Global, Regional, and National Female Infertility Burden and Trends from 1990 to 2021: A Systematic Analysis for the Global Burden of Disease Study 2021

Global, Regional, and National Female Infertility Burden and Trends from 1990 to 2021: A Systematic Analysis for the Global Burden of Disease Study 2021 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Global, Regional, and National Female Infertility Burden and Trends from 1990 to 2021: A Systematic Analysis for the Global Burden of Disease Study 2021 Jie Liu, Yi Qin, Hui Liu, Yonglin Liu, Yi Yang, Yumei Ning, Huijun Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5573774/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 May, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background Female infertility represents a significant reproductive health issue that critically affects global fertility rates. In this study, we utilized the most recent data from the Global Burden of Disease Study 2021. Initially, we assessed the global number of female infertility prevalence and disability-adjusted life years (DALYs), along with the age-standardized rate (ASR) per 100,000 individuals, stratified by age, sex, sociodemographic index (SDI), nationality, and region. Furthermore, linear regression models were employed to examine the temporal trends of disease burden from 1990 to 2021. Cluster analysis facilitated the evaluation of disease burden change patterns across different GBD regions. Lastly, the Autoregressive Composite Moving Average (ARIMA) model was applied to forecast future disease burdens. In 2021, the global prevalence of female infertility was estimated at 110,089,459, contributing to 6,210,145 DALYs, which corresponds to 0.65% of the global prevalence and 0.24% of global DALYs. There was an observed increase of 76.11% in prevalence and 56.37% in DALYs since 1990. The highest burden occurred among individuals aged 35–39, with the most rapid increase observed in the 30–34 age group. The burden of female infertility displayed considerable variability across GBD regions and countries, with areas of high-meduim SDI facing elevated risks. Projections indicate a continuing rise in the ASR of prevalence and DALYs for female infertility over the next two decades. The global burden of female infertility has intensified from 1990 to 2021, with notable disparities across different SDI regions and countries. Women aged 35–39 face the highest risk, and there is a trend toward earlier onset of infertility. Health sciences/Medical research/Epidemiology Health sciences/Diseases/Reproductive disorders/Infertility Health sciences/Health care/Disease prevention/Preventive medicine Health sciences/Health care/Health care economics Female infertility Global burden disease Age-standardized prevalence rate disability-adjusted life years Age-standardized DALYs rate Estimated annual percentage change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Female infertility represents a significant public health concern globally, defined as the inability to conceive after more than 12 months of regular unprotected intercourse without contraception [ 1 – 2 ] . This condition is categorized into primary and secondary infertility. In the United States, an estimated 7–15.5% of women of childbearing age are afflicted with infertility [ 3 – 4 ] . Worldwide, infertility impacts millions of families, affecting approximately one in seven couples in developed countries and one in four in developing countries [ 5 ] . In China, over 50 million women of childbearing age suffer from infertility, constituting 15% of this demographic [ 6 ] . The repercussions of infertility extend beyond the inability to conceive, adversely affecting the physical and mental health of couples, undermining family stability, and potentially disrupting social harmony. Surveys indicate that the divorce rate among infertile couples is 2.2 times that of fertile couples [ 7 ] . The predominant cause of female infertility is fallopian tube obstruction, while secondary causes include ovulatory disorders related to diminished ovarian reserve, endocrine disruptions, and delayed childbearing. Genetic factors also contribute, with 5–10% of affected women displaying chromosomal anomalies, gene mutations, or polymorphisms [ 8 ] . Additionally, environmental influences, endocrine dysfunctions, and hormonal imbalances explain a considerable portion of infertility cases. Emerging research suggests that infertile women may face an elevated risk of gynecological cancers [ 9 – 14 ] . Moreover, conditions such as endometriosis are linked with higher incidences of melanoma, asthma, autoimmune disorders, allergic diseases, cardiovascular diseases, and ovarian cancer [ 15 ] . Polycystic ovary syndrome (PCOS) is correlated with increased waist circumference, insulin resistance, elevated serum insulin levels, an unfavorable lipoprotein profile, type II diabetes mellitus, hyperlipidemia, and central obesity, even in individuals with a normal body mass index (BMI) [ 16 – 17 ] . The multifaceted impact of female infertility underscores the necessity for a comprehensive understanding of its complexities and implications. Current epidemiological research on female infertility is notably sparse. One pivotal study analyzed data from 277 demographic and reproductive health surveys, uncovering variations in the prevalence of primary and secondary infertility across 190 countries and territories from 1990 to 2010 [ 18 ] . Notably, regions such as North Africa and the Middle East, particularly Morocco and Yemen, exhibited high rates of primary infertility but low rates of secondary infertility. Conversely, Central and Eastern Europe along with Central Asia showed higher prevalence of secondary infertility and lower incidence of primary infertility. A separate analysis on the disease burden associated with polycystic ovary syndrome (PCOS) revealed a significant increase in infertility cases linked to PCOS among women aged 15 to 49 years—from 6 million cases in 1990 to 12.13 million in 2019. The global age-standardized prevalence rate (ASPR) of infertility due to PCOS escalated from 223.50 per 100,000 in 1990 to 308.25 per 100,000 in 2019. Moreover, the global years lived with disability (YLD) attributed to PCOS surged by 98.0%, from 35,200 in 1990 to 69,700 in 2019 [ 19 ] . Research on endometriosis-related infertility indicated a slight decrease in the global burden from 1990 to 2019, though substantial regional, age-related, temporal, and cohort-based disparities persist [ 20 ] . Differences in the burden of female infertility among countries may stem from several factors, including widespread preconception testing, complexity in diagnostic processes, the adoption of assisted reproductive technologies, and disparities in medical resource distribution. The Global Burden of Disease (GBD) Study serves as a crucial resource for understanding the epidemiological status of various diseases, encompassing data on prevalence, incidence, mortality, and disability-adjusted life years (DALYs). We derived specific data on female infertility from the most recent 2021 GBD study. This study also offers a detailed breakdown of the prevalence, mortality, and DALYs of female infertility by age, gender, geographic region, and the Sociodemographic Index (SDI), emphasizing SDI distribution and the temporal patterns and trends in disease burden. Projecting the disease burden from 2020 to 2044, this analysis is aimed at aiding clinicians, epidemiologists, and health policymakers in devising and evaluating effective public health strategies to mitigate the substantial disease burden, offering considerable benefit and value. Results The Disease Burden Attributable to Female Infertility in 2021 In 2021, the global prevalence of female infertility was estimated at 110,089,459 cases, accounting for 0.56% of the global population, with a 95% uncertainty interval (UI) of 58,608,815 to 195,025,585. The ASPR (age-standardized prevalence rate, ASPR) was 1367.36 per 100,000 population. DALYs (Disability-adjusted life years, DALYs) associated with female infertility totaled 601,134, representing 0.41% of global DALYs, with an age-standardized rate of 7.48 per 100,000 population (95% UI: 2.65–18.23) (Tables 1 – 2 ). Table 1 The prevalence and age-standardized prevalence rates of female infertility in 1990 and 2021, with trends observed globally from 1990 to 2021. Characteristics 1990 2021 1990–2021 Number of Prevalence cases (95% UI) The age-standardized rate/100000(95% UI) Number of Prevalence cases (95% UI) The age-standardized rate/100000(95% UI) EAPC (95%CI) Global 59690000 (32625584–104614493) 1118.29 (601.76-1958.53) 110089459 (58608815–195025585) 1367.36 (730.34-2405.83) 0.71 (0.55–0.88) Sex Pp’ Female 59690000 (32625584–104614493) 2267.26 (1219.63-3969.94) 110089459 (58608815–195025585) 2764.62 (1476.33-4862.57) 0.7 (0.53–0.87) Age 15–19 years 791658 (97993-2338497) 152.41 (18.87-450.21) 1014989 (69016-3271028) 162.66 (11.06-524.22) -0.17 (-0.53-0.18) 20–24 years 7957251 (2863602–16004113) 1617.04 (581.93-3252.29) 13082608 (4616284–26411136) 2190.81 (773.04-4422.8) 1.04 (0.76–1.32) 25–29 years 10618578 (3352786–26006674) 2399.03 (757.49-5875.63) 19170379 (6129768–46190159) 3258.37 (1041.87-7850.89) 1.2 (0.92–1.48) 30–34 years 13279984 (3429389–30328858) 3445.57 (889.77-7868.99) 26866483 (6932324–64075582) 4444.56 (1146.82-10600.11) 0.86 (0.65–1.06) 35–39 years 17089875 (5157052–38349601) 4851.71 (1464.05-10887.21) 30599403 (8491888–69339499) 5455.74 (1514.07-12362.93) 0.46 (0.31–0.62) 40–44 years 9834566 (2599136–22294902) 3432.88 (907.26-7782.31) 19070839 (4763496–43742361) 3812.26 (952.22-8744.09) 0.32 (0.24–0.4) 45–49 years 118087 (23532–620857) 50.86 (10.13-267.39) 284758 (48245-1686995) 60.14 (10.19-356.28) -0.16 (-0.55-0.23) SDI Regions Low SDI 10739460 (6073679–17862342) 1013.23 (566.62-1755.73) 30053933 (16629265–51679485) 1469.37 (811.58-2550.34) 1.28 (0.73–1.82) Low-middle SDI 10739460 (6073679–17862342) 1013.23 (566.62-1755.73) 30053933 (16629265–51679485) 1469.37 (811.58-2550.34) 1.28 (0.73–1.82) Middle SDI 22576313 (12416219–39052724) 1304.33 (698.69-2274.38) 39038802 (20324320–70133766) 1497.32 (782.01-2673.19) 0.63 (0.52–0.74) High-middle SDI 16546621 (8904341–29353027) 1425.78 (763.92-2536.75) 21200266 (10465956–38450758) 1540.61 (792.65-2777.42) 0.23 (0.2–0.26) High SDI 5090008 (2095085–10270333) 517.46 (213.94-1042.73) 7476943 (2888374–14892481) 684.26 (262.42-1379.74) 1.35 (1.21–1.5) Table 2 The number of disability-adjusted life years (DALYs) and the age-standardized DALY rates attributable to female infertility in 1990 and 2021, along with global trends from 1990 to 2021. Characteristics 1990 2021 1990–2019 Number of DALYs cases (95% UI) The age-standardized DALYs rate/100000(95% UI) Number of DALYs cases (95% UI) The age-standardized DALYs rate/100000 (95% UI) EAPC (95%CI) Global 325937 (114823–807747) 6.08 (2.16–15.24) 601134 (213158–1468475) 7.48 (2.65–18.23) 0.73 (0.56–0.89) Sex Female 325937 (114823–807747) 12.32 (4.39–30.93) 601134 (213158–1468475) 15.12 (5.35–36.88) 0.71 (0.54–0.88) Age 15–19 years 5074 (469-17952) 0.98 (0.09–3.46) 6162 (316-23164) 0.99 (0.05–3.71) -0.39 (-0.69–0.09) 20–24 years 48663 (11823–133352) 9.89 (2.4–27.1) 78482 (19501–207846) 13.14 (3.27–34.81) 0.92 (0.67–1.17) 25–29 years 60406 (11757–172481) 13.65 (2.66–38.97) 109615 (22334–309837) 18.63 (3.8-52.66) 1.19 (0.93–1.46) 30–34 years 70119 (13341–189815) 18.19 (3.46–49.25) 142815 (26433–384261) 23.63 (4.37–63.57) 0.88 (0.67–1.09) 35–39 years 89176 (20855–261597) 25.32 (5.92–74.27) 160995 (35790–465102) 28.7 (6.38–82.93) 0.49 (0.34–0.65) 40–44 years 51868 (10293–164620) 18.11 (3.59–57.46) 101535 (19710–317437) 20.3 (3.94–63.46) 0.36 (0.28–0.44) 45–49 years 630 (95-3355) 0.27 (0.04–1.44) 1530 (209–8644) 0.32 (0.04–1.83) -0.14 (-0.52-0.25) SDI Region Low SDI 25922 (9533–58929) 6.15 (2.3-13.99) 67696 (24767–161485) 6.52 (2.41–15.87) 0.13 (-0.26-0.53) Low-middle SDI 60815 (21169–143024) 5.67 (1.99–13.47) 167400 (58563–403460) 8.16 (2.85–19.77) 1.22 (0.69–1.75) Middle SDI 121994 (42876–302346) 7 (2.43–17.25) 211708 (75088–517044) 8.14 (2.89–19.78) 0.67 (0.56–0.78) High-middle SDI 88611 (30730–229796) 7.62 (2.66–19.76) 112846 (38219–282874) 8.25 (2.84–20.21) 0.23 (0.2–0.27) High SDI 28320 (8576–75480) 2.88 (0.87–7.63) 41101 (11612–110625) 3.78 (1.06–10.35) 1.33 (1.18–1.48) The ASPR and DALYs were highest in the 35–39 age group, showing a peak before declining. Both the number of prevalence cases and DALYs followed similar age-related patterns as the age-standardized DALYs rates (Tables S1). In 2021, the prevalence and DALYs in the 35–39 age group were 1.14 and 1.13 times higher, respectively, than those in the 30–34 age group (Fig. 1 B). The corresponding age-standardized rates were 1.23-fold and 1.21-fold higher, respectively (Figure S1 , Tables 1 – 2 ). At the regional level, based on the Sociodemographic Index (SDI), the Middle SDI region recorded the highest numbers of female infertility cases at 39,038,802 and DALYs at 211,708 in 2021. However, the highest ASRs were observed in the High-middle SDI region (Fig. 1 , Tables 1 – 2 ). The relationship between SDI levels and disease burden remained consistent across countries and regions. As illustrated in Figure S2, the relationship between the ASPR and Sociodemographic Index (SDI) manifests as a "wave." Age-Standardized DALYs Rate and SDI have the same relationship. In different territories and countries where the SDI is under 0.50, the trend is predominantly stable and tends to decrease slightly. For SDI levels between 0.50 and 0.75, the trend shows mild fluctuations, whereas regions with an SDI above 0.75 experience a rapid decline in both ASPR and Age-Standardized DALY's Rate across territories and countries. Among the GBD regions, Asia reported the highest prevalence (95% UI: 44,013,820 − 140,273,972) and DALYs (440,253, 95% UI: 160,445-1,076,577), while Australasia had the lowest (prevalence: 23,946, 95% UI: 5,824 − 97,727; DALYs: 135, 95% UI: 22–593). For age-standardized rates, East Asia showed the highest prevalence (1979.77 per 100,000, 95% UI: 1020.52-3458.07) and DALYs (10.4 per 100,000, 95% UI: 3.56–26.14), with Australasia at the opposite end (prevalence: 76.86 per 100,000, 95% UI: 18.74-304.96; DALYs: 0.44 per 100,000, 95% UI: 0.07–1.86). (Figure S3). The Central African Republic exhibited the highest age-standardized prevalence of female infertility in 2021 at 3016.48 per 100,000 (95% UI: 1877.54-4852.36), followed by Gabon, Djibouti, Comoros, Mozambique, and Eritrea. Similarly, the highest age-standardized DALYs were reported in the Central African Republic (16.48 per 100,000, 95% UI: 6.06–37.78), with Gabon and Djibouti closely following. Australia displayed the lowest ASRs for prevalence cases and DALYs, succeeded by Colombia and New Zealand. In absolute terms, China and India observed the highest numbers of cases, with 29,317,000 (95% UI: 14,569,167 − 52,098,692) and 29,075,289 (95% UI: 16,070,794 − 49,483,699) respectively, followed by Indonesia and Pakistan. The smallest nations, such as Tokelau and Niue, reported the lowest counts, with zero DALYs recorded in both, followed by the Cook Islands, Greenland, and Monaco (Figure S4). Temporal Trends in the Burden of Female Infertility Disease from 1990 to 2021 Over the 31-year period, the global disease burden showed an upward trend in female infertility patients. The number of prevalence cases surged from 59,690,000 in 1990 to 110,089,459 in 2021, marking an increase of 84.44%. The corresponding ASPR experienced a 22.27% rise. A similar trend was observed in the disability-adjusted life years (DALYs), with an 84.43% increase in the number of DALY cases and a 23.03% rise in the age-standardized DALY rate. (Fig. 2 , Tables 1 – 2 ). Examining age-specific data, among women aged 35–39, the prevalence of infertility rose from 17,089,875 in 1990 to 30,599,403 in 2021—an increase of 79.05%. The corresponding age-standardized rate similarly increased by 12.45%. In the 30–34 age group, the number of infertility cases increased from 13,279,984 in 1990 to 26,866,483 in 2021, a significant rise of 102.31%, with the corresponding age-standardized rate also showing an increase of 28.99%. These data indicate that while the heaviest disease burden remains concentrated in women aged 35–39, the