(Article In Press) Global, regional, and national burden and trends of de-pressive disorders among women of childbearing age from 1990 to 2021: insights from the Global Burden of Disease Study 2021(https://doi.org/10.63386/617468)

Ruoyu Zhu^1†, Xuemin Jia^2†, Bolun Fan², Jinhao Zheng², Yanxuan Wu², Gongchang Yu²*, Zhonglin Wang^{1, 3}*

¹First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, China

²Neck-Shoulder and Lumbocrural Pain Hospital of Shandong First Medical University, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China

³Department of Neurology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China

* Corresponding authors’ email address :
Zhonglin Wang:
zydoctor7@126.com
Gongchang Yu:
yugongchang@sdfmu.edu.cn

Equal contribution and first authorship: 
Ruoyu Zhu^1† and Xuemin Jia^2†
†These authors contributed equally to this work and share first authorship

Keywords: Depressive disorders, Women of childbearing age, Disease burden, DALYs, SDI, GBD 2021

Abstract

Background: Depressive disorders are a significant mental illness that predominantly affects women, and its impact is wide-ranging and multifaceted, particularly among women of childbearing age (WCBA). Currently, there is a lack of comprehensive literature analysis on the global overall burden and trends of depression among WCBA.

Methods: Utilizing data from the GBD 2021 project, this study evaluated the depression burden in WCBA globally, regionally, and nationally over the past 32 years using key measures: prevalence, incidence, and DALYs. Our research employed point estimates with a 95% uncertainty interval (UI), and assessed trends of the disease burden using percentage change and estimated annual percentage change (EAPC).

Results: In 2021, the global prevalence, incidence, and disability-adjusted life years (DALYs) cases of depressive disorders among WCBA were 121.24 million, 133.25 million and 21.04 million, representing approximately a 70% increase compared to 1990. The EAPCs were -0.02 (95% CI: -0.17, 0.14), -0.11 (95% CI: -0.33 to 0.11), and -0.06 (95% CI: -0.25 to 0.12), respectively, indicating an overall downward trend over the 32 years. The Low SDI region reported the highest absolute numbers, with prevalence at 17.91 million, incidence at 19.80 million, and DALYs at 3.10 million, corresponding to percentage changes of approximately 160% compared to 1990. East Asia was the only region with measures lower in 2021 than in 1990, with EAPCs at -0.51 (95% CI: -0.67, -0.35) for prevalence, -1.14 (95% CI: -1.42, -0.87) for incidence, and -0.80 (95% CI: -1.02, -0.59) for DALYs. Among 204 countries, 87.7% exhibited increasing trends, particularly the United Arab Emirates and Qatar, with increases ranging from 487% to 634%. Globally, the age cohort of 45-49 years has witnessed the most pronounced growth, with a percentage change of around 127%. No significant correlation was found between SDI and this disease burden.

Conclusions: The study underscores the escalating burden of depressive disorders among WCBA globally, with significant regional and age-specific disparities. Future research should address data limitations and explore the long-term impact of unequal access to prevention, diagnosis, and treatment resources on mental health in different parts of the world, aiming to establish resilient mental health systems.

Introduction

Depressive disorders are a pervasive and debilitating mental health condition characterized by persistent feelings of sadness, loss of interest, and a range of emotional and physical problems that impair daily functioning (1). Globally, depressive disorders constitute a significant public health challenge, contributing substantially to the overall burden of disease (2). The Global Burden of Disease (GBD) 2019 study estimates that about 279.6 million individuals worldwide suffer from depressive disorders, with an age-standardized prevalence per 100,000 people of approximately 3440.1 (3). This widespread prevalence underscores the urgent need for comprehensive strategies to mitigate the impact of depression on individuals and societies alike.

Depressive disorders disproportionately affect women of childbearing age (WCBA, 15–49 years old). Studies indicate that women are nearly twice as likely as men to experience depressive episodes, a disparity attributed to a combination of biological, hormonal, and socio-cultural factors (4). Among WCBA, the prevalence of depressive disorders is even more pronounced, with pooled prevalence in a study was 14.0% (95%CI, 12.0%–15.0%) (5). This demographic is at a critical juncture in their lives, balancing roles related to education, family planning, childbearing, and career development, all of which can exacerbate the risk of developing depressive symptoms (6). The consequences of depressive disorders for WCBA are extensive and multifaceted. At an individual level, depression can disrupt daily life, impair social relationships, and reduce productivity. Among women in their childbearing years, this disruption intersects critically with key life events, such as family planning, pregnancy, and early motherhood. Perinatal mental health disorders, including antepartum and postpartum depression, are linked to adverse outcomes for both mothers and infants. Research has shown that maternal depression can contribute to suboptimal prenatal care, preterm delivery, and low birth weight, while also influencing infant bonding and long-term developmental trajectories (7, 8). Furthermore, perinatal depressive symptoms have been associated with increased parental stress, reduced breastfeeding initiation and duration, and impaired mother-child interaction, all of which can have a lasting effect on family functioning and child well-being (9).

