Abstract
Purpose
Pelvic organ prolapse (POP) is a disorder significantly compromising the physical, mental, and social health of females. The economic POP burden within women of childbearing age (WCBA) is increasing substantially. It is important to understand the worldwide implications of POP to anticipate future trends and formulate appropriate prevention and management strategies for this population.
Patients and Methods
Leveraging 2021 Global Burden of Disease (GBD) data, this study examines the 1990–2021 global, regional, and national POP burden within WCBA. Incidence and disability-adjusted life years (DALYs) were primary measures, alongside the sociodemographic index (SDI). Health disparities were examined based on the slope index of inequality (SII) and the Concentration Index. The main determinants of variation in disease burden (DB) were found via decomposition analysis. Future trends were forecast utilizing an autoregressive (AR) integrated moving average (ARIMA) model. Data were processed and visualized using R (v4.4.1; ggplot2), with final editing in Adobe Illustrator (CS5).
Results
Globally, the POP burden among WCBA declined in 2021 in comparison to 1990. Age-specific analyses demonstrated that incidence and DALY rates rose as age increases, peaking in the 45–49 cohort. Inequality analysis revealed a narrowing gap between regions with high and low SDI. Nevertheless, lower-SDI countries and regions continued to experience a disproportionately greater burden. Decomposition analysis identified aging as the primary factor in high and high-middle SDI regions, and population rise as the predominant factor in low and low-middle SDI regions. Our ARIMA model projects a continuing downward trend in the prevalence of POP among WCBA.
Conclusion
Although the POP burden among WCBA worldwide has decreased over the past 32 years, substantial health inequities persist, particularly in less developed countries and regions. Therefore, interventions targeting POP in WCBA are imperative to support progress toward the health-related Sustainable Development Goals articulated by the World Health Organization (WHO).
Keywords: pelvic organ prolapse, POP, women of childbearing age, WCBA, disease burden, DB, disability-adjusted life years, DALYs, 2021 global burden of disease, GBD
Introduction
Pelvic organ prolapse (POP) involves the downward movement of female pelvic organs like the bladder, uterus, and rectum from their standard positions. This descent primarily arises from the weakening or damage of supportive structures, such as the pelvic floor muscles and ligaments. Consequently, these organs possibly protrude into the vaginal canal or extend beyond the vaginal introitus, leading to urinary and defecatory dysfunction, sexual dysfunction, and damage to mental health.1 The global POP incidence among women is increasing annually, currently accounting for 20% to 78% of all gynecological conditions.2–5 As of 2021, the prevalence of POP reached 140 million cases worldwide,6 with women of childbearing age (WCBA) comprising over 30% of these cases, and this proportion is rising year by year.7,8 The WCBA population is primarily affected by factors like obesity, number of deliveries, and delivery methods in POP.9–11 The associated physiological, psychological, and social challenges are becoming increasingly severe, posing significant challenges to public health. Understanding its burden is crucial and carries significant implications for future population structures and social development.
The Global Burden of Disease (GBD) facilitates the assessment of the health consequences of diseases and injuries.12,13 By analyzing its data, scholars and policymakers gain insights into the prevalence, incidence, death, and disability-adjusted life years (DALYs) of various diseases.14,15 Although the impact of POP on health worldwide has been widely recognized, previous research within the GBD framework has predominantly focused on the general population or elderly women,6,16,17 thereby neglecting the specific needs of women of childbearing age. There is a notable deficiency in specialized analyses that incorporate health inequality assessments and decomposition methods to elucidate the factors driving disparities in POP-related burdens. Addressing this gap is essential for informing targeted interventions and promoting equitable maternal health outcomes. Therefore, our study leveraged 2021 GBD data to quantify the POP burden within WCBA. By applying methodologies like health inequality assessment, decomposition analysis, as well as autoregressive (AR) integrated moving average (ARIMA) modeling, the study examined regional disparities, assessed absolute and relative health inequalities across countries and regions, evaluated how aging, population increase, and epidemiological transitions influence DB, and forecasted future trends. The objective was to provide a scientific foundation for academics and policymakers to formulate effective countermeasures.
Methods
Data Sources and Case Definition
This study analyzed 2021 GBD data related to POP in WCBA via the Institute for Health Metrics and Evaluation (IHME) (https://ghdx.healthdata.org/gbd-2021/). GBD offers 1990–2021 epidemiological estimates for 371 diseases and injuries compiled from 21 GBD regions and 204 countries and regions. To address biases from heterogeneity in data sources, case definitions, and measurement approaches, advanced statistical models like MR-BRT and DisMod-MR 2.1 were leveraged to harmonize estimates across regions, age groups, sexes, and time periods. Standardization and adjustment procedures were implemented to minimize heterogeneity-related influences on the results.
This was a cross-sectional analysis. Estimates of POP incidence and DALYs in WCBA, including rates and 95% uncertainty intervals (UIs), were obtained via the Global Health Data Exchange (GHDx) to ensure objective and accurate assessment of DB.
POP was defined as per the World Health Organization (WHO) and the International Classification of Diseases, 10th Revision (ICD-10). For genital prolapse, the relevant codes are N81.0, N81.1, N81.2, N81.3, N81.4, N81.5, N81.6, N81.7, N81.8, and N81.9 from ICD-10 (2019) (https://ghdx.healthdata.org/record/ihme-data/gbd-2021-cause-icd-code-mappings).
Sociodemographic Index (SDI)
SDI, a composite indicator of social development introduced by IHME in 2015, reflects the close relation of societal advancement to health. SDI represents the geometric mean of per capita income over time, the average education of the population aged 15 and older, and the fertility rate among people under 25. To create a score scale with 0–100, SDI values in the GBD 2021 were multiplied by 100. The combination of minimal income and education with a maximum fertility rate is denoted by 0, whereas 100 reflects the utmost income and education, as well as the minimum fertility rate. The SDI is quantified on a scale from 0 to 1, with higher values indicating greater levels of social and economic advancement. As per GBD 2021,12,204 countries and regions were split into low, low-middle, middle, high-middle, and high SDI regions. The classification of SDI values is delineated as follows: high SDI (SDI≥0.805129), medium-high SDI (0.689504≤SDI<0.805129), medium SDI (0.607679≤SDI<0.689504), low-medium SDI (0.454743≤SDI<0.607679), and low SDI (SDI<0.454743).
DALY
DALY, a standard metric for evaluating DB, quantifies the overall number of years forfeited in healthy living owing to illness from beginning to end. DALYs are derived as follows:
DALYs = years of life lost (YLLs) + years lived with disability (YLDs) (1).
