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Research Article

Trends and Projections of the Burden of Uterine Prolapse in China and G20 Countries: A Comparative Study Based on Global Burden of Disease 2023

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DOI:

10.3791/71450

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July 31st, 2026

 ,  , 
1Department of Gynecology, The Third Affiliated Hospital of Zhejiang Chinese Medical University, 2Department of Gynecology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, 3Department of Ultrasound, The Third Affiliated Hospital of Zhejiang Chinese Medical University

In This Article

Summary

This study compared the historical and future disease burden of uterine prolapse between China and G20 countries using Global Burden of Disease (GBD) 2023 data. Although China showed lower age standardized burden than the G20 overall, absolute case numbers are expected to increase because of population aging. These findings highlight the need for targeted, age-specific prevention and management strategies.

Abstract

This observational longitudinal study utilized the Global Burden of Disease (GBD) 2023 database to comprehensively analyze and project the burden of uterine prolapse (UP) in China and G20 countries from 1990 to 2050. Incidence, deaths, prevalence, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs) were assessed, including their age-standardized rates (ASRs). Trends from 1990 to 2023 were analyzed using Joinpoint regression (calculating the average annual percentage change, AAPC), along with their respective 95% confidence intervals (CI), and decomposition analysis. The future burden to 2050 was projected using autoregressive integrated moving average (ARIMA) and Bayesian age-period-cohort (BAPC) models. From 1990 to 2023, the ASRs for incidence, prevalence, DALYs, and YLDs of UP in China showed a declining trend, with AAPCs of −0.47 (95% CI −0.51 to −0.44), −0.52 (95% CI −0.56 to −0.47), −0.50(95% CI −0.54 to −0.46), and −0.52 (95% CI −0.55 to −0.48), respectively. A transient increase was observed between 2010 and 2015. The ASR for deaths remained near zero, while the ASR for YLLs increased (AAPC = 2.12, 95% CI 1.32–2.93). The G20 countries showed similar declining trends for non-fatal burdens, with reductions more pronounced than in China. Projections indicate that from 2023 to 2050, the ASRs for incidence, prevalence, DALYs, and YLDs will continue to decline in both China and G20 countries, with a faster decrease anticipated in China. Consequently, the age-standardized burden among Chinese women is expected to remain lower than the G20 average. In conclusion, while China's age-standardized UP burden is historically and prospectively lower than the G20 aggregate, rising absolute numbers due to population aging indicate a substantial future healthcare burden. This underscores the need for China to develop targeted, age-group-specific prevention and control strategies.

Introduction

Uterine prolapse (UP), also known as pelvic organ prolapse (POP), is a prevalent pelvic floor disorder among women. It is characterized by the displacement of the uterus from its normal anatomical position into or outside the vagina1,2. This condition is often associated with symptoms such as lower abdominal pressure, urinary and defecatory dysfunction, and sexual discomfort, which compromise patients' quality of life and social functioning3. The anatomical changes associated with UP include alterations in the axial positions of the uterus and vagina. Previous research has demonstrated that women with prolapse frequently exhibit retroversion and retroflexion of the uterus, as well as a backward and downward shift of the uterine and vaginal axes4.

UP reflects a deterioration in the support structures of the pelvic floor, encompassing the levator ani muscle and pelvic floor fascia, leading to the descent of the uterus5. The risk factors associated with UP can be broadly categorized into non-modifiable factors, such as age, menopause, genetic predisposition, and race, and modifiable factors, which include lifestyle and fertility-related aspects. These modifiable factors include multiple pregnancies, mode of delivery, obesity, chronic constipation, chronic cough, and other activities that consistently elevate abdominal pressure and compromise connective tissue integrity6,7. Clinical evaluation of UP involves history, physical examination, and functional assessment8. Notably, different diagnostic tools have yielded varying prevalence estimates, with 25.0% reported via questionnaire and 41.8% identified through clinical examination9.

The treatment of UP is contingent upon the severity of the condition and the presenting symptoms. Management strategies may encompass pelvic floor strengthening exercises such as Kegel exercises, the use of vaginal pessaries, and various surgical interventions1. One study revealed that combining Kegels with yoga enhances pelvic floor function and improves quality of life10. Respiratory training also benefits pelvic floor function11. Although pessary treatment is effective in approximately 90% of cases, the long-term continuation rate drops to 60% due to complications like increased vaginal discharge (84%) and discomfort (20%)12. Surgery is recommended for patients with severe symptoms or those unresponsive to conservative treatment. Various surgical methods, including mesh repair and sacral colpopexy, can effectively treat pelvic organ prolapse13–17. However, surgery carries risks such as mesh exposure, pain, and recurrence18–21, and it may not completely resolve all symptoms, particularly those related to sexual function and urinary tract symptoms22,23.