The global nature of this issue further complicates prevention and intervention efforts. While high-income countries may have more established healthcare systems capable of identifying and treating depressive disorders in WCBA, these conditions often remain under-prioritized due to stigma, insufficient mental health staffing, and limitations in service integration within maternal health settings (10, 11). In low- and middle-income countries, inadequate mental health infrastructure, limited financial resources, and cultural factors further hinder early detection and effective intervention (12, 13). The COVID-19 pandemic has magnified these challenges, exacerbating mental health inequities. Women—already bearing disproportionate caregiving burdens—experienced heightened stress and depressive symptoms during the pandemic, especially those of reproductive age, accentuating pre-existing vulnerabilities (14, 15).

Despite the demonstrable burden and the far-reaching consequences of depression among WCBA, gaps in the literature remain. Previous research has often focused on either the general adult population or specific clinical contexts, overlooking the nuanced vulnerabilities of women in their reproductive years. Moreover, variations in the prevalence and severity of depressive disorders across different global regions and income settings remain insufficiently explored. Understanding geographic, cultural, and socio-economic differences is essential to developing targeted, context-specific interventions and policies. Data from GBD, now provide an opportunity to examine incidence, prevalence, and disability-adjusted life years (DALYs) associated with depression more granularly, facilitating a better understanding of global, regional, and national patterns (16).

This study focuses on providing a comprehensive assessment of the burden and trends of depressive disorders among WCBA from 1990 to 2021, using GBD 2021 data. By examining incidence, prevalence, and DALYs globally, regionally, and nationally, this research seeks to identify key patterns and determinants underlying the mental health challenges faced by this population. Such insights can inform policymakers, healthcare providers, and stakeholders in crafting context-specific, evidence-based strategies that mitigate the mental health burden, improve maternal and child health outcomes, and ultimately enhance the quality of life for women at this critical stage of their lives.

Methods

Data Sources and Case Definition

This study utilized data on depressive disorders among WCBA from the Global Burden of Disease (GBD) 2021 project. GBD 2021 provides comprehensive epidemiological estimates for 371 diseases and injuries across 204 countries and territories from 1990 to 2021 (17). All relevant data were accessed through the Global Health Data Exchange (GHDx) platform (https://ghdx.healthdata.org/gbd-2021/sources) and are freely available for public use. Detailed methodologies, including data collection, processing, and statistical modeling techniques, are thoroughly documented in previous GBD reports.

Within the GBD framework, depressive disorders are classified under the mental and substance use disorders category. This includes major depressive disorder and dysthymia, which are aggregated due to the challenges in differentiating these subtypes across various data sources (18). According to the International Classification of Diseases, Tenth Revision, Clinical Modification, depressive disorders are identified using codes F32–F33 (19). The GBD employs standardized case definitions and severity distributions to ensure consistency and comparability across diverse datasets. For this study, we adhered to the GBD case definition for depressive disorders and did not disaggregate specific subtypes, as the GBD data primarily report overall depressive disorder measures.

Socio-demographic Index (SDI)

To contextualize the burden of depressive disorders relative to national development levels, we employed the SDI. Introduced by the Institute for Health Metrics and Evaluation in 2015, SDI is a composite measure that integrates income per capita, educational attainment, and total fertility rates under the age of 25 (17). The SDI ranges from 0 to 1, with higher values indicating greater socio-demographic development. For analysis purposes in GBD 2021, SDI values were scaled by multiplying by 100, resulting in a range from 0 to 100. Countries and territories were categorized into five SDI quintiles: low, low-middle, middle, high-middle, and high. This stratification facilitates comparative analysis of depressive disorder burdens across different socioeconomic contexts and aids in identifying patterns that may inform targeted policy interventions.