YLLs were assigned a value of zero because GBD calculations do not directly attribute mortality to POP. Accordingly, DALYs were equivalent to YLDs.12 To derive sampling-level estimates, all computations were conducted with 500 iterations. Compared with earlier GBD iterations, GBD 2021 reduced the number of iterations from 1000 to 500, as simulation tests confirmed that this adjustment did not compromise the accuracy or reliability of our results. The concluding estimates were the average of the 500 sampling estimates, with 95% UIs defined by the 2.5th and 97.5th percentiles. Uncertainty was systematically incorporated throughout the estimation.12
Estimated Annual Percentage Change (EAPC) and Percentage
EAPC is widely recognized and applied for temporal trend quantification in epidemiological research, particularly in evaluating changes in measures like incidence and frequency over defined periods.18 The present study assessed variations in the incidence and DALYs of POP. The calculation of the EAPC for 1990–2021 POP incidence and DALYs was performed as follows: first, time is used as an independent variable to fit the natural logarithm rate. The observed value is transformed using the natural logarithm, plotted on a straight line, with calculations derived from the slope.19
y = α + βx + ε (2)
EAPC = 100 × (exp(β) − 1) (3)
x represents the calendar year, and y corresponds to the natural logarithm of the rate (eg, incidence or DALYs). α, β, and ε were the intercept, slope, and random error term, respectively. The 95% confidence intervals (CIs) for EAPC were obtained from the fitted model. Trend results were interpreted using 95% CIs: a growth trajectory was an indication if the bottom boundary of the 95% CI was above 0, a decreasing tendency was an indication if the highest boundary was less than 0, and no statistically significant trend change was inferred if the 95% CI encompassed 0.20 Furthermore, the study employed percent alteration to measure the alterations in incidence and DALYs covering the years 1990 to 2021:
Percent alteration = (Cases reported in 2021 − Cases reported in 1990) / Cases reported in 1990 (4).
Since open data were utilized, ethical approval was unnecessary. Our study followed the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) for cross-sectional research.
Statistical Analysis
The count and frequency of incident cases and DALYs are utilized to evaluate the impact of POP. Estimates every 100,000 individuals represent the rate, while the counts of cases indicate the overall burden by showing total counts of cases. Indicators show their 95% UI. Suitable statistical models were employed for conducting analyses, with P values under 0.05 regarded as statistically meaningful. Subsequent sections will provide comprehensive explanations of each particular analysis technique, including Cross-country inequality analysis, decomposition analysis, and ARIMA models. All data sanitization, arithmetic, and chart creation were executed via R 4.4.1. Visualization charts were produced through ggplot2, and final edits were made utilizing Adobe Illustrator CS 5.
Cross-Country Inequality Analysis
Complete and comparative inequalities in the impact of diseases were evaluated via the slope index of inequality (SII) and the Concentration Index as per WHO.21 These indices quantify the geographical variation in the burden of POP, facilitating a systematic health disparity assessment. SII was derived by performing a regression of DALY rates on SDI, using the median population value ordered by SDI. Trends in health inequalities were examined based on 1990–2021 data from 204 countries and regions. To address heterogeneity and limit bias, a robust regression model (rlm) was leveraged in place of standard linear regression (lm), enhancing resistance to outliers and improving the reliability of estimates in the presence of extreme values. The Concentration Index was derived by matching the cumulative DALY distribution with the cumulative population share ranked by SDI, with the area under the Lorenz curve determined via numerical integration.
Decomposition Analysis
To investigate temporal shifts in the 1990–2021 POP burden, decomposition analysis was performed using Das Gupta.22 This approach attributed the changes to population aging, growth, and shifts in disease patterns. By breaking down the overall burden into these core components, the approach presents how demographic expansion and changing epidemiology influenced trends. In contrast to conventional regression methods that mainly identify relationships between variables, decomposition analysis revealed the contribution of every factor to variations. The drivers behind the global trends of POP among WCBA were precisely identified.
ARIMA Model
The main model leveraged in time-series analysis incorporates AR, differencing (I), and moving average (MA) elements to capture temporal patterns utilizing historical data.23 In its p, d, q framework, p stands for AR term number, d for differencing level, and q for MA term number. Data were first transformed to the stationary stage, which was determined via the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test. The time series modeling employed an automatic ARIMA algorithm, with the optimal model selected based on the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) across all combinations of (p, d, q). Additionally, the Ljung-Box test for white noise was conducted on the residuals of the model to evaluate the adequacy of the model fit. The Ljung-Box Q test was administered to check whether model residuals fit a white noise distribution. The goodness-of-fit of various models was compared via AIC and BIC, with the model displaying the lowest criterion values being selected. The robustness of the residuals was verified through the Ljung-Box test. An ARIMA model was identified as the best linear predictor of short-term time-series outcomes when the residuals conformed to random distribution characteristics, indicating that the residuals conformed to a white noise distribution.
Results
Global Level
A significant rise was noted in incident cases and DALYs arising from POP among WCBA worldwide. Specifically, incident cases rose from 3,996.2 thousand (95% UI: 3,066.9–5,058.1 thousand) in 1990 to 5,629.9 thousand (95% UI: 4,254.7–7,148.9 thousand) in 2021, an increase of 40.9%. Likewise, DALYs rose from 73.4 thousand (95% UI: 36.1–154.4 thousand) in 1990 to 101.96 thousand (95% UI: 49.6–212.8 thousand) in 2021, a 38.9% rise (Tables 1 and S1, Figures 1 and 2). However, the incidence and DALY rates of POP in WCBA declined over the same period worldwide, with EAPCs being −0.21 (95% CI: −0.26 to −0.15) and −0.38 (95% CI: −0.48 to −0.28).
Table 1.
