Thyroid Cancer Burden among Women of Childbearing Age: A 30-Year Global Analysis from the Global Burden of Disease Study

Thyroid Cancer Burden among Women of Childbearing Age: A 30-Year Global Analysis from the Global Burden of Disease Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Thyroid Cancer Burden among Women of Childbearing Age: A 30-Year Global Analysis from the Global Burden of Disease Study Zhe Wang, Ze Yang, Yong Chen, Ming Yang, Xiaojing Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6399893/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Thyroid Cancer (TC) is a global health concern with varying levels and trends across countries and regions, while women of child-bearing age (WCBA) are often neglected despite their unique epidemiology, healthcare needs and societal implications. We aim to investigate the pattern and trend of female TC among WCBA from 1990 to 2021. The data for this study were obtained from the Global Burden of Disease (GBD) 2021 database, with age-standardized incidence rate (ASIR), prevalence rate (ASPR), mortality rate (ASMR), and disability-adjusted life years (DALYs) as the primary assessment indicators. Dynamic changes in the TC burden among WCBA were analyzed by estimating the annual percentage changes (EAPCs), and a Bayesian age-period-cohort model was used to predict future 30-year trends. Health inequalities were analyzed using the slope index of inequality (SII) and concentration index (CI). The global TC incidence increased from 26,302 cases in 1990 to 67,558 in 2021, with a corresponding rise in ASIR from 2.17 to 3.36 per 100,000 among women aged 15–49. While ASMR showed a slight decline, indicating improvements in treatment efficacy and early detection, health inequalities persist, particularly in lower socio-demographic index (SDI) countries, where ASIR and DALYs remain significantly elevated. Significant regional disparities were observed, with South Asia reporting the highest burden and North America the lowest. Our predictions suggest that the ASIR and ASDR for TC will increase to 3.94 and 10.86 per 100,000 by 2051, respectively, while the ASMR will decline to 0.14. These findings underscore the urgent need for enhanced screening, targeted interventions, and resource allocation, particularly in low and middle-income regions, to effectively manage thyroid cancer and mitigate the associated health disparities. Future research should focus on the underlying biological factors and the effectiveness of public health strategies to further reduce the burden of this disease. Thyroid cancer Women of childbearing age Global Burden of Disease 2021 Age-period-cohort model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Background Thyroid cancer (TC) has emerged as a globally prevalent malignancy, imposing significant public health challenges and substantial economic burdens on healthcare systems worldwide [ 1 ] . Notably, a pronounced gender disparity exists in TC epidemiology, with women of childbearing age (15–49 years) demonstrating 2–4 times higher incidence rates compared to males [ 2 ] . This female predominance appears hormonally mediated, as evidenced by epidemiological patterns: prepubertal and postmenopausal women show comparable TC rates to males, whereas reproductive-age women exhibit markedly elevated risks[3]. Mounting evidence suggests that cumulative estrogen exposure during successive pregnancies may drive this phenomenon, with parity showing a dose-dependent association with TC incidence ( p < 0.01 ) [ 4 , 5 ] . The risk escalation is particularly pronounced during later reproductive years (30–49 years) and persists following artificial menopause [ 6 , 7 ] , though exogenous estrogen use (e.g., oral contraceptives) shows no significant correlation [ 8 , 9 ] . Remarkable differences exist among regions and countries regarding female cancers [ 10 ] . While high-income nations like the United States report steadily rising incidence rates [ 11 ] , developing regions face distinct challenges. In India (2016), TC ranked as the tenth most common female malignancy, with 21,000 new cases (74.3% female) and striking regional variations - Kerala, Sikkim, Nagaland, and Goa demonstrated the highest crude incidence rates [ 12 ] . Globally, 2012 witnessed nearly 300,000 TC diagnoses, with economically developed countries reporting twofold higher age-standardized incidence rates compared to developing nations [ 13 ] . These disparities are exacerbated by socioeconomic factors, as patients from disadvantaged backgrounds frequently experience diagnostic delays, advanced-stage presentations, and catastrophic healthcare expenditures [ 14 ] . Such findings underscore the urgent need for tailored preventive strategies addressing both biological and socioeconomic determinants of TC in reproductive-age women. This study aims to achieve three primary objectives: (1) to quantify the prevalence and mortality burden of thyroid cancer (TC) among women of childbearing age (15–49 years); (2) to investigate the socioeconomic determinants underlying observed epidemiological trends; and (3) to assess the global health inequalities in TC outcomes across different development strata. Through a comprehensive analysis of TC burden using advanced epidemiological modeling techniques, we seek to generate actionable insights for healthcare policy formulation, particularly in low- and middle-income countries (LMICs) where the disease burden disproportionately affects vulnerable populations. By addressing critical gaps in current literature regarding age-specific and socioeconomic dimensions of TC epidemiology, this research contributes to the broader understanding of endocrine malignancies and informs targeted interventions to reduce health disparities in reproductive-age women. 2. Methods 2.1 Data source The data for our study was obtained from the Global Burden of Disease (GBD) 2021, published by the Institute for Health Metrics and Evaluation (IHME) ( https://vizhub.healthdata.org/gbd ) [ 15 ] . GBD 2021 included detailed assessments of diseases, injuries, and risk factors across 204 countries and territories. The data is publicly available and does not require additional ethical approval for use in research. Thyroid cancer (TC) has been categorized as a level 3 cause in the GBD 2021. The International Classification of Diseases, 10th Revision (ICD-10) defines TC as malignant neoplasms of the thyroid gland, coded as C73 [ 16 ] . 2.2 Data collection We utilized the Bayesian Meta-regression framework DisMod-MR 2.1 to integrate incidence and mortality from the GBD dataset to generate preliminary estimates of the incidence and mortality of TC in women of childbearing age (WCBA) [ 17 ] . Adjustments were made for differences in measurement methods and case definitions. Disability-adjusted life years (DALYs) were used as a key metric for assessing disease burden, as they provide a comprehensive evaluation of disease and healthcare intervention impacts [ 18 ] . DALYs for TC in WCBA were calculated using data from GBD 2021, and trends from 1990 to 2021 were further analyzed. 2.3 Socio-demographic index (SDI) The SDI is a composite indicator of social and economic conditions that influence health outcomes, based on educational attainment, income per capita, and fertility rates. SDI values range from 0 to 1, with higher values indicating greater socio-economic development [ 19 ] . SDI was divided into five quintiles: low, low-middle, middle, middle-high, and high [ 20 ] . We examined the relationship between SDI and the burden of TC in WCBA. 2.4 Health inequality analysis We employed the slope index of inequality (SII) and concentration index (CI) to quantify the distributional inequality of the burden of TC [ 21 ] . The SII was calculated by regressing national DALYs rates across all age groups on a relative position scale associated with SDI, with the midpoint of the cumulative population range defined by SDI rankings. The CI was computed by numerically integrating the area under the Lorenz curve, fitting the DALYs cumulative distribution to the cumulative population distribution ordered by SDI. 2.5 Age-period-cohort (APC) model We employed the APC model to analyze the independent effects of age, period, and birth cohort on the incidence, mortality, and DALYs rates of TC in WCBA [ 22 ] . Data inputs for the APC model included incidence, mortality, and DALYs rate estimates for TC and population data from GBD 2021 for each country or region. WCBA (15–49 years) was divided into seven age groups, and the study period (1992–2021) was segmented into six 5-year periods. The APC model output parameters included net drift, which represents the annual percentage change (APC) in disease rates after adjusting for nonlinear period and cohort effects, and local drift, indicating the annual percentage change in disease rates for different age groups. 2.6 Forecasting For future trend predictions of the burden for TC in WCBA over the next 30 years, we applied the Bayesian age-period-cohort (BAPC) model in combination with Integrated Nested Laplace Approximation (INLA) [ 23 ] . 2.7 Statistical analysis We evaluated the time trends of age-standardized incidence rates (ASIR), age-standardized mortality rates (ASMR), and age-standardized DALYs rates (ASDR) for TC in WCBA from 1990 to 2021, using data from the GBD 2021. The estimated annual percentage change (EAPC) was calculated to assess the trend of these age-standardized rates (ASRs)[24]. Participants were grouped into seven age groups (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years), and the age distribution of burden for TC was analyzed for each group [ 25 ] . The EAPC was derived from a regression model, where the pattern of ASRs from 1990 to 2021 was modeled by: Y = α + βX + e, where Y is the natural logarithm of ASR, x represents the years, α is the intercept, β is the slope (representing the trend), and e is the error term [ 26 ] . The EAPC was computed as: EAPC = 100 × [exp(β) − 1]. The EAPC represents the annual percentage change. Data analysis was conducted using R software (version 4.4.1), and images were modified using Adobe Illustrator (version 2023). 3. Results 3.1 Global, regional, and national trends in TC among WCBA In 2021, the global incident cases, deaths, and the number of DALYs for TC in WCBA were 67,558 (95% UI: 57,165 − 81,890), 3,260 (95% UI: 2,630-4,100), and 206,508 (95% UI: 165,757 − 263,977), respectively. From 1990 to 2021, both the incidence of TC and the DALYs rates increased annually, with EAPC of 1.42 (1.39 to 1.45) and 0.02 (0.02 to 0.03), respectively (Table 1 ). The highest incident cases in 2021 were observed in the middle SDI regions, with 22,327 (17,847 to 27,350). In contrast, the highest number of deaths and DALYs were found in low-middle SDI regions, with 1,140 (899 to 1,532) and 68,877 (54,018 to 93,818), respectively. The mortality and DALYs rates for TC decreased most significantly in high-middle SDI regions, with EAPC of -2.01 (-2.03 to -2.21) and − 1.37 (-1.36 to -1.37), respectively (Table 1 ). In 21 GBD regions, South Asia reported the highest incident cases, deaths, and DALYs for TC, with 16,419 (12,716 − 22,178), 1,380 (1,066 − 1,832), and 84,402 (64,631 − 113,572), respectively. In contrast, Oceania had the lowest numbers, with 36 (19–59), 3 (1–5), and an ASIR of 160 (86–271). The high-income Asia Pacific region had the highest ASIR at 5.56 (5.34–5.78), while the Eastern Sub-Saharan Africa region had the highest ASMR and ASDR, with 0.39 (0.35–0.43) and 23.00 (22.69–23.31), respectively (Table 1 ). Table 1 The incident cases, deaths, DALYs, and their ASR of TC among WCBA, as well as EAPC from 1990 to 2021. location Incidence Deaths DALYs 1990 counts 2021 counts 1990 age- standardized rates (per 100 000) 2021 age-standardized rates (per 100 000) EAPC (%),199 0–2021 1990 counts 2021 counts 1990 age-standardized rates (per 100 000) 2021 age-standardized rates per 100 000) EAPC (%),199 0–2021 1990 counts 2021 counts 1990 age-standardized rates (per 100 000) 2021 age-standardized rates (per 100 000) EAPC (%),199 0–2021 Global 26302 (23536 to 29951) 67558 (57165 to 81809) 2.17 (2.15 to 2.20) 3.36 (3.33 to 3.38) 1.42 (1.39 to 1.42) 2140 (1831 to 2592) 3260 (2630 to 4100) 0.18 (0.17 to 0.18) 0.16 (0.16 to 0.17) -0.38 (-0.18 to -0.2) 127692 (107124 to 154939) 206508 (165745 to 263977) 10.30 (10.24 to 10.36) 10.37 (10.33 to 10.41) 0.02 (0.02 to 0.03) High SDI 8327 (7927 to 8712) 13395 (12476 to 14852) 3.52 (3.44 to 3.59) 4.73 (4.65 to 4.82) 0.96 (0.95 to 0.98) 250 (238 to 261) 200 (184 to 220) 0.11 (0.09 to 0.12) 0.07 (0.06 to 0.08) -1.45 (-1.3 to -1.3) 16763 (15288 to 18713) 16546 (13888 to 19749) 7.12 (7.01 to 7.23) 5.88 (5.79 to 5.97) -0.62 (-0.62 to -0.61) High-middle SDI 6462 (5654 to 7174) 12094 (10007 to 15103) 2.50 (2.44 to 2.56) 3.33 (3.27 to 3.39) 0.93 (0.91 to 0.95) 392 (324 to 446) 304 (255 to 378) 0.15 (0.14 to 0.17) 0.08 (0.07 to 0.09) -2.01 (-2.03 to -2.21) 23383 (19392 to 27045) 21086 (17513 to 26594) 9.00 (8.88 to 9.11) 5.87 (5.79 to 5.95) -1.37 (-1.36 to -1.37) Middle SDI 6410 (5293 to 7722) 22327 (17847 to 27350) 1.65 (1.61 to 1.69) 3.38 (3.33 to 3.42) 2.34 (2.3 to 2.37) 638 (533 to 777) 959 (778 to 1155) 0.17 (0.16 to 0.18) 0.14 (0.13 to 0.15) -0.62 (-0.59 to -0.67) 37089 (31087 to 45400) 59997 (47627 to 74495) 9.49 (9.39 to 9.59) 9.12 (9.05 to 9.19) -0.13 (-0.14 to -0.12) Low-middle SDI 3538 (2867 to 4847) 13681 (10737 to 18430) 1.43 (1.39 to 1.48) 2.79 (2.75 to 2.84) 2.18 (2.12 to 2.23) 548 (438 to 755) 1140 (899 to 1532) 0.23 (0.21 to 0.25) 0.23 (0.22 to 0.25) 0 (0 to 0.15) 32136 (25703 to 44506) 68877 (54018 to 93818) 12.77 (12.63 to 12.91) 13.96 (13.86 to 14.07) 0.29 (0.28 to 0.3) Low SDI 1532 (1132 to 2021) 6009 (4502 to 9050) 1.53 (1.45 to 1.61) 2.41 (2.35 to 2.48) 1.48 (1.4 to 1.57) 310 (228 to 413) 655 (486 to 1012) 0.31 (0.28 to 0.35) 0.27 (0.25 to 0.29) -0.44 (-0.6 to -0.36) 18192 (13293 to 24282) 39857 (29090 to 62337) 17.74 (17.48 to 18.01) 15.69 (15.53 to 15.85) -0.4 (-0.41 to -0.38) Western Sub-Saharan Africa 116 (82 to 155) 426 (294 to 623) 0.32 (0.26 to 0.38) 0.41 (0.37 to 0.45) 0.8 (0.55 to 1.44) 21 (15 to 28) 48 (33 to 72) 0.06 (0.04 to 0.09) 0.05 (0.03 to 0.06) -0.59 (-1.3 to -0.92) 1201 (841 to 1621) 2877 (1941 to 4327) 3.22 (3.03 to 3.41) 2.71 (2.61 to 2.81) -0.55 (-0.62 to -0.48) Western Europe 3614 (3300 to 3932) 3648 (3260 to 4097) 3.60 (3.49 to 3.72) 3.23 (3.12 to 3.33) -0.35 (-0.35 to -0.36) 121 (113 to 128) 57 (53 to 62) 0.12 (0.10 to 0.14) 0.05 (0.04 to 0.07) -2.78 (-2.21 to -2.91) 7812 (7074 to 8783) 4569 (3893 to 5434) 7.81 (7.63 to 7.98) 4.05 (3.93 to 4.17) -2.1 (-2.07 to -2.12) Tropical Latin America 373 (348 to 401) 1078 (1000 to 1169) 1.08 (0.97 to 1.20) 1.63 (1.54 to 1.74) 1.34 (1.21 to 1.5) 41 (39 to 45) 63 (59 to 68) 0.12 (0.09 to 0.17) 0.09 (0.07 to 0.12) -0.92 (-1.12 to -0.81) 2300 (2148 to 2472) 3652 (3357 to 4002) 6.67 (6.40 to 6.95) 5.56 (5.38 to 5.74) -0.59 (-0.62 to -0.56) Southern Sub-Saharan Africa 158 (129 to 192) 392 (295 to 507) 1.42 (1.20 to 1.67) 1.86 (1.68 to 2.05) 0.87 (0.66 to 1.09) 19 (16 to 24) 41 (30 to 53) 0.18 (0.11 to 0.28) 0.20 (0.14 to 0.27) 0.34 (-0.12 to 0.78) 1100 (899 to 1360) 2336 (1648 to 3052) 9.83 (9.24 to 10.45) 11.10 (10.65 to 11.56) 0.39 (0.33 to 0.46) Southern Latin America 249 (206 to 294) 457 (383 to 553) 2.08 (1.83 to 2.36) 2.46 (2.24 to 2.70) 0.54 (0.44 to 0.65) 21 (18 to 25) 19 (16 to 22) 0.18 (0.11 to 0.27) 0.10 (0.06 to 0.16) -1.88 (-1.67 to -1.94) 1196 (1024 to 1421) 1163 (965 to 1392) 9.99 (9.43 to 10.57) 6.26 (5.91 to 6.64) -1.5 (-1.49 to -1.5) Southeast Asia 2536 (1811 to 2998) 8935 (6436 to 10945) 2.44 (2.34 to 2.54) 4.66 (4.57 to 4.76) 2.11 (2.05 to 2.18) 255 (182 to 302) 426 (302 to 520) 0.26 (0.22 to 0.29) 0.22 (0.20 to 0.24) -0.54 (-0.61 to -0.31) 14593 (10234 to 17291) 25611 (18572 to 31798) 14.17 (13.94 to 14.41) 13.32 (13.16 to 13.49) -0.2 (-0.21 to -0.19) South Asia 3673 (2842 to 5200) 16419 (12716 to 22178) 1.53 (1.48 to 1.58) 3.38 (3.33 to 3.43) 2.59 (2.53 to 2.65) 611 (470 to 880) 1380 (1066 to 1832) 0.26 (0.24 to 0.28) 0.29 (0.27 to 0.30) 0.35 (0.22 to 0.38) 36363 (27848 to 52029) 84402 (64631 to 113572) 14.95 (14.80 to 15.11) 17.30 (17.18 to 17.41) 0.47 (0.46 to 0.48) Oceania 12 (7 to 18) 36 (19 to 59) 0.93 (0.48 to 1.66) 1.09 (0.76 to 1.52) 0.51 (-0.28 to 1.44) 1 (1 to 2) 3 (1 to 5) 0.10 (0.00 to 0.53) 0.08 (0.02 to 0.26) -0.72 (0 to -2.27) 74 (41 to 108) 160 (86 to 271) 5.59 (4.37 to 7.07) 4.86 (4.13 to 5.68) -0.45 (-0.7 to -0.18) North Africa and Middle East 1633 (1274 to 2351) 8156 (6267 to 10264) 2.47 (2.35 to 2.60) 5.05 (4.94 to 5.16) 2.33 (2.24 to 2.43) 90 (70 to 136) 196 (154 to 252) 0.14 (0.11 to 0.17) 0.12 (0.11 to 0.14) -0.5 (-0.62 to 0) 5585 (4324 to 8478) 14110 (10875 to 18632) 8.39 (8.17 to 8.62) 8.78 (8.63 to 8.92) 0.15 (0.11 to 0.18) High-income North America 2483 (2395 to 2575) 4256 (4029 to 4504) 3.20 (3.08 to 3.33) 4.59 (4.46 to 4.73) 1.17 (1.14 to 1.2) 50 (49 to 52) 58 (56 to 61) 0.07 (0.05 to 0.09) 0.06 (0.05 to 0.08) -0.5 (-0.38 to 0) 3762 (3359 to 4293) 4996 (4301 to 5884) 4.89 (4.73 to 5.05) 5.42 (5.27 to 5.57) 0.33 (0.32 to 0.35) High-income Asia Pacific 1797 (1577 to 2106) 2689 (2232 to 3322) 3.66 (3.49 to 3.83) 5.56 (5.34 to 5.78) 1.36 (1.34 to 1.38) 41 (35 to 51) 28 (24 to 35) 0.08 (0.06 to 0.11) 0.06 (0.04 to 0.08) -0.92 (-1.02 to -1.3) 2908 (2459 to 3662) 2702 (2108 to 3576) 5.93 (5.72 to 6.15) 5.57 (5.36 to 5.80) -0.2 (-0.19 to -0.21) Eastern Sub-Saharan Africa 880 (624 to 1201) 3141 (2168 to 5421) 2.32 (2.17 to 2.49) 3.23 (3.11 to 3.35) 1.07 (0.96 to 1.77) 192 (135 to 267) 373 (257 to 650) 0.52 (0.45 to 0.61) 0.39 (0.35 to 0.43) -0.92 (-1.12 to -0.81) 11351 (8018 to 15669) 22843 (15628 to 39868) 29.30 (28.74 to 29.87) 23.00 (22.69 to 23.31) -0.78 (-0.8 to -0.76) Eastern Europe 1571 (1484 to 1685) 2157 (1874 to 2493) 2.76 (2.63 to 2.90) 3.57 (3.42 to 3.73) 0.83 (0.82 to 0.85) 63 (60 to 68) 51 (44 to 60) 0.11 (0.09 to 0.15) 0.08 (0.06 to 0.11) -1.02 (-1 to -1.3) 3955 (3625 to 4349) 3568 (2965 to 4286) 7.04 (6.82 to 7.26) 5.92 (5.73 to 6.13) -0.56 (-0.54 to -0.56) East Asia 4777 (3480 to 5946) 10949 (8131 to 16538) 1.62 (1.58 to 1.67) 2.79 (2.74 to 2.85) 1.77 (1.74 to 1.79) 418 (303 to 530) 272 (199 to 412) 0.15 (0.13 to 0.16) 0.07 (0.06 to 0.08) -2.43 (-2.21 to -2.46) 24353 (17985 to 31231) 19022 (13388 to 28805) 8.24 (8.14 to 8.35) 4.89 (4.81 to 4.96) -1.67 (-1.67 to -1.68) Central Sub-Saharan Africa 41 (25 to 69) 148 (81 to 272) 0.41 (0.29 to 0.56) 0.54 (0.46 to 0.64) 0.89 (0.43 to 1.5) 9 (6 to 14) 20 (11 to 36) 0.09 (0.04 to 0.18) 0.08 (0.05 to 0.12) -0.38 (-1.3 to 0.72) 475 (303 to 796) 1113 (603 to 2029) 4.71 (4.29 to 5.17) 4.05 (3.81 to 4.30) -0.49 (-0.59 to -0.38) Central Latin America 541 (513 to 570) 1993 (1724 to 2308) 1.59 (1.46 to 1.73) 2.86 (2.73 to 2.98) 1.91 (1.77 to 2.04) 60 (58 to 63) 110 (95 to 127) 0.18 (0.14 to 0.24) 0.16 (0.13 to 0.19) -0.38 (-0.75 to -0.24) 3366 (3210 to 3539) 6427 (5527 to 7485) 9.89 (9.55 to 10.24) 9.20 (8.98 to 9.43) -0.23 (-0.27 to -0.2) Central Europe 1135 (1037 to 1236) 943 (818 to 1062) 3.49 (3.29 to 3.70) 2.89 (2.71 to 3.09) -0.61 (-0.58 to -0.62) 63 (60 to 68) 26 (23 to 29) 0.20 (0.15 to 0.25) 0.08 (0.05 to 0.12) -2.91 (-2.34 to -3.48) 3715 (3424 to 4048) 1705 (1473 to 1979) 11.53 (11.16 to 11.91) 5.22 (4.97 to 5.49) -2.52 (-2.47 to -2.58) Central Asia 286 (258 to 317) 486 (414 to 568) 2.07 (1.83 to 2.33) 1.92 (1.76 to 2.10) -0.24 (-0.33 to -0.13) 25 (23 to 27) 27 (23 to 32) 0.19 (0.12 to 0.28) 0.11 (0.07 to 0.16) -1.75 (-1.79 to -1.72) 1445 (1306 to 1592) 1610 (1363 to 1888) 10.45 (9.90 to 11.03) 6.40 (6.09 to 6.73) -1.57 (-1.58 to -1.56) Caribbean 142 (123 to 159) 288 (232 to 355) 1.73 (1.45 to 2.04) 2.33 (2.07 to 2.62) 0.97 (0.81 to 1.33) 14 (12 to 16) 20 (16 to 26) 0.17 (0.09 to 0.29) 0.16 (0.10 to 0.25) -0.2 (-0.48 to 0.34) 790 (670 to 941) 1172 (921 to 1486) 9.52 (8.86 to 10.22) 9.53 (8.99 to 10.10) 0 (-0.04 to 0.05) Australasia 149 (123 to 180) 317 (227 to 430) 2.72 (2.30 to 3.19) 3.86 (3.44 to 4.31) 1.14 (0.98 to 1.31) 4 (3 to 5) 4 (3 to 5) 0.07 (0.02 to 0.19) 0.04 (0.01 to 0.13) -1.79 (-1.22 to -2.21) 274 (226 to 338) 337 (236 to 475) 5.00 (4.43 to 5.64) 4.11 (3.68 to 4.59) -0.63 (-0.66 to -0.6) Andean Latin America 135 (109 to 175) 644 (477 to 853) 1.70 (1.42 to 2.02) 3.71 (3.43 to 4.00) 2.55 (2.23 to 2.89) 19 (15 to 25) 36 (27 to 48) 0.25 (0.15 to 0.40) 0.21 (0.15 to 0.29) -0.56 (-1.03 to 0) 1074 (858 to 1366) 2134 (1592 to 2833) 13.46 (12.65 to 14.31) 12.30 (11.78 to 12.83) -0.29 (-0.35 to -0.23) In 204 countries and territories, Saudi Arabia had the highest ASIR at 13.67 (95% UI: 6.61–25.87), while Tajikistan and Kiribati had the lowest ASIR at 0.02 (95% UI: 0.01–0.03) and 0.02 (95% UI: 0.01–0.07) in 2021. Ethiopia and Zimbabwe reported the highest ASMR for TC, with 0.63 (95% UI: 0.35–1.31) and 0.62 (95% UI: 0.28–1.18), respectively. In contrast, the lowest ASMR was observed in Tajikistan and Kiribati. The highest ASDR was also seen in Ethiopia at 37.25 (95% UI: 20.5-77.78), while Tajikistan had the lowest ASDR at 0.09 (95% UI: 0.04–0.15). The EAPC in ASIR from 1990 to 2021 was greatest in Tajikistan (140.45) and Kiribati (134.07), and least in Libya (0.27). The largest declines in ASMR and ASDR were found in Tajikistan (-56.05 and − 40.65), while the smallest declines were observed in Ethiopia (-0.15 and − 0.11, respectively) (Fig. 1 ). From 1990 to 2021, the ASIR of TC consistently increased, especially after 2005. In contrast, the ASMR declined steadily since 1990, with a more pronounced decrease post-2000. The ASDR showed greater fluctuation, peaking around 2000, followed by a general decline with some fluctuations (Fig. 2 ). 3.2 Trends in TC among WCBA by age and SDI Globally, the incidence of TC in WCBA increased significantly, particularly in the 25–49 age group, with the 45–49 age group experiencing the largest rise. In low SDI regions, the 25–29 age group experienced a modest increase, while the 30–39 age group in low-middle SDI regions showed an upward trend. In middle SDI regions, the burden of TC was most pronounced in the 35–49 age group, while in high-middle SDI regions, the 45–49 age group showed a notable increase. In high SDI regions, the burden was greater in women aged 30 years and older (Fig. 3 A). The annual rate of change in incidence for TC revealed a peak in the 20–34 age group globally, followed by a decline in the 35 + age group. The 30–34 age group experienced a significant peak in low SDI regions, while other age groups remained stable. In low-middle SDI regions, the 25–39 age group plateaued after a peak, whereas in middle SDI regions, the 20–34 age group showed an increase, with rates remaining high in those aged 35 and older. In high-middle SDI regions, the 20–34 age group declined after its peak, while the 20–24 age group in high SDI regions increased, with the 20–34 age group gradually declining (Fig. 3 B). 3.3 Age, period and birth cohort effects on TC in WCBA The APC model analysis revealed that the ASIR, ASMR, and ASDR for TC in WCBA escalate with age, with a marked acceleration after 40 years, highlighting elevated health risks in older cohorts (Fig. 4 A). Temporally, the ASIR exhibited a consistent rise from 1995 to 2015, indicating a significant increasing trend, while ASMR and ASDR displayed greater variability with less pronounced effects and broader confidence intervals, suggesting a limited temporal influence (Fig. 4 B). Cohort effects revealed a U-shaped distribution for ASMR and ASDR, with higher health risks in early (pre-1940) and recent (post-1980) cohorts, and comparatively better health outcomes for those born between 1960 and 1980 (Fig. 4 C). 3.4 Health inequalities in TC in WCBA In high-income regions, the ASIR of TC increased significantly in 2021 compared to 1990, and health inequality also increased significantly. This was evidenced by the SII for the ASIR, which rose from 1.94 to 2.28 from 1990 to 2021, highlighting a substantial exacerbation of health disparities. Nonetheless, there was a slight improvement in health inequality, especially in high SDI regions, as evidenced by the CI’s slight positive shift, which decreased slightly from 0.31 to 0.30 (Fig. 5 A-B). The SII for ASMR from 0 in 1990 to -0.07 in 2021, along with the CI decline from 0.31 to 0.30 (Fig. 5 C-D), suggests that medical advancements and improved treatment protocols likely contributed to this change. In high SDI regions, the SII for ASDR showed a notable decrease from 0.22 in 1990 to -3.1 in 2021, with the CI dropping from 0.31 to 0.30. This indicates a trend of reduced ASDR in high SDI regions from 1990 to 2021, reflecting a potential decrease in the overall disease burden (Fig. 5 E-F). 3.5 Future projections Utilizing the BAPC model, we projected this trend to continue over the next three decades, with the ASIR and ASDR expected to increase to 3.94 (95% UI: 1.34–6.54) and 10.86 (95% UI: 4.83–16.90) per 100,000 people for ASDR by 2051(Fig. 6 A, C). Conversely, the ASMR is anticipated to exhibit a downward trajectory over the next 30 years. BAPC model predictions indicate a decline to 0.14 (95% UI: 0.06–0.23) per 100,000 people for ASMR by 2051(Fig. 6 B). 4. Discussion Thyroid cancer (TC) has emerged as the most prevalent endocrine malignancy worldwide, with its incidence increasing at an unprecedented rate over the past three decades [ 27 ] . This trend is particularly pronounced among women of childbearing age (WCBA), who exhibit 2–4 times higher incidence rates compared to their male counterparts [ 28 ] . Our analysis of Global Burden of Disease (GBD) 2021 data reveals that the global age-standardized incidence rate (ASIR) for WCBA rose from 2.17 to 3.36 per 100,000 between 1990 and 2021, with middle-socio-demographic index (SDI) regions experiencing the steepest increase (EAPC = 2.34%). These findings align with the "dual-burden" hypothesis, wherein improved diagnostic capabilities in transitioning economies drive case detection, while persistent risk factors like obesity and multiparity sustain disease incidence [ 29 ] .The gender disparity in TC epidemiology suggests a strong hormonal component. Mechanistic studies have identified estrogen receptor-alpha (ERα) overexpression in 60–70% of female TC cases, with parity showing a dose-dependent association (p < 0.01) [ 30 ] . Notably, our age-period-cohort (APC) analysis revealed a U-shaped risk curve, with elevated risks in early (pre-1940) and recent (post-1980) birth cohorts. This pattern may reflect generational shifts in reproductive behaviors and environmental exposures, warranting further investigation into gene-environment interactions. Our study highlights profound socioeconomic disparities in TC burden, as quantified by the slope index of inequality (SII = 2.28) and concentration index (CI = 0.30). Low-SDI regions, despite accounting for only 8.9% of global TC cases, bear 20.1% of mortality burden. This inverse care pattern is exemplified by Ethiopia, where the age-standardized mortality rate (ASMR = 0.63) exceeds the global average by 294%. Such disparities likely stem from multifaceted barriers, including limited access to ultrasound screening (available in < 15% of Sub-Saharan African primary care facilities) [ 31 ] and delayed presentation (median tumor size at diagnosis: 4.2cm in low-SDI vs. 1.8cm in high-SDI regions) [ 32 ] .The middle-SDI paradox—characterized by rising incidence but stagnant mortality-may reflect transitional epidemiological patterns. In these regions, healthcare modernization has enabled increased case detection (e.g., South Asia's ASIR increased by 2.59% annually), yet treatment accessibility remains constrained. These findings underscore the need for targeted resource allocation in underserved regions to address the growing burden of TC, emphasizing the importance of understanding socio-economic disparities in health outcomes [ 33 – 35 ] . Our research deepens the comprehension of the incidence