Burden Measures and Uncertainty

The primary measures for estimating disease burden were prevalence, incidence, and DALYs. DALYs combine years lived with disability (YLDs) and years of life lost (YLLs) into a single measure. As mortality directly attributable to depressive disorders is minimal in GBD estimates, YLLs were effectively zero, making DALYs approximate YLDs in this context (17). All estimates from GBD are derived through complex modeling processes that incorporate numerous data sources and iterative adjustments. Uncertainty intervals (UIs) are generated from repeated draws (n=500 in GBD 2021) to reflect the variability in underlying data and model specifications (17).

Trend Analysis

To evaluate temporal trends in the prevalence, incidence, and DALYs of depressive disorders among WCBA from 1990 to 2021, we calculated the estimated annual percentage change (EAPC) and adopted the percentage change. EAPC is a widely recognized metric for assessing trends in epidemiological indicators over time (20). The natural logarithm of each rate (prevalence, incidence, DALYs) was regressed against the calendar year using a linear regression model. The slope of the regression line was used to compute the EAPC, and 95% confidence intervals (CIs) were derived to determine the statistical significance of the trends. An EAPC with a 95% CI entirely above zero indicates a significant upward trend, while a 95% CI entirely below zero denotes a significant downward trend. If the 95% CI includes zero, the trend is considered statistically non-significant. Additionally, we adopted the percentage change in the absolute number of cases from 1990 to 2021 to provide a clear depiction of temporal shifts in disease burden.

Data Processing and Visualization

All data cleaning, statistical analyses, and computations were performed using R software (version 4.3.1) (21). The “ggplot2” package was utilized for data visualization, ensuring high-quality and informative graphical representations (21). Final graphical outputs were refined and formatted using Adobe Illustrator (version CS5) to enhance clarity and precision for publication purposes.

Results

Global Level

From 1990 to 2021, the global burden of depressive disorders among WCBA significantly increased in terms of prevalence, incidence, and DALYs. Specifically, the number of prevalent cases rose from 72.35 million (95% UI: 63.63–84.07) in 1990 to 121.24 million (95% UI: 104.69–142.43) in 2021. Incident cases surged from 77.72 million (95% UI: 65.41–93.34) in 1990 to 133.25 million (95% UI: 108.94–162.19) in 2021, and DALYs increased from 12.45 million (95% UI: 8.43–17.07) in 1990 to 21.04 million (95% UI: 14.07–29.06) in 2021, representing approximately a 70% percentage change (Table 1and S1 and S2, Fig. 1A). The prevalence rate per 100,000 individuals increased from 5,409.59 (95% UI: 4,757.83–6,285.95) in 1990 to 6,220.98 (95% UI: 5,371.96–7,308.26) in 2021. However, EAPC was -0.02 (95% CI: -0.17, 0.14), indicating that the prevalence rate has been gradually decreasing over time. Similarly, the incidence rate per 100,000 individuals rose from 5,811.65 (95% UI: 4,891.34–6,979.66) in 1990 to 6,837.27 (95% UI: 5,590.11–8,322.29) in 2021, with an EAPC of -0.11 (95% CI: -0.33 to 0.11). The DALYs rate per 100,000 individuals also increased from 930.59 (95% UI: 630.46–1,276.48) in 1990 to 1,079.73 (95% UI: 722.08–1,491.21) in 2021, with an EAPC of -0.06 (95% CI: -0.25 to 0.12) (Tables 1 and S1 and S2, Fig. 1 and S1-S4).

5 SDI Regions Level

In 2021, the burden of depressive disorders among WCBA varied across the five SDI regions. The Low SDI region reported the highest absolute numbers, with prevalence at 17.91 million (95% UI: 15.15–21.73), incidence at 19.80 million (95% UI: 16.02–24.89), and DALYs at 3.10 million (95% UI: 2.05–4.32), corresponding to percentage changes of approximately 158%, 162%, and 162% respectively compared to 1990. The Low-middle SDI region also exhibited significant growth, with prevalence, incidence, and DALYs increasing by approximately 104%, 105%, and 105%, respectively. In contrast, the High-middle SDI region experienced the most moderate absolute increases, with prevalence increasing from 14.31 million to 17.38 million, incidence from 14.97 million to 17.86 million, and DALYs from 2.45 million to 2.94 million, representing percentage changes of around 20% (Tables 1 and S1 and S2, Fig. 1A).