The diagnosis and management of UP still face several challenges, including delayed diagnosis, inadequate treatment, and insufficient patient awareness. These issues are influenced by socio-cultural, racial, and economic factors, as well as variations in health-seeking behaviors24–26. A study covering the period from 1990 to 2019 indicates that nearly 40% of women worldwide may experience uterine prolapse during their lifetime. With the global aging population, the incidence of uterine prolapse is expected to increase further in the coming decades27. A national cross-sectional study conducted in China reported a 9.6% prevalence of symptomatic uterine prolapse among adult women, with prevalence increasing progressively with age28. Regionally, the age-standardized prevalence in low Socio-Demographic Index (SDI) regions is 2.3 times higher than in high SDI regions, with the greatest disease burden observed in sub-Saharan Africa and South Asia29.

The treatment and long-term management of UP impose a substantial economic burden due to the considerable medical resources required. Although conservative management is typically more cost-effective than surgical intervention, surgery remains an indispensable option for numerous severe cases3. Furthermore, postoperative complications, including urinary dysfunction and mesh-related issues, may further escalate healthcare costs and patient burden30,31. Current disease burden studies primarily target high-mortality illnesses, with limited epidemiological data on UP, particularly in developing countries such as China. In addition, early stages of UP often go undetected or unreported, resulting in their neglect in public health policies and resource distribution. To our knowledge, no previous study has comprehensively compared the historical burden, future projections, age-specific patterns, and decomposition of uterine prolapse burden between China and the G20 aggregate using the most recent GBD 2023 dataset.

The G20 is an international forum composed of the governments of 204 major economies, including Argentina, Australia, Brazil, Canada, the European Union, France, Germany, India, Indonesia, Italy, Japan, Mexico, the Republic of Korea, and the Russian Federation32. The European Union is treated as a single entity. In this study, the G20 aggregate was used as a macro-level international benchmark to facilitate comparison between China's disease burden and broader global economic regions. Because China is a constituent member of the G20, the comparison should be interpreted as an assessment of China's relative position within the overall G20 burden rather than a comparison with an entirely independent external population. This study aimed to compare the historical burden, temporal trends, age-specific patterns, driving factors, and future projections of uterine prolapse in China and the G20 aggregate using data from the Global Burden of Disease 2023 study. The objective is to elucidate the population distribution, temporal trends, and regional variations of UP, and to assess the attributable burden. The findings may provide a scientific basis for national governments to formulate prevention strategies, optimize the allocation of medical resources, enhance awareness of female-specific health issues, promote early diagnosis and treatment, and integrate female pelvic floor health into chronic disease prevention and aging health promotion initiatives. A detailed summary of this comprehensive analytical framework is provided in Figure 1.

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Protocol

Ethical Approval and Consent to Participate

This study was an analysis of existing, publicly available, and de-identified summary data from the Global Burden of Disease Study 2023 (GBD 2023). As the research did not involve direct interaction with human or animal subjects, the collection of new primary data, or access to any individually identifiable information, it was deemed exempt from the requirement for formal approval by an institutional review board (IRB) or ethics committee. The study was conducted in accordance with the principles outlined in the Declaration of Helsinki. The use of GBD data complies with its terms of use and data access policies.

Data Sources

This study is a secondary analysis of de-identified aggregated data published in the GBD 2023 study. The GHDx online query tool (https://ghdx.healthdata.org/gbd–results–tool) was employed to obtain disease burden data related to uterine prolapse in China and G20 countries from 1990 to 2023. Uterine prolapse cases were identified using the GBD 2023 cause hierarchy under the category “Genital prolapse,” which encompasses uterine prolapse. The corresponding ICD-10 codes mapped by the Institute for Health Metrics and Evaluation (IHME) include N81.2 (incomplete uterovaginal prolapse), N81.3 (complete uterovaginal prolapse), N81.4 (unspecified uterovaginal prolapse), and N81.9 (unspecified female genital prolapse). The GBD Cause ID for uterine prolapse were obtained directly from the GBD Results Tool, and disease burden estimates were extracted following the standardized GBD cause definitions and coding procedures. The disease burden analysis in GBD 2023 estimated the incidence, prevalence, death, YLDs, YLLs, and DALYs. The study examined epidemiological traits by analyzing geographic and age-group differences in disease burden over time and space. Data were processed using EAPC (Estimated Annual Percentage Change) analysis, the joinpoint model, the ARIMA model, the BAPC model, and decomposition analysis. The specific calculation methods are consistent with those reported in previous studies27,33,34,35.

EAPC Analysis

EAPC was a widely accepted measure to quantify the trend of ASRs over specific time intervals, and it was calculated based on the regression model fitted to the natural logarithm of the rates. The regression model was defined as: ln (rate)=α+βx+ε, and EAPC was calculated as 100×(exp(β)- 1). The 95% confidence interval (CI) was also determined by the linear regression model.

where ln (rate)is the natural logarithm of ASR, x denotes calendar year, α is the intercept, β represents the slope coefficient, and ε is the error term. If EAPC > 0 and its 95% CI is also > 0, there is a significant upward trend. If EAPC < 0 and 95% CI is < 0, there is a significant downward trend. If the 95% CI includes 0, the trend is not statistically significant; that is, the change was stable over time.

