Global and regional trends and age-period-cohort effects in polycystic ovary syndrome burden from 1990 to 2019, with predictions to 2040

Global and regional trends and age-period-cohort effects in polycystic ovary syndrome burden from 1990 to 2019, with predictions to 2040 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global and regional trends and age-period-cohort effects in polycystic ovary syndrome burden from 1990 to 2019, with predictions to 2040 Ruijie Li, Ling Zhang, Yi Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4260677/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives We aimed to analyze the secular trends of global and regional polycystic ovary syndrome (PCOS) burden, the effects of age, period, and birth cohort, and forecast the global burden over time. Material and methods Based on the incidence, prevalence, and years lived with disability (YLDs) data of PCOS from the 2019 GBD database for the years 1990 to 2019, we used the estimated annual percentage change (EAPC) and the annual percentage change (APC) calculated using the joinpoint regression model to describe the burden trends. An age-period-cohort model was utilized to analyze the effects of age, period, and birth cohort on the PCOS age-standardized rate. The burden of PCOS was projected by conducting the Bayesian age-period-cohort (BAPC) model. Results Globally, there were significant increases in age-standardized incidence rate (ASIR) (EAPC = 0.85, 95%UI:0.82—0.87), age-standardized prevalence rate (ASPR) (EAPC = 0.84, 95%UI:0.80—0.88), and age-standardized YLD rate (EAPC = 0.82, 95%UI:0.78—0.87) of PCOS from1990-2019. Period RR and cohort RR showed an upward trend in global and most SDI regions, indicating an increased risk of PCOS for new generations. Meanwhile, the BAPC model predicts that the burden will continue to rise. Conclusions The global burden of PCOS increased over the past 30 years, with variability across different regions, and this trend will continue in the future. Bayesian age-period-cohort prediction polycystic ovary syndrome socio-demographic index Joinpoint regression analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Polycystic ovary syndrome (PCOS) is one of the most prevalent metabolic and reproductive diseases affecting women of childbearing age. While the exact cause is unknown, research suggests that it may be due to the interaction of certain genes with environmental factors, which can impair a woman's fertility and threaten her overall health(De Leo et al., 2016 ; Merkin et al., 2016 ; Patel, 2018 ). Signature features of PCOS include hyperandrogenism, persistent anovulation, and polycystic ovarian changes(Walters et al., 2018 ). Women with PCOS have a significantly higher risk of menstrual disorders, obesity, acne, dyslipidemia, insulin resistance, and type 2 diabetes(Sirmans & Pate, 2013 ). The prevalence of PCOS was 6.1%, 19.9%, and 15.3%, respectively, according to the National Institutes of Health (NIH) criteria, the Rotterdam criteria, and the Androgen Excess-PCOS Society (AE-PCOS) criteria(Azziz et al., 2009 ; Yildiz et al., 2012 ). The dangers of PCOS cannot be ignored. With the improvement of socio-economic level and increased access to medical resources, women's reproductive health has received more and more attention, and more and more patients with PCOS are found in the population. However, global and large-scale epidemiological studies on PCOS are insufficient. Studies have shown that there are differences in the diagnosis and treatment patterns of PCOS between gynecologists and reproductive endocrinologists, and that the insufficient number of reproductive endocrinologists also prevents the PCOS population from accessing adequate specialized medical care(Cussons et al., 2005 ; Dokras et al., 2017 ). The harm of PCOS to women's fertility and long-term health needs urgent attention, and effective prevention and treatment measures should be taken immediately. Currently, several interventions have been proposed to alleviate symptoms and reduce comorbidities, but the medical and emotional costs are high(Azziz et al., 2016 ). In 2004, the economic burden of PCOS in the United States exceeded $ 4 billion(Azziz et al., 2005 ). In 2014, the estimated healthcare cost for PCOS in the UK was at least £237 million(Ding et al., 2018 ).Given the significant social, physical, and public health impacts of the disease, there is a need for a systematic assessment of the changes and long-term trends in the burden of PCOS, with projections at the global and regional levels, to reflect the effectiveness of public health strategies and to inform policies regarding disease screening and resource allocation. Although several previous studies have reviewed the burden of PCOS in multiple regions and countries(Gao et al., 2023 ; Liu et al., 2021 ; Safiri et al., 2022 ), there are some shortcomings. For example, disability adjusted life years (DALYs) are reported, rather than years lived with disability (YLDs). The former are not suitable to describe the burden of non-fatal diseases such as PCOS, as there is no mortality for PCOS. In addition, all current studies have only described age-standardized rates (ASR) and/or estimated annual percentage change of ASR when presenting trends in PCOS, but these methods remain descriptive and non-parametric. In order to overcome the intrinsic limitations of the nonparametric methods and further fully exploit the limited longitudinal epidemiological data, our study used the age-period-cohort (APC) model to simultaneously assess the age effects, period effects, and cohort effects on the temporal trends of PCOS. Additionally, previous studies have focused more attention on the overall trend of PCOS burden, neglecting the monitoring of stage-specific changes, and have lacked calculations of future long-term trends. Our study utilized the joinpoint regression model to depict local change characteristics, and also predicted future disease burden of PCOS using the Bayesian age-period-cohort (BAPC) model. Therefore, in this study, we described the long-term and partial time trends in PCOS incidence, prevalence, and YLDs at the global and regional levels. Furthermore, we utilized the age-period-cohort and Bayesian age-period-cohort models to analyze the contribution of different elements to the epidemiological outcome of PCOS and project future incidence, prevalence, and YLDs up to 2040. 2. Methods 2.1 Overview The GBD 2019 database ( https://ghdx.healthdata.org/gbd-2019 ), led by the Institute for Health Metrics and Evaluation (IHME), provides the most comprehensive and up-to-date data assessment of the descriptive epidemiology of diseases in 21 regions and 204 countries and territories from 1990 to 2019, using all available data(Murray et al., 2020 ). All data is calculated by direct query and downloaded from the GBD results tool. A detailed description of the method can be found on the help page of the database and other publication(Vos et al., 2020 ). The GBD collects health data from life records, censuses, registers, health surveys, population surveillance, administrative reports, scientific research, discharge records, records of outpatient visits and health insurance claims, as well as many other sources. These are then input into an algorithm to generate an estimate of the burden of disease. In the GBD study, disease estimates were generated by age, year, and location using the Bayesian meta-regression tool DisMod-MR 2.1 to ensure consistent epidemiological parameters for the conditions under study. 2.2 Data source Data on the global burden of PCOS were obtained from published sources using the Global Health Data Exchange Query Tool. This study obtained global, regional, and SDI quintile data on incidence, prevalence, and YLDs of PCOS from 1990 to 2019 from the GBD. YLDs were estimated as the product of prevalence estimate and disability weight for health states of each mutually exclusive sequela adjusted for comorbidity. The age range is limited to between 10 and 54 years old, divided into nine 5-year-old age groups. GBD divides the socio-demographic index (SDI) of 21 regions and 204 countries and territories into five components (high, high-middle, middle, low-middle, and low) based on total fertility rate, per capita income, and average years of education. SDI ranges from 0 to 1, with higher values indicating higher levels of socio-economic development. In addition, GBD regions are not actual geopolitical units, but groupings of countries created for analysis. 2.3 Statistical analyzes 2.3.1 Calculation of the estimated annual percentage change Age standardized rates of incidence, prevalence, and YLD from 1990 to 2019 were used to assess the burden of polycystic ovary syndrome. Temporal trends of burden over thirty years are reflected by the estimated annual percentage change (EAPC). The EAPC and its 95% confidence interval (CI) are obtained from the formula EAPC = 100 ∗ (exp (β) − 1), where β is the annual change in ln (ASR). Positive EAPC and lower bound of its 95% CI indicate an upward trend in ASR, while negative EAPC and upper bound of its 95% CI indicate a downward trend in ASR. Otherwise, the ASR will be considered stable over time. 2.3.2 Joinpoint regression analysis In order to detect changes in parameter trends of PCOS health metrics, the joinpoint regression model was utilized, which can be implemented by Joinpoint software version 4.9.0.1 from the National Cancer Institute ( http://surveillance.cancer.gov/joinpoint ). The joinpoint regression program's advantage is that it can indicate if changes over time are statistically significant(Kim et al., 2000 ). Therefore, we analyzed the age-standardized rate of PCOS incidence, prevalence and, YLDs by different SDI regions, calculated the number of junction points and the position of each junction point by Monte Carlo permutation test, and the corresponding test statistic P value ( α = 0.05). For convenience of understanding, slopes are often converted to annual percentage changes (APCs) and average annual percent change (AAPC); that is, the estimated annual percentage change from one connection point to the next(Li & Du, 2020 ). 2.3.3 Age-period-cohort analysis and projections To estimate the effect of age, year period, and birth cohort, we performed the age-period-cohort model. This model illustrates the various risks associated with various age groups (age effects), the impact of environmental and historical factors (period effects), and the impacts of risk factor exposure on a population with the same birth year (cohort effects). The age-period-cohort model can be expressed as follows: Ln(R i j κ )=µ + α i + β j + γ κ + ε, in which µ is the constant, and R ijk represents the attributable mortality rate in the i th age group, j th time period, and k th birth cohort. α i , β j , γ κ , and ε are the effects of age, period, cohort, and random error, respectively(Rosenberg & Anderson, 2011 ). In the age-period-cohort analysis, the relative risk (RR) is defined as the exponential value of the estimations of α i , β j , and γ κ . The overall log-linear trend by year period and birth cohort was then computed, and this is known as the local drift. Through the age-period-cohort Web Tool ( https://analysistools.nci.nih.gov/apc/ ), the age-period-cohort model was estimated(Rosenberg et al., 2014 ). In the present study, based on packages “BAPC” and “INLA” in R software, the BAPC model was used to project the burden of PCOS from 2020 to 2040. The primary data collation and calculation and the plotting of graphs in this study were performed in the R program version 4.1.0. A two-sided P value less than 0.05 was considered statistically significant. 3. Results 3.1 The burden of PCOS at the regional level, 1990–2019 Globally, the incident cases of PCOS increased from 1.38 million in 1990 to 2.13 million in 2019, and the age-standardized incidence rate (ASIR) increased from 46.1 in 1990 to 59.8 in 2019 per 100,000, with an EAPC of 0.85 (95%UI:0.82—0.87). The EAPC of ASIR from 1990 to 2019 was found to be highest in the low-middle SDI regions (1.86, 95%UI: 1.81—1.92) and high SDI regions had the lowest EAPC (-0.01, 95%UI: -0.17—0.13). Regionally, the EAPC for PCOS-related ASIR ranged from − 0.82 (95%UI:-1.23—-0.42) in High-income North America to 2.58 (95%UI: 2.48—2.68) in Southeast Asia(Table 1 ) (Fig. 1 ). Table 1 The incidence of PCOS in 1990 and 2019 and temporal trends between 1990 and 2019. 