Compared to 1990, the prevalence, incidence, and DALYs rates per 100,000 individuals in the five SDI regions increased in absolute terms in 2021, especially between 2019 and 2021, with a surging trend (Table 1 and S1 and S2, Figure 1C and S1 and S2). However, the EAPCs for prevalence, incidence, and DALYs rates in the Low SDI, Low-middle SDI, Middle SDI, and High-middle SDI regions showed a decreasing trend, except for the Middle SDI region’s prevalence rate, which had an EAPC of 0.03 (95% CI: -0.13 to 0.19). In stark contrast, the High SDI region exhibited continuous growth trends, with EAPCs of 0.33 (95% CI: 0.15 to 0.50) for prevalence, 0.49 (95% CI: 0.26 to 0.72) for incidence, and 0.40 (95% CI: 0.20 to 0.60) for DALYs rates, indicating sustained increases (Tables 1 and S1 and S2, Fig. 1B).

21 GBD Regions Level

Across the 21 GBD regions, the absolute numbers of prevalent cases, incident cases, and DALYs for depressive disorders among WCBA notably increased in both 2019 and 2021, observed in the vast majority of regions. The largest percentage changes occurred in Central Sub-Saharan Africa, Western Sub-Saharan Africa, and Eastern Sub-Saharan Africa, with approximately 175% increases each, all of which are Low SDI regions. Central Europe exhibited more moderate changes, with absolute numbers remaining relatively stable over the two years. In contrast, only East Asia showed a significant decline, with percentage changes in prevalent cases, incident cases, and DALYs of -10%, -29%, and -19%, respectively. Over the past 32 years, among the 21 GBD regions, only East Asia had all three measures (prevalence, incidence, and DALY rates) in 2021 lower than those in 1990. Its EAPCs were the lowest, at -0.51 (95% CI: -0.67, -0.35) for prevalence, -1.14 (95% CI: -1.42, -0.87) for incidence, and -0.80 (95% CI: -1.02, -0.59) for DALYs, indicating a significant downward trend. A similar trend was observed in South Asia; however, the absolute numbers for all three measures slightly increased in 2021 compared to 1990. The most pronounced growth trend was observed in Central Latin America, which falls under the High-middle SDI category, characterized by substantial increases in prevalence, incidence, and DALYs absolute numbers, as well as exceptionally high EAPC values of 0.97 (95% CI: 0.79,1.16) for prevalence, 1.16 (95% CI: 0.93,1.38) for incidence, and 1.07 (95% CI: 0.87,1.27) for DALYs rates (Tables 1 and S1 and S2, Fig. 2 andS3 and S4).

204 Countries Level

From 1990 to 2021, approximately 87.7% of countries showed an increasing trend in the prevalence, incidence, and DALYs of depressive disorders among WCBA. The United Arab Emirates and Qatar experienced the highest percentage changes, with increases ranging from 487% to 634%, both classified as high SDI regions, far surpassing other countries. Additionally, only 12.3% of countries exhibited a decrease in prevalence, incidence, and DALYs, such as Bosnia and Herzegovina (high SDI region) and Cuba (high-middle SDI), with percentage changes ranging from -35% to -31%. This suggests a polarizing trend in the increase of depressive disorder cases among WCBA in countries within high SDI regions. Over the past 32 years, approximately two-thirds of countries showed upward trends in prevalence, incidence, and DALY rates, with the largest increases observed in Mexico, with EAPCs of 1.73 (95% CI: 1.48,1.98), 2.07 (95% CI: 1.76,2.38), and 1.91 (95% CI: 1.63 to 2.19), respectively. In contrast, about one-third of countries exhibited a downward trend in these rates, with Singapore showing the most significant decrease, with EAPCs of -1.99 (95% CI: -2.30, -1.68), -2.07 (95% CI: -2.38, -1.76), and -1.91 (95% CI: -2.19, -1.63), respectively (Tables S3-S5, Fig. 3, S5 and S6).

Age Patterns4

From 1990 to 2021, depressive disorder cases of prevalence, incidence, and DALYs in WCBA have significantly increased globally. The percentage change generally shows an increasing trend with age, though there are exceptions. For example, the percentage change in the 15-19 years age group is greater than that in the 20-24 years age group. The percentage changes in the 20-24 years group are the lowest, at 37%, 40%, and 38%, respectively. In contrast, the 45-49 years age group showed the highest percentage changes, at 124%, 131%, and 127%, approximately 3-4 times higher than in the 15-19 years age group. Across the five SDI regions, from 1990 to 2021, the majority of age groups in WCBA exhibited increases in depressive disorder prevalent, incident, and DALY cases. Notably, only the High-middle SDI region saw decreases in all three measures for the 15-19 years, 20-24 years, and 25-29 years age groups. Consistently, the highest percentage of changes across all age groups was observed in Low SDI regions, where changes were generally above 150%. However, the percentage changes did not necessarily decrease with improvements in economic conditions. For example, in the Middle SDI region, the percentage changes in depressive disorder prevalent, incident, and DALY cases for the 45-49 years age group were higher than those in the Low-middle SDI region. Similarly, the percentage changes for the 15-19 years and 20-24 years age groups in High SDI regions were higher than those in High-middle SDI and even Middle SDI regions (Tables 2 and S6 and S7, Fig. 4B and S7 andS8).

