Burden and Trends of Six Gynecological Diseases Among Chinese Women Aged 15-49 Years, 1990-2021: Evidence from GBD 2021

Gynecological disorders represent a critical public health issue affecting women worldwide. Among women of reproductive age (15–49 years), these conditions carry significant consequences, including high prevalence rates, infertility, chronic pain, and even premature mortality. Moreover, they substantially increase socioeconomic burdens, lowering women’s quality of life. 1 In China, women of reproductive age form the core driving force for family and societal development. Their health is profoundly influenced by multiple factors, including rapid socioeconomic transformation, urbanization, shifts in population policy (from the one-child policy to the universal two-child policy), and reforms within the healthcare system. From 1990 to 2019, China saw a 40.0% increase in uterine fibroid incidence (747,648 to 1,046,738), a 242.8% increase in uterine fibroid deaths (138 to 473), and a 75.6% increase in uterine fibroid DALYs (90,389 to 159,558). 2 In China, there was a significant increase in the incidence of cervical cancer cases, which increased by 169.80% from 40,680.98 in 1990 to 109,759.90 in 2019. 3 The burden of polycystic ovary syndrome (PCOS) in China has exhibited an increasing trend over time, with a rate of increase surpassing the global average. 4 In 2022, the number of new cases of uterine corpus cancer in China was 50,100, while that of ovarian cancer was 39,100. 5 The age-standardized incidence and prevalence of endometriosis in China have remained consistently higher than the global averages over the long term. 6 In addition, studies have shown that the burden of gynecological diseases has increased alongside delayed childbearing, population aging, and lifestyle transitions, posing substantial challenges to women’s reproductive health and healthcare systems. However, existing research primarily focuses on individual diseases or short-term trends, with most studies limited to specific regions, hospital-based populations, or cross-sectional investigations, thereby lacking comprehensive national-level evidence on the long-term temporal patterns and disease burden of multiple gynecological conditions in China. From 1990 to 2021, China experienced rapid economic growth, accelerated aging, and shifts in disease patterns, likely resulting in dynamic changes in the burden of gynecological diseases. Nevertheless, comprehensive analyses of the incidence and DALYs for six key gynecological conditions—uterine fibroids, polycystic ovarian syndrome, endometriosis, cervical cancer, ovarian cancer, and uterine cancer—remain insufficient. In particular, there is a lack of comparative analyses of multiple gynecological diseases over a 32-year period, stratified by age and SDI context. This gap leaves policymakers lacking precise evidence. Building upon its 2019 predecessor, the the 2021 Global Burden of Disease (GBD) database offers extensive epidemiological estimates for 371 diseases and 88 attributable risk factors, covering 204 countries and territories over the period 1990–2021. Accordingly, it can illustrate current long-term dynamics of disease burden. Our study aims to address this gap by quantifying the incidence and DALY rates of these diseases among Chinese women aged 15–49 from 1990 to 2021. This will provide scientific evidence for optimizing maternal and child health resource allocation, strengthening preventive interventions, and advancing the Healthy China 2030 strategy—thereby addressing the growing challenges in women’s health. Additionally, this study compares China—as a specific country—with socio-demographic index (SDI) regions. The SDI is a composite metric developed by the GBD study to quantify a region’s socio-demographic development level. Unlike geographical or cultural groupings, SDI regions are defined along a development continuum. This comparison allows China’s disease burden to be evaluated against a global benchmark of regions at similar developmental stages, offering valuable insights for policy formulation.

The data for this study were sourced from the GBD 2021 database, hosted by the Center for Health Metrics and Evaluation at the University of Washington and maintained by the Global Health Data Exchange (Database URL: https://ghdx.healthdata.org/gbd-2021/sources ). The Bayesian mixed-effects meta-regression model Dismod-MR 2.1 was employed to estimate the disease burden across 204 countries and regions worldwide, covering 256 diseases and injuries, 371 causes of death, and 88 risk factors. 