1990 2019 1990–2019 (%) Location Incidence_Number_1000 (95%UI) ASIR per 100,000 (95%UI) Incidence_Number_1000 (95%UI) ASIR per 100,000 (95%UI) EAPC of incidence rate Global 1377.9 (941.8 to 1817) 46.1 (31.6 to 61) 2125.5 (1490 to 2803.3) 59.8 (41.7 to 78.9) 0.85 (0.82–0.87) SDI region High SDI 414.7 (284.6 to 562.4) 121.3 (82.9 to 162.4) 442 (328 to 564) 136.3 (100.6 to 172.3) -0.02 (-0.17-0.13) High-middle SDI 258.8 (177 to 343.2) 45.6 (31.2 to 60.9) 314.7 (216.7 to 418.1) 68.5 (46.5 to 91.5) 1.60 (1.53–1.67) Middle SDI 481 (325.1 to 641.4) 46.5 (31.4 to 62.1) 789.5 (541.6 to 1049.8) 77.2 (52.7 to 103.2) 1.76 (1.72–1.80) Low-middle SDI 165.2 (109.7 to 221.5) 23.9 (15.9 to 32) 392.1 (261 to 527.3) 39.9 (26.6 to 53.8) 1.86 (1.81–1.92) Low SDI 57.4 (36.8 to 78.2) 17.9 (11.7 to 24) 185.7 (121 to 253.8) 25.1 (16.6 to 34) 1.24 (1.21–1.28) GBD region Andean Latin America 23.6 (15.8 to 32.6) 93.9 (63 to 129.7) 40.4 (27.4 to 55.5) 129.3 (87.4 to 177.8) 1.10 (1.00-1.19) Australasia 15.5 (11.1 to 20.4) 168.1 (120.9 to 222.3) 19.9 (13.3 to 26.4) 198.4 (131.8 to 263) 0.37 (0.25–0.48) Caribbean 8.2 (5.6 to 11.2) 39.7 (27 to 54.2) 11.2 (7.5 to 15.2) 51.3 (34.4 to 70) 0.91 (0.84–0.99) Central Asia 4.7 (3 to 6.6) 12.2 (7.8 to 16.9) 7.7 (5.1 to 10.6) 18 (11.8 to 24.7) 1.28 (1.18–1.37) Central Europe 3.9 (2.4 to 5.6) 6.9 (4.3 to 9.8) 3 (2 to 4) 8.6 (5.7 to 11.7) 0.72 (0.67–0.78) Central Latin America 109.7 (72.6 to 152.1) 99.5 (66 to 137.5) 135.6 (91.3 to 182.7) 109.3 (72.8 to 147.9) -0.17 (-0.31–0.03) Central Sub-Saharan Africa 5.1 (3.2 to 7.1) 15 (9.4 to 20.7) 19.8 (12.8 to 27.3) 22.9 (15 to 31.1) 1.32 (1.14–1.50) East Asia 195.8 (129.4 to 264.4) 28.6 (18.9 to 38.9) 233 (159.2 to 310.7) 55.2 (37 to 74.4) 2.22 (2.00-2.43) Eastern Europe 7.4 (4.8 to 10.2) 7.6 (4.9 to 10.4) 7 (4.7 to 9.5) 10.3 (6.7 to 14.2) 1.04 (1.00-1.08) Eastern Sub-Saharan Africa 21.8 (13.8 to 30) 17.7 (11.5 to 24.1) 63.8 (40.8 to 87.5) 22.9 (14.9 to 31) 0.90 (0.85–0.94) High-income Asia Pacific 159.6 (107.2 to 225.2) 189.9 (127.5 to 263.9) 111.3 (74.6 to 154.5) 221.7 (150.4 to 306.8) 0.40 (0.32–0.48) High-income North America 128.1 (85.7 to 173.3) 112.8 (75 to 153.8) 165.5 (130.9 to 203.6) 122 (96.4 to 149.9) -0.82 (-1.23–0.42) North Africa and Middle East 127.2 (85 to 172.5) 57.7 (38.9 to 77.8) 236.3 (158.3 to 322.4) 77.2 (51.6 to 105.4) 1.12 (1.06–1.19) Oceania 1.8 (1.2 to 2.4) 44 (29.9 to 59.2) 4.6 (3.1 to 6.3) 63.3 (42.3 to 85.4) 1.00 (0.80–1.20) South Asia 150.4 (100.8 to 201.4) 23.5 (15.9 to 31.3) 398.4 (267.8 to 533) 40.1 (27 to 53.6) 2.06 (1.91–2.21) Southeast Asia 154.3 (100.9 to 209) 52.4 (34.5 to 70.8) 334.2 (226.9 to 448.5) 103.5 (70.5 to 138.9) 2.58 (2.48–2.68) Southern Latin America 11.2 (7.4 to 15.4) 42.4 (27.9 to 58.2) 20.7 (13.6 to 28.3) 71.4 (46.6 to 97.1) 1.79 (1.55–2.03) Southern Sub-Saharan Africa 10.7 (6.9 to 14.7) 30.3 (20 to 41.7) 16.4 (10.7 to 22.3) 39.1 (25.8 to 53.5) 0.86 (0.76–0.96) Tropical Latin America 21 (13.9 to 28.5) 22.1 (14.7 to 29.7) 24 (16.2 to 32) 24.2 (16.1 to 32.5) -0.24 (-0.40–0.07) Western Europe 197.1 (133.9 to 265.9) 136.2 (91.9 to 185.2) 197.4 (134.5 to 266.1) 149.7 (101.4 to 203.1) 0.22 (0.16–0.27) Western Sub-Saharan Africa 20.8 (13.2 to 28.6) 17.1 (11.1 to 23.2) 75.3 (48.3 to 103.6) 24.4 (15.8 to 33.2) 0.97 (0.76–1.18) The number and prevalence rate of PCOS were also increasing globally. In 1990, the prevalent counts were 34.26 million, while up to 2019, the count arrived at 65.99 million. The age-standardized prevalence rate (ASPR) increased from 1286.2 in 1990 to 1677.8 in 2019 per 100,000, with an EAPC of 0.84 (95%UI: 0.80—0.88). The EAPC of ASIR from 1990 to 2019 was found to be highest in the low-middle SDI regions (2.06, 95%UI: 2.00—2.12) and high SDI regions had the lowest EAPC (0.02, 95%UI: -0.13—0.17). Regionally, the EAPC for PCOS-related ASPR ranged from − 0.76 (95%UI: -1.16—-0.37) in High-income North America to 2.59 (95%UI: 2.48—2.69) in Southeast Asia (Table S1 ) (Fig. 1 ). As for years lived with disability (YLDs), the incident cases increased to 0.58 million in 2019 compared to 1990 (0.30 million). And the age-standardized YLD rate increased from 11.3 in 1990 to 14.7 in 2019 per 100,000, with an EAPC of 0.82 (95%UI:0.78—0.87). As far as SDI is concerned, the EAPC from 1990 to 2019 was found to be highest in the low-middle SDI regions (2.03, 95%UI: 1.98—2.09) and high SDI regions had the lowest EAPC (0.02, 95%UI: -0.13—0.16). Regionally, the EAPC ranged from − 0.77 (95%UI: -1.16—-0.38) in High-income North America to 2.55 (95%UI: 2.45—2.65) in Southeast Asia (Table S2 ) (Fig. 1 ). 3.2 Joinpoint regression analysis of PCOS burden The trends of ASIR in global, high-middle SDI, middle SDI, low-middle SDI, and low SDI were similar, showing a year-on-year upward trend, with AAPC values ranging from 0.9 to 1.8, and the trends were both statistically significant ( P < 0.05) (Table 2 ). The tendency of ASIR in high SDI was relatively more complex, with significant increases from 1990 to 2001 and 2010 to 2019, but decreases from 2001 to 2010 (Fig. 2 ). Table 2 The trends in PCOS burden by Joinpoint regression Incidence Prevalence YLD SDI factor Index Period Estimate(%)(95%UI) Period Estimate(%)(95%UI) Period Estimate(%)(95%UI) Global APC 1990–1996 0.8 (0.8,0.9) 1990–1996 1 (0.9,1) 1990–1996 1 (1,1) 1996–1999 1.3 (1.1,1.4) 1996–1999 1.5 (1.4,1.7) 1996–1999 1.5 (1.4,1.6) 1999–2004 0.9 (0.9,1) 1999–2004 0.9 (0.8,0.9) 1999–2004 0.9 (0.8,0.9) 2004–2009 0.6 (0.5,0.7) 2004–2010 0.4 (0.4,0.4) 2004–2010 0.4 (0.4,0.4) 2009–2017 0.8 (0.8,0.8) 2010–2017 0.9 (0.9,0.9) 2010–2017 0.9 (0.8,0.9) 2017–2019 1.5 (1.4,1.7) 2017–2019 1.6 (1.4,1.7) 2017–2019 1.6 (1.5,1.7) AAPC 1990–2019 0.9 (0.9,0.9) 1990–2019 0.9 (0.9,0.9) 1990–2019 0.9 (0.9,0.9) High SDI APC 1990–2001 0.7 (0.7,0.8) 1990–2000 0.9 (0.9,1) 1990–2000 1 (0.9,1) 2001–2005 -0.6 (-1,-0.2) 2000–2005 -0.2 (-0.4,-0.1) 2000–2005 -0.3 (-0.4,-0.2) 2005–2010 -1.9 (-2.1,-1.6) 2005–2010 -1.8 (-1.9,-1.7) 2005–2010 -1.8 (-1.9,-1.7) 2010–2016 1 (0.9,1.2) 2010–2016 0.8 (0.7,0.9) 2010–2016 0.8 (0.7,0.9) 2016–2019 2.8 (2.4,3.2) 2016–2019 2.5 (2.3,2.7) 2016–2019 2.5 (2.3,2.7) AAPC 1990–2019 0.4 (0.3,0.4) 1990–2019 0.4 (0.4,0.4) 1990–2019 0.4 (0.3,0.4) High-middle SDI APC 1990–1992 0.3 (0.1,0.4) 1990–1995 1 (1,1.1) 1990–1996 1.2 (1.1,1.2) 1992–1996 0.5 (0.4,0.6) 1995–2000 2.1 (2.1,2.2) 1996–1999 2.5 (2.3,2.6) 1996–2001 1.9 (1.8,2) 2000–2004 1.5 (1.4,1.6) 1999–2004 1.6 (1.5,1.6) 2001–2005 2.2 (2.1,2.3) 2004–2010 0.9 (0.8,0.9) 2004–2010 0.8 (0.8,0.8) 2005–2012 1.7 (1.7,1.8) 2010–2017 1.1 (1.1,1.1) 2010–2017 1.1 (1,1.1) 2012–2019 1.1 (1.1,1.2) 2017–2019 1.5 (1.3,1.6) 2017–2019 1.5 (1.4,1.7) AAPC 1990–2019 1.4 (1.4,1.4) 1990–2019 1.3 (1.3,1.3) 1990–2019 1.3 (1.3,1.3) Middle SDI APC 1990–1993 2.3 (2.2,2.5) 1990–1996 2.1 (2,2.1) 1990–1996 2.1 (2.1,2.2) 1993–2003 1.7 (1.7,1.8) 1996–1999 2.8 (2.5,3.1) 1996–1999 2.8 (2.4,3.1) 2003–2010 2 (2,2.1) 1999–2004 2 (1.9,2.1) 1999–2004 2 (1.9,2.2) 2010–2014 1.5 (1.3,1.7) 2004–2014 1.7 (1.7,1.7) 2004–2014 1.7 (1.6,1.7) 2014–2017 1 (0.7,1.4) 2014–2017 1.2 (0.9,1.5) 2014–2017 1.1 (0.8,1.5) 2017–2019 1.7 (1.3,2) 2017–2019 2 (1.6,2.3) 2017–2019 2 (1.7,2.4) AAPC 1990–2019 1.8 (1.7,1.8) 1990–2019 1.9 (1.9,2) 1990–2019 1.9 (1.9,2) Low-middle SDI APC 1990–1996 1.4 (1.3,1.4) 1990–1995 1.5 (1.4,1.6) 1990–1996 1.6 (1.5,1.6) 1996–2005 1.8 (1.7,1.8) 1995–2003 1.9 (1.8,1.9) 1996–2005 1.9 (1.9,2) 2005–2010 2.7 (2.6,2.7) 2003–2006 2.1 (1.8,2.4) 2005–2010 2.9 (2.9,3) 2010–2019 1.6 (1.5,1.6) 2006–2009 3.3 (3,3.5) 2010–2019 1.6 (1.6,1.6) 2009–2012 2.1 (1.9,2.4) 2012–2019 1.7 (1.7,1.7) AAPC 1990–2019 1.8 (1.8,1.8) 1990–2019 2 (1.9,2) 1990–2019 1.9 (1.9,1.9) Low SDI APC 1990–1995 0.5 (0.4,0.6) 1990–1995 0.7 (0.6,0.8) 1990–1996 0.8 (0.7,0.9) 1995–2007 1.3 (1.3,1.3) 1995–2001 1.3 (1.2,1.4) 1996–2000 1.3 (1.1,1.6) 2007–2010 1.6 (1.1,2) 2001–2019 1.5 (1.5,1.5) 2000–2019 1.4 (1.4,1.4) 2010–2019 1.2 (1.2,1.3) AAPC 1990–2019 1.2 (1.1,1.2) 1990–2019 1.3 (1.3,1.3) 1990–2019 1.3 (1.2,1.3) Table 3 Predictions of PCOS burden to 2040 Incidence Prevalence YLD year No._100000 ASRs per 100000 No._100000 ASRs per 100000 No._100000 ASRs per 100000 1990 13.84 46.12 372.81 1286.38 3.04 11.30 1991 14.04 46.54 383.04 1297.76 3.13 11.40 1992 14.26 46.97 393.61 1310.57 3.21 11.52 1993 14.51 47.39 404.39 1323.41 3.30 11.63 1994 14.77 47.77 415.31 1335.88 3.40 11.74 1995 15.05 48.09 426.37 1347.99 3.49 11.85 1996 15.40 48.49 438.59 1363.32 3.59 11.99 1997 15.84 49.07 452.14 1382.77 3.71 12.16 1998 16.32 49.73 466.50 1403.78 3.83 12.34 1999 16.79 50.37 480.54 1423.36 3.94 12.50 2000 17.19 50.85 493.53 1438.16 4.05 12.64 2001 17.54 51.27 505.52 1450.54 4.15 12.74 2002 17.87 51.76 517.64 1463.01 4.26 12.86 2003 18.17 52.25 529.65 1475.02 4.36 12.96 2004 18.42 52.70 541.10 1485.80 4.45 13.05 2005 18.60 53.06 551.54 1493.96 4.54 13.12 2006 18.71 53.37 560.96 1499.72 4.62 13.17 2007 18.79 53.68 570.12 1504.55 4.70 13.21 2008 18.84 53.99 578.99 1509.35 4.77 13.26 2009 18.89 54.33 587.90 1515.48 4.85 13.31 2010 18.97 54.72 597.68 1524.65 4.93 13.39 2011 19.10 55.16 608.62 1537.45 5.02 13.49 2012 19.27 55.62 620.05 1552.10 5.11 13.61 2013 19.46 56.09 631.41 1567.45 5.20 13.74 2014 19.67 56.54 642.26 1582.33 5.29 13.86 2015 19.89 56.95 652.27 1595.46 5.37 13.97 2016 20.15 57.41 662.55 1609.17 5.45 14.08 2017 20.45 57.95 673.47 1624.68 5.54 14.21 2018 20.84 58.70 687.12 1647.33 5.65 14.41 2019 21.34 59.73 704.03 1677.96 5.80 14.68 2020 21.92 60.79 725.11 1703.90 5.96 14.90 2021 22.36 61.65 740.79 1730.86 6.09 15.13 2022 22.81 62.50 756.93 1758.35 6.23 15.37 2023 23.27 63.36 773.77 1786.60 6.37 15.61 2024 23.75 64.24 791.01 1815.51 6.51 15.86 2025 24.23 65.13 808.50 1844.76 6.66 16.12 2026 24.71 66.03 826.20 1874.17 6.81 16.37 2027 25.20 66.94 844.27 1903.98 6.96 16.63 2028 25.64 67.86 862.80 1934.49 7.12 16.90 2029 26.08 68.80 881.85 1965.75 7.28 17.17 2030 26.52 69.76 901.24 1997.52 7.44 17.45 2031 26.95 70.72 920.77 2029.45 7.61 17.73 2032 27.38 71.70 940.55 2061.73 7.78 18.02 2033 27.81 72.69 960.34 2094.70 7.95 18.31 2034 28.23 73.70 980.41 2128.53 8.12 18.61 2035 28.65 74.72 1000.81 2163.03 8.29 18.91 2036 29.07 75.76 1021.42 2197.66 8.47 19.22 2037 29.50 76.81 1042.46 2232.46 8.65 19.53 2038 29.98 77.88 1063.74 2267.76 8.83 19.85 2039 30.46 78.96 1085.31 2303.77 9.02 20.17 2040 30.96 80.05 1107.36 2340.37 9.21 20.50 From 1990 to 2019, ASPR in global, high-middle SDI, middle SDI, low-middle SDI, and low SDI showed a steady upward trend (Table 2 ). However, ASPR in high SDI showed a downward trend from 2000 to 2010 and an upward trend for the rest of the years (Fig. S1 ). Except for high SDI, which exhibited a downward tendency from 2000 to 2010 (2000–2005 APC=-0.27; 2005–2010 APC=-1.80), and an upward trend in the remaining years (Fig. S2 ), the age-standardized YLD rate showed a year-on-year increase in both global and other SDI regions (AAPC > 0, P < 0.05) (Table 2 ). 3.3 Age-based description of the burden of PCOS We analyzed the age-specific rate for incidence, prevalence, and YLDs according to age groups, respectively (Fig. 3 ). High incidence rate was concentrated in the 10–14 and 15–19 age groups, with the highest age-specific incidence rate among all age groups in high SDI regions. High prevalence rate and high YLD rate were concentrated in the 20-44-year-olds, and the prevalence rate and YLD rate were highest among all age groups in high SDI regions. Over the past three decades, the burden of PCOS in different age groups has increased worldwide and in most SDI regions. However, in the high SDI regions, the tendency of burden was relatively more complex. 3.4 Age–period–cohort effect of PCOS burden The age-period-cohort effects on PCOS incidence are shown in Fig. 4 . The age effect showed that the incidence rate was highest in the 10–20 age group in global and all SDI regions, followed by a sharp decline, and decreased with age (Fig. 4 A). The period effect showed that the risk of incidence increased first and then decreased with the increasing years, and the period RR in high SDI regions was consistently less than 1(Fig. 4 B). The global incidence risk showed a relatively stable trend across the entire birth cohort; in the high-middle SDI, middle SDI, low-middle SDI, and low SDI, the RR value of cohort was also stable born from 1940 to 1990, and the risk of cohort effect showed a significant upward trend after 1990; in high SDI regions, the cohort RR showed a relatively complex variation, with an overall declining trend as the birth cohort developed (Fig. 4 C). The age-period-cohort effects on PCOS prevalence are shown in Fig. S3 . The age effect showed an inverted U-shaped trend, that is, the prevalence rate initially increased and then decreased with age, except for the high SDI regions, where the effect value peaked in the 30–34 age group, the effect values peaked in the 40–44 age group in global and other SDI regions (Fig. S3 A). In global, high-middle SDI, middle SDI, low-middle SDI, and low SDI, the period RR showed an upward trend with the increasing years; however, in high SDI regions, the RR value first increased, then decreased, and then increased again, and was always less than 1, indicating a low risk of period effect (Fig. S3 B). The cohort effect showed that the prevalence risk of PCOS was generally increasing in global, high-middle SDI, middle SDI, low-middle SDI, and low SDI regions; in high SDI regions, cohort RR fluctuated with birth year, showing an overall downward trend(Fig. S3 C). The age-period-cohort effects on PCOS YLDs are shown in Fig. S4 . For the age factor, the YLD rate first raised and then decreased with age. The effect values peaked at the age of 20–24 years in high SDI regions, while in global and other SDI regions, the effect values peaked at the age of 40–44 years (Fig. S4 A). The period RR showed an increasing trend over time in global, high-middle SDI, middle SDI, low-middle SDI, and low SDI regions, but in high SDI regions, the RR value increased first, then decreased and then increased again, and always remained below 1, indicating that the period effect risk was low (Fig. S4 B). With regard to the cohort factor, the risk of cohort effect was generally upward in global, high-middle SDI, middle SDI, low-middle SDI, and low SDI regions; while cohort RR fluctuated with birth year in high SDI regions, and showed an overall decreasing tendency (Fig. S4 C). 3.5 Predictions of incidence, prevalence, and YLDs of PCOS from 2020 to 2040 Counts of global PCOS in 2019 and projected 2040 were 2134,202 and 3095,928 for incident cases, and ASIR will rise to 80.05 cases per 100,000 population in 2040 (Fig. 5 A, 6 A). The ASPR of PCOS is likely to rise to 2340/100,000 and prevalence cases will increase to 110736,186 by 2040 (Fig. 5 B, 6 B). The age-standardized YLD rate in 1990, 2020, and 2040 were 11.30/100,000, 14.90/100,000, and 20.50/100,000, respectively. And there are 920,923 YLD counts projected for 2040 (Fig. 5 C, 6 C). Compared to the past three decades, the incidence, prevalence, and YLD of PCOS are all expected to show an upward trend over the next two decades. 