number of infertility cases in women aged 30–34 is rising rapidly (Fig. 3 ). Regionally, different trends were observed across various sociodemographic index (SDI) levels. Except for the high SDI regions, which showed a slow increase followed by a decreasing trend, the ASPR and the age-standardized DALYs rate in other SDI regions exhibited an overall upward trend. Notably, the ASR in the low and low-middle SDI regions initially displayed a decrease, followed by a sharp increase starting around 2010. For the number of prevalence cases, the low-medium SDI area initially increased, then decreased, and subsequently experienced a rapid rise starting in 2010. Conversely, in China, the number of cases and DALYs continuously increased. Meanwhile, the prevalence and DALYs in high and high-middle SDI regions remained stable (Figure S5, Tables 1 – 2 ). The burden of female infertility displays considerable variability across GBD regions. Hierarchical cluster analysis was conducted to identify regions with similar patterns of change in disease burden. According to Fig. 4 , Andean Latin America exhibited a significant increase in both ASPR and age-standardized DALYs rate, whereas regions like Oceania, Eastern Sub-Saharan Africa, Southern Sub-Saharan Africa, and Southern Africa showed significant decreases, alongside regions with minimal health systems. Among the 204 evaluated countries, from 1990 to 2021, the changes in the number of prevalence cases and DALYs followed similar patterns, with Peru experiencing the most significant increases in both metrics. Conversely, Armenia witnessed substantial declines (prevalence: -76.36%; DALYs: -76.57%), followed by Malawi, Albania, and Rugia, which also saw decreases in both case numbers and DALYs (Figs. 5 C, D). Regarding ASR, Ecuador registered the largest increase in ASPR and age-standardized DALYs rate burden over the period from 1990 to 2021, with an Estimated Annual Percent Change (EAPC) in prevalence of 9.32, 95% confidence interval (CI) from 7.26 to 11.41; and DALYs EAPC of 9.13, 95% CI from 7.12 to 11.18. This trend was followed by Peru and Bolivia (Plurinational State). The most significant decrease was observed in Malawi (number of cases: EAPC = -6.06, 95% CI from − 6.53 to -5.6; DALYs: EAPC = -6.04, 95% CI from − 6.50 to -5.57), followed by Pakistan and Uganda (Figs. 5 A, B). Factors Influencing EAPC and Predicted Results from 2022 to 2046 An analysis was conducted to assess the relationship between EAPC and Age-Standardized Rates (ASR), EAPC and Human Development Index (HDI) in 2021 (Fig. 6 ). The 2021 ASR for female infertility serves as a baseline measure of the disease reservoir, while the 2021 HDI acts as a surrogate indicator of healthcare availability at the country level. We observed a correlation between EAPC and ASR; at relatively low ASR levels, a negative association emerged between EAPC and ASR in the number of affected individuals (ASPR: ρ = -0.007, p = 0.92) and a positive correlation with ASR in DALY (ρ = 0.004, p = 0.95), although these correlations were not statistically significant (Fig. 6 A). Conversely, a statistically significant positive association was found between EAPC and HDI (ASPR: ρ = 0.26, p < 0.01; Age-Standardized DALYs Rates: ρ = 0.26, p < 0.01) (Fig. 6 B), indicating that in countries with high HDI, the incidence of female infertility increased in 2021 but showed a slowing trend when HDI exceeded 0.8. Projections based on the Autoregressive Integrated Moving Average (ARIMA) model suggest that from 2022 to 2050, the global prevalence of female infertility is expected to gradually decline. However, the corresponding ASR is projected to continue rising. Similarly, DALYs are anticipated to slowly decrease during the period, yet the corresponding ASR is expected to persistently increase (Figure S6). Discussion Infertility represents a significant global health issue, affecting over 9% of women of childbearing age worldwide. The World Health Organization (WHO) recognizes infertility as a global public health concern with profound implications for individuals and societies [ 21 ] . In 2021, female infertility contributed to a substantial disease burden, with notable variations across different ages, Sociodemographic Index (SDI) regions, territories and countries. To our knowledge, this study is the first to comprehensively assess and quantify the burden of female infertility across 204 countries and territories from 2019 to 2021, also projecting future disease burden trends. This research aims to enhance understanding of the current and future dynamics of female infertility. The data for this study was sourced from the Global Burden of Disease study, which includes household surveys, demographic statistics, and other relevant data, and was conducted across multiple countries [ 22 – 23 ] . Thus, GBD studies provide robust estimates of disease burden. While previous research has estimated the burden of female infertility, most such studies were confined to single regions or countries [ 24 ] or focused solely on specific factors related to female infertility [ 25 ] . Few studies have provided a global perspective, Utilizing data from the GBD 2017 study, Sun et al. explored the global prevalence of infertility and its DALYs from 1990 to 2017, revealing an increasing global burden of infertility disease, with a notably higher prevalence among women than men [ 26 ] . However, these analyses lacked depth in trend projection and further exploration. Leveraging data from GBD 2021, our study offers a comprehensive assessment of the global burden of female infertility. We found that in 2021, the number of prevalence female infertility cases accounted for 0.56% of the global prevalence, predominantly concentrated in the medium SDI region, with the largest numbers reported in Asia. When examining ASPR and age-standardized DALYs rate, these were primarily found in the medium-high SDI region, with the East Asian region bearing the heaviest burden. This distribution may be attributed to factors such as demographics, lifestyle, dietary patterns, environmental exposures, and enhanced access to healthcare. In conjunction with previous scholarly estimates, our findings underscore that female infertility imposes a significant disease burden. From 1990 to 2021, we noted a marked increase in the global number of female infertility prevalence, along with rises in disability-adjusted life years (DALYs) and their corresponding age-standardized rates (ASRs). Female age remains a crucial determinant of natural conception and the success of treatment-related conceptions, with fertility notably declining with advancing age, particularly after 35 years [ 27 – 29 ] . Upon analyzing the global burden of female infertility across all age groups, it becomes apparent that the highest concentration of cases is within the 35–39 age group. However, the most significant increases were observed in the 30–34 and 25–29 age groups, both in terms of prevalence and DALYs, as well as their corresponding ASRs. For instance, the prevalence of infertility in the 30–34 age group rose by 103.68%, with the ASR increasing by 29.91%. These changes may be attributed to factors such as delayed childbearing, urbanization, and increased emissions from industry and vehicles [ 30 ] , as well as advancements in infertility detection and diagnostic methods. This trend aligns with current understandings of women's fertility levels. Based on a survey involving 7,172 women, researchers have found that women over the age of 35 are twice as likely to suffer from unexplained infertility compared to younger women [ 31 ] . Additionally, studies indicate a significant increase in chromosomal degeneration and aneuploidy in infertile patients over the age of 35 [ 32 – 33 ] . It has also been documented that older eggs exhibit considerably greater mitochondrial damage, and mitochondrial DNA (mtDNA) mutations in ovarian tissue significantly increase after the age of 45 [ 34 – 35 ] . Given these findings, proactive measures and policies are essential for the diagnosis and treatment of female infertility, aiming to reduce the associated disease burden. Enhanced awareness and improved healthcare interventions could play pivotal roles in addressing this growing challenge. Our regional analysis over the period from 1990 to 2021 reveals a consistent downward trend in the number of female infertility prevalence, disability-adjusted life years (DALYs), and their corresponding age-standardized rates (ASR) in high Sociodemographic Index (SDI) areas. Further regression adaptation analysis indicated a nonlinear relationship between SDI and the prevalence of female infertility, DALYs, and corresponding ASR. Notably, a negative correlation emerged when SDI was greater than 0.75, suggesting that higher SDI values are associated with decreased trends in the prevalence and burden of female infertility. This finding aligns with the association analysis of Estimated Annual Percent Change (EAPC) and Human Development Index (HDI) in female infertility for the year 2021, where EAPC demonstrated a decreasing trend with HDI values over 0.8. SDI and HDI are critical indicators for assessing the impact of socio-economic development on health and disease burden. Regions with high SDI and HDI typically exhibit superior health, economic, and social development, potentially correlating with lower disease burdens and higher quality of life [ 36 ] . This association likely reflects advantages such as better medical facilities, more equitable access to fertility treatments, higher socio-economic conditions, and enhanced overall health, well-being, and advocacy in high SDI regions [ 37 ] . These factors contribute to fewer reported fertility issues. Significant regional and country-specific disparities in the burden of female infertility were noted. For instance, between 1990 and 2021, the number of female infertility prevalence, DALYs, and corresponding ASR significantly increased in Latin American countries such as Peru and Ecuador. Conversely, a notable decrease in the corresponding ASR was observed in Armenia, likely due to varying environmental conditions, cultural factors, preventive healthcare programs, and economic development levels. For example, data from the Peruvian Institute of Statistics indicate that the interval between pregnancies among Peruvian women has lengthened, with an average gap of four years in 2009 [ 38 ] . This extended interval may increase the age at second pregnancy, consequently elevating infertility rates. Overall, while female infertility remains a significant public health issue in developed countries, its impact is more pronounced in developing regions where rapid population growth and widespread industrialization contribute to environmental and noise pollution, posing serious health threats [ 39 ] . This observation is corroborated by the presence of the highest disease burdens in this study occurring in areas with medium and medium - high SDI. The projected results indicate a diverging trend in the disease burden of female infertility from 2022 onwards: while the number of affected individuals is expected to decrease through 2055, and the number of disability-adjusted life years (DALYs) is anticipated to follow a downward trend until 2046, the corresponding age-standardized rate (ASR) is projected to exhibit a continuous upward trend. These opposing trends can be attributed to several interrelated factors. First, the global population continues to age, and a significant increase in the proportion of women over 30 within the childbearing population may elevate the risk of infertility. This structural aging is likely to result in an upward trend in the ASPR, despite an actual decline in the number of affected individuals due to overall population decrease. Second, the impact of lifestyle and environmental risk factors such as delayed childbearing age, occupational stress, and environmental pollution is expected to heighten the relative risk of infertility among women of childbearing age. This increase could drive up the ASPR. Third, as diagnostic capabilities improve and awareness of fertility issues heightens, more cases of infertility are likely to be diagnosed. This could lead to an increase in the age-standardized prevalence, even in regions experiencing population declines. In summary, the contrasting trends in the actual number of people with infertility and the ASR are likely a reflection of demographic shifts, escalating high-risk behaviors, and socioeconomic developments expected in the coming years. Consequently, it is imperative to integrate tertiary prevention measures into early health interventions to manage and mitigate the disease burden effectively. Deficiencies of the Study This study is subject to certain limitations stemming from its reliance on the Global Burden of Disease (GBD) database data. A significant challenge is the absence of detailed data from smaller administrative divisions such as counties, provinces, and states [ 40 ] . While the GBD database encompasses numerous countries and regions worldwide, the accuracy and completeness of the data can vary substantially, particularly in low- and middle-income countries where data may be less detailed or more susceptible to bias. Additionally, the availability of raw data within this database presents another considerable limitation [ 41 – 42 ] . Although burden of disease data were estimated using standardized Bayesian regression tools, the limited data scope introduces uncertainties, notably the global outbreak of the COVID-19 pandemic may further complicate our results. Lastly, the GBD database provides population-level data, which does not include detailed personal patient information, thus limiting the depth of analysis possible regarding the epidemiological characteristics of specific subtypes or patient populations of female infertility. Conclusion Overall, female infertility imposes a significant disease burden and critically impacts global fertility, particularly in regions with high-meduim levels of economic development. This study reaffirms the significant influence of age on female fertility, highlighting an emerging trend where the onset of female infertility is occurring at progressively younger ages. We also observe that while the absolute number of cases may decline over the next 25 years, the ASPR and age-standardized DALYs are projected to continue showing an upward trend. This underscores that female infertility remains a pressing public health issue that demands sustained research attention. Our findings should guide policymakers in prioritizing women’s infertility healthcare and underscore the necessity for effective prevention and management interventions to mitigate the escalating burden of infertility. However, further research is needed to explore the risk factors for female infertility to devise and implement effective strategies to reduce this disease's burden. Methods 2.1 Overview Data specific to female infertility, including prevalence, disability-adjusted life years (DALYs), and corresponding age-standardized rates, were obtained from the Global Health Data Exchange (GHDx) website ( https://vizhub.healthdata.org/gbd-results/ ). The Global Burden of Disease (GBD) 2021 study represents the most extensive and scientifically rigorous effort to assess epidemiological burdens globally, encompassing 371 diseases and injuries along with 84 risk factors. GBD 2021 generated estimates across 204 countries and territories, categorized into 21 regions and seven super-regions [ 43 ] . These countries and territories were further segmented into five groups based on the sociodemographic index (SDI) [ 44 ] . The data sources for the 2021 GBD study included household surveys, demographic statistics, and other pertinent sources [ 45 ] . For disease burden estimation, we utilized DisMod-MR, a Bayesian meta-regression tool, which serves as the standard GBD modeling tool for delineating the burden of disease by sex, age, location, and year. We assessed the burden of disease using various criteria, with adjustment factors estimated using the MR-BRT tool to correct for systematic biases through crosstabulation. Additionally, this study involved the collection of background information such as the SDI, for subsequent correlation analyses. The SDI values, which range from 0 to 1, reflect a country’s level of social development. According to the GBD 2021 study, countries are globally classified into five quintiles based on SDI—high, medium-high, medium, medium-low, and low—and 21 geographic regions [ 43 – 44 ] . Furthermore, this research utilized the Human Development Index (HDI), introduced by the United Nations Development Programme (UNDP) in 1990, which comprises educational attainment, life expectancy, and gross national income components. The HDI serves as a comprehensive measure employed by the United Nations to evaluate the economic and social development levels of its Member States. Since its inception, the HDI has been instrumental in guiding the development strategies of developing countries. The UNDP annually publishes HDI values for nations worldwide, employing them in the Human Development Report to assess countries' human development levels [ 46 – 47 ] . 