The Incidence of Pelvic Organ Prolapse Cases and Rates Among WCBA in 1990 and 2021, and the Trends from 1990 to 2021
| Location | Incidence Cases | Incidence Rates | ||||
|---|---|---|---|---|---|---|
| 1990 (95%UI) | 2021 (95%UI) | Percentage Change (100%) |
1990 (95%UI) | 2021 (95%UI) | EAPC (95% CI) | |
| Global | 3996209.86 (3066924.39,5058089.54) | 5629892.91 (4254669.02,7148872.62) | 0.409 | 298.81 (229.33,378.21) | 288.88 (218.32,366.82) | −0.21 (−0.26,-0.15) |
| Low SDI | 519818.77 (392573.22,671251.77) | 920784.57 (691658.53,1199361.53) | 0.771 | 465.43 (351.5,601.02) | 335.67 (252.14,437.23) | −1.08 (−1.15,-1) |
| Low-middle SDI | 1425651.17 (1103815.98,1818284.37) | 1835986.89 (1408897.22,2359250.74) | 0.288 | 522.38 (404.45,666.24) | 362.65 (278.29,466.01) | −1.3 (−1.36,-1.23) |
| Middle SDI | 1076123.09 (838369.99,1357426.57) | 1617771.93 (1220972.23,2054997.93) | 0.503 | 240.71 (187.53,303.63) | 261.58 (197.42,332.27) | 0.18 (0.11,0.25) |
| High-middle SDI | 476168.61 (353713.59,617326.52) | 667067.46 (480569.15,886518.13) | 0.401 | 171.44 (127.35,222.26) | 218.62 (157.5,290.54) | 0.94 (0.8,1.07) |
| High SDI | 495304.35 (359034.28,649681.54) | 583979.6 (426919.35,767209.99) | 0.179 | 218.48 (158.37,286.57) | 240.16 (175.57,315.52) | −0.09 (−0.23,0.06) |
| Andean Latin America | 44945.72 (32392.82,60316.24) | 71667.76 (50575.34,98247.19) | 0.595 | 473.9 (341.54,635.96) | 410.65 (289.79,562.94) | −0.52 (−0.6,-0.43) |
| Australasia | 9191.35 (6187.48,13186.33) | 14510.54 (9671.65,21205.11) | 0.579 | 171.24 (115.28,245.67) | 201 (133.97,293.74) | 0.58 (0.46,0.7) |
| Caribbean | 39395.78 (28440.34,54068.85) | 45912.45 (32570.25,63460.37) | 0.165 | 422.63 (305.11,580.05) | 381.69 (270.77,527.57) | −0.33 (−0.41,-0.25) |
| Central Asia | 16715.97 (10512.32,25139.06) | 32032.15 (19526.99,48718.52) | 0.916 | 99.64 (62.66,149.84) | 132.01 (80.47,200.77) | 1.51 (1.3,1.72) |
| Central Europe | 60184.45 (38734.68,88849.73) | 70158.5 (46176.34,99961.64) | 0.166 | 195.96 (126.12,289.3) | 272.42 (179.3,388.15) | 0.95 (0.65,1.25) |
| Central Latin America | 118799.97 (86128.39,155486.6) | 203348.93 (145154.69,273050.11) | 0.712 | 283.44 (205.49,370.97) | 298.19 (212.85,400.4) | −0.32 (−0.5,-0.15) |
| Central Sub-Saharan Africa | 34793.84 (24976.86,46526.14) | 75506.46 (53111.78,102092.96) | 1.17 | 281.47 (202.05,376.38) | 231.24 (162.66,312.66) | −0.56 (−0.67,-0.45) |
| East Asia | 371320.21 (275864.03,488782.8) | 502234.67 (353681.6,686080.71) | 0.353 | 111.38 (82.75,146.61) | 151.78 (106.88,207.33) | 1.21 (0.98,1.44) |
| Eastern Europe | 95810.38 (67892.44,129312.88) | 111547.59 (77540.39,150419.33) | 0.164 | 173.27 (122.78,233.86) | 231.22 (160.73,311.8) | 1.48 (1.3,1.66) |
| Eastern Sub-Saharan Africa | 121639.51 (89070.74,159052.77) | 259192.75 (190760.81,339398.64) | 1.131 | 281.9 (206.42,368.6) | 242.01 (178.11,316.9) | −0.4 (−0.43,-0.36) |
| High-income Asia Pacific | 30863.56 (20689.61,43640.06) | 26415.12 (17113.96,38722.66) | −0.144 | 67.48 (45.23,95.41) | 69.44 (44.99,101.8) | 0.1 (−0.07,0.27) |
| High-income North America | 165395.91 (118907.69,221663.74) | 174627.56 (121063.21,236072.05) | 0.056 | 222.4 (159.89,298.07) | 207.84 (144.08,280.96) | −1.09 (−1.48,-0.7) |
| North Africa and Middle East | 307580.94 (219388.21,422070.88) | 679117.06 (479129.69,932254.87) | 1.208 | 393.7 (280.82,540.25) | 426.19 (300.69,585.05) | 0.38 (0.32,0.43) |
| Oceania | 2015.39 (1384.19,2809.78) | 4950.23 (3272.72,7086.68) | 1.456 | 129.7 (89.08,180.82) | 142.62 (94.29,204.17) | 0.33 (0.3,0.36) |
| South Asia | 1622124.95 (1267976.27,2071697.1) | 1995843.21 (1528655.97,2556112.43) | 0.23 | 636.4 (497.46,812.77) | 403.9 (309.36,517.29) | −1.66 (−1.75,-1.57) |
| Southeast Asia | 141147.06 (102838.38,187396.42) | 240160.66 (169620.92,326807.19) | 0.701 | 117.44 (85.56,155.92) | 131.09 (92.59,178.39) | 0.48 (0.38,0.59) |
| Southern Latin America | 37492.83 (25260.49,52151.29) | 51018.74 (34517.21,71514.97) | 0.361 | 302.53 (203.83,420.82) | 292.71 (198.04,410.3) | −0.16 (−0.2,-0.12) |
| Southern Sub-Saharan Africa | 31825.8 (23963.01,40445.08) | 52707.7 (38714.8,68707.86) | 0.656 | 239.43 (180.28,304.28) | 242.76 (178.31,316.45) | 0 (−0.03,0.03) |
| Tropical Latin America | 272542.63 (214177.72,346698.89) | 289086.94 (229977.64,368386) | 0.061 | 683.22 (536.91,869.12) | 476.94 (379.42,607.77) | −1.55 (−1.68,-1.42) |
| Western Europe | 302078.85 (215903.9,410110.03) | 342074.41 (243189.89,465224.31) | 0.132 | 316.15 (225.96,429.22) | 367.11 (260.99,499.28) | 0.47 (0.44,0.51) |
| Western Sub-Saharan Africa | 170344.73 (129365.49,218378.02) | 387779.47 (291060.81,499631.79) | 1.276 | 390.66 (296.68,500.81) | 323.45 (242.77,416.74) | −0.45 (−0.54,-0.36) |
Abbreviations: WCBA, Women of Childbearing Age; EAPC, Estimated Annual Percentage Change; CI, Confidence Intervals; UI, Uncertainty Intervals; SDI, Socio-Demographic Index.
Figure 1.
Temporal trend of pelvic organ prolapse burden in WCBA in global and 5 territories. (A) Percentage change in cases of incident and DALYs in 1990 and 2021. (B) The EAPC of incidence and DALY rates from 1990 to 2021. (C) and (D) The rates of incidence and DALYs from 1990 to 2021.
Abbreviations: WCBA, Women of Childbearing Age; EAPC, Estimated Annual Percentage Change; DALYs, Disability-Adjusted Life Years.