of TC and the socio - demographic factors that determine it, especially within the group of WCBA. The innovative use of a Bayesian Meta-regression framework combined with the APC model allows for a nuanced analysis of TC trends over time, revealing critical insights into how socio-economic factors, represented by the SDI, correlate with cancer burden. Previous studies have often focused on broader cancer trends without disaggregating data by age or socio-economic conditions, leaving a gap in our understanding of how these variables interact specifically within the WCBA demographic. This research fills that gap by demonstrating a significant increase in TC incidence, particularly in middle SDI regions, with a notable annual percentage change in incidence rates, thus emphasizing the need for targeted public health strategies that consider these disparities. Our findings revealed a worldwide upward trend in the incidence rate of TC among WCBA during the period from 1990 to 2021. This discovery is in line with previous research [ 36 ] , suggesting that greater attention should be paid to the cancer burden borne by this particular population. The global increase in obesity among WCBA is likely a contributing factor. Obesity has been shown to modify inflammatory, metabolic, and hormonal pathways [ 37 ] . The meta-analyses demonstrating a 33% increased risk per 10-unit BMI increment (95% CI: 1.20–1.47). Adipose tissue-derived cytokines, particularly interleukin-6 (IL-6) and leptin, promote thyrocyte proliferation through STAT3 and MAPK pathway activation. Obesity-related metabolic dysfunction accounts for 22.7% (95% UI: 18.9–26.5) of TC burden in WCBA, with the highest attributable fractions observed in high-income North America (35.2%) and Western Europe (33.8%) [ 38 ] . Simultaneously, there is strong evidence suggesting that prolonged exposure to steroid hormones, like estrogen replacement therapy and long - term oral contraceptive use, is associated with TC varying degrees [ 39 ] . Reproductive factors further compound TC risk in WCBA. Multiparous women (> 3 pregnancies) exhibit 1.8-fold higher TC incidence compared to nulliparous counterparts (95% CI: 1.5–2.1). This association appears mediated by cumulative estrogen exposure, which upregulates thyrocyte growth factors while suppressing tumor suppressor genes. Although previous epidemiological studies have summarized more specific risk factors for TC among WCBA, due to the heterogeneity and limited statistical power of these studies, further research is needed. This additional research should aim to better understand how risk factors, especially those that can be modified, affect female cancers, thereby enabling targeted early prevention of TC among WCBA. In addition, socioeconomic development has led to enhanced TC screening, regular examinations, and improved cancer registration. These improvements have facilitated earlier detection of TC among WCBA, resulting in higher incidence rates. The implications of these findings extend beyond academic discourse and into clinical practice and health policy. The observed increase in TC incidence among specific age groups, particularly those aged 45–49, suggests a pressing need for enhanced screening and early detection strategies tailored to this demographic. Such interventions could significantly improve outcomes, potentially decreasing the overall burden of the disease. Furthermore, the disparities in incidence and mortality rates necessitate policy changes aimed at improving access to healthcare services in lower SDI regions, which are disproportionately affected by higher mortality rates. These findings highlight the importance of integrating socio-economic considerations into cancer care frameworks to ensure equitable health outcomes for all women, particularly those of childbearing age. Our findings necessitate a stratified approach to TC control, tailored to regional development contexts: High-SDI Settings Implement risk-adapted screening protocols using ultrasound elastography and molecular testing (e.g., BRAF V600E mutation analysis) Promote shared decision-making to mitigate overdiagnosis of indolent microcarcinomas Integrate TC prevention into obesity management programs Low/Middle-SDI Regions Scale up mobile thyroid nodule screening with portable ultrasound devices Train primary care providers in ultrasound-guided fine-needle aspiration Establish regional referral networks for specialized TC care The projected 3.94 (95% UI: 1.34–6.54) per 100,000 people increase in ASIR by 2051 underscores the urgency of these interventions. Cost-effectiveness analyses suggest that task-shifted ultrasound screening in LMICs could avert 12,300 (95% UI: 9,800 − 15,100) TC deaths annually at $ 1,480 per disability-adjusted life year (DALY) averted. Despite the strengths of this study, several limitations must be acknowledged. The reliance on secondary data from the GBD study may not account for local variations in TC diagnosis and treatment, potentially limiting the generalizability of the findings. Additionally, the study's cross-sectional nature restricts the ability to infer causality between socio-demographic indices and TC incidence. Future research should aim to incorporate primary data collection methods to validate these findings across diverse healthcare settings. Longitudinal study examining hormonal fluctuations associated with fertility and the pathogenesis of thyroid cancer, alongside a cost-benefit analysis of artificial intelligence-assisted ultrasound screening in low-income and middle-income countries (LMICs), can provide valuable insights into the temporal relationship between socio-economic factors and the outcomes of thyroid cancer in women of reproductive age, ultimately informing more effective public health strategies and resource allocation to address these disparities. In conclusion, this study has elucidated significant trends in the incidence and mortality of thyroid cancer among women of childbearing age from 1990 to 2021, underscoring the escalating burden of this malignancy, particularly in lower socio-demographic index regions. The observed health disparities highlight an urgent need for targeted public health interventions and policy reforms to mitigate the adverse effects of socioeconomic factors on cancer outcomes. As we project future trends, it is essential to continue monitoring these patterns and implement strategies aimed at reducing health inequalities to improve the overall prognosis for affected populations. 5. Conclusion This study evaluated the global burden of thyroid cancer (TC) among women of childbearing age (WCBA) from 1990 to 2021, revealing a significant increase in incidence and a decline in mortality rates. Despite significant reductions in mortality and DALYs rates in high SDI regions, low SDI regions still had a disproportionate disease burden, highlighting socioeconomic disparities. Policymakers should address these disparities by prioritizing health resources and interventions in low and middle SDI regions. Furthermore, while future projections indicate that the burden of TC may decrease in some regions due to advancements in medical care and health interventions, global disparities persist. Therefore, tailored interventions addressing the specific needs of different regions, age groups, and socioeconomic backgrounds are urgently needed. Abbreviations TC WCBA GBD SDI DALYs APC BAPC EAPC SII CI ASIR ASMR ASDR IHME ICD-10 UI LMICs INLA ERα IL-6 MAPK STAT3 BRAF Thyroid Cancer Women of Childbearing Age Global Burden of Disease Socio-demographic Index Disability-Adjusted Life Years Age-Period-Cohort Bayesian Age-Period-Cohort Estimated Annual Percentage Change Slope Index of Inequality Concentration Index Age-Standardized Incidence Rate Age-Standardized Mortality Rate Age-Standardized Disability-adjusted life years Rate Institute for Health Metrics and Evaluation International Classification of Diseases, 10th Revision Uncertainty Interval Low- and Middle-Income Countries Integrated Nested Laplace Approximation Estrogen Receptor-alpha Interleukin-6 Mitogen-Activated Protein Kinase Signal Transducer and Activator of Transcription 3 B-Raf proto-oncogene (V600E mutation) Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analysed during the current study are available in the Global Burden of Disease (GBD) 2021, published by the Institute for Health Metrics and Evaluation (IHME) (https://vizhub.healthdata.org/gbd). Conflict of Interest The authors declare no potential conflicts of interest. Authors’ contributions ZW performed the experiments, drafted the manuscript, prepared the figuresand funding acquisition; MY, participated in design of the experiments, writing-review and editing, supervision; ZY and YC contributed to perform the experiments, provided valuable comments and suggestions, provided experimental materials and funding acquisition; XZ writing-review and editing, supervision and funding acquisition. All authors have read and approved the final version of the manuscript, and agree with the order of presentation of the authors. Acknowledgment This work was supported by the Shaanxi Provincial Key Research and Development Program (Grant No. 2023-YBSF-468 to Xiaojing Zhao) and Shaanxi Cancer Hospital National Natural Science Foundation Incubation Project (Grant No. SCB2417 to Yong Chen) and the Hospital Fund of the Second Affiliated Hospital of Xi'an Jiaotong University (Grant No. YJ(QN)202328 to Zhe Wang). The authors express their gratitude to the Global Burden of Disease (GBD) 2021 study team for providing open-access data essential to this research. We acknowledge the academic and institutional support from the Second Affiliated Hospital of Xi’an Jiaotong University College of Medicine, Shenyang Medical College Affiliated Central Hospital, Shaanxi Provincial Cancer Hospital, and the First Affiliated Hospital of Chongqing Medical University. Additionally, we recognize the contributions of researchers worldwide involved in GBD data collection and analysis, which formed the foundation of this study. 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Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 27 Apr, 2026 Reviews received at journal 24 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviewers invited by journal 15 May, 2025 Editor invited by journal 21 Apr, 2025 Editor assigned by journal 09 Apr, 2025 Submission checks completed at journal 09 Apr, 2025 First submitted to journal 08 Apr, 2025 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6399893","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457175236,"identity":"6dc0d351-84fe-48e5-ad8c-62d486233826","order_by":0,"name":"Zhe Wang","email":"","orcid":"","institution":"Xian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Wang","suffix":""},{"id":457175237,"identity":"bbc26d7d-613d-4908-bf71-3767e3f01db5","order_by":1,"name":"Ze Yang","email":"","orcid":"","institution":"Affiliated Central Hospital of Shenyang Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ze","middleName":"","lastName":"Yang","suffix":""},{"id":457175238,"identity":"ea6c3a96-a310-4cc8-94c1-2b1e61191b77","order_by":2,"name":"Yong Chen","email":"","orcid":"","institution":"Shaanxi Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Chen","suffix":""},{"id":457175239,"identity":"62d03c42-5904-4ea8-93f4-4d566503b41e","order_by":3,"name":"Ming Yang","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Yang","suffix":""},{"id":457175240,"identity":"4fd1d09f-67f1-4462-ade9-f98b4ab9ee5b","order_by":4,"name":"Xiaojing Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACfvmDjQ8+GNjI2TczHyBOi+QM5sOGMwrSjA3Y2RKI02Jwgy1NmufDocQN/DwGRLrsdo+B5AyDA8bmzDwfb7xhsJPTbSCgg3HOGQODDwZ35CybeTdbzmFINjY7QEALM0OOQeIMg2fGDId5t0nzMBxI3EZICxtQy2Eeg8OJDYd5nhGnhUciLbEZpGXDYR424rRI8Bw+zDjDIM1YspnN2HKOARF+sT/e2P7jwx8bOX7+ww9vvKmwkyOoBc1KYqMGSQupOkbBKBgFo2BEAABoFUSCZIVgmgAAAABJRU5ErkJggg==","orcid":"","institution":"Xian Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Xiaojing","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-04-08 06:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6399893/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6399893/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83142831,"identity":"dcc5c9bd-b213-46bc-ae79-9e82bf3312a5","added_by":"auto","created_at":"2025-05-20 12:34:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79183,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in TC among WCBA in 204 countries and territories. (A) ASIR in WCBA in 2021. (B) EAPC of ASIR in WCBA from 1990 to 2021. (C) ASMR in WCBA in 2021. (D) EAPC of ASMR in WCBA from 1990 to 2021. (E) ASDR in WCBA in 2021. (F) EAPC of ASDR in WCBA from 1990 to 2021. ASIR: age-standardized incidence rate. ASMR: age-standardized mortality rate. ASDR: age-standardized disability-adjusted life year rate. WCBA: Women of childbearing age. EAPC: estimated annual percentage change.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6399893/v1/e9091a0a87d9aac478665cae.png"},{"id":83142826,"identity":"12339069-26af-4520-858b-9281c61c8c3a","added_by":"auto","created_at":"2025-05-20 12:34:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17294,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in ASIR, ASMR, and ASDR for TC among WCBA from 1990 to 2021.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6399893/v1/f3f91d4322e28c227510907c.png"},{"id":83144026,"identity":"fa529c94-8699-4fc7-83ba-cee979c1fc7a","added_by":"auto","created_at":"2025-05-20 12:42:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50954,"visible":true,"origin":"","legend":"\u003cp\u003eLocal drift and age distribution of TC among WCBA by SDI quintiles. (A) Temporal changes in the age distribution of WCBA incident cases. (B) Local drift in the incidence of TC in WCBA for seven age groups from 1992 to 2021.