7 The dataset is comprehensive and highly representative. This study defines the scope based on disease nomenclature: for gynecological diseases among individuals aged 15–49, the Chinese population within this age range was included, further subdivided into subgroups: 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years. Data mining employed the International Classification of Diseases (ICD-10) coding standards: Uterine fibroids ICD-10 code: D25-D26.9, D28.2, Polycystic ovarian syndrome ICD-10 code: E28.2, Endometriosis ICD-10 code: N80–N80.9, Cervical cancer ICD-10 code: C53–C53.9, Ovarian cancer ICD-10 code: C56–C56.2, C56.9, Uterine cancer ICD-10 codes: C54–C54.3, C54.8–C54.9). Uterine fibroids are made of muscle cells and other tissues that grow in and around the wall of the uterus, or womb, they can vary in number and size and grow as one tumor or in clusters. Polycystic Ovary Syndrome is a complex disorder characterized by infertility, hirsutism; obesity; and various menstrual disturbances such as oligomenorrhea; amenorrhea; anovulation. Endometriosis is a condition in which functional endometrial tissue is present outside the uterus. It is often confined to the pelvis involving the ovary, the ligaments, cul-de-sac, and the uterovesical peritoneum. Cervical cancer is cancer that starts in the cells of the cervix. Ovarian cancer is a cancerous tumor that forms in the tissues of an ovary. Uterine cancer is a cancer that forms in tissues of the uterus. In the GBD data, sequentially select “measure” as incidence and disability-adjusted life years (DALY), “metric” as “number” (absolute count) and “rate,” “cause” as the corresponding disease, “location” as “China or Global or SDI regions”, “age” as the age group of the study population, and “year” as all years between 1990 and 2021. This approach yielded the burden of responsibility for all studied diseases during the research period. Excel software was used to organize and clean the data. R 4.4.1 software was employed to plot line graphs showing changes in disease burden metrics over time. Joinpoint regression was conducted with Joinpoint Regression Program 5.2.0. Age-standardized rates (ASRs) were natural log-transformed prior to fitting. A maximum of 3 joinpoints was allowed, given the number of data points. The optimal number of joinpoints was determined by a Monte Carlo permutation test (default 4,499 permutations) with a significance level of α = 0.05, using Bonferroni correction for multiple comparisons. Uncorrelated errors (heteroscedastic) were assumed, and the variance of the dependent variable was estimated via the empirical quantile method. The minimum number of observations between joinpoints, or from a joinpoint to the end of the series, was set at 2. Age-standardized rates (ASRs) were natural logarithm-transformed prior to model fitting, and the annual percentage change (APC) with its 95% confidence interval (CI) was calculated for each segment. To characterize the overall temporal trend, the estimated annual percentage change (EAPC) was derived using a log-linear regression model: ln(Y) = α + βX + ε, where Y represents the natural logarithm of the ASR, X denotes calendar year, and β is the regression coefficient. The EAPC was calculated as 100 × [exp(β) − 1]. The 95% CI of the EAPC was used to determine the statistical significance of the trend: an EAPC > 0 (with its 95% CI excluding zero) indicates an increasing trend, whereas an EAPC < 0 indicates a decreasing trend. 8 A trend was considered not statistically significant if the 95% CI included zero. Uncertainty in the original disease burden estimates was described using the 95% uncertainty interval (UI) generated by the GBD study. P < 0.05 is considered statistically significant. The socio-demographic index (SDI) was incorporated to reflect regional development levels. The SDI comprises per capita income, average years of schooling among residents aged ≥15 years, and total fertility rate among those under 25 years (range 0–1). Regions were categorized into five groups—high, high-middle, middle, low-middle, and low—based on the integrated GBD 2021 data. This study was approved by the Ethics Review Committee of Ningbo University Affiliated People’s Hospital (202500232).

In 2021, the burden of six gynecological diseases among Chinese women aged 15–49 varied significantly. Uterine fibroids had the highest incidence at 960,418.32 cases (95% UI: 688,515.35–1,263,435.83), followed by endometriosis (412,969.69 cases; 95% UI: 296,826.28–554,233.11), while uterine cancer had the lowest incidence at 14,312.09 cases (95% UI: 9,842.02–20,370.33). In 2021, the disease with the highest DALY burden among Chinese women aged 15–49 was cervical cancer, totaling 467,647.35 (95% UI: 335,980.55–621,267.21). Endometriosis ranked second (241,180.83; 95% UI: 142,226.55–387,507.39), while uterine fibroids had the lowest DALYs (7,954.00; 95% UI: 4,730.35–11,633.17) ( Table 1 ). Table 1 The Incidence and DALYs for Six Gynecological System Diseases in Female Aged 15–49 years in China from 1990 to 2021 Year Uterine Fibroids Polycystic Ovarian Syndrome Endometriosis Cervical Cancer Ovarian Cancer Uterine Cancer 1990 