2.2 Study Data The incidence of female infertility was assessed using the International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10). Based on the physiological characteristics of women, it was presumed that there was no burden of infertility in women under the age of 15. Consequently, the study population was segmented into seven age groups: 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years. 2.3 Statistics Initially, the global prevalence of female infertility, along with disability-adjusted life years (DALYs) and corresponding age-standardized rates (ASRs), were reported for 2021. These were further delineated by different subtypes, including age, sociodemographic index (SDI), region, and country. Temporal trends in disease burden from 1990 to 2021 were subsequently explored both globally and by subtype. Estimated Annual Percent Change (EAPC) values were calculated using linear regression models. Based on these EAPC values, hierarchical cluster analysis was employed to identify patterns of change in the disease burden across the Global Burden of Disease (GBD) regions. These regions were classified into four categories: significant increase, small increase, stable or small decrease, and significant decrease. Furthermore, the relationships among EAPC, ASR, and the Human Development Index (HDI) in 2021 were evaluated. Given the normal distribution of these three variables, Spearman correlation analysis was utilized to examine the associations. Projections of the future burden of disease from 2022 to 2050 were made using an autoregressive composite moving average (ARIMA) model under a maximum likelihood framework. Statistical significance was established at a P-value of less than 0.05. All data organization, management, and analysis were conducted using R software (version 4.1.2). 2.1 Overview Data specific to female infertility, including prevalence, disability-adjusted life years (DALYs), and corresponding age-standardized rates, were obtained from the Global Health Data Exchange (GHDx) website ( https://vizhub.healthdata.org/gbd-results/ ). The Global Burden of Disease (GBD) 2021 study represents the most extensive and scientifically rigorous effort to assess epidemiological burdens globally, encompassing 371 diseases and injuries along with 84 risk factors. GBD 2021 generated estimates across 204 countries and territories, categorized into 21 regions and seven super-regions [ 43 ] . These countries and territories were further segmented into five groups based on the sociodemographic index (SDI) [ 44 ] . The data sources for the 2021 GBD study included household surveys, demographic statistics, and other pertinent sources [ 45 ] . For disease burden estimation, we utilized DisMod-MR, a Bayesian meta-regression tool, which serves as the standard GBD modeling tool for delineating the burden of disease by sex, age, location, and year. We assessed the burden of disease using various criteria, with adjustment factors estimated using the MR-BRT tool to correct for systematic biases through crosstabulation. Additionally, this study involved the collection of background information such as the SDI, for subsequent correlation analyses. The SDI values, which range from 0 to 1, reflect a country’s level of social development. According to the GBD 2021 study, countries are globally classified into five quintiles based on SDI—high, medium-high, medium, medium-low, and low—and 21 geographic regions [ 43 – 44 ] . Furthermore, this research utilized the Human Development Index (HDI), introduced by the United Nations Development Programme (UNDP) in 1990, which comprises educational attainment, life expectancy, and gross national income components. The HDI serves as a comprehensive measure employed by the United Nations to evaluate the economic and social development levels of its Member States. Since its inception, the HDI has been instrumental in guiding the development strategies of developing countries. The UNDP annually publishes HDI values for nations worldwide, employing them in the Human Development Report to assess countries' human development levels [ 46 – 47 ] . 2.2 Study Data The incidence of female infertility was assessed using the International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10). Based on the physiological characteristics of women, it was presumed that there was no burden of infertility in women under the age of 15. Consequently, the study population was segmented into seven age groups: 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years. 2.3 Statistics Initially, the global prevalence of female infertility, along with disability-adjusted life years (DALYs) and corresponding age-standardized rates (ASRs), were reported for 2021. These were further delineated by different subtypes, including age, sociodemographic index (SDI), region, and country. Temporal trends in disease burden from 1990 to 2021 were subsequently explored both globally and by subtype. Estimated Annual Percent Change (EAPC) values were calculated using linear regression models. Based on these EAPC values, hierarchical cluster analysis was employed to identify patterns of change in the disease burden across the Global Burden of Disease (GBD) regions. These regions were classified into four categories: significant increase, small increase, stable or small decrease, and significant decrease. Furthermore, the relationships among EAPC, ASR, and the Human Development Index (HDI) in 2021 were evaluated. Given the normal distribution of these three variables, Spearman correlation analysis was utilized to examine the associations. Projections of the future burden of disease from 2022 to 2050 were made using an autoregressive composite moving average (ARIMA) model under a maximum likelihood framework. Statistical significance was established at a P-value of less than 0.05. All data organization, management, and analysis were conducted using R software (version 4.1.2). Declarations Acknowledgements The authors gratefully acknowledge all participants of the GBD 2021 for their contribution. Author contributions Jie Liu, Yi Qin: Study design，Conceptualization, formal analysis, methodology, software, visualization, and writing-original draft; Hui Liu, Yonglin Liu, Yi Yang: formal analysis, software and visualization.; Yumei Ning, Huijun Ye: Investigation, project administration, supervision, and writing-review & editing. All the authors have reviewed and approved the manuscript for publication. Competing interests The authors have no competing interest to declare. Additional information Correspondence and requests for data should be addressed to corresponding author Availability of data and materials Publicly available datasets were analyzed in this study. The data can be found here: https://vizhub.healthdata.org/gbd-results/ Ethics Approval: Not applicable. Role of the funding source: This research received the found of General scientific research project of Zhejiang Provincial Department of Education(Y202351402) and Zhejiang Provincial Traditional Chinese Medicine Science and Technology Program (2023ZL428). References Weiss, M. S., Dokras, A. & Marsh, E. E. Beyond awareness - National Infertility Awareness Week 2023. Fertil. Steril. 119 , 570–571. https://doi.org:10.1016/j.fertnstert.2023.02.026 (2023). Caballero, B. Humans against Obesity: Who Will Win? Adv Nutr 10, S4–S9 (2019). https://doi.org:10.1093/advances/nmy055 Ding, C. et al. Association of weight-adjusted-waist index with all-cause and cardiovascular mortality in China: A prospective cohort study. Nutr. Metab. Cardiovasc. Dis. 32 , 1210–1217. https://doi.org:10.1016/j.numecd.2022.01.033 (2022). Gesink Law, D. C., Maclehose, R. F. & Longnecker, M. P. Obesity and time to pregnancy. Hum. Reprod. 22 , 414–420. https://doi.org:10.1093/humrep/del400 (2007). Greil, A. L., Johnson, K. M., McQuillan, J. & Lacy, N. Are prior pregnancy outcomes relevant for models of fertility-specific distress or infertility helpseeking? Hum. Fertil. (Camb) . 14 , 160–166. https://doi.org:10.3109/14647273.2011.587229 (2011). Fu, B. et al. Development and validation of an Infertility Stigma Scale for Chinese women. J. Psychosom. Res. 79 , 69–75. https://doi.org:10.1016/j.jpsychores.2014.11.014 (2015). Bunnewell, S. J. et al. Diminished ovarian reserve in recurrent pregnancy loss: a systematic review and meta-analysis. Fertil Steril 113, 818–827 e813 (2020). https://doi.org:10.1016/j.fertnstert.2019.11.014 Tarin, J. J., Garcia-Perez, M. A., Hamatani, T. & Cano, A. Infertility etiologies are genetically and clinically linked with other diseases in single meta-diseases. Reprod. Biol. Endocrinol. 13 , 31. https://doi.org:10.1186/s12958-015-0029-9 (2015). Venn, A. et al. Breast and ovarian cancer incidence after infertility and in vitro fertilisation. Lancet 346 , 995–1000. https://doi.org:10.1016/s0140-6736(95)91687-3 (1995). Escobedo, L. G., Lee, N. C., Peterson, H. B. & Wingo, P. A. Infertility-associated endometrial cancer risk may be limited to specific subgroups of infertile women. Obstet. Gynecol. 77 , 124–128 (1991). Dor, J. et al. Cancer incidence in a cohort of infertile women who underwent in vitro fertilization. Fertil. Steril. 77 , 324–327 (2002). Althuis, M. D. et al. Uterine cancer after use of clomiphene citrate to induce ovulation. Am. J. Epidemiol. 161 , 607–615 (2005). Silva, I. S. et al. Ovulation-stimulation drugs and cancer risks: a long-term follow-up of a British cohort. Br. J. Cancer . 100 , 1824–1831. https://doi.org:10.1038/sj.bjc.6605086 (2009). Lerner-Geva, L. et al. Are infertility treatments a potential risk factor for cancer development? Perspective of 30 years of follow-up. Gynecol. Endocrinology: Official J. Int. Soc. Gynecol. Endocrinol. 28 , 809–814. https://doi.org:10.3109/09513590.2012.671391 (2012). Kvaskoff, M. et al. Endometriosis: a high-risk population for major chronic diseases? Hum. Reprod. Update . 21 , 500–516. https://doi.org:10.1093/humupd/dmv013 (2015). Hart, R. & Doherty, D. A. The potential implications of a PCOS diagnosis on a woman's long-term health using data linkage. J. Clin. Endocrinol. Metab. 100 , 911–919. https://doi.org:10.1210/jc.2014-3886 (2015). Kheirollahi, A. et al. Evaluation of lipid ratios and triglyceride-glucose index as risk markers of insulin resistance in Iranian polycystic ovary syndrome women. Lipids Health Dis. 19 , 235. https://doi.org:10.1186/s12944-020-01410-8 (2020). Mascarenhas, M. N., Flaxman, S. R., Boerma, T., Vanderpoel, S. & Stevens, G. A. National, regional, and global trends in infertility prevalence since 1990: a systematic analysis of 277 health surveys. PLoS Med. 9 , e1001356. https://doi.org:10.1371/journal.pmed.1001356 (2012). Liu, X., Zhang, J. & Wang, S. Global, regional, and national burden of infertility attributable to PCOS, 1990–2019. Hum. Reprod. 39 , 108–118. https://doi.org:10.1093/humrep/dead241 (2024). Liu, J. et al. Spatiotemporal Trends and Age-Period-Cohort Analysis for the Burden of Endometriosis-Related Infertility: An Analysis of the Global Burden of Disease Study 2019. J. Pers. Med. 13 https://doi.org:10.3390/jpm13091284 (2023). Lei, H. L., Wei, H. J., Ho, H. Y., Liao, K. W. & Chien, L. C. Relationship between risk factors for infertility in women and lead, cadmium, and arsenic blood levels: a cross-sectional study from Taiwan. BMC Public. Health . 15 , 1220. https://doi.org:10.1186/s12889-015-2564-x (2015). Collaborators, G. B. D. M. A. R. Global, regional, and national burden of meningitis and its aetiologies, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 22 , 685–711. https://doi.org:10.1016/S1474-4422(23)00195-3 (2023). Collaborators, G. B. D. S. C. I. Global, regional, and national burden of spinal cord injury, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 22 , 1026–1047. https://doi.org:10.1016/S1474-4422(23)00287-9 (2023). Yu, J. et al. Burden of female infertility in China from 1990 to 2019: a temporal trend analysis and forecasting, and comparison with the global level. Sex. Health . 20 , 577–584. https://doi.org:10.1071/SH23029 (2023). Liu, J. et al. Spatiotemporal Trends and Age-Period-Cohort Analysis for the Burden of Endometriosis-Related Infertility: An Analysis of the Global Burden of Disease Study 2019. J. Personalized Med. 13 https://doi.org:10.3390/jpm13091284 (2023). Sun, H. et al. Global, regional, and national prevalence and disability-adjusted life-years for infertility in 195 countries and territories, 1990–2017: results from a global burden of disease study, 2017. Aging (Albany NY) . 11 , 10952–10991. https://doi.org:10.18632/aging.102497 (2019). García, D., Brazal, S., Rodríguez, A., Prat, A. & Vassena, R. Knowledge of age-related fertility decline in women: A systematic review. Eur. J. Obstet. Gynecol. Reprod. Biol. 230 , 109–118. https://doi.org:10.1016/j.ejogrb.2018.09.030 (2018). Kasaven, L. S. et al. Exploring the knowledge, attitudes, and perceptions of women of reproductive age towards fertility and elective oocyte cryopreservation for age-related fertility decline in the UK: a cross-sectional survey. Hum. Reprod. (Oxford England) . 38 , 2478–2488. https://doi.org:10.1093/humrep/dead200 (2023). Huang, W., Li, X., Yang, H. & Huang, H. The impact of maternal age on aneuploidy in oocytes: Reproductive consequences, molecular mechanisms, and future directions. Ageing Res. Rev. 97 https://doi.org:10.1016/j.arr.2024.102292 (2024). Cohen, A. J. et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet 389 , 1907–1918 https://doi.org:10.1016/S0140-6736(17)30505-6 (2017). Maheshwari, A., Hamilton, M. & Bhattacharya, S. Effect of female age on the diagnostic categories of infertility. Hum. Reprod. 23 , 538–542. https://doi.org:10.1093/humrep/dem431 (2008). Mikwar, M., MacFarlane, A. J. & Marchetti, F. Mechanisms of oocyte aneuploidy associated with advanced maternal age. Mutat. Research/Reviews Mutat. Res. 785 https://doi.org:10.1016/j.mrrev.2020.108320 (2020). Verdyck, P. et al. Aneuploidy in oocytes from women of advanced maternal age: analysis of the causal meiotic errors and impact on embryo development. Hum. Reprod. 38 , 2526–2535. https://doi.org:10.1093/humrep/dead201 (2023). May-Panloup, P. et al. Ovarian ageing: the role of mitochondria in oocytes and follicles. Hum. Reprod. Update . 22 , 725–743. https://doi.org:10.1093/humupd/dmw028 (2016). Smits, M. A. J. et al. Human ovarian aging is characterized by oxidative damage and mitochondrial dysfunction. Hum. Reprod. 38 , 2208–2220. https://doi.org:10.1093/humrep/dead177 (2023). Bai, J., Cui, J., Shi, F. & Yu, C. Global Epidemiological Patterns in the Burden of Main Non-Communicable Diseases, 1990–2019: Relationships With Socio-Demographic Index. Int. J. Public. Health . 68 https://doi.org:10.3389/ijph.2023.1605502 (2023). Davila-Cervantes, C. A. The burden of type 2 diabetes in adolescents and young adults in Mexico: analysis from the Global Burden of Disease Study, 1990 to 2019. J. Diabetes Metab. Disord . 22 , 1673–1684. https://doi.org:10.1007/s40200-023-01299-x (2023). Instituto Nacional de Estadistica e Informatica (INEI) and & Macro, O. R. C. Perú: Encuesta Demográfica y de Salud Familiar-ENDES Continua, 2009 (2010), accessed at www.measuredhs.com, on Nov. 3, 2010.网页未导入. Nieuwenhuijsen, M. J. et al. Air pollution and human fertility rates. Environ. Int. 70 , 9–14. https://doi.org:10.1016/j.envint.2014.05.005 (2014). Xu, Q. et al. The incidence of asthma attributable to temperature variability: An ecological study based on 1990–2019 GBD data. Sci. Total Environ. 