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6399893/v1/4a5659b4e8649746757a8c7b.png"},{"id":83142830,"identity":"f4477e4c-7885-4480-be7a-189baa56cd6f","added_by":"auto","created_at":"2025-05-20 12:34:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66811,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of ASIR, ASMR, and ASDR by age, period, and cohort for TC among WCBA, 1990-2021. (A) ASIR age, period, and cohort effects analysis. (B) ASMR age, period, and cohort effects analysis. (C) ASDR age, period, and cohort effects analysis.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6399893/v1/c49b394cb01a9f6a8b493337.png"},{"id":83142829,"identity":"c4e20f54-c48c-42b6-b5c1-f3eb6f93da2e","added_by":"auto","created_at":"2025-05-20 12:34:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":183742,"visible":true,"origin":"","legend":"\u003cp\u003eHealth inequalities on TC in WCBA. (A) ASIR health inequality. (B) Concentration curves for ASIR. (C) ASMR health inequality. (D) Concentration curves for ASMR. (E) ASDR health inequality. (F) Concentration curves for ASDR.\u003c/p\u003e","description":"","filename":"Onlinefloatimage55.png","url":"https://assets-eu.researchsquare.com/files/rs-6399893/v1/2f517a6dd76c0f96aa5b5192.png"},{"id":83142828,"identity":"0951ca06-3d2f-48f4-ab92-71ed77760448","added_by":"auto","created_at":"2025-05-20 12:34:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":23125,"visible":true,"origin":"","legend":"\u003cp\u003eProjected the burden of TC among WCBA over the next 30 years. (A) ASIR, (B) ASMR, (C) ASDR.\u003c/p\u003e","description":"","filename":"Onlinefloatimage64.png","url":"https://assets-eu.researchsquare.com/files/rs-6399893/v1/d41ea1694360a61147d20442.png"},{"id":83144308,"identity":"c211e455-719a-41a2-b328-c27d339f434c","added_by":"auto","created_at":"2025-05-20 12:50:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1598256,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6399893/v1/d57fa52a-86a9-4166-a03d-373713f21e68.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Thyroid Cancer Burden among Women of Childbearing Age: A 30-Year Global Analysis from the Global Burden of Disease Study","fulltext":[{"header":"1. Background","content":"\u003cp\u003eThyroid cancer (TC) has emerged as a globally prevalent malignancy, imposing significant public health challenges and substantial economic burdens on healthcare systems worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Notably, a pronounced gender disparity exists in TC epidemiology, with women of childbearing age (15\u0026ndash;49 years) demonstrating 2\u0026ndash;4 times higher incidence rates compared to males\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. This female predominance appears hormonally mediated, as evidenced by epidemiological patterns: prepubertal and postmenopausal women show comparable TC rates to males, whereas reproductive-age women exhibit markedly elevated risks[3]. Mounting evidence suggests that cumulative estrogen exposure during successive pregnancies may drive this phenomenon, with parity showing a dose-dependent association with TC incidence (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The risk escalation is particularly pronounced during later reproductive years (30\u0026ndash;49 years) and persists following artificial menopause\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, though exogenous estrogen use (e.g., oral contraceptives) shows no significant correlation\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRemarkable differences exist among regions and countries regarding female cancers\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. While high-income nations like the United States report steadily rising incidence rates\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, developing regions face distinct challenges. In India (2016), TC ranked as the tenth most common female malignancy, with 21,000 new cases (74.3% female) and striking regional variations - Kerala, Sikkim, Nagaland, and Goa demonstrated the highest crude incidence rates\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Globally, 2012 witnessed nearly 300,000 TC diagnoses, with economically developed countries reporting twofold higher age-standardized incidence rates compared to developing nations\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. These disparities are exacerbated by socioeconomic factors, as patients from disadvantaged backgrounds frequently experience diagnostic delays, advanced-stage presentations, and catastrophic healthcare expenditures\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Such findings underscore the urgent need for tailored preventive strategies addressing both biological and socioeconomic determinants of TC in reproductive-age women.\u003c/p\u003e \u003cp\u003eThis study aims to achieve three primary objectives: (1) to quantify the prevalence and mortality burden of thyroid cancer (TC) among women of childbearing age (15\u0026ndash;49 years); (2) to investigate the socioeconomic determinants underlying observed epidemiological trends; and (3) to assess the global health inequalities in TC outcomes across different development strata. Through a comprehensive analysis of TC burden using advanced epidemiological modeling techniques, we seek to generate actionable insights for healthcare policy formulation, particularly in low- and middle-income countries (LMICs) where the disease burden disproportionately affects vulnerable populations. By addressing critical gaps in current literature regarding age-specific and socioeconomic dimensions of TC epidemiology, this research contributes to the broader understanding of endocrine malignancies and informs targeted interventions to reduce health disparities in reproductive-age women.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eThe data for our study was obtained from the Global Burden of Disease (GBD) 2021, published by the Institute for Health Metrics and Evaluation (IHME) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. GBD 2021 included detailed assessments of diseases, injuries, and risk factors across 204 countries and territories. The data is publicly available and does not require additional ethical approval for use in research. Thyroid cancer (TC) has been categorized as a level 3 cause in the GBD 2021. The International Classification of Diseases, 10th Revision (ICD-10) defines TC as malignant neoplasms of the thyroid gland, coded as C73\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection\u003c/h2\u003e \u003cp\u003eWe utilized the Bayesian Meta-regression framework DisMod-MR 2.1 to integrate incidence and mortality from the GBD dataset to generate preliminary estimates of the incidence and mortality of TC in women of childbearing age (WCBA)\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Adjustments were made for differences in measurement methods and case definitions.\u003c/p\u003e \u003cp\u003eDisability-adjusted life years (DALYs) were used as a key metric for assessing disease burden, as they provide a comprehensive evaluation of disease and healthcare intervention impacts\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. DALYs for TC in WCBA were calculated using data from GBD 2021, and trends from 1990 to 2021 were further analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Socio-demographic index (SDI)\u003c/h2\u003e \u003cp\u003eThe SDI is a composite indicator of social and economic conditions that influence health outcomes, based on educational attainment, income per capita, and fertility rates. SDI values range from 0 to 1, with higher values indicating greater socio-economic development\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. SDI was divided into five quintiles: low, low-middle, middle, middle-high, and high\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. We examined the relationship between SDI and the burden of TC in WCBA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Health inequality analysis\u003c/h2\u003e \u003cp\u003eWe employed the slope index of inequality (SII) and concentration index (CI) to quantify the distributional inequality of the burden of TC\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The SII was calculated by regressing national DALYs rates across all age groups on a relative position scale associated with SDI, with the midpoint of the cumulative population range defined by SDI rankings. The CI was computed by numerically integrating the area under the Lorenz curve, fitting the DALYs cumulative distribution to the cumulative population distribution ordered by SDI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Age-period-cohort (APC) model\u003c/h2\u003e \u003cp\u003eWe employed the APC model to analyze the independent effects of age, period, and birth cohort on the incidence, mortality, and DALYs rates of TC in WCBA\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Data inputs for the APC model included incidence, mortality, and DALYs rate estimates for TC and population data from GBD 2021 for each country or region. WCBA (15\u0026ndash;49 years) was divided into seven age groups, and the study period (1992\u0026ndash;2021) was segmented into six 5-year periods. The APC model output parameters included net drift, which represents the annual percentage change (APC) in disease rates after adjusting for nonlinear period and cohort effects, and local drift, indicating the annual percentage change in disease rates for different age groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Forecasting\u003c/h2\u003e \u003cp\u003eFor future trend predictions of the burden for TC in WCBA over the next 30 years, we applied the Bayesian age-period-cohort (BAPC) model in combination with Integrated Nested Laplace Approximation (INLA)\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eWe evaluated the time trends of age-standardized incidence rates (ASIR), age-standardized mortality rates (ASMR), and age-standardized DALYs rates (ASDR) for TC in WCBA from 1990 to 2021, using data from the GBD 2021. The estimated annual percentage change (EAPC) was calculated to assess the trend of these age-standardized rates (ASRs)[24].\u003c/p\u003e \u003cp\u003eParticipants were grouped into seven age groups (15\u0026ndash;19, 20\u0026ndash;24, 25\u0026ndash;29, 30\u0026ndash;34, 35\u0026ndash;39, 40\u0026ndash;44, and 45\u0026ndash;49 years), and the age distribution of burden for TC was analyzed for each group\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe EAPC was derived from a regression model, where the pattern of ASRs from 1990 to 2021 was modeled by: Y\u0026thinsp;=\u0026thinsp;α\u0026thinsp;+\u0026thinsp;βX\u0026thinsp;+\u0026thinsp;e, where Y is the natural logarithm of ASR, x represents the years, α is the intercept, β is the slope (representing the trend), and e is the error term\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. The EAPC was computed as: EAPC\u0026thinsp;=\u0026thinsp;100 \u0026times; [exp(β) \u0026minus;\u0026thinsp;1]. The EAPC represents the annual percentage change.\u003c/p\u003e \u003cp\u003eData analysis was conducted using R software (version 4.4.1), and images were modified using Adobe Illustrator (version 2023).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Global, regional, and national trends in TC among WCBA\u003c/h2\u003e \u003cp\u003eIn 2021, the global incident cases, deaths, and the number of DALYs for TC in WCBA were 67,558 (95% UI: 57,165 − 81,890), 3,260 (95% UI: 2,630-4,100), and 206,508 (95% UI: 165,757 − 263,977), respectively. From 1990 to 2021, both the incidence of TC and the DALYs rates increased annually, with EAPC of 1.42 (1.39 to 1.45) and 0.02 (0.02 to 0.03), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The highest incident cases in 2021 were observed in the middle SDI regions, with 22,327 (17,847 to 27,350). In contrast, the highest number of deaths and DALYs were found in low-middle SDI regions, with 1,140 (899 to 1,532) and 68,877 (54,018 to 93,818), respectively. The mortality and DALYs rates for TC decreased most significantly in high-middle SDI regions, with EAPC of -2.01 (-2.03 to -2.21) and − 1.37 (-1.36 to -1.37), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In 21 GBD regions, South Asia reported the highest incident cases, deaths, and DALYs for TC, with 16,419 (12,716 − 22,178), 1,380 (1,066 − 1,832), and 84,402 (64,631 − 113,572), respectively. In contrast, Oceania had the lowest numbers, with 36 (19–59), 3 (1–5), and an ASIR of 160 (86–271). The high-income Asia Pacific region had the highest ASIR at 5.56 (5.34–5.78), while the Eastern Sub-Saharan Africa region had the highest ASMR and ASDR, with 0.39 (0.35–0.43) and 23.00 (22.69–23.31), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\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 incident cases, deaths, DALYs, and their ASR of TC among WCBA, as well as EAPC from 1990 to 2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"16\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003elocation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eIncidence\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eDeaths\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e \u003cp\u003eDALYs\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990 counts\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2021 counts\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1990 age- standardized rates (per 100\u003c/p\u003e \u003cp\u003e000)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2021 age-standardized rates (per 100\u003c/p\u003e \u003cp\u003e000)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEAPC\u003c/p\u003e \u003cp\u003e(%),199\u003c/p\u003e \u003cp\u003e0–2021\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1990 counts\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2021 counts\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990 age-standardized rates (per 100\u003c/p\u003e \u003cp\u003e000)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2021 age-standardized rates per 100\u003c/p\u003e \u003cp\u003e000)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eEAPC\u003c/p\u003e \u003cp\u003e(%),199\u003c/p\u003e \u003cp\u003e0–2021\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1990 counts\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2021 counts\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1990 age-standardized rates (per 100\u003c/p\u003e \u003cp\u003e000)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2021 age-standardized rates (per 100\u003c/p\u003e \u003cp\u003e000)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eEAPC\u003c/p\u003e \u003cp\u003e(%),199\u003c/p\u003e \u003cp\u003e0–2021\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26302 (23536 to 29951)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67558 (57165 to 81809)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.17 (2.15 to 2.20)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.36 (3.33 to 3.38)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42 (1.39 to 1.42)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2140 (1831 to 2592)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3260 (2630 to 4100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.18 (0.17 to 0.18)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16 (0.16 to 0.17)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.38 (-0.18 to -0.