Incidence Case (95%UI) 763924.54(540362.20,1053786.07) 134474.66(89646.06,194982.78) 665906.21(449532.00,920429.73) 28,243.67(22277.05,35813.10) 8587.93(5020.09,11724.89) 7608.80(4290.70,10082.08) DALYs Case (95%UI) 4726.20(2823.79,7771.55) 44078.48(18966.74,91450.10) 334190.60(185051.79,546,553.69) 516820.44(407041.90,652148.23) 153874.33(91308.47,209935.53) 115737.06(62139.62,154752.10) ASIR (95%UI) 247.18(246.62,247.74) 36.86(36.67,37.06) 203.07(202.57,203.57) 10.22(10.10,10.35) 3.00(2.94,3.07) 2.89(2.82,2.95) Age-standardized DALY rate (95%UI) 1.68(1.63,1.73) 13.70(13.57,13.83) 108.10(107.73,108.47) 189.66(189.13,190.18) 55.66(55.38,55.94) 43.18(42.93,43.43) 2021 Incidence Case (95%UI) 960418.32(688515.35,1263435.83) 130403.31(85354.87,193461.44) 412969.69(296826.28,554233.11) 49796.24(35518.21,65634.10) 11339.35(8278.88,15199.54) 14312.09(9842.02,20370.33) DALYs Case (95%UI) 7954.00(4730.35,11633.17) 81337.60(35559.16,168662.71) 241180.83(142226.55,387507.39) 467647.35(335980.55,621267.21) 176403.44(126934.17,236354.71) 83645.38(57606.29,117195.51) ASIR (95%UI) 271.82(271.27,272.38) 60.50(60.17,60.84) 130.91(130.50,131.33) 12.71(12.60,12.83) 3.03(2.97,3.09) 3.51(3.45,3.57) Age-standardized DALY rate (95%UI) 2.04(2.00,2.09) 25.57(25.39,25.76) 69.72(69.43,70.01) 117.11(116.77,117.45) 45.04(44.82,45.26) 20.82(20.67,20.96) EAPC 1990–2021 Incidence Case (95%UI) 0.57(0.25,0.89) 0.08(−0.51,0.69) −1.45(−1.62,-1.28) 2.48(2.12,2.85) 0.91(0.72,1.10) 2.23(1.58,2.89) DALYs Case (95%UI) 2.63(1.98,3.29) 2.23(1.87,2.59) −1.07(−1.26,-0.89) 0.13(−0.19,0.46) 0.34(0.14,0.54) −1.17(−1.76,-0.58) ASIR (95%UI) 0.24(0.10,0.38) 1.62(1.47,1.77) −1.55(−1.74,-1.36) 1.24(1.03,1.44) −0.16(−0.26,-0.06) 0.62(0.21,1.03) Age-standardized DALY rate (95%UI) 1.46(1.02,1.90) 2.09(1.92,2.26) −1.56(−1.75,-1.37) −1.23(−1.38,-1.09) −0.98(−1.10,-0.86) −2.61(−2.98,-2.25) The Incidence and DALYs for Six Gynecological System Diseases in Female Aged 15–49 years in China from 1990 to 2021 The highest Age-standardized incidence rate (ASIR) was observed for uterine fibroids (271.82/100,000; 95% UI: 271.27–272.38), followed by endometriosis (130.91/100,000; 95% UI: 130.50–131.33), while ovarian cancer had the lowest ASIR (3.03/100,000; 95% UI: 2.97–3.09). The highest age-standardized DALY rate was observed for cervical cancer (117.11/100,000; 95% UI: 116.77–117.45), followed by endometriosis (69.72/100,000; 95% UI: 69.43–70.01), while uterine fibroids had the lowest rate (2.04/100,000; 95% UI: 2.00–2.09) ( Table 1 , Figure 1A and B ). Figure 1 Temporal trends of rate and number of ovarian cancer attribute to high body-mass index in female aged over 55 years from 1990 to 2021. ( A ) DALY change cases and age-standardized DALY rate. ( B ) Death change cases and age-standardized death rate. Panel A (Incidence) shows age-standardized rates per 100,000 people from 1990 to 2020. The x-axis is labeled Year and the y-axis is labeled Age-standardized rate. Uterine fibroids have the highest incidence, peaking around 2005, then stabilizing. Endometriosis shows a gradual decline. Polycystic ovarian syndrome rises slightly. Cervical, uterine and ovarian cancers remain low and stable. Panel B (DALYs) shows age-standardized rates per 100,000 people. Cervical cancer has the highest DALYs, decreasing over time. Endometriosis declines steadily. Ovarian cancer, polycystic ovarian syndrome, uterine cancer and uterine fibroids remain low. Lines are distinguished by different markers and styles. The graphs highlight the burden of these conditions over time, with uterine fibroids having the highest incidence and cervical cancer the highest DALYs. Multi-line graphs showing incidence and DALYs for gynecologic diseases from 1990 to 2020. Abbreviations : DALY, disability-adjusted life years; SDI, Socio-Demographic Index. Temporal trends of rate and number of ovarian cancer attribute to high body-mass index in female aged over 55 years from 1990 to 2021. ( A ) DALY change cases and age-standardized DALY rate. ( B ) Death change cases and age-standardized death rate. Among the EAPC trends for ASIR from 1990 to 2021: Polycystic ovarian syndrome showed the fastest growth (EAPC = 1.62, 95% CI: 1.47–1.77); Cervical cancer had the second-fastest growth (EAPC = 1.24, 95% CI: 1.03–1.44); Endometriosis showed the steepest decline (EAPC = −1.55, 95% CI: −1.74 to −1.36). Age-standardized DALY rate EAPC trends: Polycystic ovarian syndrome exhibited the most pronounced increase (EAPC = 2.09, 95% CI: 1.92–2.26); Uterine fibroids showed the second-fastest increase (EAPC = 1.46, 95% CI: 1.02–1.90); Uterine cancer exhibited the most pronounced decrease (EAPC = −2.61, 95% CI: −2.98 to −2.25) ( Table 1 , Figure 1A and B ). The burden of gynecological diseases exhibits distinct characteristics across different age groups. The numbers of incident cases of uterine fibroids peaked in the 30–34 age group (316,334.44, 95% UI: 