Figure 2.
Temporal trend of pelvic organ prolapse burden in WCBA in regions. (A) Incidence rate per 100,000 population in 1990 and 2021. (B) Percentage change in cases of incidence and DALYs in 1990 and 2021. (C) EAPC of rates of incidence and DALYs from 1990 to 2021.
Abbreviations: WCBA, Women of Childbearing Age; EAPC, Estimated Annual Percentage Change; DALYs, Disability-Adjusted Life Years.
SDI Regional Level
In 2021, the low-middle SDI region reported the greatest absolute numbers of incident POP cases and DALYs among WCBA: 1.836 million cases (95% UI: 1.409–2.359 million) and 37,000 DALYs (95% UI: 17,000–76,000) (Tables 1 and S1, Figure 1). Ranking by absolute counts, the order was: low-middle, middle, low, high-middle, and high SDI. A substantial relative rise of nearly 72–77% was noted in the low SDI region, whereas the high SDI region had only a minimal change of about 12–18%. From 1990 to 2021, the high-middle SDI region recorded the quickest rise in incidence and DALY rates, with EAPCs being 0.94 (95% CI: 0.80–1.07) and 0.91 (95% CI: 0.77–1.04). Although the low-middle SDI region had the greatest POP burden, the high-middle SDI region had the fastest growth rate.
GBD Regional Level
Across most GBD regions, the absolute numbers of POP incidence and DALYs within WCBA grew over time. In contrast, the high-income Asia Pacific region exhibited reductions in incidence and DALYs, whereas high-income North America reported a decrease in DALYs alone. Regarding rates, nearly half of the regions experienced sustained rises during the 32 years. Central Asia and Eastern Europe showed the most pronounced growth, with EAPCs of 1.51 (95% CI: 1.30–1.72) and 1.47 (95% CI: 1.30–1.66) for incidence, and 1.60 (95% CI: 1.38–1.81) and 1.40 (95% CI: 1.24–1.57) for DALYs. By contrast, South Asia and Latin America displayed notable declines in incidence rates, with EAPCs being −1.66 (95% CI: −1.75 to −1.57) and −1.55 (95% CI: −1.68 to −1.42). Similarly, DALYs decreased substantially in high-income North America (EAPC −2.37, 95% CI: −3.36 to −1.37) and South Asia (EAPC −1.83, 95% CI: −1.98 to −1.68) (Tables 1 and S1, Figures 2 and 3).
Figure 3.
Temporal trend of pelvic organ prolapse burden in WCBA globally. (A) Percentage change in Incidence cases across 204 countries in 1990 and 2021. (B) EAPC in Incidence rates across 204 countries from 1990 to 2021.
Abbreviations: WCBA, Women of Childbearing Age; EAPC, Estimated Annual Percentage Change.
Country Level
In 2021, India recorded the most incident POP cases among WCBA: 1,403,005.37 cases (95% UI: 1,078,130.08–1,793,803.06), followed by China (485,663.78, 95% UI: 342,596.88–661,497.35) and Pakistan (336,858.52, 95% UI: 264,437.78–423,576.59). From 1990 to 2021, over 80% of countries experienced rises in incidence and DALYs. The most pronounced growth was observed in high-SDI countries Qatar and the UAE, with rises of 535%-631%, far exceeding the modest changes in other high-SDI regions. Conversely, nearly 15% of countries recorded declines, including the Virgin Islands and Puerto Rico, where reductions ranged from −41% to −51%. Regarding rates, nearly 46% of countries exhibited upward trends over the 32 years. Mongolia and Georgia recorded the largest rises, with EAPCs being 2.46 (95% CI: 2.03–2.89) and 1.89 (95% CI: 1.68–2.10) for incidence, and 2.49 (95% CI: 2.02–2.96) and 2.05 (95% CI: 1.80–2.29) for DALYs. In contrast, more than half of the countries demonstrated declining incidence rates, with Nepal and India showing the steepest reductions, at −2.35 (95% CI: −2.70 to −2.00) and −1.87 (95% CI: −1.98 to −1.75) (Tables S2 and S3, Figure 3).
Age Patterns
Over the past 32 years, detailed temporal trends in POP cases among WCBA worldwide and across the five SDI regions are provided in Tables 2 and S4, Figures 4 and S1. The percentage change in POP incidence and DALYs of WCBA rose with age worldwide. The 15–19 cohort displayed the smallest percentage change, with a 26% rise in incidence and a 3% decline in DALYs, whereas the 45–49 group demonstrated the most substantial rise, with a 70% rise in incidence and a 58% rise in DALYs, markedly exceeding the changes observed in the youngest cohort. Across the five SDI regions, distinct age-related patterns were noted. For the low-SDI region, the 15–19 group exhibited the greatest percentage rise (127%), followed by the 45–49 cohort (90%). In the low-middle SDI region, the 45–49 group showed the largest rise (52%), followed by the 40–44 and 15–19 groups, with rises of 39% and 38%. In regions with middle-to-high SDI, the percentage change in POP cases progressively rose as age increased. In the middle-SDI region, the percentage change ranged from −7% in 15–19 to 99% in 45–49. As socioeconomic development advances, the extent of the change has dropped in every age cohort. Specifically, the percentage change in the 45–49 cohort decreased from 99% in the middle-SDI region to 39% in the high-SDI region. Notably, in the low-SDI region, the 15–19 group experienced the most pronounced rise, reaching 127%, the highest percentage change across age cohorts and SDI regions. Trend analysis of 1990–2021 POP incidence globally revealed an upward trajectory in the 15–19 group, with an EAPC being 0.06 (95% CI: 0 to 0.12), while downward trends were evident across the 20–49 cohort. Therefore, globally, the incidence and DALYs linked to POP within WCBA have risen with age over the past 32 years, with the sharpest growth in the 45–49 cohort (Tables 2 and S4, Figures 4 and S1).
Table 2.