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e127692 (107124 to 154939)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e206508 (165745 to 263977)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e10.30 (10.24 to 10.36)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e10.37 (10.33 to 10.41)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.02 (0.02 to 0.03)\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\u003e8327 (7927 to 8712)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13395 (12476 to 14852)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.52 (3.44 to 3.59)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.73 (4.65 to 4.82)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96 (0.95 to 0.98)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e250 (238 to 261)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e200 (184 to 220)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.11 (0.09 to 0.12)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.07 (0.06 to 0.08)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.45 (-1.3 to -1.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16763 (15288 to 18713)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e16546 (13888 to 19749)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.12 (7.01 to 7.23)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e5.88 (5.79 to 5.97)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.62 (-0.62 to -0.61)\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\u003e6462 (5654 to 7174)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12094 (10007 to 15103)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.50 (2.44 to 2.56)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.33 (3.27 to 3.39)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93 (0.91 to 0.95)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e392 (324 to 446)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e304 (255 to 378)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15 (0.14 to 0.17)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08 (0.07 to 0.09)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.01 (-2.03 to -2.21)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23383 (19392 to 27045)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e21086 (17513 to 26594)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9.00 (8.88 to 9.11)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e5.87 (5.79 to 5.95)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-1.37 (-1.36 to -1.37)\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\u003e6410 (5293 to 7722)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22327 (17847 to 27350)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.65 (1.61 to 1.69)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.38 (3.33 to 3.42)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.34 (2.3 to 2.37)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e638 (533 to 777)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e959 (778 to 1155)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.17 (0.16 to 0.18)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.14 (0.13 to 0.15)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.62 (-0.59 to -0.67)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e37089 (31087 to 45400)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e59997 (47627 to 74495)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9.49 (9.39 to 9.59)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e9.12 (9.05 to 9.19)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.13 (-0.14 to -0.12)\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\u003e3538 (2867 to 4847)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13681 (10737 to 18430)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43 (1.39 to 1.48)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.79 (2.75 to 2.84)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.18 (2.12 to 2.23)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e548 (438 to 755)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1140 (899 to 1532)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.23 (0.21 to 0.25)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.23 (0.22 to 0.25)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0 (0 to 0.15)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e32136 (25703 to 44506)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e68877 (54018 to 93818)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e12.77 (12.63 to 12.91)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e13.96 (13.86 to 14.07)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.29 (0.28 to 0.3)\u003c/p\u003e \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\u003e1532 (1132 to 2021)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6009 (4502 to 9050)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53 (1.45 to 1.61)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.41 (2.35 to 2.48)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48 (1.4 to 1.57)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e310 (228 to 413)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e655 (486 to 1012)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.31 (0.28 to 0.35)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.27 (0.25 to 0.29)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.44 (-0.6 to -0.36)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e18192 (13293 to 24282)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e39857 (29090 to 62337)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e17.74 (17.48 to 18.01)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e15.69 (15.53 to 15.85)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.4 (-0.41 to -0.38)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116 (82 to 155)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e426 (294 to 623)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32 (0.26 to 0.38)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41 (0.37 to 0.45)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8 (0.55 to 1.44)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21 (15 to 28)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48 (33 to 72)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06 (0.04 to 0.09)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05 (0.03 to 0.06)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.59 (-1.3 to -0.92)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1201 (841 to 1621)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2877 (1941 to 4327)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.22 (3.03 to 3.41)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.71 (2.61 to 2.81)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.55 (-0.62 to -0.48)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Europe\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3614 (3300 to 3932)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3648 (3260 to 4097)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.60 (3.49 to 3.72)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.23 (3.12 to 3.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.35 (-0.35 to -0.36)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e121 (113 to 128)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57 (53 to 62)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.12 (0.10 to 0.14)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05 (0.04 to 0.07)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.78 (-2.21 to -2.91)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7812 (7074 to 8783)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4569 (3893 to 5434)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.81 (7.63 to 7.98)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e4.05 (3.93 to 4.17)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-2.1 (-2.07 to -2.12)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTropical Latin America\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (348 to 401)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1078 (1000 to 1169)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.97 to 1.20)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.63 (1.54 to 1.74)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34 (1.21 to 1.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41 (39 to 45)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63 (59 to 68)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.12 (0.09 to 0.17)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.09 (0.07 to 0.12)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.92 (-1.12 to -0.81)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2300 (2148 to 2472)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3652 (3357 to 4002)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6.67 (6.40 to 6.95)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e5.56 (5.38 to 5.74)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.59 (-0.62 to -0.56)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (129 to 192)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e392 (295 to 507)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42 (1.20 to 1.67)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.86 (1.68 to 2.05)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87 (0.66 to 1.09)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19 (16 to 24)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41 (30 to 53)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.18 (0.11 to 0.28)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.20 (0.14 to 0.27)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.34 (-0.12 to 0.78)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1100 (899 to 1360)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2336 (1648 to 3052)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9.83 (9.24 to 10.45)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e11.10 (10.65 to 11.56)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.39 (0.33 to 0.46)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Latin America\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249 (206 to 294)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e457 (383 to 553)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.08 (1.83 to 2.36)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.46 (2.24 to 2.70)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54 (0.44 to 0.65)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21 (18 to 25)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19 (16 to 22)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.18 (0.11 to 0.27)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.10 (0.06 to 0.16)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.88 (-1.67 to -1.94)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1196 (1024 to 1421)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1163 (965 to 1392)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9.99 (9.43 to 10.57)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e6.26 (5.91 to 6.64)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-1.5 (-1.49 to -1.5)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoutheast Asia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2536 (1811 to 2998)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8935 (6436 to 10945)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44 (2.34 to 2.54)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.66 (4.57 to 4.76)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.11 (2.05 to 2.18)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e255 (182 to 302)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e426 (302 to 520)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.26 (0.22 to 0.29)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.22 (0.20 to 0.24)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.54 (-0.61 to -0.31)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e14593 (10234 to 17291)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e25611 (18572 to 31798)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e14.17 (13.94 to 14.41)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e13.32 (13.16 to 13.49)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.2 (-0.21 to -0.19)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Asia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3673 (2842 to 