4. Discussion Our study indicated that during the period of 1990–2019, the global ASIR, ASPR, and age-standardized YLD rate for PCOS showed an increasing trend. However, EAPC varied widely across different SDI regions and GBD regions. The examinations of age, period, and cohort effects differentiate the source of trends in incidence, prevalence, and YLD by different ages, periods, and birth cohorts for the globe and different SDI regions. It was estimated that the incidence, prevalence, and YLD of PCOS would increase from 2020 to 2040. From 1990 to 2019, the incidence, prevalence, and YLDs of PCOS were on the rise worldwide, highlighting a growing concern in public health. However, this upward trend varies widely across SDI regions and GBD regions, suggesting divergent epidemiological patterns in different socioeconomic settings. The EAPC being highest in the low-middle SDI regions may reflect burgeoning health awareness and improved diagnostic capabilities in these areas, furthermore an increasingly prosperous diet content with urbanization may lead to the increasing probability of developing PCOS(Kulkarni et al., 2019 ). Conversely, the minimal change in high SDI regions might indicate a plateau in medical care consciousness or possibly the impact of better healthcare access and preventive strategies attenuating the rise in incident cases. This variation necessitates targeted public health strategies that address the specific needs and challenges of each region. Our joinpoint regression analysis further delineates the complexity of PCOS epidemiology, revealing that ASRs of incidence, prevalence, and YLD have a tendency to rise first, then decline, and then rise again across different periods in high SDI regions. These fluctuating trends suggest the impact of health awareness activities, policy changes, or the evolution of diagnostic criteria over time. For example, before the 21st century, the diagnostic criteria for PCOS were not standardized or unified. The Rotterdam criteria and the Androgen Excess-PCOS Society criteria, established in 2003 and 2006 respectively, made the diagnosis of PCOS more accurate, reflecting an increased attention to polycystic ovary syndrome in related countries during this period. The age-based description of PCOS burden, with high incidence rates in young age groups of 10–19 years old sets the target population for early detection and treatment of this condition. However, it is difficult to diagnose PCOS during adolescence(Legro et al., 2013 ), as the manifestations overlap with the physiological changes of puberty(Witchel et al., 2019 ). Therefore, pediatric and gynecologic medical personnel must use accurate methods to distinguish between the early manifestations of PCOS and the physiology of puberty. High prevalence and YLD rates in the 20-44-year-olds, underscores the chronic nature of PCOS and its significant impact on women’s health throughout their reproductive years. The highest burden in high SDI regions might mirror better diagnostic practices and greater health-seeking behavior, on the other hand, due to the high compliance of developed countries with Westernized diets, the risks of obesity, insulin resistance, and early puberty are higher, all of which are associated with PCOS(Chen et al., 2018 ; Kopp, 2019 ). Limitations in accessing screening, treatment, and disease management services may also lead to an underestimation of PCOS in developing countries. These dynamics emphasize the need for early intervention and sustained management to mitigate PCOS’s long-term impacts. The analysis of the age, period, and cohort effects on PCOS revealed the complexity of the epidemiology of this condition, showing varying trends across different SDI regions. The incidence of PCOS was observed to be highest in the 10-20-year age group, then sharply declined with increasing age. This may reflect the biological reality that PCOS begins during adolescence and early adulthood, a phase when significant hormonal changes occur in the body(Bremer, 2010 ), and when teenagers are subjected to a slew of emotional fluctuations caused by setbacks, interpersonal relationships, and peer pressure(Adone & Fulmali, n.d.), all of which could trigger PCOS. Both prevalence and YLDs increase then decline with advancing age, further emphasizing the significant impact of PCOS on women of reproductive age. In Global and most SDI regions, the period RR and cohort RR of prevalence and YLD are on an upward trend, indicating that with time, new generations face an increased risk of the disease. However, in high SDI regions, the period RR is always less than 1, suggesting that the risk of PCOS is not increasing but decreasing or stabilizing. Overall, these results highlight the complex interplay of generations, social role changes, and potential exposure factors influencing the burden of PCOS. These findings call for global public health policymakers to adopt customized preventive measures and treatment strategies for populations across different regions and age groups(Teede et al., 2018 ). Predictions indicate that the burden of PCOS will continue to rise globally, with obesity driven by globalization and urbanization cited as one of the significant reasons(Mu et al., 2019 ). Lifestyle changes around the world, such as unhealthy dietary habits, lack of exercise, smoking, and others, are risk factors for PCOS(Eleftheriadou et al., 2012 ; Kulkarni et al., 2019 ; Tao et al., 2021 ). In addition, environmental changes, including pollution and exposure to chemical substances, can also lead to women developing PCOS and causing reproductive disorders(Chiang et al., 2017 ). The continuous growth of the global population and the aging demographic structure imply that more women will enter the age range potentially affected by PCOS. With increasing public awareness of PCOS and advances in medical technology, more undiagnosed cases may be identified and recorded, thus increasing the number of cases. Therefore, the prevention, diagnosis, and treatment strategies for PCOS will need to adapt to these changes and challenges. The global health system will need to increase resource allocation and enhance public awareness to address this growing disease burden. This study is the first to parametrically analyze the disease burden of PCOS globally using the age-period-cohort model and predict the future epidemiological trends of PCOS using the Bayesian age-period-cohort model. Our research conducted an in-depth secondary analysis of global PCOS based on GBD data, but there are still some limitations. Firstly, this study was based on summarized data rather than individual-level data, which means we could not comprehensively describe incidence, prevalence, and YLDs in detail. Secondly, the predictions were based on the GBD 2019 database, which means the quality of the data from original registries greatly affects the accuracy and robustness of estimates in the database, potentially leading to bias in the estimates. Thirdly, in the APC model analysis, it's assumed that the cohort is equal to the period minus the age, hence there's an issue of multicollinearity among these three variables. Although there are several algorithms available for this issue(Luo, 2013 ), more factors should be considered when using the model and interpreting the results. Finally, given the differences in the burden and influential factors of PCOS among women of different ethnic backgrounds(VanHise et al., 2023 ), it would be meaningful to provide subgroup analyses by ethnicity, future research may focus on this issue. 5. 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Human Reproduction , 27 (10), 3067–3073. https://doi.org/10.1093/humrep/des232 Additional Declarations No competing interests reported. Supplementary Files TableS1.docx TableS2.docx fig.S1.tif Figure S1. Joinpoint regression analysis in ASPR of PCOS from 1990 to 2019 by SDI region, * P <0.05. fig.S2.tif Figure S2. Joinpoint regression analysis in age-standardized YLD rate of PCOS from 1990 to 2019 by SDI region, * P <0.05. fig.S3.tif Figure S3. Age (A), period (B), and cohort (C) effects on PCOS prevalence by SDI region. fig.S4.tif Figure S4. Age (A), period (B), and cohort (C) effects on PCOS YLD by SDI region. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4260677","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291592458,"identity":"7e36c9f2-c25a-403c-ab43-f7839bb2d905","order_by":0,"name":"Ruijie Li","email":"","orcid":"","institution":"Wuhan Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruijie","middleName":"","lastName":"Li","suffix":""},{"id":291592459,"identity":"69394d94-7297-47e3-9a08-a2961ded38e3","order_by":1,"name":"Ling Zhang","email":"","orcid":"","institution":"Wuhan Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Zhang","suffix":""},{"id":291592463,"identity":"07225c3b-809a-490a-a456-8df1fba2febc","order_by":2,"name":"Yi Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACPmYQaWAjx9jeAxU6QEALG1hLQZoxc88ZICOBGC1g8sPhxPYZOcRqYWfeJl1gcDixd+bbY5I/fzDI8d1IYPxcgNdhbGXSMwzSjWfOzkuT5klgMJa8kcAsPQOvFh4zaR4Da9mNs3PMpIEOS9xwIwEoSFgLM+P+m2fMJH8kMNQTq8VZsXEGj5kE0GEJBoS1sBVb8xikGTP25Bhb86RJGM4887BZGp8Wfv7DG2/z/AFF5RnDmz9sbOT5jicf/IxPCxAYIHMkgJixAb8GNC2jYBSMglEwCjABAF3vP97WePdCAAAAAElFTkSuQmCC","orcid":"","institution":"Wuhan Union Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yi","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-04-13 06:44:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4260677/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4260677/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54920545,"identity":"5059c8b1-a5a9-47c7-92f3-7986fd6f3e0a","added_by":"auto","created_at":"2024-04-18 15:14:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":324631,"visible":true,"origin":"","legend":"\u003cp\u003eThe EAPC for ASIR, ASPR and Age Standardized YLD Rate of PCOS at the regional level. EAPC, estimated annual percentage change; ASIR, age-standardized incidence rate; ASPR, age-standardized prevalence rate; YLDs, years lived with disability; PCOS, polycystic ovary syndrome.\u003c/p\u003e","description":"","filename":"fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4260677/v1/41327bce3ebb9550fcbd57df.png"},{"id":54921262,"identity":"7cd49423-f264-49c1-8a9a-56d433e8440d","added_by":"auto","created_at":"2024-04-18 15:22:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":608767,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint regression analysis in ASIR of PCOS from 1990 to 2019 by SDI region. SDI: sociodemographic index; APC, annual percentage change; * \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4260677/v1/aac5d80ae41ef4f6b7a076b2.png"},{"id":54920543,"identity":"50d1167b-595a-45a5-a81e-b528fc6ebe3b","added_by":"auto","created_at":"2024-04-18 15:14:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":208733,"visible":true,"origin":"","legend":"\u003cp\u003eAge-specific rate of incidence (A)，prevalence (B), and YLD (C) of PCOS from 1990 to 2019.\u003c/p\u003e","description":"","filename":"fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4260677/v1/67ab93bb7785092fb7bfb877.png"},{"id":54920540,"identity":"7d396fcb-5aaa-4bb8-bb8c-fbe3c325e75b","added_by":"auto","created_at":"2024-04-18 15:14:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2198975,"visible":true,"origin":"","legend":"\u003cp\u003eAge (A), period (B) and cohort (C) effects on PCOS incidence by SDI region.\u003c/p\u003e","description":"","filename":"fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4260677/v1/dc1bf6fc3a24484cb5c6a6d1.png"},{"id":54920544,"identity":"bd0f31ae-051f-48c3-992c-dc1cc7b80c93","added_by":"auto","created_at":"2024-04-18 15:14:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":193001,"visible":true,"origin":"","legend":"\u003cp\u003eThe projections of the global age-standardized rate of incidence (A), prevalence (B), and YLD (C) per 100,000 population of PCOS from the BAPC model, 1990–2040. BAPC: Bayesian age-period-cohort.\u003c/p\u003e","description":"","filename":"fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4260677/v1/6b3579faa658ad5e422b3819.png"},{"id":54920547,"identity":"513e9f52-ee39-421d-9e14-fbe92f5621b0","added_by":"auto","created_at":"2024-04-18 15:14:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":96104,"visible":true,"origin":"","legend":"\u003cp\u003eThe projections of global incidence (A), prevalence (B), and YLD (C) numbers of PCOS in the total population, 1990–2040.\u003c/p\u003e","description":"","filename":"fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-4260677/v1/697d4e44b6508950eace47b9.png"},{"id":68043072,"identity":"3b11fa21-c629-4a5a-8068-5df47e9ab4d3","added_by":"auto","created_at":"2024-11-01 17:02:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5090668,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4260677/v1/cf7f3603-ad2c-4bea-a4f6-f9577308c9c2.pdf"},{"id":54920541,"identity":"408f3e2a-700a-4415-8434-3b1780c2c356","added_by":"auto","created_at":"2024-04-18 15:14:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18675,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4260677/v1/524de30cd99e0674be0add16.docx"},{"id":54921261,"identity":"7434b7a2-a862-494d-8079-93cf5caae183","added_by":"auto","created_at":"2024-04-18 15:22:49","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18106,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4260677/v1/0f0a97b6b84e6566f9313a7f.docx"},{"id":54920548,"identity":"8ad48357-a055-46ea-868a-ed2bb07f0e34","added_by":"auto","created_at":"2024-04-18 15:14:50","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3451452,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1.\u003c/strong\u003e Joinpoint regression analysis in ASPR of PCOS from 1990 to 2019 by SDI region, * \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"fig.S1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4260677/v1/ed8108942351234ce7d47d70.tif"},{"id":54921263,"identity":"764e7dd6-f938-4116-818a-4478f21fc40f","added_by":"auto","created_at":"2024-04-18 15:22:50","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3198004,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2.