates of UFs have changed significantly, with the highest ASIR in 37.5-year-olds and the highest ASPR in 42.5-year-olds. ASIR and ASPR decline in the 40\u0026ndash;60 age group, while period and cohort risks trend downward and stabilize (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003eS6\u003c/span\u003e). For UC, ASIR (0.89%, 95% CI 0.83 to 0.96) and ASPR (1.05, 95% CI 0.98 to 1.13) show upward trends, with a significant increase in the 57.5-year-old population; however, cohort risks for those born after 1985 have declined (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003eS6\u003c/span\u003e). CC shows an annual decline in ASIR (-0.60, 95% CI -0.64 to -0.56), peaking in the 57.5-year-old group. Both period and cohort risks trend downward, while ASPR remains stable across age groups, with cohort risks stabilizing for those born after 1970 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003eS6\u003c/span\u003e). OC shows minor local drifts in ASIR (-0.25, 95% CI -0.28 to -0.22) and ASPR (-0.04, 95% CI -0.07 to -0.01), with rates increasing with age. However, cohort risks for those born after 1980 are rising (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003eS6\u003c/span\u003e). TC shows the largest annual changes, with the highest ASIR and ASPR in the 30-year-olds, while period and cohort risks have worsened recently (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003eS6\u003c/span\u003e). For PC, the age distribution of ASIR and ASPR has changed, with annual local variations exceeding 0.82% in the 22.5\u0026ndash;57.5 age group. Although ASIR and ASPR have shown some alleviation since 2007, cohort risks have increased (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eUFs show rising ASMR (0.32, 95% CI 0.03 to 0.60) and ASDR of (0.21, 95% CI 0.10 to 0.32), with ASMR significantly increased in the 77.5-year-old population and severe ASDR in the 47.5-year-old group. Both period and cohort risks show an upward trend (Fig. S7 and S8). For UC, ASMR and ASDR decline across all age groups, with mortality increasing with age. The 67.5-year-old population bears the heaviest disability burden, while period and cohort risks demonstrate a downward trend (Fig. S7 and S8). CC shows the largest decline in ASMR (1.3, 95% CI -1.36 to -1.25)) and ASDR (1.28, 95% CI -1.34 to -1.22), with stable mortality in the 57.5-year-old population but the highest disability burden in this group. Period and cohort risks have improved (Fig. S7 and S8). OC exhibits an overall downward trend in ASMR and ASDR, with the 67.5-year-old population experiencing the most significant disability burden, while risks for populations born after 1980 are rising (Fig. S7 and S8). TC shows a declining trend, with the highest disability burden in the 27.5-year-old population, and both cohort and period risks are improving (Fig. S7 and S8). For PC, ASMR and ASDR remain low and stable in the 0-62.5 age group, with significant risk declines across different periods and birth cohorts (Fig. S7 and S8).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePredictive analysis of the burden of genital neoplasms by 2035\u003c/h2\u003e\n \u003cp\u003eWe forecasted the case numbers and age-standardized rates (ASR) for incidence, prevalence, and DALYs related to six genital neoplasms until 2035. Globally, the incidence and prevalence of genital tumors are anticipated to rise (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003eS9\u003c/span\u003e). By 2035, the incidence and prevalence of UFs, UC, OC, TC, and PC will increase (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003eS9\u003c/span\u003e). Conversely, CC cases may fluctuate initially but exhibit a stable trend over time (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003eS9\u003c/span\u003e). The ASIR shows different trends. The ASIRs for UFs and OC are expected to rise over time, while the ASIRs for UC, CC, TC, and PC show a downward trend (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Regarding the ASPR, except for the ASPR of OC, which is expected to increase over time, the ASPR for the other tumors all show a decreasing trend (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Additionally, the number of DALY cases related to UFs, UC, OC, and TC is expected to show an upward trend (Fig. S10). In contrast, the overall trends for the number of DALY cases of CC and PC remain relatively stable (Fig. S10). The ASDR for UC, CC, and TC are forecasted to decline; the ASDR for PC is subject to inevitable fluctuations, and the ASDR for UFs and OC is relatively stable (Fig. S10).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study offers a comprehensive analysis of genital neoplasms using 1990\u0026ndash;2021 GBD data, applying EAPC, age-period-cohort, inequality, and predictive analyses. Findings show increasing global rates of UFs, UC, TC, and PC, while CC and OC rates declined. Prevalence rose for all tumors, particularly TC. Mortality and DALYs fell for all except UFs, with CC seeing the largest decrease. Low- and middle-income countries exhibited higher EAPC for ASIR and ASPR, whereas high-income countries had lower EAPC. Mortality and DALYs EAPC were elevated in sub-Saharan Africa, South Asia, and Latin America, but lower in Western Europe, North America, and Oceania. The study emphasizes improving prevention and screening for rising tumor incidences like TC. It suggests expanding successful strategies for declining mortality and DALYs in CC. Clinicians can adapt their diagnostic and treatment approaches to enhance patient care outcomes.