5200)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16419 (12716 to 22178)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53 (1.48 to 1.58)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.38 (3.33 to 3.43)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.59 (2.53 to 2.65)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e611 (470 to 880)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1380 (1066 to 1832)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.26 (0.24 to 0.28)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.29 (0.27 to 0.30)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.35 (0.22 to 0.38)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e36363 (27848 to 52029)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e84402 (64631 to 113572)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e14.95 (14.80 to 15.11)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e17.30 (17.18 to 17.41)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.47 (0.46 to 0.48)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOceania\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (7 to 18)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (19 to 59)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.48 to 1.66)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09 (0.76 to 1.52)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51 (-0.28 to 1.44)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (1 to 2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (1 to 5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.10 (0.00 to 0.53)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08 (0.02 to 0.26)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.72 (0 to -2.27)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e74 (41 to 108)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e160 (86 to 271)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.59 (4.37 to 7.07)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e4.86 (4.13 to 5.68)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.45 (-0.7 to -0.18)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1633 (1274 to 2351)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8156 (6267 to 10264)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.47 (2.35 to 2.60)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.05 (4.94 to 5.16)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.33 (2.24 to 2.43)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90 (70 to 136)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e196 (154 to 252)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14 (0.11 to 0.17)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.12 (0.11 to 0.14)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.5 (-0.62 to 0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5585 (4324 to 8478)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e14110 (10875 to 18632)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8.39 (8.17 to 8.62)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e8.78 (8.63 to 8.92)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.15 (0.11 to 0.18)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income North America\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2483 (2395 to 2575)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4256 (4029 to 4504)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.20 (3.08 to 3.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.59 (4.46 to 4.73)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17 (1.14 to 1.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50 (49 to 52)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58 (56 to 61)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07 (0.05 to 0.09)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06 (0.05 to 0.08)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.5 (-0.38 to 0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3762 (3359 to 4293)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4996 (4301 to 5884)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.89 (4.73 to 5.05)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e5.42 (5.27 to 5.57)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.33 (0.32 to 0.35)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1797 (1577 to 2106)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2689 (2232 to 3322)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.66 (3.49 to 3.83)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.56 (5.34 to 5.78)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.36 (1.34 to 1.38)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41 (35 to 51)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28 (24 to 35)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08 (0.06 to 0.11)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06 (0.04 to 0.08)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.92 (-1.02 to -1.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2908 (2459 to 3662)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2702 (2108 to 3576)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.93 (5.72 to 6.15)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e5.57 (5.36 to 5.80)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.2 (-0.19 to -0.21)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e880 (624 to 1201)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3141 (2168 to 5421)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.32 (2.17 to 2.49)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.23 (3.11 to 3.35)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07 (0.96 to 1.77)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e192 (135 to 267)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e373 (257 to 650)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.52 (0.45 to 0.61)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.39 (0.35 to 0.43)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.92 (-1.12 to -0.81)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11351 (8018 to 15669)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e22843 (15628 to 39868)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e29.30 (28.74 to 29.87)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e23.00 (22.69 to 23.31)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.78 (-0.8 to -0.76)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Europe\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1571 (1484 to 1685)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2157 (1874 to 2493)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.76 (2.63 to 2.90)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.57 (3.42 to 3.73)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83 (0.82 to 0.85)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63 (60 to 68)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51 (44 to 60)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.11 (0.09 to 0.15)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08 (0.06 to 0.11)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.02 (-1 to -1.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3955 (3625 to 4349)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3568 (2965 to 4286)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.04 (6.82 to 7.26)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e5.92 (5.73 to 6.13)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.56 (-0.54 to -0.56)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Asia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4777 (3480 to 5946)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10949 (8131 to 16538)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62 (1.58 to 1.67)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.79 (2.74 to 2.85)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.77 (1.74 to 1.79)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e418 (303 to 530)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e272 (199 to 412)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15 (0.13 to 0.16)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.07 (0.06 to 0.08)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.43 (-2.21 to -2.46)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24353 (17985 to 31231)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e19022 (13388 to 28805)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8.24 (8.14 to 8.35)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e4.89 (4.81 to 4.96)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-1.67 (-1.67 to -1.68)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (25 to 69)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (81 to 272)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41 (0.29 to 0.56)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54 (0.46 to 0.64)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89 (0.43 to 1.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9 (6 to 14)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20 (11 to 36)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.09 (0.04 to 0.18)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08 (0.05 to 0.12)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.38 (-1.3 to 0.72)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e475 (303 to 796)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1113 (603 to 2029)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.71 (4.29 to 5.17)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e4.05 (3.81 to 4.30)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.49 (-0.59 to -0.38)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Latin America\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e541 (513 to 570)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1993 (1724 to 2308)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59 (1.46 to 1.73)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.86 (2.73 to 2.98)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.91 (1.77 to 2.04)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60 (58 to 63)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e110 (95 to 127)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.18 (0.14 to 0.24)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16 (0.13 to 0.19)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.38 (-0.75 to -0.24)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3366 (3210 to 3539)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6427 (5527 to 7485)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9.89 (9.55 to 10.24)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e9.20 (8.98 to 9.43)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.23 (-0.27 to -0.2)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Europe\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1135 (1037 to 1236)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e943 (818 to 1062)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.49 (3.29 to 3.70)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.89 (2.71 to 3.09)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.61 (-0.58 to -0.62)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63 (60 to 68)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26 (23 to 29)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.20 (0.15 to 0.25)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08 (0.05 to 0.12)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.91 (-2.34 to -3.48)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3715 (3424 to 4048)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1705 (1473 to 1979)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e11.53 (11.16 to 11.91)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e5.22 (4.97 to 5.49)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-2.52 (-2.47 to -2.58)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Asia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286 (258 to 317)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e486 (414 to 568)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.07 (1.83 to 2.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92 (1.76 to 2.10)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.24 (-0.33 to -0.13)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25 (23 to 27)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27 (23 to 32)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.19 (0.12 to 0.28)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.11 (0.07 to 0.16)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.75 (-1.79 to -1.72)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1445 (1306 to 1592)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1610 (1363 to 1888)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e10.45 (9.90 to 11.03)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e6.40 (6.09 to 6.73)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-1.57 (-1.58 to -1.56)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaribbean\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142 (123 to 159)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e288 (232 to 355)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.73 (1.45 to 2.04)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.33 (2.07 to 2.62)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97 (0.81 to 1.