The Incidence of Pelvic Organ Prolapse Cases and Rates Among WCBA in 1990 and 2021, and the Trends in Age Patterns from 1990 to 2021
| Location | Age | Incidence Cases | Incidence Rates | ||||
|---|---|---|---|---|---|---|---|
| 1990 (95%UI) | 2021 (95%UI) | Percentage Change (100%) |
1990 (95%UI) | 2021 (95%UI) | EAPC (95% CI) | ||
| Global | 15-19 years | 36614.67 (12819.97,73973.81) | 46160.16 (16942.29,93086.62) | 0.26 | 14.33 (5.02,28.95) | 15.2 (5.58,30.66) | 0.06 (0,0.12) |
| Global | 20-24 years | 202475.86 (131958.29,295791.15) | 219628.23 (140451.48,316258.94) | 0.08 | 82.94 (54.05,121.16) | 74.77 (47.81,107.66) | −0.96 (−1.22,-0.7) |
| Global | 25-29 years | 376470.26 (231484.19,594526.9) | 403773.65 (239516.08,654049.92) | 0.07 | 171.05 (105.17,270.12) | 138.76 (82.31,224.77) | −1.37 (−1.63,-1.12) |
| Global | 30-34 years | 556789.45 (394827.86,761908.26) | 672988.81 (471296.26,942430.49) | 0.21 | 292.88 (207.68,400.77) | 225.13 (157.66,315.27) | −1.09 (−1.22,-0.97) |
| Global | 35-39 years | 753538.21 (399609.47,1229324.01) | 995174.27 (531512.51,1606553.98) | 0.32 | 434.43 (230.38,708.73) | 358.23 (191.33,578.31) | −0.69 (−0.78,-0.61) |
| Global | 40-44 years | 947747.55 (634930.22,1299284.05) | 1389008.01 (929732.14,1906122.39) | 0.47 | 675.87 (452.79,926.57) | 559.88 (374.76,768.32) | −0.55 (−0.61,-0.5) |
| Global | 45-49 years | 1122573.86 (633333.45,1746141.33) | 1903159.79 (1087427.55,2962162.68) | 0.7 | 986.44 (556.53,1534.39) | 807.65 (461.47,1257.06) | −0.43 (−0.55,-0.3) |
| High-middle SDI | 15-19 years | 3615.69 (1303.69,7182.35) | 2407.11 (840.66,4882.8) | −0.33 | 7.63 (2.75,15.17) | 6.99 (2.44,14.18) | −0.2 (−0.34,-0.06) |
| High-middle SDI | 20-24 years | 11574.7 (6159.35,18911.54) | 7981.66 (4293.95,13030.3) | −0.31 | 24.04 (12.79,39.28) | 22.42 (12.06,36.61) | −0.45 (−0.69,-0.21) |
| High-middle SDI | 25-29 years | 16893.29 (8519.24,29787.37) | 13410.54 (6702.99,23850.99) | −0.21 | 36.93 (18.62,65.12) | 33.27 (16.63,59.17) | −0.66 (−0.81,-0.51) |
| High-middle SDI | 30-34 years | 39445.56 (25144.27,59625.28) | 41795.62 (26450.69,62366.03) | 0.06 | 94.48 (60.23,142.82) | 81.17 (51.37,121.12) | −0.34 (−0.43,-0.25) |
| High-middle SDI | 35-39 years | 75055.95 (40239.89,125224.21) | 88272.9 (47344.27,149076.72) | 0.18 | 190.03 (101.88,317.05) | 178.15 (95.55,300.86) | 0.06 (−0.11,0.22) |
| High-middle SDI | 40-44 years | 133841.1 (92967.15,181081.23) | 181007.83 (125275.52,245229.13) | 0.35 | 435.34 (302.39,588.99) | 397.65 (275.21,538.74) | −0.08 (−0.3,0.14) |
| High-middle SDI | 45-49 years | 195742.33 (116869.38,298546.7) | 332191.77 (200021.91,501239.51) | 0.7 | 798.38 (476.68,1217.69) | 688.79 (414.74,1039.31) | −0.22 (−0.42,-0.02) |
| High SDI | 15-19 years | 2702.99 (937.46,5540.94) | 2243.49 (731.84,4655.44) | −0.17 | 8.48 (2.94,17.39) | 7.71 (2.52,16) | −0.55 (−0.72,-0.38) |
| High SDI | 20-24 years | 10178.16 (5436.38,16502.77) | 8511.48 (4658.65,13471.7) | −0.16 | 30.31 (16.19,49.15) | 27 (14.78,42.74) | −0.52 (−0.91,-0.13) |
| High SDI | 25-29 years | 16899.23 (7905.6,30881.38) | 15197.83 (7318.97,27129.23) | −0.1 | 47.14 (22.05,86.15) | 43.98 (21.18,78.5) | −0.49 (−1.04,0.07) |
| High SDI | 30-34 years | 41625.74 (25682.29,62782.74) | 40375.96 (25643.34,60050.89) | −0.03 | 117.31 (72.38,176.93) | 107.86 (68.5,160.42) | −1.04 (−1.55,-0.53) |
| High SDI | 35-39 years | 79630.1 (41833.04,132502.27) | 80975.75 (43268.65,135389.33) | 0.02 | 238.13 (125.1,396.25) | 213.47 (114.07,356.92) | −1.29 (−1.81,-0.77) |
| High SDI | 40-44 years | 146568.41 (99983.63,200216) | 161393.9 (112319.62,216566.88) | 0.1 | 469.42 (320.22,641.24) | 439.63 (305.95,589.91) | −0.58 (−0.71,-0.46) |
| High SDI | 45-49 years | 197699.72 (114057.83,307831.92) | 275281.18 (166019.33,416030.31) | 0.39 | 782.45 (451.42,1218.33) | 766.67 (462.37,1158.66) | 0.09 (−0.2,0.38) |
| Low-middle SDI | 15-19 years | 13021.36 (4471.76,26749.39) | 17944.56 (6552.41,36464.32) | 0.38 | 22.16 (7.61,45.53) | 19.85 (7.25,40.34) | −0.47 (−0.53,-0.41) |
| Low-middle SDI | 20-24 years | 92397.93 (62678.34,133336.81) | 99436 (65838.79,143855.78) | 0.08 | 177.16 (120.18,255.66) | 114.28 (75.67,165.33) | −2 (−2.21,-1.79) |
| Low-middle SDI | 25-29 years | 179147.25 (111957.68,277384.41) | 191740.09 (116029.24,305295.62) | 0.07 | 399.14 (249.44,618.02) | 236 (142.81,375.76) | −2.4 (−2.66,-2.14) |
| Low-middle SDI | 30-34 years | 240333.83 (174470.35,322870.93) | 279969.67 (198705.51,383431.5) | 0.16 | 641.85 (465.95,862.28) | 378.81 (268.86,518.8) | −2.09 (−2.26,-1.91) |
| Low-middle SDI | 35-39 years | 291807.97 (154432.31,470871.6) | 360698.23 (189341.61,586117.39) | 0.24 | 910.93 (482.09,1469.91) | 541.7 (284.35,880.23) | −1.71 (−1.8,-1.62) |
| Low-middle SDI | 40-44 years | 303840.1 (196785.04,431405.78) | 421064.59 (272271.92,601446.11) | 0.39 | 1171.12 (758.48,1662.8) | 729.98 (472.02,1042.7) | −1.38 (−1.46,-1.31) |
| Low-middle SDI | 45-49 years | 305102.72 (167017.16,487514.68) | 465133.75 (249121.12,750686.58) | 0.52 | 1405.67 (769.48,2246.08) | 940.86 (503.92,1518.47) | −1.04 (−1.13,-0.96) |
| Low SDI | 15-19 years | 5591.89 (1913.59,11500.76) | 12697.61 (4438.34,26119.76) | 1.27 | 22.25 (7.62,45.77) | 20.59 (7.2,42.36) | −0.36 (−0.44,-0.28) |