\u003c/p\u003e \u003cp\u003eAPC analysis is crucial for understanding disease patterns and formulating prevention strategies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This study used APC analysis to discern the impacts of age, period, and cohort on genital neoplasm burden changes. For UFs, ASIR and ASPR peak at ages 37.5 and 42.5, then decline due to reproductive and hormonal changes; hormone fluctuations stimulate fibroid development, while stable levels between 40 and 60 restrict growth [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In UC, ASIR and ASPR rose for those aged 57.5 but fell for those born after 1985, as menopausal estrogen fluctuations increased risk in older women, while better health management lowered it in younger women [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For CC, the decline in ASIR, ASMR, and ASDR indicates the effectiveness of CC vaccines and screening programs like smear tests and HPV detection, which should be further promoted [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. OC risk rises with age as tissue aging and reduced cell repair and immune functions increase susceptibility, and the cohort risk for those born after 1980 has increased due to factors like environmental pollution and changing fertility patterns [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Although OC mortality and disability rates have generally decreased, late diagnosis due to lack of early symptoms burdens the 67.5-year-old population, but improved medical treatments have enhanced prognosis and lowered these rates [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor PC, annual changes in the 22.5\u0026ndash;57.5 age group may relate to androgen levels, high-fat diets, lack of exercise, and occupational exposure [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The low ASMR and ASDR in the 0-62.5 age group are likely due to the low incidence of PC and effective early screening and treatment [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Declining risks across periods and birth cohorts may benefit from advancements in screening technologies and improved treatment methods [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This study found that the ASIR and ASPR of TC significantly increased from 1990 to 2021, with worsening period and cohort risks. The trend of global population aging was evident, leading to a growing elderly population [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Although TC is more common in young people, the number of patients will increase due to changing age demographics [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The 27.5-year-old population bears the heaviest DALYs burden, with the high-incidence age group being 15\u0026ndash;35. This group may be more vulnerable to environmental, lifestyle, and reproductive health issues [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Analyzing these causes can provide a scientific basis for targeted prevention and control strategies for TC and PC.\u003c/p\u003e \u003cp\u003eHigh-SDI regions boast abundant medical resources and advanced technologies, enabling accurate disease detection and diagnosing previously undetected tumors, leading to increased ASIR and ASPR [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. For instance, advanced screening can identify early-stage micro-tumors. Additionally, lifestyle and environmental factors in these areas, such as high-calorie diets, inactivity, and pollution, may raise certain tumor risks. In low SDI regions, as economies grow and healthcare improves, tumor diagnoses and treatments rise, increasing patient diagnoses [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This initially elevates ASMR. However, with further development in high-SDI regions, their superior medical technology and public health systems enhance cancer prevention and treatment, leading to better mortality control [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Consequently, ASMR in some high-SDI countries in Europe and America has stabilized or declined thanks to advanced cancer treatments and personalized plans [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. UFs are non-fatal diseases [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In high-SDI regions with adequate medical resources, fibroid-related issues can be addressed quickly, reducing death risk [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Thus, there's a negative correlation between ASMR and SDI. In high-SDI regions, prevention and treatment of CC are more effective [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Vaccination and screening have significantly reduced CC mortality, and as SDI increases, ASMR for CC declines [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe global dissemination of medical technologies has allowed low-SDI regions to improve cancer treatment and control. The gap between low-SDI and high-SDI regions has narrowed, reducing DALYs inequality for UC, CC, OC, and TC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. International aid and technical exchanges have enhanced medical standards in the low-SDI regions. UFs typically have a mild impact on DALYs, and their effects are stable across regions. PC pathogenesis and treatment effects are consistent across SDI regions, leading to low DALYs burden inequality and stable SII [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. PC is ordinary in older men, especially in high-SDI regions where population aging is pronounced, leading to more patients [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Diagnosis and treatment are better standardized in these areas. While survival time has improved, the number of patients living with the disease has risen, shifting the DALYs burden to high-SDI regions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFormulate prevention and control strategies for genital tumors based on their features, incidence, prevalence, mortality, and factors like SDI [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. For UFs, health management for women aged 37.5 to 42.5 should be improved, regular check-ups encouraged, health promotion focused on 40-60-year-olds, a healthy lifestyle adopted, and medical services for older women enhanced. UC should increase screening for women aged 57.5, utilize advanced techniques for early diagnosis, promote successful strategies for those born after 1985, and strengthen rehabilitation for elderly patients. For CC, treatment and rehabilitation for 57.5-year-old patients should be enhanced to improve their quality of life. For OC, monitoring of high-risk groups like those with family history and nulliparous women should be strengthened, genetic testing and preventive measures carried out, and early-diagnosis technologies invested in. For TC, research investment is crucial, and treatment and rehabilitation for 27.5-year-old young patients should be optimized. For PC, research on high-risk factors like lifestyle and genetics is essential to develop targeted prevention strategies given the gradually increasing cohort risk despite low and stable ASMR and ASDR in the 0 to 62.5 age group.