The trend analysis of depressive disorder prevalence, incidence, and DALY rates from 1990 to 2021 shows a significant decline globally, and in four out of five SDI regions, except for the High SDI region. The 15-19 years age group showed only a small decrease, and, an increase in prevalent, incident, and DALY rates was observed in both the global and High-middle SDI regions. In contrast, all age groups in the High SDI region showed an increasing trend in prevalence, incidence, and DALY rates. The fastest increase in prevalence, incidence, and DALYs was observed in the 15-19 years age group in the High SDI region, with EAPCs of 1.22 (95% CI: 0.99, 1.46), 1.29 (95% CI: 1.03, 1.56), and 1.35 (95% CI: 1.09, 1.60), respectively. This indicates that, in 2021, the 15-19 years age group in the High SDI region faced the most significant burden of depressive disorders globally (Tables 2 and S6 and S7, Fig. 4C and S9 and S10).

Among the 21 regions, East Asia showed the most significant increase in the proportion of depressive disorder cases in the 45-49 years age group in WCBA. The proportion of prevalent cases in this age group increased from 11% in 1990 to 26% in 2021, the incidence proportion increased from 8% to 24%, and the DALY proportion increased from 10% to 25%. Additionally, the proportion of cases in the 15-19 years age group decreased the most in East Asia, from 11% to 5%, from 15% to 7%, and from 12% to 6%, respectively. Therefore, there is a growing trend in the proportion of depressive disorder cases in older age groups in WCBA (Fig. 4A, S11 and S12).

The association between depressive disorders burden and SDI

From 1990 to 2021, the analysis indicates that there is no significant correlation between SDI regions and the prevalence, incidence, and DALY rates of depressive disorders in WCBA. For example, as shown in Figure 5A (R = -0.01, 95% CI: -0.08 to 0.07, p = 0.80), the results suggest that SDI alone does not account for the variability in prevalence rates across the analyzed regions. Similarly, in 2021, the analysis of the relationship between SDI and the prevalence, incidence, and DALY rates of depressive disorders across 204 GBD countries reveals no correlation between SDI and prevalence rates. As seen in Figure 5B (R = 0.07, 95% CI: -0.07 to 0.02, p = 0.35), this further confirms the absence of a significant association. Overall, the analysis of SDI and prevalence rates across multiple regions and countries does not support a statistically significant correlation between these two variables (fig. 5, S13 and S14).

Discussion

This study utilized data from the GBD 2021 project to analyze depressive disorders among WCBA from 1990 to 2021. Employing standardized case definitions and advanced statistical modeling, the research evaluated key burden measures—prevalence, incidence, and DALYs—across global, regional, and national levels. The SDI was incorporated to contextualize the burden relative to socio-economic development. The findings reveal a significant global increase in the absolute number of depressive disorder cases among WCBA, with notable regional disparities and age-specific trends. Low and Low-middle SDI regions experienced the most substantial rises, while High SDI regions, particularly high-income countries, also showed considerable increases. The age-specific analysis highlighted that older WCBA (45–49 years) and younger WCBA (15–19 years) in High SDI regions are especially affected. Despite EAPCs for prevalence, incidence, and DALYs rates were predominantly negative, suggesting a slight decline in these rates over time, the rise in absolute numbers. Additionally, no significant correlation was found between SDI and the burden of depressive disorders, indicating that factors beyond socio-demographic development influence these trends.