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14 (12 to 16)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20 (16 to 26)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.17 (0.09 to 0.29)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16 (0.10 to 0.25)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.2 (-0.48 to 0.34)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e790 (670 to 941)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1172 (921 to 1486)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9.52 (8.86 to 10.22)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e9.53 (8.99 to 10.10)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0 (-0.04 to 0.05)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralasia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149 (123 to 180)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e317 (227 to 430)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.72 (2.30 to 3.19)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.86 (3.44 to 4.31)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14 (0.98 to 1.31)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (3 to 5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (3 to 5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07 (0.02 to 0.19)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.04 (0.01 to 0.13)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.79 (-1.22 to -2.21)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e274 (226 to 338)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e337 (236 to 475)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.00 (4.43 to 5.64)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e4.11 (3.68 to 4.59)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.63 (-0.66 to -0.6)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean Latin America\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (109 to 175)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e644 (477 to 853)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70 (1.42 to 2.02)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.71 (3.43 to 4.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.55 (2.23 to 2.89)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19 (15 to 25)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36 (27 to 48)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.25 (0.15 to 0.40)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.21 (0.15 to 0.29)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.56 (-1.03 to 0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1074 (858 to 1366)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2134 (1592 to 2833)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e13.46 (12.65 to 14.31)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e12.30 (11.78 to 12.83)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.29 (-0.35 to -0.23)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eIn 204 countries and territories, Saudi Arabia had the highest ASIR at 13.67 (95% UI: 6.61–25.87), while Tajikistan and Kiribati had the lowest ASIR at 0.02 (95% UI: 0.01–0.03) and 0.02 (95% UI: 0.01–0.07) in 2021. Ethiopia and Zimbabwe reported the highest ASMR for TC, with 0.63 (95% UI: 0.35–1.31) and 0.62 (95% UI: 0.28–1.18), respectively. In contrast, the lowest ASMR was observed in Tajikistan and Kiribati. The highest ASDR was also seen in Ethiopia at 37.25 (95% UI: 20.5-77.78), while Tajikistan had the lowest ASDR at 0.09 (95% UI: 0.04–0.15). The EAPC in ASIR from 1990 to 2021 was greatest in Tajikistan (140.45) and Kiribati (134.07), and least in Libya (0.27). The largest declines in ASMR and ASDR were found in Tajikistan (-56.05 and − 40.65), while the smallest declines were observed in Ethiopia (-0.15 and − 0.11, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom 1990 to 2021, the ASIR of TC consistently increased, especially after 2005. In contrast, the ASMR declined steadily since 1990, with a more pronounced decrease post-2000. The ASDR showed greater fluctuation, peaking around 2000, followed by a general decline with some fluctuations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Trends in TC among WCBA by age and SDI\u003c/h2\u003e \u003cp\u003eGlobally, the incidence of TC in WCBA increased significantly, particularly in the 25–49 age group, with the 45–49 age group experiencing the largest rise. In low SDI regions, the 25–29 age group experienced a modest increase, while the 30–39 age group in low-middle SDI regions showed an upward trend. In middle SDI regions, the burden of TC was most pronounced in the 35–49 age group, while in high-middle SDI regions, the 45–49 age group showed a notable increase. In high SDI regions, the burden was greater in women aged 30 years and older (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The annual rate of change in incidence for TC revealed a peak in the 20–34 age group globally, followed by a decline in the 35 + age group. The 30–34 age group experienced a significant peak in low SDI regions, while other age groups remained stable. In low-middle SDI regions, the 25–39 age group plateaued after a peak, whereas in middle SDI regions, the 20–34 age group showed an increase, with rates remaining high in those aged 35 and older. In high-middle SDI regions, the 20–34 age group declined after its peak, while the 20–24 age group in high SDI regions increased, with the 20–34 age group gradually declining (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Age, period and birth cohort effects on TC in WCBA\u003c/h2\u003e \u003cp\u003eThe APC model analysis revealed that the ASIR, ASMR, and ASDR for TC in WCBA escalate with age, with a marked acceleration after 40 years, highlighting elevated health risks in older cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Temporally, the ASIR exhibited a consistent rise from 1995 to 2015, indicating a significant increasing trend, while ASMR and ASDR displayed greater variability with less pronounced effects and broader confidence intervals, suggesting a limited temporal influence (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Cohort effects revealed a U-shaped distribution for ASMR and ASDR, with higher health risks in early (pre-1940) and recent (post-1980) cohorts, and comparatively better health outcomes for those born between 1960 and 1980 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Health inequalities in TC in WCBA\u003c/h2\u003e \u003cp\u003eIn high-income regions, the ASIR of TC increased significantly in 2021 compared to 1990, and health inequality also increased significantly. This was evidenced by the SII for the ASIR, which rose from 1.94 to 2.28 from 1990 to 2021, highlighting a substantial exacerbation of health disparities. Nonetheless, there was a slight improvement in health inequality, especially in high SDI regions, as evidenced by the CI’s slight positive shift, which decreased slightly from 0.31 to 0.30 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). The SII for ASMR from 0 in 1990 to -0.07 in 2021, along with the CI decline from 0.31 to 0.30 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D), suggests that medical advancements and improved treatment protocols likely contributed to this change. In high SDI regions, the SII for ASDR showed a notable decrease from 0.22 in 1990 to -3.1 in 2021, with the CI dropping from 0.31 to 0.30. This indicates a trend of reduced ASDR in high SDI regions from 1990 to 2021, reflecting a potential decrease in the overall disease burden (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Future projections\u003c/h2\u003e \u003cp\u003eUtilizing the BAPC model, we projected this trend to continue over the next three decades, with the ASIR and ASDR expected to increase to 3.94 (95% UI: 1.34–6.54) and 10.86 (95% UI: 4.83–16.90) per 100,000 people for ASDR by 2051(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, C). Conversely, the ASMR is anticipated to exhibit a downward trajectory over the next 30 years. BAPC model predictions indicate a decline to 0.14 (95% UI: 0.06–0.23) per 100,000 people for ASMR by 2051(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThyroid cancer (TC) has emerged as the most prevalent endocrine malignancy worldwide, with its incidence increasing at an unprecedented rate over the past three decades\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. This trend is particularly pronounced among women of childbearing age (WCBA), who exhibit 2–4 times higher incidence rates compared to their male counterparts\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Our analysis of Global Burden of Disease (GBD) 2021 data reveals that the global age-standardized incidence rate (ASIR) for WCBA rose from 2.17 to 3.36 per 100,000 between 1990 and 2021, with middle-socio-demographic index (SDI) regions experiencing the steepest increase (EAPC = 2.34%). These findings align with the \"dual-burden\" hypothesis, wherein improved diagnostic capabilities in transitioning economies drive case detection, while persistent risk factors like obesity and multiparity sustain disease incidence\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.The gender disparity in TC epidemiology suggests a strong hormonal component. Mechanistic studies have identified estrogen receptor-alpha (ERα) overexpression in 60–70% of female TC cases, with parity showing a dose-dependent association (p \u0026lt; 0.01)\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Notably, our age-period-cohort (APC) analysis revealed a U-shaped risk curve, with elevated risks in early (pre-1940) and recent (post-1980) birth cohorts. This pattern may reflect generational shifts in reproductive behaviors and environmental exposures, warranting further investigation into gene-environment interactions.\u003c/p\u003e\u003cp\u003eOur study highlights profound socioeconomic disparities in TC burden, as quantified by the slope index of inequality (SII = 2.28) and concentration index (CI = 0.30). Low-SDI regions, despite accounting for only 8.9% of global TC cases, bear 20.1% of mortality burden. This inverse care pattern is exemplified by Ethiopia, where the age-standardized mortality rate (ASMR = 0.63) exceeds the global average by 294%. Such disparities likely stem from multifaceted barriers, including limited access to ultrasound screening (available in \u0026lt; 15% of Sub-Saharan African primary care facilities)\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e and delayed presentation (median tumor size at diagnosis: 4.2cm in low-SDI vs. 1.8cm in high-SDI regions)\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.The middle-SDI paradox—characterized by rising incidence but stagnant mortality-may reflect transitional epidemiological patterns. In these regions, healthcare modernization has enabled increased case detection (e.g., South Asia's ASIR increased by 2.59% annually), yet treatment accessibility remains constrained. These findings underscore the need for targeted resource allocation in underserved regions to address the growing burden of TC, emphasizing the importance of understanding socio-economic disparities in health outcomes\u003csup\u003e[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e–\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur research deepens the comprehension of the incidence of TC and the socio - demographic factors that determine it, especially within the group of WCBA. The innovative use of a Bayesian Meta-regression framework combined with the APC model allows for a nuanced analysis of TC trends over time, revealing critical insights into how socio-economic factors, represented by the SDI, correlate with cancer burden. Previous studies have often focused on broader cancer trends without disaggregating data by age or socio-economic conditions, leaving a gap in our understanding of how these variables interact specifically within the WCBA demographic. This research fills that gap by demonstrating a significant increase in TC incidence, particularly in middle SDI regions, with a notable annual percentage change in incidence rates, thus emphasizing the need for targeted public health strategies that consider these disparities.\u003c/p\u003e\u003cp\u003eOur findings revealed a worldwide upward trend in the incidence rate of TC among WCBA during the period from 1990 to 2021. This discovery is in line with previous research\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, suggesting that greater attention should be paid to the cancer burden borne by this particular population. The global increase in obesity among WCBA is likely a contributing factor. Obesity has been shown to modify inflammatory, metabolic, and hormonal pathways\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. The meta-analyses demonstrating a 33% increased risk per 10-unit BMI increment (95% CI: 1.20–1.47). Adipose tissue-derived cytokines, particularly interleukin-6 (IL-6) and leptin, promote thyrocyte proliferation through STAT3 and MAPK pathway activation. Obesity-related metabolic dysfunction accounts for 22.7% (95% UI: 18.9–26.5) of TC burden in WCBA, with the highest attributable fractions observed in high-income North America (35.2%) and Western Europe (33.8%)\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Simultaneously, there is strong evidence suggesting that prolonged exposure to steroid hormones, like estrogen replacement therapy and long - term oral contraceptive use, is associated with TC varying degrees\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Reproductive factors further compound TC risk in WCBA. Multiparous women (\u0026gt; 3 pregnancies) exhibit 1.8-fold higher TC incidence compared to nulliparous counterparts (95% CI: 1.5–2.1). This association appears mediated by cumulative estrogen exposure, which upregulates thyrocyte growth factors while suppressing tumor suppressor genes. Although previous epidemiological studies have summarized more specific risk factors for TC among WCBA, due to the heterogeneity and limited statistical power of these studies, further research is needed. This additional research should aim to better understand how risk factors, especially those that can be modified, affect female cancers, thereby enabling targeted early prevention of TC among WCBA. In addition, socioeconomic development has led to enhanced TC screening, regular examinations, and improved cancer registration. These improvements have facilitated earlier detection of TC among WCBA, resulting in higher incidence rates.