181,996.64,482,209.34). The ASIR for uterine fibroids reached its highest point in the 35–39 age group (561.53 per 100,000, 95% UI: 308.19–841.23), while the lowest ASIR was observed in the 15–19 age group (24.42 per 100,000; 95% UI: 8.82–50.28). Both the case number of DALYs and the age-standardized DALY rate peaked in the 45–49 age group (2,603.35, 95% UI: 1,396.44–3,698.11 and 4.80, 95% UI: 2.57–6.82, respectively). The ASIR showed the greatest increase in the 35–39 age group (EAPC = 0.87); the age-standardized DALY rate increased most significantly in the 45–49 age group (EAPC = 2.24) ( Supplementary Table 1 and Figures 2–4 ). Figure 2 Age-specific DALY rate trends for six gynecological diseases, 1990–2021. The image features six graphs depicting age-specific DALY rates from 1990 to 2021 for cervical cancer, endometriosis, ovarian cancer, polycystic ovarian syndrome, uterine cancer and uterine fibroids. The x-axis shows years, while the y-axis indicates age-specific rates. Each graph includes lines for age groups: 15-19, 20-24, 25-29, 30-34, 35-39, 40-44 and 45-49 years, differentiated by colors and markers. Cervical cancer rates drop from 400 to 200 for ages 45-49. Endometriosis rates decline overall, starting at 100 and ending around 50 for ages 45-49. Ovarian cancer rates vary, peaking at 150 for ages 45-49. Polycystic ovarian syndrome rates rise steadily, reaching 25 for ages 45-49. Uterine cancer peaks at 100 for ages 45-49. Uterine fibroids peak at 6 for ages 45-49. These graphs illustrate disease burden trends across age groups. A multi-line graph showing age-specific DALY rates for six gynecological diseases from 1990 to 2021. Abbreviation : DALY, disability-adjusted life years. Figure 3 Age-specific incidence rate trends for six gynecological diseases, 1990–2021. The image contains six multi-line graphs depicting the incidence rates of various conditions from 1990 to 2020. The x-axis represents the year and the y-axis represents the age-specific rate. Each graph includes lines for age groups: 15-19, 20-24, 25-29, 30-34, 35-39, 40-44 and 45-49 years, differentiated by color. 1. Cervical cancer: Rates generally increase, peaking around 2010 for ages 45-49. 2. Endometriosis: A decline is observed from 2000, with the highest rates in ages 35-39. 3. Ovarian cancer: Fluctuations occur, with the highest rates in ages 45-49. 4. Polycystic ovarian syndrome: A steady increase, especially in ages 15-19. 5. Uterine cancer: Peaks around 2010, with ages 45-49 showing the highest rates. 6. Uterine fibroids: Rates rise, peaking for ages 40-44. The legend maps colors to age groups, aiding in distinguishing the lines. The graphs highlight trends and variations in incidence rates across different age groups and conditions. Graphs of incidence rates by age for cervical, ovarian, uterine cancers, endometriosis, PCOS and fibroids. Abbreviations : DALY, disability-adjusted life years; EAPC, estimated annual percentage change. Figure 4 Case number of six gynecological diseases in female aged 15–49 years in 2021. The 'Composition by Age Group' stacked bar graph illustrates the percentage distribution of conditions across age groups. The horizontal axis shows percentage (0%-100%) and the vertical axis lists conditions: Cervical cancer, Endometriosis, Ovarian cancer, Polycystic ovarian syndrome, Uterine cancer and Uterine fibroids. Age groups are 15-19, 20-24, 25-29, 30-34, 35-39, 40-44 and 45-49 years. Cervical cancer is most prevalent in ages 35-49, less in 30-34 and minimal in younger groups. Endometriosis is significant from 25-49 years, with presence in 15-24 years. Ovarian cancer is most common in ages 40-49, followed by 30-39, with smaller segments in younger groups. Polycystic ovarian syndrome is notable across all ages, including 15-24 years. Uterine cancer is prevalent in ages 40-49, followed by 30-39, with minor younger segments. Uterine fibroids are large in ages 30-49, with smaller segments in younger groups. A stacked bar graph showing composition by age group for six gynecological diseases. Age-specific DALY rate trends for six gynecological diseases, 1990–2021. Age-specific incidence rate trends for six gynecological diseases, 1990–2021. Case number of six gynecological diseases in female aged 15–49 years in 2021. The incidence of polycystic ovarian syndrome (PCOS) is concentrated among young women. In 2021, the 15–19 age group had the highest numbers of incident cases and ASIR (109,706.90, 95% UI: 67,434.38–175,302.11 and 317.17 per 100,000, 95% UI: 194.96–506.81, respectively), and this group also exhibited the fastest ASIR increase (EAPC = 1.96). The highest case number of DALYs was observed in the 30–34 age group (15,750.10, 95% UI: 6,775.30–33,539.69), while the age-standardized DALY rate peaked in the 20–24 age group (28.32 per 100,000, 95% UI: 12.52–58.89) and exhibited the fastest increase (EAPC = 2.29) ( Supplementary