| Low SDI | 20-24 years | 30016.52 (19418.95,45014.98) | 53815.06 (33665.68,78650.3) | 0.79 | 138.19 (89.4,207.24) | 102.05 (63.84,149.15) | −1.45 (−1.65,-1.25) |
| Low SDI | 25-29 years | 54555.48 (31791.08,88378.19) | 86804.32 (48846.15,146248.97) | 0.59 | 294.52 (171.63,477.12) | 197.06 (110.89,332.01) | −1.92 (−2.17,-1.67) |
| Low SDI | 30-34 years | 78867.91 (55106.39,110696.89) | 128816.84 (86037.35,183355.97) | 0.63 | 518.49 (362.28,727.75) | 347.04 (231.79,493.98) | −1.6 (−1.79,-1.42) |
| Low SDI | 35-39 years | 104285.27 (53668.6,172091.68) | 173677.19 (86991.75,290698.31) | 0.67 | 809.38 (416.53,1335.64) | 545.03 (272.99,912.26) | −1.19 (−1.32,-1.06) |
| Low SDI | 40-44 years | 116113.47 (75335.05,164725.78) | 217412.79 (139670.67,310225.68) | 0.87 | 1171.07 (759.8,1661.35) | 832.36 (534.73,1187.7) | −0.91 (−1.01,-0.81) |
| Low SDI | 45-49 years | 130388.24 (70303.94,212218.98) | 247560.75 (130497.29,403640.64) | 0.9 | 1570.63 (846.87,2556.34) | 1192.06 (628.37,1943.62) | −0.69 (−0.78,-0.61) |
| Middle SDI | 15-19 years | 11657.04 (4316.79,23007.75) | 10840.91 (4128.06,21328.85) | −0.07 | 12.64 (4.68,24.95) | 12.34 (4.7,24.28) | −0.3 (−0.41,-0.18) |
| Middle SDI | 20-24 years | 58192.46 (37401.7,83899.33) | 49770.83 (31323.7,72609.4) | −0.14 | 65.88 (42.34,94.98) | 57.43 (36.14,83.78) | −1.15 (−1.47,-0.83) |
| Middle SDI | 25-29 years | 108770.74 (67300.44,170244.83) | 96420.78 (59435.28,156397.44) | −0.11 | 145.21 (89.84,227.27) | 106.43 (65.6,172.63) | −1.6 (−1.82,-1.39) |
| Middle SDI | 30-34 years | 156206.21 (112207.92,211126.6) | 181630.27 (127237.62,252126.37) | 0.16 | 260.17 (186.89,351.65) | 183.92 (128.84,255.31) | −1.19 (−1.29,-1.09) |
| Middle SDI | 35-39 years | 202318.8 (114579.08,323577.52) | 290888.31 (165142.36,461873.17) | 0.44 | 364.99 (206.7,583.74) | 317.4 (180.19,503.97) | −0.59 (−0.72,-0.45) |
| Middle SDI | 40-44 years | 246571.58 (167868.03,335536.97) | 407005.22 (277071.85,546278.08) | 0.65 | 583.52 (397.26,794.06) | 497.25 (338.51,667.41) | −0.53 (−0.6,-0.46) |
| Middle SDI | 45-49 years | 292406.26 (168685.59,450384.58) | 581215.62 (335338.35,897211.39) | 0.99 | 862.73 (497.7,1328.84) | 716.57 (413.43,1106.15) | −0.44 (−0.52,-0.37) |
Figure 4.
Temporal trends of the burden of pelvic organ prolapse among WCBA stratified by age patterns in different regions. (A) Incidence cases from 1990 to 2021 across seven age groups (15–49 years, with 5-year intervals) and five regions (from low to high SDI). (B) Percentage change and EAPC in incidence cases from 1990 to 2021 across seven age groups and five regions globally.
Abbreviations: WCBA, women of childbearing age; EAPC, estimated annual percentage change; SDI, sociodemographic index; GBD, Global Burden of Disease.
Relationship Between POP Burden and SDI
In 2021, POP incidence and DALY rates within WCBA were negatively correlated with SDI. As socioeconomic conditions improved, the overall DB demonstrated a gradual decline. The POP burden was slightly lower than expected worldwide. Across 21 GBD regions, a reduction in the POP burden was observed when SDI values ranged between 0.5 and 0.7. Tropical Latin America and South Asia displayed a greater burden than forecast, whereas high-income Asia Pacific, Central, East, and Southeast Asia demonstrated lower-than-expected burdens. At the national level, the Islamic Republic of Iran, Pakistan, and Brazil experienced higher burdens than projected, while Japan, Singapore, and the Republic of Korea recorded lower burdens (Figures 5 and S2-S4).
Figure 5.
The association between SDI and the incidence rates per 100,000 population of pelvic organ prolapse in WCBA across 21 GBD regions.
Abbreviations: WCBA, women of childbearing age; SDI, sociodemographic index; GBD, Global Burden of Disease.
Health Inequalities
Marked absolute and relative disparities in the POP burden were observed with respect to SDI. Although the inequality gap between high- and low-SDI regions narrowed over time, lower-SDI countries and regions continued to shoulder a disproportionately great burden (Figure 6, p < 0.05). SII indicated that the differences in incidence and DALY rates across the highest and lowest SDI regions declined to 11 (95% CI: −33.2 to 55.51) and −0.11 (95% CI: −1.17 to 0.95) in 2021 from −117.33 (95% CI: −173.42 to −61.24) and −2.29 (95% CI: −3.58 to −0.99) in 1990. The Concentration Index also dropped to −0.174 and −0.21 in 2021 from −0.208 and −0.238 in 1990. Therefore, absolute and relative health inequalities linked to POP have diminished from 1990 to 2021.
Figure 6.
Health inequality regression curves and concentration curves for the DALYs of pelvic organ prolapse worldwide among WCBA in 1990 and 2021. Panels (A and C) illustrate the slope index of inequality, depicting the relationship between SDI and Incidence (DALYs) rates for each condition, with points representing individual countries and territories sized by population. Panels (B and D) present the concentration index, which quantifies relative inequalities by integrating the area under the Lorenz curve, aligning Incidence and DALYs distribution with population distribution by SDI. Blue represents data from 1990, and red represents data from 2021.