904 , 166726. https://doi.org:10.1016/j.scitotenv.2023.166726 (2023). Diseases, G. B. D. & Injuries, C. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396 , 1204–1222. https://doi.org:10.1016/S0140-6736(20)30925-9 (2020). Collaborators, G. B. D. R. F. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396 , 1223–1249. https://doi.org:10.1016/S0140-6736(20)30752-2 (2020). Diseases, G. B. D. & Injuries, C. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 403 , 2133–2161. https://doi.org:10.1016/S0140-6736(24)00757-8 (2024). Xie, J. et al. Global burden of type 2 diabetes in adolescents and young adults, 1990–2019: systematic analysis of the Global Burden of Disease Study 2019. BMJ 379 https://doi.org:10.1136/bmj-2022-072385 (2022). e072385. Tabatabaei-Malazy, O. et al. Regional burden of chronic kidney disease in North Africa and Middle East during 1990–2019; Results from Global Burden of Disease study 2019. Front. Public. Health . 10 , 1015902. https://doi.org:10.3389/fpubh.2022.1015902 (2022). Fallahzadeh, H. et al. Distribution incidence, mortality of tuberculosis and human development index in Iran: estimates from the global burden of disease study 2019. BMC Public. Health . 24 , 661. https://doi.org:10.1186/s12889-024-18021-y (2024). Karamitanha, F., Ahmadi, F. & Fallahabadi, H. Difference Between Various Countries in Mortality and Incidence Rate of the Atrial Fibrillation Based on Human Development Index in Worldwide: Data From Global Burden of Disease 2010–2019. Curr. Probl. Cardiol. 48 , 101438. https://doi.org:10.1016/j.cpcardiol.2022.101438 (2023). Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 21 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Feb, 2025 Reviews received at journal 01 Feb, 2025 Reviewers agreed at journal 23 Jan, 2025 Reviews received at journal 10 Jan, 2025 Reviewers agreed at journal 27 Dec, 2024 Reviewers invited by journal 27 Dec, 2024 Editor assigned by journal 27 Dec, 2024 Editor invited by journal 20 Dec, 2024 Submission checks completed at journal 19 Dec, 2024 First submitted to journal 03 Dec, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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2021.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5573774/v1/436d7dd4052cc26434207884.png"},{"id":72187910,"identity":"6b5c9889-cca5-4730-be8a-33e009fd04c6","added_by":"auto","created_at":"2024-12-23 13:48:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":119258,"visible":true,"origin":"","legend":"\u003cp\u003eAge-standardized prevalence rates and the number of cases of female infertility prevalence and DALYs from 1990 to 2021, highlighting global trends over this period.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5573774/v1/a8255f621323cf9f8a7e7d12.png"},{"id":72189161,"identity":"72d58d5b-8b41-4da0-959b-f819dd961b37","added_by":"auto","created_at":"2024-12-23 13:56:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":369407,"visible":true,"origin":"","legend":"\u003cp\u003eAge-standardized prevalence rates and the number of cases of female infertility prevalence and DALYs across different age groups from 1990 to 2021, with global trends depicted for the same period.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5573774/v1/59a75e38b6e9797acc4f53c4.png"},{"id":72189406,"identity":"fb9e517b-345a-465c-ba24-daa117d799a6","added_by":"auto","created_at":"2024-12-23 14:04:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":116494,"visible":true,"origin":"","legend":"\u003cp\u003eResults of cluster analysis based on the EAPC in age-standardized rates for prevalence and DALYs attributable to female infertility from 1990 to 2021.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5573774/v1/33fd204a4e57a899d632e044.png"},{"id":72187914,"identity":"8ebc5ab0-4ccb-4197-99c2-65f86d2b3f24","added_by":"auto","created_at":"2024-12-23 13:48:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":125651,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in prevalence and DALYs attributable to female infertility across countries and territories from 1990 to 2021, along with the EAPC for the corresponding ASR.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5573774/v1/fae1f8251b818d9bedb39877.png"},{"id":72187917,"identity":"c5d21d9d-7225-4280-b523-173fddf0aa0c","added_by":"auto","created_at":"2024-12-23 13:48:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":208212,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between EAPC and age-standardized rates (ASR), and EAPC and the Human Development Index (HDI) in 2021.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5573774/v1/4516fa93389e76b8f1f62498.png"},{"id":83459963,"identity":"ba730985-6c46-47c5-b28a-3c332a48ce80","added_by":"auto","created_at":"2025-05-26 16:06:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2272277,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5573774/v1/7b6a657b-8ffd-4b59-b147-e4f5df4589c5.pdf"},{"id":72187911,"identity":"e691011e-9b95-4fc2-83fb-88f4a29295a1","added_by":"auto","created_at":"2024-12-23 13:48:27","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1510050,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5573774/v1/5e48869222187d2fbfe7061c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global, Regional, and National Female Infertility Burden and Trends from 1990 to 2021: A Systematic Analysis for the Global Burden of Disease Study 2021","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFemale infertility represents a significant public health concern globally, defined as the inability to conceive after more than 12 months of regular unprotected intercourse without contraception \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. This condition is categorized into primary and secondary infertility. In the United States, an estimated 7\u0026ndash;15.5% of women of childbearing age are afflicted with infertility \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Worldwide, infertility impacts millions of families, affecting approximately one in seven couples in developed countries and one in four in developing countries \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. In China, over 50\u0026nbsp;million women of childbearing age suffer from infertility, constituting 15% of this demographic \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The repercussions of infertility extend beyond the inability to conceive, adversely affecting the physical and mental health of couples, undermining family stability, and potentially disrupting social harmony. Surveys indicate that the divorce rate among infertile couples is 2.2 times that of fertile couples \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe predominant cause of female infertility is fallopian tube obstruction, while secondary causes include ovulatory disorders related to diminished ovarian reserve, endocrine disruptions, and delayed childbearing. Genetic factors also contribute, with 5\u0026ndash;10% of affected women displaying chromosomal anomalies, gene mutations, or polymorphisms \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Additionally, environmental influences, endocrine dysfunctions, and hormonal imbalances explain a considerable portion of infertility cases. Emerging research suggests that infertile women may face an elevated risk of gynecological cancers \u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Moreover, conditions such as endometriosis are linked with higher incidences of melanoma, asthma, autoimmune disorders, allergic diseases, cardiovascular diseases, and ovarian cancer \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Polycystic ovary syndrome (PCOS) is correlated with increased waist circumference, insulin resistance, elevated serum insulin levels, an unfavorable lipoprotein profile, type II diabetes mellitus, hyperlipidemia, and central obesity, even in individuals with a normal body mass index (BMI) \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The multifaceted impact of female infertility underscores the necessity for a comprehensive understanding of its complexities and implications.\u003c/p\u003e \u003cp\u003eCurrent epidemiological research on female infertility is notably sparse. One pivotal study analyzed data from 277 demographic and reproductive health surveys, uncovering variations in the prevalence of primary and secondary infertility across 190 countries and territories from 1990 to 2010 \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Notably, regions such as North Africa and the Middle East, particularly Morocco and Yemen, exhibited high rates of primary infertility but low rates of secondary infertility. Conversely, Central and Eastern Europe along with Central Asia showed higher prevalence of secondary infertility and lower incidence of primary infertility. A separate analysis on the disease burden associated with polycystic ovary syndrome (PCOS) revealed a significant increase in infertility cases linked to PCOS among women aged 15 to 49 years\u0026mdash;from 6\u0026nbsp;million cases in 1990 to 12.13\u0026nbsp;million in 2019. The global age-standardized prevalence rate (ASPR) of infertility due to PCOS escalated from 223.50 per 100,000 in 1990 to 308.25 per 100,000 in 2019. Moreover, the global years lived with disability (YLD) attributed to PCOS surged by 98.0%, from 35,200 in 1990 to 69,700 in 2019 \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Research on endometriosis-related infertility indicated a slight decrease in the global burden from 1990 to 2019, though substantial regional, age-related, temporal, and cohort-based disparities persist \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDifferences in the burden of female infertility among countries may stem from several factors, including widespread preconception testing, complexity in diagnostic processes, the adoption of assisted reproductive technologies, and disparities in medical resource distribution.\u003c/p\u003e \u003cp\u003eThe Global Burden of Disease (GBD) Study serves as a crucial resource for understanding the epidemiological status of various diseases, encompassing data on prevalence, incidence, mortality, and disability-adjusted life years (DALYs). We derived specific data on female infertility from the most recent 2021 GBD study. This study also offers a detailed breakdown of the prevalence, mortality, and DALYs of female infertility by age, gender, geographic region, and the Sociodemographic Index (SDI), emphasizing SDI distribution and the temporal patterns and trends in disease burden. Projecting the disease burden from 2020 to 2044, this analysis is aimed at aiding clinicians, epidemiologists, and health policymakers in devising and evaluating effective public health strategies to mitigate the substantial disease burden, offering considerable benefit and value.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe Disease Burden Attributable to Female Infertility in 2021\u003c/h2\u003e \u003cp\u003eIn 2021, the global prevalence of female infertility was estimated at 110,089,459 cases, accounting for 0.56% of the global population, with a 95% uncertainty interval (UI) of 58,608,815 to 195,025,585. The ASPR (age-standardized prevalence rate, ASPR) was 1367.36 per 100,000 population. DALYs (Disability-adjusted life years, DALYs) associated with female infertility totaled 601,134, representing 0.41% of global DALYs, with an age-standardized rate of 7.48 per 100,000 population (95% UI: 2.65\u0026ndash;18.23) (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe prevalence and age-standardized prevalence rates of female infertility in 1990 and 2021, with trends observed globally from 1990 to 2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1990\u0026ndash;2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Prevalence\u003c/p\u003e \u003cp\u003ecases (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe age-standardized rate/100000(95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of Prevalence\u003c/p\u003e \u003cp\u003ecases (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThe age-standardized rate/100000(95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEAPC (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59690000\u003c/p\u003e \u003cp\u003e(32625584\u0026ndash;104614493)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1118.29\u003c/p\u003e \u003cp\u003e(601.76-1958.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110089459\u003c/p\u003e \u003cp\u003e(58608815\u0026ndash;195025585)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1367.36\u003c/p\u003e \u003cp\u003e(730.34-2405.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003cp\u003e(0.55\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePp\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59690000\u003c/p\u003e \u003cp\u003e(32625584\u0026ndash;104614493)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2267.26\u003c/p\u003e \u003cp\u003e(1219.63-3969.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110089459\u003c/p\u003e \u003cp\u003e(58608815\u0026ndash;195025585)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2764.62\u003c/p\u003e \u003cp\u003e(1476.33-4862.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003cp\u003e(0.53\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;19 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e791658\u003c/p\u003e \u003cp\u003e(97993-2338497)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152.41\u003c/p\u003e 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\u003cp\u003e(4616284\u0026ndash;26411136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2190.81\u003c/p\u003e \u003cp\u003e(773.04-4422.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003cp\u003e(0.76\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10618578\u003c/p\u003e \u003cp\u003e(3352786\u0026ndash;26006674)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2399.03\u003c/p\u003e \u003cp\u003e(757.49-5875.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19170379\u003c/p\u003e \u003cp\u003e(6129768\u0026ndash;46190159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3258.37\u003c/p\u003e \u003cp\u003e(1041.87-7850.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003cp\u003e(0.92\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13279984\u003c/p\u003e \u003cp\u003e(3429389\u0026ndash;30328858)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3445.57\u003c/p\u003e \u003cp\u003e(889.77-7868.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26866483\u003c/p\u003e \u003cp\u003e(6932324\u0026ndash;64075582)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4444.56\u003c/p\u003e \u003cp\u003e(1146.82-10600.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003cp\u003e(0.65\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17089875\u003c/p\u003e \u003cp\u003e(5157052\u0026ndash;38349601)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4851.71\u003c/p\u003e \u003cp\u003e(1464.05-10887.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30599403\u003c/p\u003e \u003cp\u003e(8491888\u0026ndash;69339499)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5455.74\u003c/p\u003e \u003cp\u003e(1514.07-12362.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003cp\u003e(0.31\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9834566\u003c/p\u003e \u003cp\u003e(2599136\u0026ndash;22294902)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3432.88\u003c/p\u003e \u003cp\u003e(907.26-7782.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19070839\u003c/p\u003e \u003cp\u003e(4763496\u0026ndash;43742361)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3812.26\u003c/p\u003e \u003cp\u003e(952.22-8744.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003cp\u003e(0.24\u0026ndash;0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118087\u003c/p\u003e \u003cp\u003e(23532\u0026ndash;620857)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.86\u003c/p\u003e \u003cp\u003e(10.13-267.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e284758\u003c/p\u003e \u003cp\u003e(48245-1686995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.14\u003c/p\u003e \u003cp\u003e(10.19-356.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003cp\u003e(-0.55-0.