Table 1 and Figures 2–4 ). The numbers of incident cases of endometriosis peaked in the 40–44 age group (71,844.86, 95% UI: 35,019.53–120,774.90), with the highest ASIR in the 20–24 age group (173.14 per 100,000, 95% UI: 92.35–277.42). The highest case number of DALYs was observed in the 45–49 age group (49,243.18, 95% UI: 26,715.49–82,183.52), while the highest age-standardized DALY rate was in the 40–44 age group (143.38 per 100,000, 95% UI: 76.73–261.51). Both ASIR and age-standardized DALY rates declined across all age groups, with the slowest ASIR decline observed in the 45–49 age group (EAPC = −0.84); the slowest DALY rate decline occurred in the 15–19 age group (EAPC = −1.20) ( Supplementary Table 1 and Figures 2–4 ). The incidence and DALY burden of cervical cancer, ovarian cancer, and uterine cancer all increased significantly with age, peaking in the 45–49 age group. This group had the highest numbers of incident cases (16,171.11, 95% UI: 11,381.34–21,735.90, 4,222.15, 95% UI: 2,946.91–5,675.88 and 6,549.65, 95% UI: 4,590.32–9,249.38, respectively) and the highest DALY counts (182,526.98, 95% UI: 129,647.74–247,656.33, 77,372.78, 95% UI: 54,093.26–104,355.52, 35,736.66, 95% UI: 25,133.79–50,139.18). Cervical cancer exhibited the fastest ASIR growth in the 45–49 age group (EAPC = 1.49), while its age-standardized DALY rate declined across all age groups, with the steepest decrease observed in the 15–19 age group (EAPC = −2.83). The ASIR for ovarian cancer increased most rapidly in the 20–24 age group (EAPC = 0.68), while its age-standardized DALY rate decreased across all age groups, with the steepest decline observed in the 15–19 age group (EAPC = −2.10). The ASIR for uterine cancer increased most rapidly in the 20–24 age group (EAPC = 1.13), but the age-standardized DALY rate decreased across all age groups, with the steepest decline observed in the 35–39 age group (EAPC = −2.85) ( Supplementary Table 1 and Figures 2–4 ). In 2021, significant variations in disease burden were observed across different SDI regions. Cervical cancer, endometriosis ASIR, and age-standardized DALY rates were highest in low SDI regions (Cervical cancer ASIR: 20.84/100,000, 95% UI: 20.65–21.04; Endometriosis ASIR: 209.16/100,000, 95% UI: 208.62–209.70), while the lowest rates were observed in high SDI regions (11.71, 95% UI: 11.58–11.84 and 152.02, 95% UI: 151.51–152.53, respectively). The ASIRs for polycystic ovarian syndrome, uterine cancer, and uterine fibroids were highest in high SDI regions (165.92/100,000, 95% UI: 165.31–166.52, 5.16/100,000, 95% UI: 5.08–5.25, and 522.99/100,000, 95% UI: 522.11–523.88, respectively) and lowest in low SDI regions (31.40, 95% UI: 31.21–31.58, 0.94, 95% UI: 0.90–0.98, and 464.48, 95% UI: 463.62–465.34, respectively). The ASIR for ovarian cancer was highest in middle SDI regions (4.61/100,000). Regarding age-standardized DALY rates, cervical cancer (428.77, 95% UI: 427.90–429.64), endometriosis (126.12, 95% UI: 125.68–126.55), uterine fibroids (7.71, 95% UI: 7.59–7.82) were highest in low SDI regions, while polycystic ovarian syndrome (60.33, 95% UI: 60.02–60.65) peaked in high SDI regions. Ovarian cancer (66.07, 95% UI: 65.84–66.30) reached its highest rate in low-middle SDI regions, and uterine cancer (22.77, 95% UI: 22.62–22.92) was highest in high-middle SDI regions ( Table 2 ). Table 2 ASIR and Age-Standardized DALYs Rate for Six Gynecological System Diseases in Female Aged 15–49 years in China, SDI Regions and Worldwide in 2021 Incidence DALYs (Disability-Adjusted Life Years) Cervical cancer Endometriosis Ovarian Cancer Polycystic Ovarian Syndrome Uterine Cancer Uterine Fibroids Cervical Cancer Endometriosis Ovarian Cancer Polycystic ovarian syndrome Uterine Cancer Uterine Fibroids ASR China 12.71(12.60,12.83) 130.91(130.50,131.33) 3.03(2.97,3.09) 60.50(60.17,60.84) 3.51(3.45,3.57) 271.82(271.27,272.38) 117.11(116.77,117.45) 69.72(69.43,70.01) 45.04(44.82,45.26) 25.57(25.39,25.76) 20.82(20.67,20.96) 2.04(2.00,2.09) Global 15.17(15.12,15.23) 178.70(178.51,178.89) 4.27(4.24,4.30) 64.54(64.42,64.65) 2.86(2.84,2.88) 493.33(493.02,493.64) 206.17(205.97,206.36) 98.69(98.55,98.83) 63.90(63.79,64.01) 29.51(29.43,29.58) 18.24(18.18,18.30) 4.91(4.88,4.94) High-middle SDI 13.72(13.60,13.85) 167.64(167.16,168.13) 4.43(4.36,4.50) 68.37(68.01,68.72) 4.70(4.63,4.77) 504.47(503.71,505.24) 133.99(133.62,134.37) 91.25(90.91,91.59) 65.58(65.31,65.84) 30.39(30.19,30.59) 22.77(22.62,22.92) 3.16(3.10,3.22) High SDI 11.71(11.58,11.84) 152.02(151.51,152.53) 4.37(4.30,4.45) 165.92(165.31,166.52) 5.16(5.08,5.25) 522.99(522.11,523.88) 75.45(75.13,75.77) 84.04(83.68,84.40) 58.62(58.34,58.90) 60.33(60.02,60.65) 18.47(18.31,18.62) 2.46(2.41,2.52) Low-middle SDI 15.96(15.84,16.07) 190.06(189.69,190.44) 4.10(4.05,4.16) 46.72(46.53,46.90) 1.35(1.32,1.38) 514.78(514.15,515.42) 267.68(267.21,268.15) 106.62(106.33,106.91) 