Abbreviations: DALYs, disability-adjusted life-years; SDI, sociodemographic index.
Decomposition Analysis
The contributions of aging, population expansion, and changes in disease patterns to trends in 1990–2021 POP incidence and DALYs among WCBA were examined through decomposition analysis (Figure 7, Tables S5 and S6). Overall, incidence and DALYs showed an upward trajectory globally and across SDI regions. The largest rises were found in the middle-SDI region. Globally, population aging and increase contributed positively to the DB, accounting for 48.58% and 110.61% of the change. Nonetheless, the relative impact of these drivers varied by SDI region: in high- and high-middle-SDI regions, the burden arose predominantly from aging (90.8% and 107.34%), whereas in low- and low-middle-SDI regions, population increase was the principal reason (162.6% and 253.77%). Conversely, changes in disease patterns contributed to reductions in incidence and DALYs, notably in low-middle-SDI regions, with decreases being 199.35% and 216.46%.
Figure 7.
Analysis of the incidence (A) and DALYs (B) breakdown of pelvic organ prolapse among WCBA globally and in SDI countries from 1990 to 2021. Dots represent overall change values caused by aging, epidemiological changes, and population growth.
ARIMA Model
The impact of POP on WCBA over the upcoming two decades was forecast via an ARIMA model. In this study, the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test was conducted on the Disability-Adjusted Life Years (DALYs) rate and the incidence rate. The results indicated that the KPSS test p-values for both the DALYs rate and the incidence rate were less than 0.05, suggesting that the series were non-stationary. Consequently, the order of differencing (d = 2) was determined based on the KPSS criterion, and the KPSS test was subsequently reapplied. The results demonstrated that the KPSS test statistic for the DALYs rate was 0.394 (p = 0.079), while the test statistic for the incidence rate was 0.377 (p = 0.087), indicating that the time series had achieved stationarity. Table 3 summarizes the optimal parameters of the model, alongside AIC, BIC, and p-values derived from the Ljung-Box Q test. The Ljung-Box test confirmed white noise residuals in all models, indicating model stability and strong concordance with the observed data. Figure 8 presents the forecast trends in POP incidence and DALYs from 2022 to 2041, which are expected to decline steadily during this period. By 2041, the incidence rate is projected to decrease to 243.28 per 100,000 population, while the DALY rate is predicted to fall to 4.62 per 100,000. Leveraging GBD, this study comprehensively evaluated 1990–2021 POP incidence and DALYs among WCBA globally, incorporating health inequality measures to examine SDI-linked complete and comparative disparities, decomposition analyses to unravel the impacts of aging, population expansion, and alterations in disease patterns, and ARIMA modeling to predict future trajectories over the next two decades. The foregoing analyses provide a robust and comprehensive assessment of global POP burden within WCBA.
Table 3.
ARIMA Model Parameters and Their Corresponding AIC and BIC for Predicting Incidence and DALYs Rate (per 100,000) AIC and BIC (Over the Next 20 years Worldwide)
| Measures | Parameters | AIC | BIC | Ljung–Box test p-value |
|---|---|---|---|---|
| DALYs | Arima (2, 2, 0) | −169.14 | −164.93 | 0.629 |
| Incidence | Arima (2, 2, 0) | 51.8 | 56 | 0.484 |
Figure 8.
The projected future trends in pelvic organ prolapse incidence (A) and DALYs (B) (per 100,000) from 2022 to 2041.
Discussion
This research employed data from the GBD database to analyze the incidence and DALYs linked to POP in WCBA globally spanning the years 1990 to 2021. The assessment incorporated health inequality measures to examine complete and comparative disparities related to the SDI, utilized decomposition analysis to evaluate the impacts of aging, population expansion, and alterations in disease patterns, and applied ARIMA modeling to predict the impact of disease for the upcoming two decades, offering a thorough assessment of the POP load in WCBA. The findings demonstrate a significant global increase in the absolute number of new cases of POP and DALYs in 2021. This pattern might be linked to the cumulative effects of global population growth and an expanding elderly demographic. However, when compared to 1990, the EAPC in both incidence and DALY rates exhibited a declining trend. As of 2021, incidence and DALYs were inversely correlated with the SDI, showing a steady decrease in the total disease burden in conjunction with socioeconomic development. Globally, the burden of POP was marginally lower than anticipated. This reduction may be ascribed to significant advancements in pelvic floor therapy in the last thirty years, as well as accumulated clinical expertise in diagnosis, therapeutic interventions, and prognosis assessment, which have culminated in the creation of a thorough framework incorporating well-known diagnostic and treatment strategies.24 Nonetheless, the widespread presence of numerous factors that might pose a risk, including body mass index, birth weight, way of delivery, number of births, and high-intensity physical labor—continues to contribute to POP development in WCBA.25 These elements should be considered critical in addressing the prevalence of POP. Consequently, there is an urgent need for increased societal focus on the disease burden within this demographic.
Our analysis of both complete and comparative inequalities tied to the SDI uncovers a decrease in these inequalities concerning incidence and DALYs, suggesting a shrinking disparity between nations with low and high SDI. However, regions with low SDI continue to experience a unevenly higher disease burden, particularly among the 15–19 age group, which exhibited the largest percentage increase. This pattern may be related to factors such as imperfect local government policies, unequal regional medical resources, poverty, population malnutrition, early marriage, high fertility and long duration of childbirth.26,27 Therefore, it is necessary to carry out targeted interventions in low SDI areas. These interventions should include government measures to evenly distribute medical resources, improve population nutrition, promote late childbearing, prolong the interval between two pregnancies, and strengthen education and awareness on prenatal care and the importance of professional birth care personnel. Such measures are essential to reduce delivery-related trauma that contributes to POP, thereby mitigating its prevalence.