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSDI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10739460\u003c/p\u003e \u003cp\u003e(6073679\u0026ndash;17862342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1013.23\u003c/p\u003e \u003cp\u003e(566.62-1755.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30053933\u003c/p\u003e \u003cp\u003e(16629265\u0026ndash;51679485)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1469.37\u003c/p\u003e \u003cp\u003e(811.58-2550.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003cp\u003e(0.73\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10739460\u003c/p\u003e \u003cp\u003e(6073679\u0026ndash;17862342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1013.23\u003c/p\u003e \u003cp\u003e(566.62-1755.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30053933\u003c/p\u003e \u003cp\u003e(16629265\u0026ndash;51679485)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1469.37\u003c/p\u003e \u003cp\u003e(811.58-2550.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003cp\u003e(0.73\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22576313\u003c/p\u003e \u003cp\u003e(12416219\u0026ndash;39052724)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1304.33\u003c/p\u003e \u003cp\u003e(698.69-2274.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39038802\u003c/p\u003e \u003cp\u003e(20324320\u0026ndash;70133766)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1497.32\u003c/p\u003e \u003cp\u003e(782.01-2673.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003cp\u003e(0.52\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16546621\u003c/p\u003e \u003cp\u003e(8904341\u0026ndash;29353027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1425.78\u003c/p\u003e \u003cp\u003e(763.92-2536.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21200266\u003c/p\u003e \u003cp\u003e(10465956\u0026ndash;38450758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1540.61\u003c/p\u003e \u003cp\u003e(792.65-2777.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003cp\u003e(0.2\u0026ndash;0.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5090008\u003c/p\u003e \u003cp\u003e(2095085\u0026ndash;10270333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e517.46\u003c/p\u003e \u003cp\u003e(213.94-1042.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7476943\u003c/p\u003e \u003cp\u003e(2888374\u0026ndash;14892481)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e684.26\u003c/p\u003e \u003cp\u003e(262.42-1379.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003cp\u003e(1.21\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe number of disability-adjusted life years (DALYs) and the age-standardized DALY rates attributable to female infertility in 1990 and 2021, along with global trends from 1990 to 2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNumber of DALYs\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ecases\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(95% UI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eThe age-standardized DALYs rate/100000(95% UI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNumber of DALYs\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ecases\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(95% UI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eThe age-standardized DALYs rate/100000\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(95% UI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eEAPC\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(95%CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e325937\u003c/p\u003e \u003cp\u003e(114823\u0026ndash;807747)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.08\u003c/p\u003e \u003cp\u003e(2.16\u0026ndash;15.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e601134\u003c/p\u003e \u003cp\u003e(213158\u0026ndash;1468475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.48\u003c/p\u003e \u003cp\u003e(2.65\u0026ndash;18.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003cp\u003e(0.56\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e325937 (114823\u0026ndash;807747)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.32\u003c/p\u003e \u003cp\u003e(4.39\u0026ndash;30.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e601134\u003c/p\u003e \u003cp\u003e(213158\u0026ndash;1468475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.12\u003c/p\u003e \u003cp\u003e(5.35\u0026ndash;36.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003cp\u003e(0.54\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;19 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5074\u003c/p\u003e \u003cp\u003e(469-17952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e(0.09\u0026ndash;3.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6162\u003c/p\u003e \u003cp\u003e(316-23164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e(0.05\u0026ndash;3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003cp\u003e(-0.69\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48663\u003c/p\u003e \u003cp\u003e(11823\u0026ndash;133352)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.89\u003c/p\u003e \u003cp\u003e(2.4\u0026ndash;27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78482\u003c/p\u003e \u003cp\u003e(19501\u0026ndash;207846)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.14\u003c/p\u003e \u003cp\u003e(3.27\u0026ndash;34.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003cp\u003e(0.67\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60406\u003c/p\u003e \u003cp\u003e(11757\u0026ndash;172481)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.65\u003c/p\u003e \u003cp\u003e(2.66\u0026ndash;38.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109615\u003c/p\u003e \u003cp\u003e(22334\u0026ndash;309837)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.63\u003c/p\u003e \u003cp\u003e(3.8-52.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003cp\u003e(0.93\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70119\u003c/p\u003e \u003cp\u003e(13341\u0026ndash;189815)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.19\u003c/p\u003e \u003cp\u003e(3.46\u0026ndash;49.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142815\u003c/p\u003e \u003cp\u003e(26433\u0026ndash;384261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.63\u003c/p\u003e \u003cp\u003e(4.37\u0026ndash;63.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003cp\u003e(0.67\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89176\u003c/p\u003e \u003cp\u003e(20855\u0026ndash;261597)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.32\u003c/p\u003e \u003cp\u003e(5.92\u0026ndash;74.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160995\u003c/p\u003e \u003cp\u003e(35790\u0026ndash;465102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003cp\u003e(6.38\u0026ndash;82.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003cp\u003e(0.34\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51868\u003c/p\u003e \u003cp\u003e(10293\u0026ndash;164620)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.11\u003c/p\u003e \u003cp\u003e(3.59\u0026ndash;57.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e101535\u003c/p\u003e \u003cp\u003e(19710\u0026ndash;317437)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003cp\u003e(3.94\u0026ndash;63.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003cp\u003e(0.28\u0026ndash;0.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e630\u003c/p\u003e \u003cp\u003e(95-3355)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003cp\u003e(0.04\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1530\u003c/p\u003e \u003cp\u003e(209\u0026ndash;8644)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003cp\u003e(0.04\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003cp\u003e(-0.52-0.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSDI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25922\u003c/p\u003e \u003cp\u003e(9533\u0026ndash;58929)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.15\u003c/p\u003e \u003cp\u003e(2.3-13.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67696\u003c/p\u003e \u003cp\u003e(24767\u0026ndash;161485)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.52\u003c/p\u003e \u003cp\u003e(2.41\u0026ndash;15.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003cp\u003e(-0.26-0.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60815\u003c/p\u003e \u003cp\u003e(21169\u0026ndash;143024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003cp\u003e(1.99\u0026ndash;13.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e167400\u003c/p\u003e \u003cp\u003e(58563\u0026ndash;403460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003cp\u003e(2.85\u0026ndash;19.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003cp\u003e(0.69\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121994\u003c/p\u003e \u003cp\u003e(42876\u0026ndash;302346)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(2.43\u0026ndash;17.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e211708\u003c/p\u003e \u003cp\u003e(75088\u0026ndash;517044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.14\u003c/p\u003e \u003cp\u003e(2.89\u0026ndash;19.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003cp\u003e(0.56\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-middle SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88611\u003c/p\u003e \u003cp\u003e(30730\u0026ndash;229796)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.62\u003c/p\u003e \u003cp\u003e(2.66\u0026ndash;19.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e112846\u003c/p\u003e \u003cp\u003e(38219\u0026ndash;282874)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.25\u003c/p\u003e \u003cp\u003e(2.84\u0026ndash;20.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003cp\u003e(0.2\u0026ndash;0.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28320\u003c/p\u003e \u003cp\u003e(8576\u0026ndash;75480)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003cp\u003e(0.87\u0026ndash;7.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41101\u003c/p\u003e \u003cp\u003e(11612\u0026ndash;110625)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003cp\u003e(1.06\u0026ndash;10.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003cp\u003e(1.18\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe ASPR and DALYs were highest in the 35\u0026ndash;39 age group, showing a peak before declining. Both the number of prevalence cases and DALYs followed similar age-related patterns as the age-standardized DALYs rates (Tables S1). In 2021, the prevalence and DALYs in the 35\u0026ndash;39 age group were 1.14 and 1.13 times higher, respectively, than those in the 30\u0026ndash;34 age group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The corresponding age-standardized rates were 1.23-fold and 1.21-fold higher, respectively (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the regional level, based on the Sociodemographic Index (SDI), the Middle SDI region recorded the highest numbers of female infertility cases at 39,038,802 and DALYs at 211,708 in 2021. However, the highest ASRs were observed in the High-middle SDI region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The relationship between SDI levels and disease burden remained consistent across countries and regions. As illustrated in Figure S2, the relationship between the ASPR and Sociodemographic Index (SDI) manifests as a \"wave.\" Age-Standardized DALYs Rate and SDI have the same relationship. In different territories and countries where the SDI is under 0.50, the trend is predominantly stable and tends to decrease slightly. For SDI levels between 0.50 and 0.75, the trend shows mild fluctuations, whereas regions with an SDI above 0.75 experience a rapid decline in both ASPR and Age-Standardized DALY's Rate across territories and countries.\u003c/p\u003e \u003cp\u003eAmong the GBD regions, Asia reported the highest prevalence (95% UI: 44,013,820\u0026thinsp;\u0026minus;\u0026thinsp;140,273,972) and DALYs (440,253, 95% UI: 160,445-1,076,577), while Australasia had the lowest (prevalence: 23,946, 95% UI: 5,824\u0026thinsp;\u0026minus;\u0026thinsp;97,727; DALYs: 135, 95% UI: 22\u0026ndash;593). For age-standardized rates, East Asia showed the highest prevalence (1979.77 per 100,000, 95% UI: 1020.52-3458.07) and DALYs (10.4 per 100,000, 95% UI: 3.56\u0026ndash;26.14), with Australasia at the opposite end (prevalence: 76.86 per 100,000, 95% UI: 18.74-304.96; DALYs: 0.44 per 100,000, 95% UI: 0.07\u0026ndash;1.86). (Figure S3).\u003c/p\u003e \u003cp\u003eThe Central African Republic exhibited the highest age-standardized prevalence of female infertility in 2021 at 3016.48 per 100,000 (95% UI: 1877.54-4852.36), followed by Gabon, Djibouti, Comoros, Mozambique, and Eritrea. Similarly, the highest age-standardized DALYs were reported in the Central African Republic (16.48 per 100,000, 95% UI: 6.06\u0026ndash;37.78), with Gabon and Djibouti closely following. Australia displayed the lowest ASRs for prevalence cases and DALYs, succeeded by Colombia and New Zealand. In absolute terms, China and India observed the highest numbers of cases, with 29,317,000 (95% UI: 14,569,167\u0026thinsp;\u0026minus;\u0026thinsp;52,098,692) and 29,075,289 (95% UI: 16,070,794\u0026thinsp;\u0026minus;\u0026thinsp;49,483,699) respectively, followed by Indonesia and Pakistan. The smallest nations, such as Tokelau and Niue, reported the lowest counts, with zero DALYs recorded in both, followed by the Cook Islands, Greenland, and Monaco (Figure S4).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTemporal Trends in the Burden of Female Infertility Disease from 1990 to 2021\u003c/h3\u003e\n\u003cp\u003eOver the 31-year period, the global disease burden showed an upward trend in female infertility patients. The number of prevalence cases surged from 59,690,000 in 1990 to 110,089,459 in 2021, marking an increase of 84.44%. The corresponding ASPR experienced a 22.27% rise. A similar trend was observed in the disability-adjusted life years (DALYs), with an 84.43% increase in the number of DALY cases and a 23.03% rise in the age-standardized DALY rate. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExamining age-specific data, among women aged 35\u0026ndash;39, the prevalence of infertility rose from 17,089,875 in 1990 to 30,599,403 in 2021\u0026mdash;an increase of 79.05%. The corresponding age-standardized rate similarly increased by 12.45%. In the 30\u0026ndash;34 age group, the number of infertility cases increased from 13,279,984 in 1990 to 26,866,483 in 2021, a significant rise of 102.31%, with the corresponding age-standardized rate also showing an increase of 28.99%. These data indicate that while the heaviest disease burden remains concentrated in women aged 35\u0026ndash;39, the number of infertility cases in women aged 30\u0026ndash;34 is rising rapidly (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegionally, different trends were observed across various sociodemographic index (SDI) levels. Except for the high SDI regions, which showed a slow increase followed by a decreasing trend, the ASPR and the age-standardized DALYs rate in other SDI regions exhibited an overall upward trend. Notably, the ASR in the low and low-middle SDI regions initially displayed a decrease, followed by a sharp increase starting around 2010. For the number of prevalence cases, the low-medium SDI area initially increased, then decreased, and subsequently experienced a rapid rise starting in 2010. Conversely, in China, the number of cases and DALYs continuously increased. Meanwhile, the prevalence and DALYs in high and high-middle SDI regions remained stable (Figure S5, Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe burden of female infertility displays considerable variability across GBD regions. Hierarchical cluster analysis was conducted to identify regions with similar patterns of change in disease burden. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Andean Latin America exhibited a significant increase in both ASPR and age-standardized DALYs rate, whereas regions like Oceania, Eastern Sub-Saharan Africa, Southern Sub-Saharan Africa, and Southern Africa showed significant decreases, alongside regions with minimal health systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the 204 evaluated countries, from 1990 to 2021, the changes in the number of prevalence cases and DALYs followed similar patterns, with Peru experiencing the most significant increases in both metrics. Conversely, Armenia witnessed substantial declines (prevalence: -76.36%; DALYs: -76.57%), followed by Malawi, Albania, and Rugia, which also saw decreases in both case numbers and DALYs (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding ASR, Ecuador registered the largest increase in ASPR and age-standardized DALYs rate burden over the period from 1990 to 2021, with an Estimated Annual Percent Change (EAPC) in prevalence of 9.32, 95% confidence interval (CI) from 7.26 to 11.41; and DALYs EAPC of 9.13, 95% CI from 7.12 to 11.18. This trend was followed by Peru and Bolivia (Plurinational State). The most significant decrease was observed in Malawi (number of cases: EAPC = -6.06, 95% CI from \u0026minus;\u0026thinsp;6.53 to -5.6; DALYs: EAPC = -6.04, 95% CI from \u0026minus;\u0026thinsp;6.50 to -5.57), followed by Pakistan and Uganda (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B).\u003c/p\u003e\n\u003ch3\u003eFactors Influencing EAPC and Predicted Results from 2022 to 2046\u003c/h3\u003e\n\u003cp\u003eAn analysis was conducted to assess the relationship between EAPC and Age-Standardized Rates (ASR), EAPC and Human Development Index (HDI) in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The 2021 ASR for female infertility serves as a baseline measure of the disease reservoir, while the 2021 HDI acts as a surrogate indicator of healthcare availability at the country level. We observed a correlation between EAPC and ASR; at relatively low ASR levels, a negative association emerged between EAPC and ASR in the number of affected individuals (ASPR: ρ = -0.007, p\u0026thinsp;=\u0026thinsp;0.92) and a positive correlation with ASR in DALY (ρ\u0026thinsp;=\u0026thinsp;0.004, p\u0026thinsp;=\u0026thinsp;0.95), although these correlations were not statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Conversely, a statistically significant positive association was found between EAPC and HDI (ASPR: ρ\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Age-Standardized DALYs Rates: ρ\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), indicating that in countries with high HDI, the incidence of female infertility increased in 2021 but showed a slowing trend when HDI exceeded 0.8.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eProjections based on the Autoregressive Integrated Moving Average (ARIMA) model suggest that from 2022 to 2050, the global prevalence of female infertility is expected to gradually decline. However, the corresponding ASR is projected to continue rising. Similarly, DALYs are anticipated to slowly decrease during the period, yet the corresponding ASR is expected to persistently increase (Figure S6).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eInfertility represents a significant global health issue, affecting over 9% of women of childbearing age worldwide. The World Health Organization (WHO) recognizes infertility as a global public health concern with profound implications for individuals and societies \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In 2021, female infertility contributed to a substantial disease burden, with notable variations across different ages, Sociodemographic Index (SDI) regions, territories and countries. To our knowledge, this study is the first to comprehensively assess and quantify the burden of female infertility across 204 countries and territories from 2019 to 2021, also projecting future disease burden trends. This research aims to enhance understanding of the current and future dynamics of female infertility.\u003c/p\u003e \u003cp\u003eThe data for this study was sourced from the Global Burden of Disease study, which includes household surveys, demographic statistics, and other relevant data, and was conducted across multiple countries \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Thus, GBD studies provide robust estimates of disease burden. While previous research has estimated the burden of female infertility, most such studies were confined to single regions or countries \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e or focused solely on specific factors related to female infertility \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFew studies have provided a global perspective, Utilizing data from the GBD 2017 study, Sun et al. explored the global prevalence of infertility and its DALYs from 1990 to 2017, revealing an increasing global burden of infertility disease, with a notably higher prevalence among women than men \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. However, these analyses lacked depth in trend projection and further exploration. Leveraging data from GBD 2021, our study offers a comprehensive assessment of the global burden of female infertility. We found that in 2021, the number of prevalence female infertility cases accounted for 0.56% of the global prevalence, predominantly concentrated in the medium SDI region, with the largest numbers reported in Asia. When examining ASPR and age-standardized DALYs rate, these were primarily found in the medium-high SDI region, with the East Asian region bearing the heaviest burden. This distribution may be attributed to factors such as demographics, lifestyle, dietary patterns, environmental exposures, and enhanced access to healthcare. In conjunction with previous scholarly estimates, our findings underscore that female infertility imposes a significant disease burden.\u003c/p\u003e \u003cp\u003eFrom 1990 to 2021, we noted a marked increase in the global number of female infertility prevalence, along with rises in disability-adjusted life years (DALYs) and their corresponding age-standardized rates (ASRs). Female age remains a crucial determinant of natural conception and the success of treatment-related conceptions, with fertility notably declining with advancing age, particularly after 35 years \u003csup\u003e[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUpon analyzing the global burden of female infertility across all age groups, it becomes apparent that the highest concentration of cases is within the 35\u0026ndash;39 age group. However, the most significant increases were observed in the 30\u0026ndash;34 and 25\u0026ndash;29 age groups, both in terms of prevalence and DALYs, as well as their corresponding ASRs. For instance, the prevalence of infertility in the 30\u0026ndash;34 age group rose by 103.68%, with the ASR increasing by 29.91%. These changes may be attributed to factors such as delayed childbearing, urbanization, and increased emissions from industry and vehicles \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, as well as advancements in infertility detection and diagnostic methods.\u003c/p\u003e \u003cp\u003eThis trend aligns with current understandings of women's fertility levels. Based on a survey involving 7,172 women, researchers have found that women over the age of 35 are twice as likely to suffer from unexplained infertility compared to younger women \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Additionally, studies indicate a significant increase in chromosomal degeneration and aneuploidy in infertile patients over the age of 35 \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. It has also been documented that older eggs exhibit considerably greater mitochondrial damage, and mitochondrial DNA (mtDNA) mutations in ovarian tissue significantly increase after the age of 45 \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Given these findings, proactive measures and policies are essential for the diagnosis and treatment of female infertility, aiming to reduce the associated disease burden. Enhanced awareness and improved healthcare interventions could play pivotal roles in addressing this growing challenge.\u003c/p\u003e \u003cp\u003eOur regional analysis over the period from 1990 to 2021 reveals a consistent downward trend in the number of female infertility prevalence, disability-adjusted life years (DALYs), and their corresponding age-standardized rates (ASR) in high Sociodemographic Index (SDI) areas. Further regression adaptation analysis indicated a nonlinear relationship between SDI and the prevalence of female infertility, DALYs, and corresponding ASR. Notably, a negative correlation emerged when SDI was greater than 0.75, suggesting that higher SDI values are associated with decreased trends in the prevalence and burden of female infertility. This finding aligns with the association analysis of Estimated Annual Percent Change (EAPC) and Human Development Index (HDI) in female infertility for the year 2021, where EAPC demonstrated a decreasing trend with HDI values over 0.8.\u003c/p\u003e \u003cp\u003eSDI and HDI are critical indicators for assessing the impact of socio-economic development on health and disease burden. Regions with high SDI and HDI typically exhibit superior health, economic, and social development, potentially correlating with lower disease burdens and higher quality of life \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. This association likely reflects advantages such as better medical facilities, more equitable access to fertility treatments, higher socio-economic conditions, and enhanced overall health, well-being, and advocacy in high SDI regions \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. These factors contribute to fewer reported fertility issues.\u003c/p\u003e \u003cp\u003eSignificant regional and country-specific disparities in the burden of female infertility were noted. For instance, between 1990 and 2021, the number of female infertility prevalence, DALYs, and corresponding ASR significantly increased in Latin American countries such as Peru and Ecuador. Conversely, a notable decrease in the corresponding ASR was observed in Armenia, likely due to varying environmental conditions, cultural factors, preventive healthcare programs, and economic development levels. For example, data from the Peruvian Institute of Statistics indicate that the interval between pregnancies among Peruvian women has lengthened, with an average gap of four years in 2009 \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. This extended interval may increase the age at second pregnancy, consequently elevating infertility rates.\u003c/p\u003e \u003cp\u003eOverall, while female infertility remains a significant public health issue in developed countries, its impact is more pronounced in developing regions where rapid population growth and widespread industrialization contribute to environmental and noise pollution, posing serious health threats \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. This observation is corroborated by the presence of the highest disease burdens in this study occurring in areas with medium and medium - high SDI.\u003c/p\u003e \u003cp\u003eThe projected results indicate a diverging trend in the disease burden of female infertility from 2022 onwards: while the number of affected individuals is expected to decrease through 2055, and the number of disability-adjusted life years (DALYs) is anticipated to follow a downward trend until 2046, the corresponding age-standardized rate (ASR) is projected to exhibit a continuous upward trend. These opposing trends can be attributed to several interrelated factors. First, the global population continues to age, and a significant increase in the proportion of women over 30 within the childbearing population may elevate the risk of infertility. This structural aging is likely to result in an upward trend in the ASPR, despite an actual decline in the number of affected individuals due to overall population decrease. Second, the impact of lifestyle and environmental risk factors such as delayed childbearing age, occupational stress, and environmental pollution is expected to heighten the relative risk of infertility among women of childbearing age. This increase could drive up the ASPR. Third, as diagnostic capabilities improve and awareness of fertility issues heightens, more cases of infertility are likely to be diagnosed. This could lead to an increase in the age-standardized prevalence, even in regions experiencing population declines. In summary, the contrasting trends in the actual number of people with infertility and the ASR are likely a reflection of demographic shifts, escalating high-risk behaviors, and socioeconomic developments expected in the coming years. Consequently, it is imperative to integrate tertiary prevention measures into early health interventions to manage and mitigate the disease burden effectively.\u003c/p\u003e \u003cp\u003eDeficiencies of the Study\u003c/p\u003e \u003cp\u003eThis study is subject to certain limitations stemming from its reliance on the Global Burden of Disease (GBD) database data. A significant challenge is the absence of detailed data from smaller administrative divisions such as counties, provinces, and states \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. While the GBD database encompasses numerous countries and regions worldwide, the accuracy and completeness of the data can vary substantially, particularly in low- and middle-income countries where data may be less detailed or more susceptible to bias. Additionally, the availability of raw data within this database presents another considerable limitation \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Although burden of disease data were estimated using standardized Bayesian regression tools, the limited data scope introduces uncertainties, notably the global outbreak of the COVID-19 pandemic may further complicate our results. Lastly, the GBD database provides population-level data, which does not include detailed personal patient information, thus limiting the depth of analysis possible regarding the epidemiological characteristics of specific subtypes or patient populations of female infertility.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, female infertility imposes a significant disease burden and critically impacts global fertility, particularly in regions with high-meduim levels of economic development. This study reaffirms the significant influence of age on female fertility, highlighting an emerging trend where the onset of female infertility is occurring at progressively younger ages. We also observe that while the absolute number of cases may decline over the next 25 years, the ASPR and age-standardized DALYs are projected to continue showing an upward trend. This underscores that female infertility remains a pressing public health issue that demands sustained research attention. Our findings should guide policymakers in prioritizing women’s infertility healthcare and underscore the necessity for effective prevention and management interventions to mitigate the escalating burden of infertility. However, further research is needed to explore the risk factors for female infertility to devise and implement effective strategies to reduce this disease's burden.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e2.1 Overview\u003c/p\u003e \u003cp\u003eData specific to female infertility, including prevalence, disability-adjusted life years (DALYs), and corresponding age-standardized rates, were obtained from the Global Health Data Exchange (GHDx) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Global Burden of Disease (GBD) 2021 study represents the most extensive and scientifically rigorous effort to assess epidemiological burdens globally, encompassing 371 diseases and injuries along with 84 risk factors. GBD 2021 generated estimates across 204 countries and territories, categorized into 21 regions and seven super-regions \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. These countries and territories were further segmented into five groups based on the sociodemographic index (SDI) \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. The data sources for the 2021 GBD study included household surveys, demographic statistics, and other pertinent sources \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor disease burden estimation, we utilized DisMod-MR, a Bayesian meta-regression tool, which serves as the standard GBD modeling tool for delineating the burden of disease by sex, age, location, and year. We assessed the burden of disease using various criteria, with adjustment factors estimated using the MR-BRT tool to correct for systematic biases through crosstabulation.