66.07(65.84,66.30) 19.97(19.85,20.09) 15.35(15.24,15.46) 7.05(6.97,7.12) Low SDI 20.84(20.65,21.04) 209.16(208.62,209.70) 3.12(3.05,3.19) 31.40(31.21,31.58) 0.94(0.90,0.98) 464.48(463.62,465.34) 428.77(427.90,429.64) 126.12(125.68,126.55) 56.97(56.65,57.29) 11.91(11.78,12.05) 14.10(13.94,14.26) 7.71(7.59,7.82) Middle SDI 14.94(14.85,15.03) 166.30(165.97,166.63) 4.61(4.56,4.66) 71.24(71.02,71.47) 2.43(2.39,2.47) 468.93(468.40,469.46) 182.27(181.95,182.59) 89.70(89.46,89.93) 64.80(64.61,64.99) 32.95(32.81,33.10) 18.74(18.63,18.84) 4.28(4.23,4.33) EAPC China 1.24(1.03,1.44) −1.55(−1.74,-1.36) −0.16(−0.26,-0.06) 1.62(1.47,1.77) 0.62(0.21,1.03) 0.24(0.10,0.38) −1.23(−1.38,-1.09) −1.56(−1.75,-1.37) −0.98(−1.10,-0.86) 2.09(1.92,2.26) −2.61(−2.98,-2.25) 1.46(1.02,1.90) Global −0.38(−0.48,-0.29) −1.01(−1.05,-0.96) 0.07(0.01,0.13) 0.65(0.62,0.69) 0.44(0.32,0.56) 0.26(0.25,0.27) −1.25(−1.34,-1.16) −1.01(−1.06,-0.96) −0.37(−0.45,-0.30) 0.72(0.68,0.76) −1.24(−1.34,-1.13) 0.04(−0.01,0.08) High-middle SDI 0.60(0.52,0.69) −0.72(−0.82,-0.61) −0.53(−0.60,-0.46) 1.37(1.30,1.45) 0.35(0.20,0.51) −0.26(−0.32,-0.21) −1.09(−1.15,-1.02) −0.71(−0.82,-0.61) −1.12(−1.21,-1.02) 1.20(1.16,1.25) −1.90(−2.10,-1.70) −0.91(−1.01,-0.82) High SDI −1.46(−1.63,-1.28) −0.92(−1.00,-0.85) −1.21(−1.32,-1.10) 0.21(−0.05,0.46) 1.78(1.69,1.87) 0.08(−0.10,0.26) −2.06(−2.17,-1.96) −0.85(−0.95,-0.76) −1.73(−1.79,-1.67) 0.08(−0.09,0.25) 0.40(0.30,0.51) −0.50(−0.60,-0.41) Low-middle SDI −0.68(−0.79,-0.56) −1.50(−1.52,-1.47) 1.69(1.61,1.76) 1.28(1.23,1.32) 1.00(0.92,1.07) 0.67(0.61,0.74) −1.63(−1.74,-1.53) −1.65(−1.68,-1.61) 1.29(1.22,1.35) 1.59(1.55,1.63) −0.10(−0.17,-0.03) −0.31(−0.39,-0.23) Low SDI −1.17(−1.28,-1.05) −1.41(−1.46,-1.36) 1.37(1.25,1.48) 0.92(0.89,0.94) 0.49(0.33,0.64) 0.29(0.26,0.32) −1.84(−1.95,-1.74) −1.36(−1.44,-1.28) 0.99(0.87,1.10) 1.21(1.19,1.24) −0.35(−0.46,-0.25) −0.35(−0.50,-0.20) Middle SDI −0.24(−0.33,-0.14) −1.05(−1.13,-0.97) 1.03(0.97,1.09) 1.39(1.34,1.43) 0.41(0.28,0.55) 0.60(0.57,0.63) −1.58(−1.68,-1.48) −1.03(−1.12,-0.95) 0.38(0.29,0.47) 1.72(1.67,1.77) −1.78(−1.90,-1.66) 0.33(0.24,0.43) ASIR and Age-Standardized DALYs Rate for Six Gynecological System Diseases in Female Aged 15–49 years in China, SDI Regions and Worldwide in 2021 From 1990 to 2021, the ASIR for cervical cancer declined most rapidly in high SDI regions (EAPC = −1.46) and increased most rapidly in China (EAPC = 1.24). The ASIR for endometriosis decreased most rapidly in China (EAPC = −1.55) and most slowly in high-middle SDI regions (EAPC = −0.72). Polycystic ovarian syndrome ASIR increased most rapidly in China (EAPC = 1.62) and grew slowest in high SDI regions (EAPC = 0.21). Uterine cancer ASIR increased most rapidly in high SDI regions (EAPC = 1.78) and declined most rapidly in high-middle SDI regions (EAPC = −0.26). Uterine fibroids showed the fastest growth in low-middle SDI regions (EAPC=0.67) and the fastest decline in high-middle SDI regions (EAPC=−0.26) ( Table 2 ). Trends in EAPC for age-standardized DALY rates: Endometriosis and cervical cancer showed declining trends across all regions, with cervical cancer decreasing most rapidly in low SDI regions (EAPC = −1.84); Endometriosis declined most rapidly in low-middle SDI regions (EAPC = −1.65); Ovarian cancer increased most rapidly in low-middle SDI regions (EAPC = 1.29) and declined most rapidly in high SDI regions (EAPC = −1.73); Polycystic ovarian syndrome (EAPC=2.09) and uterine fibroids (EAPC=1.46) both increased most rapidly in China; uterine cancer increased most rapidly in high SDI regions (EAPC=0.40) and decreased most rapidly in high-middle SDI regions (EAPC=−1.90) ( Table 2 ).

This study examined the differential burden of six gynecological diseases among Chinese women aged 15–49 years from 1990 to 2021. Uterine fibroids had the highest incidence, and cervical cancer contributed the greatest health burden. Polycystic ovary syndrome showed the largest increases in both ASIR and age-standardized DALY rate over time. Endometriosis ASIR and uterine cancer DALY rates declined. Uterine fibroids predominantly affected women aged 30–39 years, while polycystic ovarian syndrome had the highest incidence and fastest growth among adolescents aged 15–19 years. The disease burden for cervical cancer, ovarian cancer, and uterine cancer is concentrated in the 45–49 age group. For endometriosis, the burden is characterized by higher incidence in younger age groups, with the greatest burden observed in the middle-aged and older group (45–49 years). Low Socio-Demographic Index (SDI) regions bear a heavier burden of cervical cancer and endometriosis, while high SDI regions exhibit higher incidence rates of polycystic ovarian syndrome and uterine cancer. These findings provide crucial evidence-based value for the precise prevention and control of various diseases across different patient groups, regions, and even age cohorts.