Joinpoint Analysis

The Joinpoint regression model was employed to calculate the annual percentage change (APC) and the average annual percentage change (AAPC), along with their respective 95% confidence intervals (CI), in order to determine the long-term trends of significant changes for UP in China and G20 countries from 1990 to 2023. This model establishes segmented regression based on the temporal characteristics of disease distribution, dividing the time range into different intervals, each of which undergoes trend fitting and optimization, effectively avoiding the subjectivity of typical trend analyses based on linear trends. The trend direction was determined based on the AAPC calculated from the final model; specifically, when the 95% CI does not include 0, it means that the trend is significant, AAPC > 0 indicates an increasing trend, and APPC < 0 indicates a decreasing trend.

BAPC Analysis

The APC model was extended using Bayesian statistical methods (BAPC), which integrate historical data patterns, uncertainty factors, and prior knowledge to improve prediction accuracy and robustness, thereby effectively handling noise and ambiguities in the data. BAPC projections for 2024 to 2038 were created using a smoothing parameter of 5 for age, period, and cohort effects, with standardized weights based on the world standard population.

ARIMA Analysis

The ARIMA model is a popular model in econometrics that can analyze the behavior of stationary and non-stationary time series as well as the impact of plans and policies on specific outcomes over time34. In the ARIMA (p, d, q) model, 'p' represents the count of autoregressive terms, 'd' signifies the order of differencing, and 'q' denotes the count of moving average terms. The specific calculation methods are consistent with those reported in previous studies. For each prediction, the predicted Value, upper limit, lower limit and 95%CI of each year and the performance parameters of the prediction model will be obtained (_eval.csv file). The ARIMA time series model was used to predict the next 27 years (2024-2050), and the model performance was evaluated. Model performance was assessed using standard forecasting diagnostics, including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), residual autocorrelation analysis, and goodness-of-fit evaluation. Residual plots demonstrated no major violations of model assumptions, supporting the suitability of the selected forecasting models for burden projection. Auto.arima was used to automatically select the optimal ARIMA(p,d,q) parameters and AICc (modified AIC) was used to select the best model. Based on the bootstrap prediction method, the prediction value and confidence interval were calculated by simulating the possible future paths.

Decomposition analyses

We performed a Das Gupta decomposition​ (Kitagawa–Das Gupta factorization) of the change in absolute counts between 1990 and 2023. The analysis was primarily decomposed into three components: the aging effect (changes in age structure), the population size effect (growth in total population), and the epidemiological change effect (changes in age-specific rates). The data column names are as follows: 'overall_difference' denotes the total change; 'a_effect' indicates the aging effect; 'p_effect' represents the population size effect; 'r_effect' signifies the epidemiological change effect; 'a_percent', 'p_percent', and 'r_percent' denote the percentage contribution of each respective effect; 'val_1990' and 'val_2023' represent the number of cases in 1990 and 2023, respectively; and 'change' refers to the difference in 'diff1'. The magnitude of each factor on the resulting plot illustrates its proportional influence, with positive and negative values indicating an increase or mitigation effect, respectively. Black dots on the plot serve as markers for the total change.

Analysis software

Data statistical analysis and visualization in this study were performed using the R software package and the Joinpoint software program (Refer to Table of Materials for details). A p-value < 0.05 was considered statistically significant.

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Results

Overall trends of uterine prolapse DALYs, Deaths, Incidence, Prevalence, YLDs, YLLs in China and G20 Countries

Overall, the number of uterine prolapse cases in China increased from 1051,197.19 in 1990 (95% UI: 871,473.64–1254,781.07) to 2218,831.76 in 2023 (95% UI: 1829,927.18–2655,003.13), representing a growth of 111.08%. During the same period, the cumulative amount of prevalence in China increased by 126.06% year-on-year. In 2023, a total of 19,054,548.16 women were affect...

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Discussion

This study conducted a comprehensive analysis of the changes in the disease burden of uterine prolapse in China and the G20 countries from 1990 to 2023, employing age stratification and decomposition analysis. To predict and analyze future disease burden, the ARIMA time series model and the Bayesian age-period-cohort (BAPC) model were utilized to predict and analyze the future disease burden. The findings revealed both similarities and notable differences. The disease burden (such as the numbers of prevalence and DALYs) ...

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Disclosures

The authors declare that they have no conflicts of interest to report.

Acknowledgements

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data Availability

The data used in this study were sourced from the publicly available Global Burden of Disease Study 2023 (GBD 2023). The authors gratefully acknowledge the work of the Institute for Health Metrics and Evaluation (IHME) and the entire GBD collaborative network in producing and sharing this invaluable resource. All raw data used in this study have been submitted as supplementary materials.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
ARIMA modelR (forecast package)–Time series forecasting of disease burden
BAPC modelR (BAPC package)Version 0.0.36 (relies on INLA (v24.11.25).)Bayesian age-period-cohort prediction
GBD 2023 dataGlobal Health Data Exchange–Source of uterine prolapse burden data
Joinpoint Regression ProgramNational Cancer InstituteVersion 5.1.0.0Trend analysis using APC and AAPC
R softwareR Foundation for Statistical ComputingVersion 4.4.1Statistical analysis and visualization

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Age-Standardized RatesDisability Adjusted Life YearsYears Lived With DisabilityYears Of Life LostJoinpoint RegressionARIMA ModelBayesian Age Period CohortPopulation Aging
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