\u003c/p\u003e \u003cp\u003eAdditionally, this study involved the collection of background information such as the SDI, for subsequent correlation analyses. The SDI values, which range from 0 to 1, reflect a country’s level of social development. According to the GBD 2021 study, countries are globally classified into five quintiles based on SDI—high, medium-high, medium, medium-low, and low—and 21 geographic regions \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e–\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Furthermore, this research utilized the Human Development Index (HDI), introduced by the United Nations Development Programme (UNDP) in 1990, which comprises educational attainment, life expectancy, and gross national income components. The HDI serves as a comprehensive measure employed by the United Nations to evaluate the economic and social development levels of its Member States. Since its inception, the HDI has been instrumental in guiding the development strategies of developing countries. The UNDP annually publishes HDI values for nations worldwide, employing them in the Human Development Report to assess countries' human development levels \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e2.2 Study Data\u003c/p\u003e \u003cp\u003eThe incidence of female infertility was assessed using the International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10). Based on the physiological characteristics of women, it was presumed that there was no burden of infertility in women under the age of 15. Consequently, the study population was segmented into seven age groups: 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years.\u003c/p\u003e \u003cp\u003e2.3 Statistics\u003c/p\u003e \u003cp\u003eInitially, the global prevalence of female infertility, along with disability-adjusted life years (DALYs) and corresponding age-standardized rates (ASRs), were reported for 2021. These were further delineated by different subtypes, including age, sociodemographic index (SDI), region, and country. Temporal trends in disease burden from 1990 to 2021 were subsequently explored both globally and by subtype. Estimated Annual Percent Change (EAPC) values were calculated using linear regression models. Based on these EAPC values, hierarchical cluster analysis was employed to identify patterns of change in the disease burden across the Global Burden of Disease (GBD) regions. These regions were classified into four categories: significant increase, small increase, stable or small decrease, and significant decrease.\u003c/p\u003e \u003cp\u003eFurthermore, the relationships among EAPC, ASR, and the Human Development Index (HDI) in 2021 were evaluated. Given the normal distribution of these three variables, Spearman correlation analysis was utilized to examine the associations. Projections of the future burden of disease from 2022 to 2050 were made using an autoregressive composite moving average (ARIMA) model under a maximum likelihood framework. Statistical significance was established at a P-value of less than 0.05. All data organization, management, and analysis were conducted using R software (version 4.1.2).\u003c/p\u003e \u003c/div\u003e\u003cp\u003e2.1 Overview\u003c/p\u003e\u003cp\u003eData specific to female infertility, including prevalence, disability-adjusted life years (DALYs), and corresponding age-standardized rates, were obtained from the Global Health Data Exchange (GHDx) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Global Burden of Disease (GBD) 2021 study represents the most extensive and scientifically rigorous effort to assess epidemiological burdens globally, encompassing 371 diseases and injuries along with 84 risk factors. GBD 2021 generated estimates across 204 countries and territories, categorized into 21 regions and seven super-regions \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. These countries and territories were further segmented into five groups based on the sociodemographic index (SDI) \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. The data sources for the 2021 GBD study included household surveys, demographic statistics, and other pertinent sources \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor disease burden estimation, we utilized DisMod-MR, a Bayesian meta-regression tool, which serves as the standard GBD modeling tool for delineating the burden of disease by sex, age, location, and year. We assessed the burden of disease using various criteria, with adjustment factors estimated using the MR-BRT tool to correct for systematic biases through crosstabulation.\u003c/p\u003e\u003cp\u003eAdditionally, this study involved the collection of background information such as the SDI, for subsequent correlation analyses. The SDI values, which range from 0 to 1, reflect a country’s level of social development. According to the GBD 2021 study, countries are globally classified into five quintiles based on SDI—high, medium-high, medium, medium-low, and low—and 21 geographic regions \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e–\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Furthermore, this research utilized the Human Development Index (HDI), introduced by the United Nations Development Programme (UNDP) in 1990, which comprises educational attainment, life expectancy, and gross national income components. The HDI serves as a comprehensive measure employed by the United Nations to evaluate the economic and social development levels of its Member States. Since its inception, the HDI has been instrumental in guiding the development strategies of developing countries. The UNDP annually publishes HDI values for nations worldwide, employing them in the Human Development Report to assess countries' human development levels \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e2.2 Study Data\u003c/p\u003e\u003cp\u003eThe incidence of female infertility was assessed using the International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10). Based on the physiological characteristics of women, it was presumed that there was no burden of infertility in women under the age of 15. Consequently, the study population was segmented into seven age groups: 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years.\u003c/p\u003e\u003cp\u003e2.3 Statistics\u003c/p\u003e\u003cp\u003eInitially, the global prevalence of female infertility, along with disability-adjusted life years (DALYs) and corresponding age-standardized rates (ASRs), were reported for 2021. These were further delineated by different subtypes, including age, sociodemographic index (SDI), region, and country. Temporal trends in disease burden from 1990 to 2021 were subsequently explored both globally and by subtype. Estimated Annual Percent Change (EAPC) values were calculated using linear regression models. Based on these EAPC values, hierarchical cluster analysis was employed to identify patterns of change in the disease burden across the Global Burden of Disease (GBD) regions. These regions were classified into four categories: significant increase, small increase, stable or small decrease, and significant decrease.\u003c/p\u003e\u003cp\u003eFurthermore, the relationships among EAPC, ASR, and the Human Development Index (HDI) in 2021 were evaluated. Given the normal distribution of these three variables, Spearman correlation analysis was utilized to examine the associations. Projections of the future burden of disease from 2022 to 2050 were made using an autoregressive composite moving average (ARIMA) model under a maximum likelihood framework. Statistical significance was established at a P-value of less than 0.05. All data organization, management, and analysis were conducted using R software (version 4.1.2).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge all participants of the GBD 2021 for their contribution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJie Liu, Yi Qin: Study design，Conceptualization, formal analysis, methodology, software, visualization, and writing-original draft; Hui Liu, Yonglin Liu, Yi Yang: formal analysis, software and visualization.; Yumei Ning, Huijun Ye: Investigation, project administration, supervision, and writing-review \u0026amp; editing. All the authors have reviewed and approved the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for data should be addressed to corresponding author\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. The data can be found here: https://vizhub.healthdata.org/gbd-results/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of the funding source:\u0026nbsp;\u003c/strong\u003eThis research received the found of General scientific research project of Zhejiang Provincial Department of Education(Y202351402) and Zhejiang Provincial Traditional Chinese Medicine Science and Technology Program (2023ZL428).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWeiss, M. S., Dokras, A. \u0026amp; Marsh, E. E. Beyond awareness - National Infertility Awareness Week 2023. \u003cem\u003eFertil. Steril.\u003c/em\u003e \u003cb\u003e119\u003c/b\u003e, 570\u0026ndash;571. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.fertnstert.2023.02.026\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.fertnstert.2023.02.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaballero, B. Humans against Obesity: Who Will Win? Adv Nutr 10, S4\u0026ndash;S9 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1093/advances/nmy055\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1093/advances/nmy055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing, C. et al. Association of weight-adjusted-waist index with all-cause and cardiovascular mortality in China: A prospective cohort study. \u003cem\u003eNutr. Metab. Cardiovasc. Dis.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 1210\u0026ndash;1217. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.numecd.2022.01.033\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.numecd.2022.01.033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGesink Law, D. C., Maclehose, R. F. \u0026amp; Longnecker, M. P. Obesity and time to pregnancy. \u003cem\u003eHum. Reprod.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 414\u0026ndash;420. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1093/humrep/del400\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1093/humrep/del400\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreil, A. L., Johnson, K. M., McQuillan, J. \u0026amp; Lacy, N. Are prior pregnancy outcomes relevant for models of fertility-specific distress or infertility helpseeking? \u003cem\u003eHum. Fertil. (Camb)\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, 160\u0026ndash;166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3109/14647273.2011.587229\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3109/14647273.2011.587229\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu, B. et al. Development and validation of an Infertility Stigma Scale for Chinese women. \u003cem\u003eJ. Psychosom. Res.\u003c/em\u003e \u003cb\u003e79\u003c/b\u003e, 69\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.jpsychores.2014.11.014\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.jpsychores.2014.11.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBunnewell, S. J. et al. Diminished ovarian reserve in recurrent pregnancy loss: a systematic review and meta-analysis. Fertil Steril 113, 818\u0026ndash;827 e813 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.fertnstert.2019.11.014\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.fertnstert.2019.11.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTarin, J. J., Garcia-Perez, M. A., Hamatani, T. \u0026amp; Cano, A. Infertility etiologies are genetically and clinically linked with other diseases in single meta-diseases. \u003cem\u003eReprod. Biol. Endocrinol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1186/s12958-015-0029-9\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1186/s12958-015-0029-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenn, A. et al. Breast and ovarian cancer incidence after infertility and in vitro fertilisation. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e346\u003c/b\u003e, 995\u0026ndash;1000. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/s0140-6736(95)91687-3\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/s0140-6736(95)91687-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1995).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEscobedo, L. G., Lee, N. C., Peterson, H. B. \u0026amp; Wingo, P. A. Infertility-associated endometrial cancer risk may be limited to specific subgroups of infertile women. \u003cem\u003eObstet. Gynecol.\u003c/em\u003e \u003cb\u003e77\u003c/b\u003e, 124\u0026ndash;128 (1991).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDor, J. et al. Cancer incidence in a cohort of infertile women who underwent in vitro fertilization. \u003cem\u003eFertil. Steril.\u003c/em\u003e \u003cb\u003e77\u003c/b\u003e, 324\u0026ndash;327 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlthuis, M. D. et al. Uterine cancer after use of clomiphene citrate to induce ovulation. \u003cem\u003eAm. J. Epidemiol.\u003c/em\u003e \u003cb\u003e161\u003c/b\u003e, 607\u0026ndash;615 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva, I. S. et al. Ovulation-stimulation drugs and cancer risks: a long-term follow-up of a British cohort. \u003cem\u003eBr. J. Cancer\u003c/em\u003e. \u003cb\u003e100\u003c/b\u003e, 1824\u0026ndash;1831. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1038/sj.bjc.6605086\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1038/sj.bjc.6605086\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLerner-Geva, L. et al. Are infertility treatments a potential risk factor for cancer development? Perspective of 30 years of follow-up. \u003cem\u003eGynecol. Endocrinology: Official J. Int. Soc. Gynecol. Endocrinol.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 809\u0026ndash;814. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3109/09513590.2012.671391\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3109/09513590.2012.671391\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKvaskoff, M. et al. Endometriosis: a high-risk population for major chronic diseases? \u003cem\u003eHum. Reprod. Update\u003c/em\u003e. \u003cb\u003e21\u003c/b\u003e, 500\u0026ndash;516. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1093/humupd/dmv013\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1093/humupd/dmv013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHart, R. \u0026amp; Doherty, D. A. The potential implications of a PCOS diagnosis on a woman's long-term health using data linkage. \u003cem\u003eJ. Clin. Endocrinol. Metab.\u003c/em\u003e \u003cb\u003e100\u003c/b\u003e, 911\u0026ndash;919. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1210/jc.2014-3886\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1210/jc.2014-3886\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKheirollahi, A. et al. Evaluation of lipid ratios and triglyceride-glucose index as risk markers of insulin resistance in Iranian polycystic ovary syndrome women. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1186/s12944-020-01410-8\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1186/s12944-020-01410-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMascarenhas, M. N., Flaxman, S. R., Boerma, T., Vanderpoel, S. \u0026amp; Stevens, G. A. National, regional, and global trends in infertility prevalence since 1990: a systematic analysis of 277 health surveys. \u003cem\u003ePLoS Med.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, e1001356. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1371/journal.pmed.1001356\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1371/journal.pmed.1001356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, X., Zhang, J. \u0026amp; Wang, S. Global, regional, and national burden of infertility attributable to PCOS, 1990\u0026ndash;2019. \u003cem\u003eHum. Reprod.