\u003c/strong\u003e Joinpoint regression analysis in age-standardized YLD rate of PCOS from 1990 to 2019 by SDI region, * \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"fig.S2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4260677/v1/14ff5fcf897d13304b92c388.tif"},{"id":54920549,"identity":"958ceca4-421f-4255-9bd3-82d13693a9e0","added_by":"auto","created_at":"2024-04-18 15:14:50","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":4517416,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3. \u003c/strong\u003eAge (A), period (B), and cohort (C) effects on PCOS prevalence by SDI region.\u003c/p\u003e","description":"","filename":"fig.S3.tif","url":"https://assets-eu.researchsquare.com/files/rs-4260677/v1/4b4beb94750a7c56d50ca298.tif"},{"id":54920550,"identity":"2854fbc6-1098-4115-b481-ba0b75e4be99","added_by":"auto","created_at":"2024-04-18 15:14:50","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2497604,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S4.\u003c/strong\u003e Age (A), period (B), and cohort (C) effects on PCOS YLD by SDI region.\u003c/p\u003e","description":"","filename":"fig.S4.tif","url":"https://assets-eu.researchsquare.com/files/rs-4260677/v1/75dd91e9ddad2f04c504125a.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global and regional trends and age-period-cohort effects in polycystic ovary syndrome burden from 1990 to 2019, with predictions to 2040","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePolycystic ovary syndrome (PCOS) is one of the most prevalent metabolic and reproductive diseases affecting women of childbearing age. While the exact cause is unknown, research suggests that it may be due to the interaction of certain genes with environmental factors, which can impair a woman's fertility and threaten her overall health(De Leo et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Merkin et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Patel, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Signature features of PCOS include hyperandrogenism, persistent anovulation, and polycystic ovarian changes(Walters et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Women with PCOS have a significantly higher risk of menstrual disorders, obesity, acne, dyslipidemia, insulin resistance, and type 2 diabetes(Sirmans \u0026amp; Pate, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The prevalence of PCOS was 6.1%, 19.9%, and 15.3%, respectively, according to the National Institutes of Health (NIH) criteria, the Rotterdam criteria, and the Androgen Excess-PCOS Society (AE-PCOS) criteria(Azziz et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yildiz et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The dangers of PCOS cannot be ignored.\u003c/p\u003e \u003cp\u003eWith the improvement of socio-economic level and increased access to medical resources, women's reproductive health has received more and more attention, and more and more patients with PCOS are found in the population. However, global and large-scale epidemiological studies on PCOS are insufficient. Studies have shown that there are differences in the diagnosis and treatment patterns of PCOS between gynecologists and reproductive endocrinologists, and that the insufficient number of reproductive endocrinologists also prevents the PCOS population from accessing adequate specialized medical care(Cussons et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Dokras et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The harm of PCOS to women's fertility and long-term health needs urgent attention, and effective prevention and treatment measures should be taken immediately. Currently, several interventions have been proposed to alleviate symptoms and reduce comorbidities, but the medical and emotional costs are high(Azziz et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In 2004, the economic burden of PCOS in the United States exceeded \u003cspan\u003e$\u003c/span\u003e4\u0026nbsp;billion(Azziz et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In 2014, the estimated healthcare cost for PCOS in the UK was at least \u0026pound;237\u0026nbsp;million(Ding et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).Given the significant social, physical, and public health impacts of the disease, there is a need for a systematic assessment of the changes and long-term trends in the burden of PCOS, with projections at the global and regional levels, to reflect the effectiveness of public health strategies and to inform policies regarding disease screening and resource allocation.\u003c/p\u003e \u003cp\u003eAlthough several previous studies have reviewed the burden of PCOS in multiple regions and countries(Gao et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Safiri et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), there are some shortcomings. For example, disability adjusted life years (DALYs) are reported, rather than years lived with disability (YLDs). The former are not suitable to describe the burden of non-fatal diseases such as PCOS, as there is no mortality for PCOS. In addition, all current studies have only described age-standardized rates (ASR) and/or estimated annual percentage change of ASR when presenting trends in PCOS, but these methods remain descriptive and non-parametric. In order to overcome the intrinsic limitations of the nonparametric methods and further fully exploit the limited longitudinal epidemiological data, our study used the age-period-cohort (APC) model to simultaneously assess the age effects, period effects, and cohort effects on the temporal trends of PCOS. Additionally, previous studies have focused more attention on the overall trend of PCOS burden, neglecting the monitoring of stage-specific changes, and have lacked calculations of future long-term trends. Our study utilized the joinpoint regression model to depict local change characteristics, and also predicted future disease burden of PCOS using the Bayesian age-period-cohort (BAPC) model.\u003c/p\u003e \u003cp\u003eTherefore, in this study, we described the long-term and partial time trends in PCOS incidence, prevalence, and YLDs at the global and regional levels. Furthermore, we utilized the age-period-cohort and Bayesian age-period-cohort models to analyze the contribution of different elements to the epidemiological outcome of PCOS and project future incidence, prevalence, and YLDs up to 2040.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Overview\u003c/h2\u003e \u003cp\u003eThe GBD 2019 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ghdx.healthdata.org/gbd-2019\u003c/span\u003e\u003cspan address=\"https://ghdx.healthdata.org/gbd-2019\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), led by the Institute for Health Metrics and Evaluation (IHME), provides the most comprehensive and up-to-date data assessment of the descriptive epidemiology of diseases in 21 regions and 204 countries and territories from 1990 to 2019, using all available data(Murray et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). All data is calculated by direct query and downloaded from the GBD results tool. A detailed description of the method can be found on the help page of the database and other publication(Vos et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The GBD collects health data from life records, censuses, registers, health surveys, population surveillance, administrative reports, scientific research, discharge records, records of outpatient visits and health insurance claims, as well as many other sources. These are then input into an algorithm to generate an estimate of the burden of disease. In the GBD study, disease estimates were generated by age, year, and location using the Bayesian meta-regression tool DisMod-MR 2.1 to ensure consistent epidemiological parameters for the conditions under study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data source\u003c/h2\u003e \u003cp\u003eData on the global burden of PCOS were obtained from published sources using the Global Health Data Exchange Query Tool. This study obtained global, regional, and SDI quintile data on incidence, prevalence, and YLDs of PCOS from 1990 to 2019 from the GBD. YLDs were estimated as the product of prevalence estimate and disability weight for health states of each mutually exclusive sequela adjusted for comorbidity. The age range is limited to between 10 and 54 years old, divided into nine 5-year-old age groups. GBD divides the socio-demographic index (SDI) of 21 regions and 204 countries and territories into five components (high, high-middle, middle, low-middle, and low) based on total fertility rate, per capita income, and average years of education. SDI ranges from 0 to 1, with higher values indicating higher levels of socio-economic development. In addition, GBD regions are not actual geopolitical units, but groupings of countries created for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analyzes\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Calculation of the estimated annual percentage change\u003c/h2\u003e \u003cp\u003eAge standardized rates of incidence, prevalence, and YLD from 1990 to 2019 were used to assess the burden of polycystic ovary syndrome. Temporal trends of burden over thirty years are reflected by the estimated annual percentage change (EAPC). The EAPC and its 95% confidence interval (CI) are obtained from the formula EAPC\u0026thinsp;=\u0026thinsp;100 \u0026lowast; (exp (β)\u0026thinsp;\u0026minus;\u0026thinsp;1), where β is the annual change in ln (ASR). Positive EAPC and lower bound of its 95% CI indicate an upward trend in ASR, while negative EAPC and upper bound of its 95% CI indicate a downward trend in ASR. Otherwise, the ASR will be considered stable over time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Joinpoint regression analysis\u003c/h2\u003e \u003cp\u003eIn order to detect changes in parameter trends of PCOS health metrics, the joinpoint regression model was utilized, which can be implemented by Joinpoint software version 4.9.0.1 from the National Cancer Institute (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://surveillance.cancer.gov/joinpoint\u003c/span\u003e\u003cspan address=\"http://surveillance.cancer.gov/joinpoint\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The joinpoint regression program's advantage is that it can indicate if changes over time are statistically significant(Kim et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Therefore, we analyzed the age-standardized rate of PCOS incidence, prevalence and, YLDs by different SDI regions, calculated the number of junction points and the position of each junction point by Monte Carlo permutation test, and the corresponding test statistic \u003cem\u003eP\u003c/em\u003e value (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05). For convenience of understanding, slopes are often converted to annual percentage changes (APCs) and average annual percent change (AAPC); that is, the estimated annual percentage change from one connection point to the next(Li \u0026amp; Du, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Age-period-cohort analysis and projections\u003c/h2\u003e \u003cp\u003eTo estimate the effect of age, year period, and birth cohort, we performed the age-period-cohort model. This model illustrates the various risks associated with various age groups (age effects), the impact of environmental and historical factors (period effects), and the impacts of risk factor exposure on a population with the same birth year (cohort effects). The age-period-cohort model can be expressed as follows: Ln(R\u003csub\u003e\u003cem\u003ei j κ\u003c/em\u003e\u003c/sub\u003e)=\u0026micro;\u0026thinsp;+\u0026thinsp;α\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;γ\u003csub\u003eκ\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε, in which \u0026micro; is the constant, and \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eijk\u003c/em\u003e\u003c/sub\u003e represents the attributable mortality rate in the \u003cem\u003ei\u003c/em\u003eth age group, \u003cem\u003ej\u003c/em\u003eth time period, and \u003cem\u003ek\u003c/em\u003eth birth cohort. α\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, β \u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e, γ\u003csub\u003eκ\u003c/sub\u003e, and ε are the effects of age, period, cohort, and random error, respectively(Rosenberg \u0026amp; Anderson, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In the age-period-cohort analysis, the relative risk (RR) is defined as the exponential value of the estimations of α\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, β \u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e, and γ\u003csub\u003eκ\u003c/sub\u003e. The overall log-linear trend by year period and birth cohort was then computed, and this is known as the local drift. Through the age-period-cohort Web Tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://analysistools.nci.nih.gov/apc/\u003c/span\u003e\u003cspan address=\"https://analysistools.nci.nih.gov/apc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the age-period-cohort model was estimated(Rosenberg et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the present study, based on packages \u0026ldquo;BAPC\u0026rdquo; and \u0026ldquo;INLA\u0026rdquo; in R software, the BAPC model was used to project the burden of PCOS from 2020 to 2040. The primary data collation and calculation and the plotting of graphs in this study were performed in the R program version 4.1.0. A two-sided \u003cem\u003eP\u003c/em\u003e value less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 The burden of PCOS at the regional level, 1990\u0026ndash;2019\u003c/h2\u003e\n\u003cp\u003eGlobally, the incident cases of PCOS increased from 1.38\u0026nbsp;million in 1990 to 2.13\u0026nbsp;million in 2019, and the age-standardized incidence rate (ASIR) increased from 46.1 in 1990 to 59.8 in 2019 per 100,000, with an EAPC of 0.85 (95%UI:0.82\u0026mdash;0.87). The EAPC of ASIR from 1990 to 2019 was found to be highest in the low-middle SDI regions (1.86, 95%UI: 1.81\u0026mdash;1.92) and high SDI regions had the lowest EAPC (-0.01, 95%UI: -0.17\u0026mdash;0.13). Regionally, the EAPC for PCOS-related ASIR ranged from \u0026minus;\u0026thinsp;0.82 (95%UI:-1.23\u0026mdash;-0.42) in High-income North America to 2.58 (95%UI: 2.48\u0026mdash;2.68) in Southeast Asia(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe incidence of PCOS in 1990 and 2019 and temporal trends between 1990 and 2019.