To our knowledge, this is the first integrated, age- and SDI-stratified analysis of six major gynecological diseases among Chinese women of reproductive age over a 32-year period, providing a comprehensive evidence base for future public health policy and resource allocation in China. The findings of this study closely align with the study objective of comprehensively assessing the burden and temporal trends of six gynecological diseases among Chinese women aged 15–49 years from 1990 to 2021. Substantial heterogeneity was observed across diseases and age groups. Also, this research comprehensively assessing the burden and temporal trends of six gynecological diseases among SDI regions and worldwide women aged 15–49 years from 1990 to 2021 By age, disease burden clusters distinctly: Uterine fibroids peak among women aged 30–39 during childbearing years, while polycystic ovarian syndrome shows highest incidence and growth rate in adolescents (15–19 years). Cervical cancer, ovarian cancer, and uterine cancer both exhibit concentrated incidence and health damage burdens among those aged 45–49. Endometriosis, however, demonstrates a distinct pattern: its incidence declines rapidly in younger age groups, while the greatest burden falls on middle-aged and older women (45–49 years). Disease burden distribution varies significantly across SDI regions. Low-SDI areas bear the heaviest burden from cervical cancer and endometriosis incidence and health damage, while high-SDI regions exhibit the highest ASIRs for polycystic ovarian syndrome and uterine cancer. Trend analysis indicates the most pronounced improvement in cervical cancer burden in high-SDI countries and regions, while ovarian cancer burden grows fastest in low- and middle-SDI regions. Compared to other SDI groups, China experiences the highest increase in health damage burden from polycystic ovarian syndrome and uterine fibroids. The findings from this study on the disease burden and evolving characteristics of gynecological conditions among Chinese women aged 15–49 reflect the complex interplay of social, medical, and lifestyle factors. Uterine fibroids rank highest in both absolute prevalence and age-standardized incidence. This is migh t due to increased detection rates from development of imaging examinations (such as ultrasound), coupled with possible risk factors among women of reproductive age—including work pressure, persistent anxiety and depression, history of gynecological diseases, history of miscarriage—which have led to broader identification and reporting of uterine fibroids. 9 , 10 Cervical cancer, ranking first in DALYs, stems from its high mortality rate and uneven screening coverage. Although cervical cancer screening programs (such as the “Two Cancers” screening initiative) have been promoted in China since the 2000s, but overall penetration remains suboptimal. 11 , 12 Consequently, high rates of late-stage detection and delayed treatment amplify the health impact. The incidence and DALY rates of polycystic ovarian syndrome (PCOS) have shown a marked increase, an inevitable consequence of China’s obesity epidemic in recent years (adult obesity rates rose over 15% from 2010 to 2020). Obesity-related insulin resistance and metabolic disorders have exacerbated the prevalence of PCOS, while the obesity cause by Westernization of lifestyles among young people (eg, animal source foods) may have further contributed to its rise. 13 , 14 Thus, earlier-stage prevention and control including broader screening coverage and healthier eating habits might collectively reduce the burden of these diseases. Conversely, the current decline in endometriosis incidence in China may benefit from diagnostic “advancements” (eg, implementation of International Endometriosis Society guidelines) and the emergence of early diagnosis and treatment strategies (eg, widespread adoption of laparoscopy), thereby reducing missed or misdiagnosed cases. The decline in DALYs for uterine cancer stems from the continuously refined expert consensus derived from years of systematic research on uterine cancer, 15 , 16 reducing the incidence of more harmful “advanced-stage cases”. These divergent trends reflect variations in disease burden and suggest targeted interventions: community screening for uterine fibroids requires strengthening, primary-level screening coverage for cervical cancer needs expansion, and PCOS should be integrated into obesity prevention systems. However, the declining incidence of endometriosis and uterine cancer among younger age groups indicates the effectiveness of current interventions. The age distribution of gynecological disease burden among Chinese women aged 15–49 reflects variations across life stages, influenced by physiological characteristics, societal changes, and medical intervention strategies. Uterine fibroids most commonly occur in women aged 30–39 during peak childbearing years, might primarily due to the combined effects of elevated estrogen levels and high fertility rates in this age group. Uterine fibroids are often associated with obesity, high oestrogen levels and dietary habits. 17 Urbanization, increased societal stress, and altered hormonal environments further promote fibroid development. Polycystic ovarian syndrome (PCOS) is most prevalent among adolescent females aged 15–19, exhibiting both the highest incidence and fastest growth rate. This correlates with China’s substantial rise in adolescent obesity rates. 18 High-sugar diets, sedentary lifestyles, and endocrine disruptions contribute to the early onset of PCOS. 19 Therefore, targeted health education and lifestyle interventions should be provided for women aged 30–39 years and aged 15–19, emphasizing healthy dietary habits, regular physical activity, and mental well-being to support normal hormonal metabolism and thereby reduce the risk of developing uterine fibroids and polycystic ovarian syndrome. The concentrated incidence and health damage burdens of cervical cancer, ovarian cancer, and uterine cancer is concentrated in the 45–49 age group in China, reflecting the natural pattern of cancer incidence accumulating with age: women in this age bracket are in the perimenopausal stage, where hormonal fluctuations exacerbate the occurrence of gynecological diseases. By the end of 2019, the successful launch of domestically produced HPV vaccines and policy measures prioritising cervical cancer prevention and control had led to an increase in increasing human papillomavirus (HPV) vaccination rates. 20 Furthermore, the inclusion of screening for breast and cervical cancer in the basic public health service programme, coupled with the rapid development of AI-assisted colposcopy systems, is expected to reduce the burden of cervical cancer in the future through tertiary prevention. 