\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e, 108\u0026ndash;118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1093/humrep/dead241\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1093/humrep/dead241\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, J. et al. Spatiotemporal Trends and Age-Period-Cohort Analysis for the Burden of Endometriosis-Related Infertility: An Analysis of the Global Burden of Disease Study 2019. \u003cem\u003eJ. Pers. Med.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3390/jpm13091284\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3390/jpm13091284\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei, H. L., Wei, H. J., Ho, H. Y., Liao, K. W. \u0026amp; Chien, L. C. Relationship between risk factors for infertility in women and lead, cadmium, and arsenic blood levels: a cross-sectional study from Taiwan. \u003cem\u003eBMC Public. Health\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, 1220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1186/s12889-015-2564-x\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1186/s12889-015-2564-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaborators, G. B. D. M. A. R. Global, regional, and national burden of meningitis and its aetiologies, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet Neurol.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 685\u0026ndash;711. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/S1474-4422(23)00195-3\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/S1474-4422(23)00195-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaborators, G. B. D. S. C. I. Global, regional, and national burden of spinal cord injury, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet Neurol.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 1026\u0026ndash;1047. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/S1474-4422(23)00287-9\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/S1474-4422(23)00287-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu, J. et al. Burden of female infertility in China from 1990 to 2019: a temporal trend analysis and forecasting, and comparison with the global level. \u003cem\u003eSex. Health\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e, 577\u0026ndash;584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1071/SH23029\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1071/SH23029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, J. et al. Spatiotemporal Trends and Age-Period-Cohort Analysis for the Burden of Endometriosis-Related Infertility: An Analysis of the Global Burden of Disease Study 2019. \u003cem\u003eJ. Personalized Med.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3390/jpm13091284\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3390/jpm13091284\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, H. et al. Global, regional, and national prevalence and disability-adjusted life-years for infertility in 195 countries and territories, 1990\u0026ndash;2017: results from a global burden of disease study, 2017. \u003cem\u003eAging (Albany NY)\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, 10952\u0026ndash;10991. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.18632/aging.102497\u003c/span\u003e\u003cspan address=\"https://doi.org:10.18632/aging.102497\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a, D., Brazal, S., Rodr\u0026iacute;guez, A., Prat, A. \u0026amp; Vassena, R. Knowledge of age-related fertility decline in women: A systematic review. \u003cem\u003eEur. J. Obstet. Gynecol. Reprod. Biol.\u003c/em\u003e \u003cb\u003e230\u003c/b\u003e, 109\u0026ndash;118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.ejogrb.2018.09.030\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.ejogrb.2018.09.030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKasaven, L. S. et al. Exploring the knowledge, attitudes, and perceptions of women of reproductive age towards fertility and elective oocyte cryopreservation for age-related fertility decline in the UK: a cross-sectional survey. \u003cem\u003eHum. Reprod. (Oxford England)\u003c/em\u003e. \u003cb\u003e38\u003c/b\u003e, 2478\u0026ndash;2488. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1093/humrep/dead200\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1093/humrep/dead200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, W., Li, X., Yang, H. \u0026amp; Huang, H. The impact of maternal age on aneuploidy in oocytes: Reproductive consequences, molecular mechanisms, and future directions. \u003cem\u003eAgeing Res. Rev.\u003c/em\u003e \u003cb\u003e97\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.arr.2024.102292\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.arr.2024.102292\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen, A. J. et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e389\u003c/b\u003e, 1907\u0026ndash;1918 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/S0140-6736(17)30505-6\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/S0140-6736(17)30505-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaheshwari, A., Hamilton, M. \u0026amp; Bhattacharya, S. Effect of female age on the diagnostic categories of infertility. \u003cem\u003eHum. Reprod.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 538\u0026ndash;542. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1093/humrep/dem431\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1093/humrep/dem431\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMikwar, M., MacFarlane, A. J. \u0026amp; Marchetti, F. Mechanisms of oocyte aneuploidy associated with advanced maternal age. \u003cem\u003eMutat. Research/Reviews Mutat. Res.\u003c/em\u003e \u003cb\u003e785\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.mrrev.2020.108320\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.mrrev.2020.108320\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerdyck, P. et al. Aneuploidy in oocytes from women of advanced maternal age: analysis of the causal meiotic errors and impact on embryo development. \u003cem\u003eHum. Reprod.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, 2526\u0026ndash;2535. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1093/humrep/dead201\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1093/humrep/dead201\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMay-Panloup, P. et al. Ovarian ageing: the role of mitochondria in oocytes and follicles. \u003cem\u003eHum. Reprod. Update\u003c/em\u003e. \u003cb\u003e22\u003c/b\u003e, 725\u0026ndash;743. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1093/humupd/dmw028\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1093/humupd/dmw028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmits, M. A. J. et al. Human ovarian aging is characterized by oxidative damage and mitochondrial dysfunction. \u003cem\u003eHum. Reprod.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, 2208\u0026ndash;2220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1093/humrep/dead177\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1093/humrep/dead177\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai, J., Cui, J., Shi, F. \u0026amp; Yu, C. Global Epidemiological Patterns in the Burden of Main Non-Communicable Diseases, 1990\u0026ndash;2019: Relationships With Socio-Demographic Index. \u003cem\u003eInt. J. Public. Health\u003c/em\u003e. \u003cb\u003e68\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3389/ijph.2023.1605502\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3389/ijph.2023.1605502\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavila-Cervantes, C. A. The burden of type 2 diabetes in adolescents and young adults in Mexico: analysis from the Global Burden of Disease Study, 1990 to 2019. \u003cem\u003eJ. Diabetes Metab. Disord\u003c/em\u003e. \u003cb\u003e22\u003c/b\u003e, 1673\u0026ndash;1684. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1007/s40200-023-01299-x\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1007/s40200-023-01299-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInstituto Nacional de Estadistica e Informatica (INEI) and \u0026amp; Macro, O. R. C. Per\u0026uacute;: Encuesta Demogr\u0026aacute;fica y de Salud Familiar-ENDES Continua, 2009 (2010), accessed at www.measuredhs.com, on Nov. 3, 2010.网页未导\u0026amp;#20837.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNieuwenhuijsen, M. J. et al. Air pollution and human fertility rates. \u003cem\u003eEnviron. Int.\u003c/em\u003e \u003cb\u003e70\u003c/b\u003e, 9\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.envint.2014.05.005\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.envint.2014.05.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, Q. et al. The incidence of asthma attributable to temperature variability: An ecological study based on 1990\u0026ndash;2019 GBD data. \u003cem\u003eSci. Total Environ.\u003c/em\u003e \u003cb\u003e904\u003c/b\u003e, 166726. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.scitotenv.2023.166726\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.scitotenv.2023.166726\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiseases, G. B. D. \u0026amp; Injuries, C. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e396\u003c/b\u003e, 1204\u0026ndash;1222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/S0140-6736(20)30925-9\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/S0140-6736(20)30925-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaborators, G. B. D. R. F. Global burden of 87 risk factors in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e396\u003c/b\u003e, 1223\u0026ndash;1249. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/S0140-6736(20)30752-2\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/S0140-6736(20)30752-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiseases, G. B. D. \u0026amp; Injuries, C. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e403\u003c/b\u003e, 2133\u0026ndash;2161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/S0140-6736(24)00757-8\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/S0140-6736(24)00757-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie, J. et al. Global burden of type 2 diabetes in adolescents and young adults, 1990\u0026ndash;2019: systematic analysis of the Global Burden of Disease Study 2019. \u003cem\u003eBMJ\u003c/em\u003e \u003cb\u003e379\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1136/bmj-2022-072385\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1136/bmj-2022-072385\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022). e072385.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabatabaei-Malazy, O. et al. Regional burden of chronic kidney disease in North Africa and Middle East during 1990\u0026ndash;2019; Results from Global Burden of Disease study 2019. \u003cem\u003eFront. Public. Health\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e, 1015902. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3389/fpubh.2022.1015902\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3389/fpubh.2022.1015902\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFallahzadeh, H. et al. Distribution incidence, mortality of tuberculosis and human development index in Iran: estimates from the global burden of disease study 2019. \u003cem\u003eBMC Public. Health\u003c/em\u003e. \u003cb\u003e24\u003c/b\u003e, 661. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1186/s12889-024-18021-y\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1186/s12889-024-18021-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaramitanha, F., Ahmadi, F. \u0026amp; Fallahabadi, H. Difference Between Various Countries in Mortality and Incidence Rate of the Atrial Fibrillation Based on Human Development Index in Worldwide: Data From Global Burden of Disease 2010\u0026ndash;2019. \u003cem\u003eCurr. Probl. Cardiol.\u003c/em\u003e \u003cb\u003e48\u003c/b\u003e, 101438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.cpcardiol.2022.101438\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.cpcardiol.2022.101438\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Female infertility, Global burden disease, Age-standardized prevalence rate, disability-adjusted life years, Age-standardized DALYs rate, Estimated annual percentage change","lastPublishedDoi":"10.21203/rs.3.rs-5573774/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5573774/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFemale infertility represents a significant reproductive health issue that critically affects global fertility rates. In this study, we utilized the most recent data from the Global Burden of Disease Study 2021. Initially, we assessed the global number of female infertility prevalence and disability-adjusted life years (DALYs), along with the age-standardized rate (ASR) per 100,000 individuals, stratified by age, sex, sociodemographic index (SDI), nationality, and region. Furthermore, linear regression models were employed to examine the temporal trends of disease burden from 1990 to 2021. Cluster analysis facilitated the evaluation of disease burden change patterns across different GBD regions. Lastly, the Autoregressive Composite Moving Average (ARIMA) model was applied to forecast future disease burdens. In 2021, the global prevalence of female infertility was estimated at 110,089,459, contributing to 6,210,145 DALYs, which corresponds to 0.65% of the global prevalence and 0.24% of global DALYs. There was an observed increase of 76.11% in prevalence and 56.37% in DALYs since 1990. The highest burden occurred among individuals aged 35\u0026ndash;39, with the most rapid increase observed in the 30\u0026ndash;34 age group. The burden of female infertility displayed considerable variability across GBD regions and countries, with areas of high-meduim SDI facing elevated risks. Projections indicate a continuing rise in the ASR of prevalence and DALYs for female infertility over the next two decades. The global burden of female infertility has intensified from 1990 to 2021, with notable disparities across different SDI regions and countries. Women aged 35\u0026ndash;39 face the highest risk, and there is a trend toward earlier onset of infertility.\u003c/p\u003e","manuscriptTitle":"Global, Regional, and National Female Infertility Burden and Trends from 1990 to 2021: A Systematic Analysis for the Global Burden of Disease Study 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-23 13:48:22","doi":"10.21203/rs.3.rs-5573774/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-24T12:43:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-01T16:30:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96691217499331434728638211426481667260","date":"2025-01-23T20:02:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-10T18:02:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296093515993847719785070217262776791672","date":"2024-12-27T14:36:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-27T14:04:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-27T13:33:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-12-20T13:42:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-19T12:22:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-12-03T15:59:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"066ea4f1-aa95-4cb0-8a9b-ed1152d3421d","owner":[],"postedDate":"December 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41886019,"name":"Health sciences/Medical research/Epidemiology"},{"id":41886020,"name":"Health sciences/Diseases/Reproductive disorders/Infertility"},{"id":41886021,"name":"Health sciences/Health care/Disease prevention/Preventive medicine"},{"id":41886022,"name":"Health sciences/Health care/Health care economics"}],"tags":[],"updatedAt":"2025-05-26T15:59:42+00:00","versionOfRecord":{"articleIdentity":"rs-5573774","link":"https://doi.org/10.1038/s41598-025-01498-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-21 15:57:07","publishedOnDateReadable":"May 21st, 2025"},"versionCreatedAt":"2024-12-23 13:48:22","video":"","vorDoi":"10.1038/s41598-025-01498-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-01498-x","workflowStages":[]},"version":"v1","identity":"rs-5573774","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5573774","identity":"rs-5573774","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}