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1990\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019 (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLocation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncidence_Number_1000 (95%UI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eASIR per 100,000 (95%UI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncidence_Number_1000 (95%UI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eASIR per 100,000 (95%UI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEAPC of incidence rate\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlobal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1377.9 (941.8 to 1817)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.1 (31.6 to 61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2125.5 (1490 to 2803.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.8 (41.7 to 78.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.85 (0.82\u0026ndash;0.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSDI region\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh SDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e414.7 (284.6 to 562.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121.3 (82.9 to 162.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e442 (328 to 564)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e136.3 (100.6 to 172.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.02 (-0.17-0.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh-middle SDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e258.8 (177 to 343.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.6 (31.2 to 60.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e314.7 (216.7 to 418.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68.5 (46.5 to 91.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.60 (1.53\u0026ndash;1.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMiddle SDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e481 (325.1 to 641.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.5 (31.4 to 62.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e789.5 (541.6 to 1049.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77.2 (52.7 to 103.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.76 (1.72\u0026ndash;1.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow-middle SDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e165.2 (109.7 to 221.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.9 (15.9 to 32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e392.1 (261 to 527.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.9 (26.6 to 53.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.86 (1.81\u0026ndash;1.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow SDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.4 (36.8 to 78.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.9 (11.7 to 24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e185.7 (121 to 253.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.1 (16.6 to 34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.24 (1.21\u0026ndash;1.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGBD region\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAndean Latin America\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.6 (15.8 to 32.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.9 (63 to 129.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.4 (27.4 to 55.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129.3 (87.4 to 177.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.10 (1.00-1.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAustralasia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.5 (11.1 to 20.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e168.1 (120.9 to 222.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.9 (13.3 to 26.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e198.4 (131.8 to 263)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.37 (0.25\u0026ndash;0.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCaribbean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.2 (5.6 to 11.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.7 (27 to 54.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.2 (7.5 to 15.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.3 (34.4 to 70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91 (0.84\u0026ndash;0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCentral Asia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.7 (3 to 6.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.2 (7.8 to 16.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.7 (5.1 to 10.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (11.8 to 24.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.28 (1.18\u0026ndash;1.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCentral Europe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.9 (2.4 to 5.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.9 (4.3 to 9.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (2 to 4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.6 (5.7 to 11.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.72 (0.67\u0026ndash;0.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCentral Latin America\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e109.7 (72.6 to 152.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.5 (66 to 137.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135.6 (91.3 to 182.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e109.3 (72.8 to 147.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.17 (-0.31\u0026ndash;0.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.1 (3.2 to 7.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (9.4 to 20.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.8 (12.8 to 27.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.9 (15 to 31.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.32 (1.14\u0026ndash;1.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEast Asia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195.8 (129.4 to 264.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.6 (18.9 to 38.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e233 (159.2 to 310.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.2 (37 to 74.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.22 (2.00-2.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEastern Europe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.4 (4.8 to 10.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.6 (4.9 to 10.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (4.7 to 9.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.3 (6.7 to 14.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04 (1.00-1.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.8 (13.8 to 30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.7 (11.5 to 24.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.8 (40.8 to 87.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.9 (14.9 to 31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90 (0.85\u0026ndash;0.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e159.6 (107.2 to 225.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e189.9 (127.5 to 263.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e111.3 (74.6 to 154.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e221.7 (150.4 to 306.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.40 (0.32\u0026ndash;0.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh-income North America\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e128.1 (85.7 to 173.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112.8 (75 to 153.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e165.5 (130.9 to 203.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122 (96.4 to 149.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.82 (-1.23\u0026ndash;0.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e127.2 (85 to 172.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.7 (38.9 to 77.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e236.3 (158.3 to 322.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77.2 (51.6 to 105.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.12 (1.06\u0026ndash;1.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOceania\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.8 (1.2 to 2.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44 (29.9 to 59.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.6 (3.1 to 6.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.3 (42.3 to 85.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 (0.80\u0026ndash;1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSouth Asia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150.4 (100.8 to 201.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.5 (15.9 to 31.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e398.4 (267.8 to 533)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.1 (27 to 53.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.06 (1.91\u0026ndash;2.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSoutheast Asia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e154.3 (100.9 to 209)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.4 (34.5 to 70.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e334.2 (226.9 to 448.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103.5 (70.5 to 138.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.58 (2.48\u0026ndash;2.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSouthern Latin America\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.2 (7.4 to 15.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.4 (27.9 to 58.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.7 (13.6 to 28.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.4 (46.6 to 97.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.79 (1.55\u0026ndash;2.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.7 (6.9 to 14.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.3 (20 to 41.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.4 (10.7 to 22.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.1 (25.8 to 53.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86 (0.76\u0026ndash;0.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTropical Latin America\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (13.9 to 28.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.1 (14.7 to 29.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (16.2 to 32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.2 (16.1 to 32.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.24 (-0.40\u0026ndash;0.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWestern Europe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e197.1 (133.9 to 265.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e136.2 (91.9 to 185.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e197.4 (134.5 to 266.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149.7 (101.4 to 203.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.22 (0.16\u0026ndash;0.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.8 (13.2 to 28.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.1 (11.1 to 23.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75.3 (48.3 to 103.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.4 (15.8 to 33.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.97 (0.76\u0026ndash;1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe number and prevalence rate of PCOS were also increasing globally. In 1990, the prevalent counts were 34.26\u0026nbsp;million, while up to 2019, the count arrived at 65.99\u0026nbsp;million. The age-standardized prevalence rate (ASPR) increased from 1286.2 in 1990 to 1677.8 in 2019 per 100,000, with an EAPC of 0.84 (95%UI: 0.80\u0026mdash;0.88). The EAPC of ASIR from 1990 to 2019 was found to be highest in the low-middle SDI regions (2.06, 95%UI: 2.00\u0026mdash;2.12) and high SDI regions had the lowest EAPC (0.02, 95%UI: -0.13\u0026mdash;0.17). Regionally, the EAPC for PCOS-related ASPR ranged from \u0026minus;\u0026thinsp;0.76 (95%UI: -1.16\u0026mdash;-0.37) in High-income North America to 2.59 (95%UI: 2.48\u0026mdash;2.69) in Southeast Asia (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAs for years lived with disability (YLDs), the incident cases increased to 0.58\u0026nbsp;million in 2019 compared to 1990 (0.30\u0026nbsp;million). And the age-standardized YLD rate increased from 11.3 in 1990 to 14.7 in 2019 per 100,000, with an EAPC of 0.82 (95%UI:0.78\u0026mdash;0.87). As far as SDI is concerned, the EAPC from 1990 to 2019 was found to be highest in the low-middle SDI regions (2.03, 95%UI: 1.98\u0026mdash;2.09) and high SDI regions had the lowest EAPC (0.02, 95%UI: -0.13\u0026mdash;0.16). Regionally, the EAPC ranged from \u0026minus;\u0026thinsp;0.77 (95%UI: -1.16\u0026mdash;-0.38) in High-income North America to 2.55 (95%UI: 2.45\u0026mdash;2.65) in Southeast Asia (Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Joinpoint regression analysis of PCOS burden\u003c/h2\u003e\n\u003cp\u003eThe trends of ASIR in global, high-middle SDI, middle SDI, low-middle SDI, and low SDI were similar, showing a year-on-year upward trend, with AAPC values ranging from 0.9 to 1.8, and the trends were both statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The tendency of ASIR in high SDI was relatively more complex, with significant increases from 1990 to 2001 and 2010 to 2019, but decreases from 2001 to 2010 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe trends in PCOS burden by Joinpoint regression\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIncidence\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePrevalence\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYLD\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSDI factor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePeriod\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEstimate(%)(95%UI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePeriod\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEstimate(%)(95%UI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePeriod\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEstimate(%)(95%UI)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlobal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8 (0.8,0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.9,1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (1,1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1996\u0026ndash;1999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3 (1.1,1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1996\u0026ndash;1999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.5 (1.4,1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1996\u0026ndash;1999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.5 (1.4,1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1999\u0026ndash;2004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9 (0.9,1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1999\u0026ndash;2004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9 (0.8,0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1999\u0026ndash;2004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9 (0.8,0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2004\u0026ndash;2009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6 (0.5,0.