In East Asia, where the aging population is on the rise, pelvic floor exercises, such as Kegel exercises, are beneficial for postpartum women in preventing the exacerbation of POP.26 Management strategies for POP differ across regions: in rural areas, the use of vaginal pessaries offers a cost-effective solution for symptom relief, highlighting the importance of educating healthcare staff in fitting pessaries and aftercare procedures. Conversely, Surgical operations in metropolitan regions, such as pelvic floor reconstruction, are accessible but frequently limited by budgetary constraints and societal prejudice. It is imperative to develop interventions that are tailored to the specific socioeconomic, cultural, and healthcare challenges of each region to effectively reduce the impact of POP.28 Moreover, significant regional disparities have been observed, with high-income areas experiencing notable declines in both incidence and DALYs associated with POP. The trend is due to rising income levels and improved educational standards, which enhance healthcare accessibility through various mechanisms, thereby mitigating the disease burden. On a larger scale, economic development strengthens public health systems, when education boosts understanding of health, collectively fostering a virtuous cycle of improved availability of healthcare, preventing diseases, and promoting healthier lifestyles.29,30
In this study, we investigated the effects of rising population, aging demographics, and changes in disease patterns on the global impact of POP among WCBA in the past thirty years. Our findings indicate a global increase in POP incidence and DALYs, with this trend evident across all SDI regions. A major increase was seen in the middle-SDI area. Population expansion and aging have become key factors in the rising global impact of POP, a finding consistent with multiple GBD-based studies. Wang et al6 attributed the significant increase in global POP cases largely to population growth, while Xu et al16 noted that population growth raised the absolute incidence of POP and aging shaped its age distribution. Similarly, Wang et al17 emphasized that population growth expands the case base and aging increases the proportion of high-risk groups—two fundamental demographic forces continuously elevating POP case numbers. However, the influence of these factors differed greatly among SDI area. Taking the incidence rate as an example, in high and high-middle SDI regions, aging was the predominant factor, whereas in low and low-middle SDI regions, population growth had a more substantial influence. Therefore, it is essential to develop adaptable health policies that are tailored to the specific circumstances of each region to address this complexity effectively. In regions characterized by high and high-middle SDI, the demographic transition towards an aging population has significantly augmented the burden of disease, particularly concerning age-related conditions. In these contexts, routine gynecological examinations are instrumental in facilitating early detection of such conditions. Concurrently, lifestyle modifications, including weight management and proper nutrition, are essential for maintaining pelvic health. Additionally, supervised pelvic floor muscle training may contribute to decelerating disease progression. Conversely, in areas classified as low and low-middle SDI, for example: India, high population density intensifies the disease burden. Implementing family planning policies to control population growth is crucial, alongside bolstering disease prevention, essential primary healthcare, treatment, and rehabilitation services. Physicians are crucial in conducting screenings, providing guidance, and offering non-operative treatment, while governments should enhance financial support, such as offering subsidies for surgeries to families with limited financial resources. Ultimately, health education, advocacy campaigns, and policy interventions are vital for reducing the impact of POP and provide equal healthcare access, thereby markedly improving pelvic health outcomes on a global scale.
We forecasted the future trends in the prevalence of POP among WCBA for the next twenty years. Utilizing the ARIMA model, our analysis anticipates a gradual decline in the global burden of POP by 2041. This tendency could be linked to advancements in global public health, which are associated with favorable economic growth. Improvements in maternal healthcare worldwide, the standardization of delivery management practices (such as a reduction in the incidence of dystocia and birth trauma), and increased awareness of pelvic floor rehabilitation are likely contributing factors to the gradual decrease in POP incidence. Additionally, changes in fertility rates may play a significant role; the persistently declining fertility rates in many countries, particularly given that multiple vaginal deliveries are a major risk factor for POP, might help lower the occurrence of this condition. Nonetheless, due to the broad range of the 95% CIs, the projections for incidence and DALYs in each region lack precise reference value.
Limitations
The data used in this research were obtained from the GBD 2021 database. However, the precision and reliability of GBD estimates are heavily contingent upon the quality and volume of the base data. The data primarily originate from administrative registries, censuses, surveillance statistics, disease registries, epidemiological surveys, and published literature. These sources form the foundation for mathematical modeling used to compute relevant metrics. However, they do not constitute rigorously designed statistical investigations specifically targeting POP, particularly among younger WCBA with mild symptoms. Consequently, discrepancies with real-world situations may arise. Additionally, variations in incidence and DALY rates may represent real trends associated with age, yet they might also be impacted by regional disparities, possibly causing bias in the analysis. Nations with less socioeconomic Countries with lower socioeconomic status often have limited health data, which impairs the accurate evaluation of the impact of conditions like POP. Insufficient report is a major problem, notably in regions where healthcare access is insufficient and data collection systems are lacking. Despite these limitations, this research delivers the latest worldwide Figures on POP and offers crucial information for public health officials to formulate successful strategies to manage the condition and its risk factors.
Conclusion
During the last 32 years, the total number of POP cases and DALYs among WCBA has continued to rise. The global community still faces a significant disease burden, with marked regional disparities, underscoring the need for nuanced public health strategies. Moving forward, it is imperative to integrate health education, optimize medical resource allocation, and implement policy interventions to effectively mitigate the burden of pelvic organ prolapse.
Funding Statement
The study was supported by the National Natural Science Foundation of China (No. 82060322), the Guangxi Key Research and Development Program (Grant Number: GuiKe AB23026011) and Scientific research projects funded by Guangxi Health Commission (Grant Number: Z20210105).
Abbreviations
POP, Pelvic organ prolapse; WCBA, women of childbearing age; GBD, Global Burden of Disease; DALYs, disability-adjusted life years; DB, disease burden; AR, autoregressive; ARIMA, autoregressive integrated moving average; IHME, Institute for Health Metrics and Evaluation; UIs, uncertainty intervals; GHDx, Global Health Data Exchange; WHO, World Health Organization; ICD-10, 10th Revision; SDI, sociodemographic index; YLLs, years of life lost; YLDs, years lived with disability; EAPC, Estimated annual percentage change; Cis, confidence intervals; GATHER, Guidelines for Accurate and Transparent Health Estimates Reporting; SII, slope index of inequality; rlm, regression mo lm, linear regression; MA, moving average; KPSS, Kwiatkowski-Phillips-Schmidt-Shin; AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion.
Data Sharing Statement
The data supporting the findings of this study were derived from the Global Burden of Disease Study 2021 (GBD 2021), which is publicly available through the Global Health Data Exchange (GHDx) platform managed by the Institute for Health Metrics and Evaluation (IHME). The data can be accessed at: https://ghdx.healthdata.org/gbd-2021/.
Ethics Statements
According to Article 32 of the Ethical Review Measures for Life Science and Medical Research Involving Human Beings of the People’s Republic of China, the data used in this study will not cause any form of harm to human beings, nor will it touch sensitive personal privacy or trade secrets, so the ethical review can be exempted. In addition, the database used in this study was publicly available and legally available.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
Caiying Pang and Xinyan Li are co-first authors for this study. The authors declare that they have no competing interests in this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data supporting the findings of this study were derived from the Global Burden of Disease Study 2021 (GBD 2021), which is publicly available through the Global Health Data Exchange (GHDx) platform managed by the Institute for Health Metrics and Evaluation (IHME). The data can be accessed at: https://ghdx.healthdata.org/gbd-2021/.