20 Concurrently, cervical cancer screening coverage remains low in rural areas, leading to predominantly late-stage diagnoses. Delayed diagnosis of ovarian and uterine cancers further exacerbates their impact and harm. The rapid decline in endometriosis incidence among younger age groups and the highest burden among middle-aged and elderly groups represent its unique characteristics. The decline in younger groups may be closely linked to advances in diagnostic technology, reducing missed diagnoses. 21 The high burden in middle-aged and elderly groups stems from the chronic progression of the disease and resulting functional impairment. Clearly, the priority for preventing and controlling cervical, ovarian, and uterine cancers is to reduce their incidence and burden among middle-aged women, whereas the priority for managing endometriosis is to lower its incidence among young women. These age-specific distributions underscore the importance of targeted interventions: Screening for cervical, ovarian, and uterine cancers should be intensified among middle-aged women, and treatment costs for these conditions must be covered by both statutory or private health insurance. For endometriosis, health education programs targeting young women are needed to reduce its incidence, thereby alleviating the disease burden associated with long-term suffering. The uneven distribution of gynecological disease burden among women aged 15–49 across regions with different SDI levels might stems from disparities in healthcare resource accessibility, medical interventions, lifestyle factors, and environmental exposures. In low SDI regions, the heavy burden of cervical cancer stems from low HPV vaccination rates, low coverage of cervical screening programs like Pap smears, weak public health and sanitation infrastructure, and delays in early diagnosis and treatment, 22 leading to high incidence and mortality rates. The higher burden of endometriosis is also associated with prevalent genital tract infections, poor sanitation, and diagnostic challenges. The absence of standardized diagnostic and treatment protocols further exacerbates outcomes. In high SDI regions, diseases like polycystic ovarian syndrome and uterine cancer are prevalent, reflecting high obesity rates, hormone-influenced lifestyles (eg, high sugar intake, lack of exercise), and improved diagnostic rates due to robust disease screening systems. High SDI regions show notable improvements in cervical cancer burden, largely due to systematic HPV vaccination and widespread screening for reproductive-age women (eg, HPV-based screening programs), which significantly prevent cervical cancer progression. In middle- and low-SDI regions, ovarian cancer burden is increasing most rapidly which might driven by accelerated population aging, enhanced diagnostic capabilities (eg, high ultrasound and MRI availability), and environmental factors (eg, significant industrial pollution hazards). For these regions, strengthening basic medical infrastructure, improving early screening and diagnosis, enhancing treatment accessibility, increasing public health education, and promoting policy development and research support would be cost-effective approaches to reducing the disease burden. 23 By revealing the distinct disease burden patterns in China and across SDI regions, our findings provide critical evidence to inform policy-making. regions with low-to-medium SDI levels may benefit from adopting China’s prevention and control strategies to reduce the burden of ovarian and cervical cancer. These strategies include integrating ovarian and cervical cancer into basic public health service programs, HPV vaccination coverage, expanding screening for both cancers, and establishing dedicated prevention and control systems. In high-SDI regions and China, reducing the burden of polycystic ovary syndrome and similar conditions requires health education that raises public awareness through disseminate information about promotion of healthy lifestyles and balanced diets. This study is the first to integrate multiple data sources from China spanning 1990–2021, precisely delineating the age, temporal, and SDI-based tripartite burden patterns of six gynecological diseases among Chinese women aged 15–49. These findings are directly relevant to the broader context of global women’s health, as they provide a comprehensive evidence base for the Healthy China 2030 strategy and similar initiatives worldwide, highlighting the need for lifecycle- and region-specific interventions. It provides robust evidence-based support for developing stratified intervention strategies. 24 Limitations of this study include: Firstly, GBD 2021 data sourced from national disease surveillance system reports, potentially subject to reporting bias or variations in data quality. While the GBD study provides the most comprehensive and standardized framework for cross-country and cross-time comparisons of disease burden, it is important to recognize potential biases inherent in its estimation process. These include reliance on statistical modeling for data-scarce regions, heterogeneity in primary data quality, and assumptions underlying the cause of death ensemble model. Nevertheless, the GBD study remains the most widely accepted reference for global disease burden due to its transparent protocols, systematic use of available data, and extensive sensitivity analyses. Secondly, failure to further account for individual behavioral health factors (eg, diet, exercise) and environmental factors, resulting in limited insight into disease burden and underlying mechanisms of variation; The use of nationally standardized SDI classifications at the provincial or county level may understate provincial and regional disparities; the extended time span (32 years) and unaccounted policy adjustments (eg, healthcare coverage rates) during specific periods limit trend analysis accuracy. Future research integrating electronic health records and multi-cohort studies may deepen understanding of disease burden mechanisms. Thirdly, the Joinpoint regression employed in this study captures overall time trends and age-stratified changes but cannot disentangle the independent effects of age, period, and birth cohort on disease burden. Age-specific comparisons without cohort decomposition may be misleading, as observed trends could be driven by period effects (eg, screening policy changes) or cohort effects (eg, differential risk exposure by birth year) rather than true biological aging. The future research should incorporate age-period-cohort (APC) modeling in subsequent analyses. This will allow for a more rigorous decomposition of the driving factors behind the observed epidemiological transitions and strengthen the validity of age-specific interpretations. Fourthly, anormal uterine bleeding (AUB) as one of the most common gynecological complaints among women of reproductive age in China, could significantly affects quality of life, work productivity, and healthcare utilization. However, it had not been included in this research due to it is not a disease entity in GBD 2021 as defined by ICD-10 codes. AUB should be included in future research to better capture the full disease burden.