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2004\u0026ndash;2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4 (0.4,0.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2004\u0026ndash;2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4 (0.4,0.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2009\u0026ndash;2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8 (0.8,0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2010\u0026ndash;2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9 (0.9,0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2010\u0026ndash;2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9 (0.8,0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.5 (1.4,1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.6 (1.4,1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.6 (1.5,1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAAPC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9 (0.9,0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9 (0.9,0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9 (0.9,0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh SDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7 (0.7,0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9 (0.9,1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.9,1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2001\u0026ndash;2005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.6 (-1,-0.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2000\u0026ndash;2005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.2 (-0.4,-0.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2000\u0026ndash;2005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.3 (-0.4,-0.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2005\u0026ndash;2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.9 (-2.1,-1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2005\u0026ndash;2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.8 (-1.9,-1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2005\u0026ndash;2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.8 (-1.9,-1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2010\u0026ndash;2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.9,1.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2010\u0026ndash;2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8 (0.7,0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2010\u0026ndash;2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8 (0.7,0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2016\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.8 (2.4,3.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2016\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.5 (2.3,2.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2016\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.5 (2.3,2.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAAPC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4 (0.3,0.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4 (0.4,0.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4 (0.3,0.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh-middle SDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1992\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3 (0.1,0.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1995\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (1,1.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.2 (1.1,1.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1992\u0026ndash;1996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5 (0.4,0.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1995\u0026ndash;2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.1 (2.1,2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1996\u0026ndash;1999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.5 (2.3,2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1996\u0026ndash;2001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.9 (1.8,2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2000\u0026ndash;2004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.5 (1.4,1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1999\u0026ndash;2004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.6 (1.5,1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2001\u0026ndash;2005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.2 (2.1,2.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2004\u0026ndash;2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9 (0.8,0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2004\u0026ndash;2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8 (0.8,0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2005\u0026ndash;2012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.7 (1.7,1.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2010\u0026ndash;2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1 (1.1,1.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2010\u0026ndash;2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1 (1,1.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2012\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1 (1.1,1.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.5 (1.3,1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.5 (1.4,1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAAPC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.4 (1.4,1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3 (1.3,1.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3 (1.3,1.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMiddle SDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1993\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3 (2.2,2.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.1 (2,2.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.1 (2.1,2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1993\u0026ndash;2003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.7 (1.7,1.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1996\u0026ndash;1999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.8 (2.5,3.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1996\u0026ndash;1999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.8 (2.4,3.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2003\u0026ndash;2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (2,2.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1999\u0026ndash;2004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (1.9,2.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1999\u0026ndash;2004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (1.9,2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2010\u0026ndash;2014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.5 (1.3,1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2004\u0026ndash;2014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.7 (1.7,1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2004\u0026ndash;2014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.7 (1.6,1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2014\u0026ndash;2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.7,1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2014\u0026ndash;2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.2 (0.9,1.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2014\u0026ndash;2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1 (0.8,1.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.7 (1.3,2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (1.6,2.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2017\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (1.7,2.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAAPC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.8 (1.7,1.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.9 (1.9,2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.9 (1.9,2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow-middle SDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.4 (1.3,1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1995\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.5 (1.4,1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.6 (1.5,1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1996\u0026ndash;2005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.8 (1.7,1.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1995\u0026ndash;2003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.9 (1.8,1.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1996\u0026ndash;2005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.9 (1.9,2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2005\u0026ndash;2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.7 (2.6,2.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2003\u0026ndash;2006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.1 (1.8,2.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2005\u0026ndash;2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.9 (2.9,3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.6 (1.5,1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2006\u0026ndash;2009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.3 (3,3.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.6 (1.6,1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2009\u0026ndash;2012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.1 (1.9,2.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2012\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.7 (1.7,1.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAAPC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.8 (1.8,1.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (1.9,2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.9 (1.9,1.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow SDI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1995\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5 (0.4,0.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1995\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7 (0.6,0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8 (0.7,0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1995\u0026ndash;2007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3 (1.3,1.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1995\u0026ndash;2001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3 (1.2,1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1996\u0026ndash;2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3 (1.1,1.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2007\u0026ndash;2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.6 (1.1,2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2001\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.5 (1.5,1.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2000\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.4 (1.4,1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.2 (1.2,1.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAAPC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.2 (1.1,1.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3 (1.3,1.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3 (1.2,1.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePredictions of PCOS burden to 2040\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIncidence\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePrevalence\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYLD\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eyear\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo._100000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eASRs per 100000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo._100000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eASRs per 100000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo._100000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eASRs per 100000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1990\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e372.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1286.