\u003c/p\u003e \u003cp\u003eThis study uses the GBD database to investigate genital neoplasms, noting data quality, disease definition, and the influence of external factors limitations. Caution is warranted when applying findings, and various approaches should address these issues [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Low- and middle-income countries' lack of dependable epidemiological data can jeopardize accuracy. Diagnostic biases in initial studies may impact the GBD database, diminishing the reliability of the research. Differences in diagnostic criteria and technology could cause misdiagnosis or underdiagnosis, leading to data that might not accurately represent the true prevalence of the disease burden. Secondly, the GBD database definitions of genital tumors may not encompass all relevant disease conditions, potentially leading to an underestimation of the disease burden. Some rare subtypes or early-stage genital neoplasms might be inaccurately defined or excluded from the statistics, thus limiting the comprehensiveness of the findings. Additionally, the lack of consideration for exceptional circumstances like the COVID-19 pandemic can skew mortality estimates. In severely affected regions, mortality data may be inaccurate, and the GBD database might not reflect these changes, leading to discrepancies between research findings and reality.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this study analyzed the GBD database to predict the global burden of genital neoplasms, revealing distinct trends and regional disparities by SDI levels. High-SDI regions showed higher incidence rates but achieved effective prevention, whereas low-SDI regions faced greater challenges. Tailored strategies are essential: high-SDI areas should advance screening and treatment technologies, while low-SDI areas need improved healthcare infrastructure and health education. Global collaboration is vital for enhanced data collection and international efforts to strengthen the prevention and control of genital neoplasms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the collaborators of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB.B. Luo researched and analyzed the data and wrote the paper. S.Q. Zuo oversaw data collation. R. Zhang assisted in the data analysis and the layout of the paper. S.Y. Wang designed, revised and directed the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China (82305021), the Natural Science Research Project of the Anhui Provincial Department of Education (2024AH030036), the Traditional Chinese Medicine Inheritance and Innovation Project of Anhui Province (2024CCCX266), the Research Project of the Second People\u0026apos;s Hospital of Hefei (2023yjc008).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data about this study is accessible at no cost via the GBD 2021 portal (http://ghdx.healthdata.org/gbd-2021). R scripts to read in R environment and the RDS files of the datasets analyzed here are available from the authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray, F, Laversanne, M, Sung, H, et al., Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries\u003cem\u003e.\u003c/em\u003e CA Cancer J Clin. 2024;74(3):229-263.\u003c/li\u003e\n\u003cli\u003eLi, T, Zhang, H, Lian, M, et al., Global status and attributable risk factors of 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2020;396(10258):1135-1159.\u003c/li\u003e\n\u003cli\u003eCollaborators, G D, Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021\u003cem\u003e.\u003c/em\u003e Lancet. 2024;403(10440):1989-2056.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Health equity, Socio-demographic Index, Genital neoplasms, Cancer disparities, Global Burden of Disease","lastPublishedDoi":"10.21203/rs.3.rs-6699229/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6699229/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eWhile global longevity increases, economic disparities drive unequal burdens of genital neoplasms. This first comprehensive study evaluates how Socio-demographic Index (SDI) shapes the epidemiology of six major genital neoplasms (uterine fibroids [UFs], prostate [PC], cervical [CC], uterine [UC], testicular [TC], and ovarian cancer [OC]), providing evidence for equitable resource allocation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eUsing 2021 Global Burden of Disease data (1990-2021), we analyzed age-standardized rates (ASRs) of incidence (ASIR), prevalence (ASPR), mortality (ASMR), and disability-adjusted life years (DALYs) (ASDR) across 204 countries, stratified by SDI quintiles, age, and region. Trend analysis employed estimated annual percentage changes (EAPCs). Inequality was quantified via slope/concentration indices (SII/CI). Age-period-cohort modeling identified risk transitions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThere are notable disparities in the burden of genital neoplasms by cancer type. UFs showed the highest global prevalence (ASPR 2,841.07/100,000), while PC dominated mortality (ASMR 12.63/100,000). Divergent trends emerged: CC burden declined (DALYs -31.45%, 1990-2021) but rose for TC (ASPR EAPC 1.80%). High-SDI regions had 3.2-fold higher PC incidence yet 67% lower CC mortality than low-SDI areas. SDI-driven inequalities narrowed for UC (SII Δ-12.46) but persisted for PC (CI crossed zero). Projections suggest rising UFs cases (+15.98% by 2035) despite stable ASRs, highlighting demographic pressures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eSDI-mediated disparities require targeted interventions, particularly CC screening in low-resource settings and TC/PC prevention in high-income regions. Limitations include underdiagnosis in low-SDI areas. These findings establish a framework for global cancer control prioritization.\u003c/p\u003e","manuscriptTitle":"Global trends and socio-demographic inequalities in the burden of genital neoplasms: a 30-year comprehensive analysis of the Global Burden of Disease Study 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 15:58:40","doi":"10.21203/rs.3.rs-6699229/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d52a3277-8c0b-417e-ae77-679cfa9ef96d","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T11:54:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 15:58:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6699229","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6699229","identity":"rs-6699229","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}