\u003c/p\u003e\u003cp\u003eThe implications of these findings extend beyond academic discourse and into clinical practice and health policy. The observed increase in TC incidence among specific age groups, particularly those aged 45–49, suggests a pressing need for enhanced screening and early detection strategies tailored to this demographic. Such interventions could significantly improve outcomes, potentially decreasing the overall burden of the disease. Furthermore, the disparities in incidence and mortality rates necessitate policy changes aimed at improving access to healthcare services in lower SDI regions, which are disproportionately affected by higher mortality rates. These findings highlight the importance of integrating socio-economic considerations into cancer care frameworks to ensure equitable health outcomes for all women, particularly those of childbearing age.\u003c/p\u003e\u003cp\u003eOur findings necessitate a stratified approach to TC control, tailored to regional development contexts:\u003c/p\u003e\u003cp\u003eHigh-SDI Settings\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eImplement risk-adapted screening protocols using ultrasound elastography and molecular testing (e.g., BRAF V600E mutation analysis)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePromote shared decision-making to mitigate overdiagnosis of indolent microcarcinomas\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIntegrate TC prevention into obesity management programs\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eLow/Middle-SDI Regions\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eScale up mobile thyroid nodule screening with portable ultrasound devices\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTrain primary care providers in ultrasound-guided fine-needle aspiration\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEstablish regional referral networks for specialized TC care\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eThe projected 3.94 (95% UI: 1.34–6.54) per 100,000 people increase in ASIR by 2051 underscores the urgency of these interventions. Cost-effectiveness analyses suggest that task-shifted ultrasound screening in LMICs could avert 12,300 (95% UI: 9,800 − 15,100) TC deaths annually at \u003cspan\u003e$\u003c/span\u003e1,480 per disability-adjusted life year (DALY) averted.\u003c/p\u003e\u003cp\u003eDespite the strengths of this study, several limitations must be acknowledged. The reliance on secondary data from the GBD study may not account for local variations in TC diagnosis and treatment, potentially limiting the generalizability of the findings. Additionally, the study's cross-sectional nature restricts the ability to infer causality between socio-demographic indices and TC incidence. Future research should aim to incorporate primary data collection methods to validate these findings across diverse healthcare settings. Longitudinal study examining hormonal fluctuations associated with fertility and the pathogenesis of thyroid cancer, alongside a cost-benefit analysis of artificial intelligence-assisted ultrasound screening in low-income and middle-income countries (LMICs), can provide valuable insights into the temporal relationship between socio-economic factors and the outcomes of thyroid cancer in women of reproductive age, ultimately informing more effective public health strategies and resource allocation to address these disparities.\u003c/p\u003e\u003cp\u003eIn conclusion, this study has elucidated significant trends in the incidence and mortality of thyroid cancer among women of childbearing age from 1990 to 2021, underscoring the escalating burden of this malignancy, particularly in lower socio-demographic index regions. The observed health disparities highlight an urgent need for targeted public health interventions and policy reforms to mitigate the adverse effects of socioeconomic factors on cancer outcomes. As we project future trends, it is essential to continue monitoring these patterns and implement strategies aimed at reducing health inequalities to improve the overall prognosis for affected populations.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study evaluated the global burden of thyroid cancer (TC) among women of childbearing age (WCBA) from 1990 to 2021, revealing a significant increase in incidence and a decline in mortality rates. Despite significant reductions in mortality and DALYs rates in high SDI regions, low SDI regions still had a disproportionate disease burden, highlighting socioeconomic disparities. Policymakers should address these disparities by prioritizing health resources and interventions in low and middle SDI regions. Furthermore, while future projections indicate that the burden of TC may decrease in some regions due to advancements in medical care and health interventions, global disparities persist. Therefore, tailored interventions addressing the specific needs of different regions, age groups, and socioeconomic backgrounds are urgently needed.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\" style=\"margin-right: calc(-2%); width: 102%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4066%;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003cp\u003eWCBA\u003c/p\u003e\n \u003cp\u003eGBD\u003c/p\u003e\n \u003cp\u003eSDI\u003c/p\u003e\n \u003cp\u003eDALYs\u003c/p\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003cp\u003eBAPC\u003c/p\u003e\n \u003cp\u003eEAPC\u003c/p\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003cp\u003eASIR\u003c/p\u003e\n \u003cp\u003eASMR\u003c/p\u003e\n \u003cp\u003eASDR\u003c/p\u003e\n \u003cp\u003eIHME\u003c/p\u003e\n \u003cp\u003eICD-10\u003c/p\u003e\n \u003cp\u003eUI\u003c/p\u003e\n \u003cp\u003eLMICs\u003c/p\u003e\n \u003cp\u003eINLA\u003c/p\u003e\n \u003cp\u003eER\u0026alpha;\u003c/p\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003cp\u003eMAPK\u003c/p\u003e\n \u003cp\u003eSTAT3\u003c/p\u003e\n \u003cp\u003eBRAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84.4507%;\"\u003e\n \u003cp\u003eThyroid Cancer\u003c/p\u003e\n \u003cp\u003eWomen of Childbearing Age\u003c/p\u003e\n \u003cp\u003eGlobal Burden of Disease\u003c/p\u003e\n \u003cp\u003eSocio-demographic Index\u003c/p\u003e\n \u003cp\u003eDisability-Adjusted Life Years\u003c/p\u003e\n \u003cp\u003eAge-Period-Cohort\u003c/p\u003e\n \u003cp\u003eBayesian Age-Period-Cohort\u003c/p\u003e\n \u003cp\u003eEstimated Annual Percentage Change\u003c/p\u003e\n \u003cp\u003eSlope Index of Inequality\u003c/p\u003e\n \u003cp\u003eConcentration Index\u003c/p\u003e\n \u003cp\u003eAge-Standardized Incidence Rate\u003c/p\u003e\n \u003cp\u003eAge-Standardized Mortality Rate\u003c/p\u003e\n \u003cp\u003eAge-Standardized Disability-adjusted life years Rate\u003c/p\u003e\n \u003cp\u003eInstitute for Health Metrics and Evaluation\u003c/p\u003e\n \u003cp\u003eInternational Classification of Diseases, 10th Revision\u003c/p\u003e\n \u003cp\u003eUncertainty Interval\u003c/p\u003e\n \u003cp\u003eLow- and Middle-Income Countries\u003c/p\u003e\n \u003cp\u003eIntegrated Nested Laplace Approximation\u003c/p\u003e\n \u003cp\u003eEstrogen Receptor-alpha\u003c/p\u003e\n \u003cp\u003eInterleukin-6\u003c/p\u003e\n \u003cp\u003eMitogen-Activated Protein Kinase\u003c/p\u003e\n \u003cp\u003eSignal Transducer and Activator of Transcription 3\u003c/p\u003e\n \u003cp\u003eB-Raf proto-oncogene (V600E mutation)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the Global Burden of Disease (GBD) 2021, published by the Institute for Health Metrics and Evaluation (IHME) (https://vizhub.healthdata.org/gbd).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZW performed the experiments, drafted the manuscript, prepared the figuresand funding acquisition; MY, participated in design of the experiments, writing-review and editing, supervision; ZY and YC contributed to perform the experiments, provided valuable comments and suggestions, provided experimental materials and funding acquisition; XZ writing-review and editing, supervision and funding acquisition. All authors have read and approved the final version of the manuscript, and agree with the order of presentation of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Shaanxi Provincial Key Research and Development Program (Grant No. 2023-YBSF-468 to Xiaojing Zhao) and Shaanxi Cancer Hospital National Natural Science Foundation Incubation Project (Grant No. SCB2417 to Yong Chen) and the Hospital Fund of the Second Affiliated Hospital of Xi\u0026apos;an Jiaotong University (Grant No. YJ(QN)202328 to Zhe Wang). The authors express their gratitude to the Global Burden of Disease (GBD) 2021 study team for providing open-access data essential to this research. We acknowledge the academic and institutional support from the Second Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University College of Medicine, Shenyang Medical College Affiliated Central Hospital, Shaanxi Provincial Cancer Hospital, and the First Affiliated Hospital of Chongqing Medical University. Additionally, we recognize the contributions of researchers worldwide involved in GBD data collection and analysis, which formed the foundation of this study. Finally, we appreciate the valuable feedback from the reviewers and editors that strengthened the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhou T, Wang X, Zhang J, Zhou E, Xu C, Shen Y, Zou J, Lu W, Su K, Huang W, et al. Global burden of thyroid cancer from 1990 to 2021: a systematic analysis from the Global Burden of Disease Study 2021. 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JAMA Surg. 2017;152(8):734\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitahara CM, Sosa JA. Understanding the ever-changing incidence of thyroid cancer. Nat Rev Endocrinol. 2020;16(11):617\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitahara CM, Pfeiffer RM, Sosa JA, Shiels MS. Impact of Overweight and Obesity on US Papillary Thyroid Cancer Incidence Trends (1995\u0026ndash;2015). J Natl Cancer Inst. 2020;112(8):810\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchonfeld SJ, Neta G, Sturgis EM, Pfeiffer RM, Hutchinson AA, Xu L, Wheeler W, Guenel P, Rajaraman P, de Vathaire F, et al. Common genetic variants in sex hormone pathway genes and papillary thyroid cancer risk. Thyroid. 2012;22(2):151\u0026ndash;6.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Thyroid cancer, Women of childbearing age, Global Burden of Disease 2021, Age-period-cohort model","lastPublishedDoi":"10.21203/rs.3.rs-6399893/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6399893/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThyroid Cancer (TC) is a global health concern with varying levels and trends across countries and regions, while women of child-bearing age (WCBA) are often neglected despite their unique epidemiology, healthcare needs and societal implications. We aim to investigate the pattern and trend of female TC among WCBA from 1990 to 2021. The data for this study were obtained from the Global Burden of Disease (GBD) 2021 database, with age-standardized incidence rate (ASIR), prevalence rate (ASPR), mortality rate (ASMR), and disability-adjusted life years (DALYs) as the primary assessment indicators. Dynamic changes in the TC burden among WCBA were analyzed by estimating the annual percentage changes (EAPCs), and a Bayesian age-period-cohort model was used to predict future 30-year trends. Health inequalities were analyzed using the slope index of inequality (SII) and concentration index (CI). The global TC incidence increased from 26,302 cases in 1990 to 67,558 in 2021, with a corresponding rise in ASIR from 2.17 to 3.36 per 100,000 among women aged 15\u0026ndash;49. While ASMR showed a slight decline, indicating improvements in treatment efficacy and early detection, health inequalities persist, particularly in lower socio-demographic index (SDI) countries, where ASIR and DALYs remain significantly elevated. Significant regional disparities were observed, with South Asia reporting the highest burden and North America the lowest. Our predictions suggest that the ASIR and ASDR for TC will increase to 3.94 and 10.86 per 100,000 by 2051, respectively, while the ASMR will decline to 0.14. These findings underscore the urgent need for enhanced screening, targeted interventions, and resource allocation, particularly in low and middle-income regions, to effectively manage thyroid cancer and mitigate the associated health disparities. Future research should focus on the underlying biological factors and the effectiveness of public health strategies to further reduce the burden of this disease.\u003c/p\u003e","manuscriptTitle":"Thyroid Cancer Burden among Women of Childbearing Age: A 30-Year Global Analysis from the Global Burden of Disease Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 12:34:48","doi":"10.21203/rs.3.rs-6399893/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"233733106899304070545450780505837252462","date":"2026-04-28T03:17:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-24T14:10:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273837175513296702142274623948377604522","date":"2025-05-15T10:02:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-15T08:42:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-21T04:32:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-09T06:21:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-09T06:17:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-04-08T06:23:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2239945f-4ce2-46fc-a26a-60aaa8988c08","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-20T12:34:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-20 12:34:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6399893","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6399893","identity":"rs-6399893","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}