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1991\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e383.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1297.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1992\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e393.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1310.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.52\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1993\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e404.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1323.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.63\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1994\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e415.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1335.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1995\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e426.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1347.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.85\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e438.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1363.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.99\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1997\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e452.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1382.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1998\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e466.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1403.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e480.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1423.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e493.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1438.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e505.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1450.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e517.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1463.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e529.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1475.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e541.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1485.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e551.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1493.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e560.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1499.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e570.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1504.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e578.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1509.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e587.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1515.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e597.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1524.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.39\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e608.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1537.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.49\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e620.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1552.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e631.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1567.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e642.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1582.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e652.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1595.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.97\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e662.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1609.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e673.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1624.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e687.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1647.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.41\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e704.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1677.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.68\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e725.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1703.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.90\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e740.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1730.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e756.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1758.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.37\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e773.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1786.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e791.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1815.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e808.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1844.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e826.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1874.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.37\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e844.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1903.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.63\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e862.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1934.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.90\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e881.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1965.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e901.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1997.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.45\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e920.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2029.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.73\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2032\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e940.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2061.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2033\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e960.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2094.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e980.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2128.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2035\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1000.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2163.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.91\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2036\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1021.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2197.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2037\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1042.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2232.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1063.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2267.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.85\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1085.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2303.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2040\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1107.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2340.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFrom 1990 to 2019, ASPR in global, high-middle SDI, middle SDI, low-middle SDI, and low SDI showed a steady upward trend (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). However, ASPR in high SDI showed a downward trend from 2000 to 2010 and an upward trend for the rest of the years (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eExcept for high SDI, which exhibited a downward tendency from 2000 to 2010 (2000\u0026ndash;2005 APC=-0.27; 2005\u0026ndash;2010 APC=-1.80), and an upward trend in the remaining years (Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e), the age-standardized YLD rate showed a year-on-year increase in both global and other SDI regions (AAPC\u0026thinsp;\u0026gt;\u0026thinsp;0, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Age-based description of the burden of PCOS\u003c/h2\u003e\n\u003cp\u003eWe analyzed the age-specific rate for incidence, prevalence, and YLDs according to age groups, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). High incidence rate was concentrated in the 10\u0026ndash;14 and 15\u0026ndash;19 age groups, with the highest age-specific incidence rate among all age groups in high SDI regions. High prevalence rate and high YLD rate were concentrated in the 20-44-year-olds, and the prevalence rate and YLD rate were highest among all age groups in high SDI regions. Over the past three decades, the burden of PCOS in different age groups has increased worldwide and in most SDI regions. However, in the high SDI regions, the tendency of burden was relatively more complex.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Age\u0026ndash;period\u0026ndash;cohort effect of PCOS burden\u003c/h2\u003e\n\u003cp\u003eThe age-period-cohort effects on PCOS incidence are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The age effect showed that the incidence rate was highest in the 10\u0026ndash;20 age group in global and all SDI regions, followed by a sharp decline, and decreased with age (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). The period effect showed that the risk of incidence increased first and then decreased with the increasing years, and the period RR in high SDI regions was consistently less than 1(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). The global incidence risk showed a relatively stable trend across the entire birth cohort; in the high-middle SDI, middle SDI, low-middle SDI, and low SDI, the RR value of cohort was also stable born from 1940 to 1990, and the risk of cohort effect showed a significant upward trend after 1990; in high SDI regions, the cohort RR showed a relatively complex variation, with an overall declining trend as the birth cohort developed (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003eThe age-period-cohort effects on PCOS prevalence are shown in Fig. \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e. The age effect showed an inverted U-shaped trend, that is, the prevalence rate initially increased and then decreased with age, except for the high SDI regions, where the effect value peaked in the 30\u0026ndash;34 age group, the effect values peaked in the 40\u0026ndash;44 age group in global and other SDI regions (Fig. \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003eA). In global, high-middle SDI, middle SDI, low-middle SDI, and low SDI, the period RR showed an upward trend with the increasing years; however, in high SDI regions, the RR value first increased, then decreased, and then increased again, and was always less than 1, indicating a low risk of period effect (Fig. \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003eB). The cohort effect showed that the prevalence risk of PCOS was generally increasing in global, high-middle SDI, middle SDI, low-middle SDI, and low SDI regions; in high SDI regions, cohort RR fluctuated with birth year, showing an overall downward trend(Fig. \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003eThe age-period-cohort effects on PCOS YLDs are shown in Fig. \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e. For the age factor, the YLD rate first raised and then decreased with age. The effect values peaked at the age of 20\u0026ndash;24 years in high SDI regions, while in global and other SDI regions, the effect values peaked at the age of 40\u0026ndash;44 years (Fig. \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003eA). The period RR showed an increasing trend over time in global, high-middle SDI, middle SDI, low-middle SDI, and low SDI regions, but in high SDI regions, the RR value increased first, then decreased and then increased again, and always remained below 1, indicating that the period effect risk was low (Fig. \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003eB). With regard to the cohort factor, the risk of cohort effect was generally upward in global, high-middle SDI, middle SDI, low-middle SDI, and low SDI regions; while cohort RR fluctuated with birth year in high SDI regions, and showed an overall decreasing tendency (Fig. \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003eC).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e3.5 Predictions of incidence, prevalence, and YLDs of PCOS from 2020 to 2040\u003c/h2\u003e\n\u003cp\u003eCounts of global PCOS in 2019 and projected 2040 were 2134,202 and 3095,928 for incident cases, and ASIR will rise to 80.05 cases per 100,000 population in 2040 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). The ASPR of PCOS is likely to rise to 2340/100,000 and prevalence cases will increase to 110736,186 by 2040 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB, \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). The age-standardized YLD rate in 1990, 2020, and 2040 were 11.30/100,000, 14.90/100,000, and 20.50/100,000, respectively. And there are 920,923 YLD counts projected for 2040 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC, \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). Compared to the past three decades, the incidence, prevalence, and YLD of PCOS are all expected to show an upward trend over the next two decades.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study indicated that during the period of 1990\u0026ndash;2019, the global ASIR, ASPR, and age-standardized YLD rate for PCOS showed an increasing trend. However, EAPC varied widely across different SDI regions and GBD regions. The examinations of age, period, and cohort effects differentiate the source of trends in incidence, prevalence, and YLD by different ages, periods, and birth cohorts for the globe and different SDI regions. It was estimated that the incidence, prevalence, and YLD of PCOS would increase from 2020 to 2040.\u003c/p\u003e \u003cp\u003eFrom 1990 to 2019, the incidence, prevalence, and YLDs of PCOS were on the rise worldwide, highlighting a growing concern in public health. However, this upward trend varies widely across SDI regions and GBD regions, suggesting divergent epidemiological patterns in different socioeconomic settings. The EAPC being highest in the low-middle SDI regions may reflect burgeoning health awareness and improved diagnostic capabilities in these areas, furthermore an increasingly prosperous diet content with urbanization may lead to the increasing probability of developing PCOS(Kulkarni et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Conversely, the minimal change in high SDI regions might indicate a plateau in medical care consciousness or possibly the impact of better healthcare access and preventive strategies attenuating the rise in incident cases. This variation necessitates targeted public health strategies that address the specific needs and challenges of each region.\u003c/p\u003e \u003cp\u003eOur joinpoint regression analysis further delineates the complexity of PCOS epidemiology, revealing that ASRs of incidence, prevalence, and YLD have a tendency to rise first, then decline, and then rise again across different periods in high SDI regions. These fluctuating trends suggest the impact of health awareness activities, policy changes, or the evolution of diagnostic criteria over time. For example, before the 21st century, the diagnostic criteria for PCOS were not standardized or unified. The Rotterdam criteria and the Androgen Excess-PCOS Society criteria, established in 2003 and 2006 respectively, made the diagnosis of PCOS more accurate, reflecting an increased attention to polycystic ovary syndrome in related countries during this period.\u003c/p\u003e \u003cp\u003eThe age-based description of PCOS burden, with high incidence rates in young age groups of 10\u0026ndash;19 years old sets the target population for early detection and treatment of this condition. However, it is difficult to diagnose PCOS during adolescence(Legro et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), as the manifestations overlap with the physiological changes of puberty(Witchel et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, pediatric and gynecologic medical personnel must use accurate methods to distinguish between the early manifestations of PCOS and the physiology of puberty. High prevalence and YLD rates in the 20-44-year-olds, underscores the chronic nature of PCOS and its significant impact on women\u0026rsquo;s health throughout their reproductive years. The highest burden in high SDI regions might mirror better diagnostic practices and greater health-seeking behavior, on the other hand, due to the high compliance of developed countries with Westernized diets, the risks of obesity, insulin resistance, and early puberty are higher, all of which are associated with PCOS(Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kopp, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Limitations in accessing screening, treatment, and disease management services may also lead to an underestimation of PCOS in developing countries. These dynamics emphasize the need for early intervention and sustained management to mitigate PCOS\u0026rsquo;s long-term impacts.\u003c/p\u003e \u003cp\u003eThe analysis of the age, period, and cohort effects on PCOS revealed the complexity of the epidemiology of this condition, showing varying trends across different SDI regions. The incidence of PCOS was observed to be highest in the 10-20-year age group, then sharply declined with increasing age. This may reflect the biological reality that PCOS begins during adolescence and early adulthood, a phase when significant hormonal changes occur in the body(Bremer, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and when teenagers are subjected to a slew of emotional fluctuations caused by setbacks, interpersonal relationships, and peer pressure(Adone \u0026amp; Fulmali, n.d.), all of which could trigger PCOS. Both prevalence and YLDs increase then decline with advancing age, further emphasizing the significant impact of PCOS on women of reproductive age. In Global and most SDI regions, the period RR and cohort RR of prevalence and YLD are on an upward trend, indicating that with time, new generations face an increased risk of the disease. However, in high SDI regions, the period RR is always less than 1, suggesting that the risk of PCOS is not increasing but decreasing or stabilizing. Overall, these results highlight the complex interplay of generations, social role changes, and potential exposure factors influencing the burden of PCOS. These findings call for global public health policymakers to adopt customized preventive measures and treatment strategies for populations across different regions and age groups(Teede et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePredictions indicate that the burden of PCOS will continue to rise globally, with obesity driven by globalization and urbanization cited as one of the significant reasons(Mu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Lifestyle changes around the world, such as unhealthy dietary habits, lack of exercise, smoking, and others, are risk factors for PCOS(Eleftheriadou et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kulkarni et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, environmental changes, including pollution and exposure to chemical substances, can also lead to women developing PCOS and causing reproductive disorders(Chiang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The continuous growth of the global population and the aging demographic structure imply that more women will enter the age range potentially affected by PCOS. With increasing public awareness of PCOS and advances in medical technology, more undiagnosed cases may be identified and recorded, thus increasing the number of cases. Therefore, the prevention, diagnosis, and treatment strategies for PCOS will need to adapt to these changes and challenges. The global health system will need to increase resource allocation and enhance public awareness to address this growing disease burden.\u003c/p\u003e \u003cp\u003eThis study is the first to parametrically analyze the disease burden of PCOS globally using the age-period-cohort model and predict the future epidemiological trends of PCOS using the Bayesian age-period-cohort model. Our research conducted an in-depth secondary analysis of global PCOS based on GBD data, but there are still some limitations. Firstly, this study was based on summarized data rather than individual-level data, which means we could not comprehensively describe incidence, prevalence, and YLDs in detail. Secondly, the predictions were based on the GBD 2019 database, which means the quality of the data from original registries greatly affects the accuracy and robustness of estimates in the database, potentially leading to bias in the estimates. Thirdly, in the APC model analysis, it's assumed that the cohort is equal to the period minus the age, hence there's an issue of multicollinearity among these three variables. Although there are several algorithms available for this issue(Luo, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), more factors should be considered when using the model and interpreting the results. Finally, given the differences in the burden and influential factors of PCOS among women of different ethnic backgrounds(VanHise et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), it would be meaningful to provide subgroup analyses by ethnicity, future research may focus on this issue.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOverall, the global burden of polycystic ovary syndrome increased over the past 30 years, with variability across SDI regions and GBD regions, and this trend is expected to continue in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.L. was responsible for methodology, data curation, formal analysis, and draft writing. L.Z. was responsible for draft writing. Y.L. was responsible for project administration and manuscript review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure Statement:\u0026nbsp;\u003c/strong\u003eThe authors declared no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecific author contributions: Ruijie Li:\u0026nbsp;\u003c/strong\u003eMethodology, Data curation, Formal analysis, Writing \u0026ndash; original draft. \u003cstrong\u003eLing Zhang\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; original draft. \u003cstrong\u003eYi Liu\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eConceptualization, Project administration, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdone, A., \u0026amp; Fulmali, D. G. (n.d.). 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Prevalence of polycystic ovary syndrome in a metabolically healthy obese population. \u003cem\u003eInternational Journal of Gynaecology and Obstetrics: The Official Organ of the International Federation of Gynaecology and Obstetrics\u003c/em\u003e, \u003cem\u003e146\u003c/em\u003e(2), 164\u0026ndash;169. https://doi.org/10.1002/ijgo.12824\u003c/li\u003e\n\u003cli\u003eMurray, C. J. L., Abbafati, C., Abbas, K. M., Abbasi, M., Abbasi-Kangevari, M., Abd-Allah, F., Abdollahi, M., Abedi, P., Abedi, A., Abolhassani, H., Aboyans, V., Abreu, L. G., Abrigo, M. R. M., Abu-Gharbieh, E., Abu Haimed, A. K., Abushouk, A. I., Acebedo, A., Ackerman, I. N., Adabi, M., \u0026hellip; Lim, S. S. (2020). Five insights from the Global Burden of Disease Study 2019. \u003cem\u003eThe Lancet\u003c/em\u003e, \u003cem\u003e396\u003c/em\u003e(10258), 1135\u0026ndash;1159. https://doi.org/10.1016/S0140-6736(20)31404-5\u003c/li\u003e\n\u003cli\u003ePatel, S. (2018). 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J., Handelsman, D. J., \u0026amp; Campbell, R. E. (2018). New Perspectives on the Pathogenesis of PCOS: Neuroendocrine Origins. \u003cem\u003eTrends in Endocrinology \u0026amp; Metabolism\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(12), 841\u0026ndash;852. https://doi.org/10.1016/j.tem.2018.08.005\u003c/li\u003e\n\u003cli\u003eWitchel, S. F., Burghard, A. C., Tao, R. H., \u0026amp; Oberfield, S. E. (2019). The diagnosis and treatment of PCOS in adolescents: An update. \u003cem\u003eCurrent Opinion in Pediatrics\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(4), 562. https://doi.org/10.1097/MOP.0000000000000778\u003c/li\u003e\n\u003cli\u003eYildiz, B. O., Bozdag, G., Yapici, Z., Esinler, I., \u0026amp; Yarali, H. (2012). Prevalence, phenotype and cardiometabolic risk of polycystic ovary syndrome under different diagnostic criteria. \u003cem\u003eHuman Reproduction\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(10), 3067\u0026ndash;3073. https://doi.org/10.1093/humrep/des232\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bayesian age-period-cohort, prediction, polycystic ovary syndrome, socio-demographic index, Joinpoint regression analysis","lastPublishedDoi":"10.21203/rs.3.rs-4260677/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4260677/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjectives\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe aimed to analyze the secular trends of global and regional polycystic ovary syndrome (PCOS) burden, the effects of age, period, and birth cohort, and forecast the global burden over time.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMaterial and methods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBased on the incidence, prevalence, and years lived with disability (YLDs) data of PCOS from the 2019 GBD database for the years 1990 to 2019, we used the estimated annual percentage change (EAPC) and the annual percentage change (APC) calculated using the joinpoint regression model to describe the burden trends. An age-period-cohort model was utilized to analyze the effects of age, period, and birth cohort on the PCOS age-standardized rate. The burden of PCOS was projected by conducting the Bayesian age-period-cohort (BAPC) model.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGlobally, there were significant increases in age-standardized incidence rate (ASIR) (EAPC\u0026thinsp;=\u0026thinsp;0.85, 95%UI:0.82\u0026mdash;0.87), age-standardized prevalence rate (ASPR) (EAPC\u0026thinsp;=\u0026thinsp;0.84, 95%UI:0.80\u0026mdash;0.88), and age-standardized YLD rate (EAPC\u0026thinsp;=\u0026thinsp;0.82, 95%UI:0.78\u0026mdash;0.87) of PCOS from1990-2019. Period RR and cohort RR showed an upward trend in global and most SDI regions, indicating an increased risk of PCOS for new generations. Meanwhile, the BAPC model predicts that the burden will continue to rise.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe global burden of PCOS increased over the past 30 years, with variability across different regions, and this trend will continue in the future.\u003c/p\u003e","manuscriptTitle":"Global and regional trends and age-period-cohort effects in polycystic ovary syndrome burden from 1990 to 2019, with predictions to 2040","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-18 15:14:45","doi":"10.21203/rs.3.rs-4260677/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"27b189e6-3734-4435-998e-6d453821415b","owner":[],"postedDate":"April 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-01T16:53:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-18 15:14:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4260677","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4260677","identity":"rs-4260677","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}