The increase in absolute numbers of depressive disorders among WCBA underscores the escalating mental health challenge within this demographic. The negative EAPCs, despite rising absolute cases, suggest that while the overall burden is increasing due to population growth, the individual risk of depressive disorders may be experiencing a slight decline. This highlights the complexity of mental health trends, where population dynamics and socio-economic factors interplay to influence disease burden. The pronounced increases in Low and Low-middle SDI regions indicate significant disparities in mental health resources and support systems. These regions may lack adequate mental health infrastructure, trained professionals, and access to care, exacerbating the burden of depressive disorders. Conversely, the substantial increases in High SDI regions, especially among younger WCBA, point to emerging mental health issues potentially linked to modern societal pressures, lifestyle changes, and increased awareness and diagnosis rates. The lack of a significant correlation between SDI and depressive disorder burden suggests that socio-economic development alone does not account for the variability in mental health outcomes, emphasizing the role of other factors such as cultural norms, social support systems, and individual lifestyle choices. The predominance of negative EAPCs in prevalent, incident, and DALY rates, despite higher absolute numbers in 2021 compared to 1990, can be attributed to several interrelated factors. Firstly, global population growth has naturally led to an increase in the total number of individuals affected by depressive disorders, even if the prevalence rate per 100,000 individuals is declining slightly (17). Secondly, advancements in mental health awareness, diagnostic criteria, and reporting mechanisms may have contributed to higher detection rates, particularly in High SDI regions. Increased mental health literacy and reduced stigma in these regions facilitate more accurate reporting and diagnosis, thereby reflecting higher incidence and prevalence rates (22). The COVID-19 pandemic has played a pivotal role in exacerbating mental health issues worldwide. The pandemic introduced unprecedented stressors, including social isolation, economic instability, and heightened health anxieties, which disproportionately affected WCBA who often balance multiple roles such as caregiving, employment, and education (23). The intersection of hormonal fluctuations related to pregnancy and postpartum periods further heightened vulnerability to depressive disorders during the pandemic (24). In High SDI regions, the significant increases in depressive disorder burden among younger WCBA (15–19 years) may be linked to factors such as academic pressures, social media influence, and changing societal expectations. High-income countries often experience higher levels of stress related to career and personal achievements, which can contribute to depressive symptoms (25). Moreover, cultural shifts towards greater openness about mental health lead to increased reporting and diagnosis, thereby reflecting higher incidence and prevalence rates (26). Additionally, the rise in adolescent mental health issues is influenced by factors like bullying, cyberbullying, and the pervasive use of social media, which can exacerbate feelings of anxiety and depression (27). For the older age group (45–49 years), the increase in depressive disorder burden may be attributed to menopause-related hormonal changes, which are associated with mood swings and increased vulnerability to depressive symptoms (27). Furthermore, midlife stressors such as career stagnation, financial pressures, and caregiving responsibilities for both children and aging parents can contribute to the heightened depressive burden in this demographic (28).

Previous studies on depressive disorders among WCBA have often been limited to specific regions or age groups, leading to varied and sometimes conflicting findings (29-31). The comprehensive global approach of the GBD study, which includes a wide range of countries and utilizes standardized methodologies, provides a more nuanced understanding of global trends compared to previous research (17). The lack of a significant correlation between SDI and depressive disorder burden in this study contrasts with some previous findings that suggest higher SDI regions have better mental health outcomes due to superior healthcare infrastructure (32). This discrepancy may be explained by the unique pressures faced in high SDI environments, such as higher stress levels and social isolation, which can offset the benefits of better healthcare systems (33). Additionally, cultural factors and stigma associated with mental health vary significantly across regions, influencing both the reporting and actual prevalence of depressive disorders (34). Furthermore, previous studies may not have accounted for the broad spectrum of socio-cultural and environmental factors that influence mental health, leading to incomplete or biased interpretations. The GBD study’s robust methodology, including extensive data sources and advanced modeling techniques, offers a more accurate and comprehensive assessment, thereby resolving some of the inconsistencies observed in earlier research(35).

The findings of this study have significant theoretical and practical implications. The absence of an association between SDI and depressive disorder burden suggests that additional factors, such as cultural norms, social support systems, and individual lifestyle choices, are critical in shaping mental health trends(36). Practically, the substantial increase in depressive disorder burden among WCBA, particularly in Low and Low-middle SDI regions, necessitates targeted public health interventions. Enhancing mental health services, increasing access to mental health care, and implementing community-based support programs are essential steps to mitigate the rising burden (37). In High SDI regions, strategies should focus on addressing the unique stressors contributing to depressive disorders, such as workplace stress management, promoting work-life balance, and fostering environments that reduce social isolation (38). Moreover, the age-specific findings highlight the need for age-tailored interventions. For younger WCBA, integrating mental health education into school curricula and providing accessible counseling services can help address early-onset depressive symptoms (39). For older WCBA, especially those approaching menopause, providing targeted hormonal and psychological support can alleviate depressive symptoms associated with hormonal fluctuations (40).

This study boasts several strengths that enhance the reliability and applicability of its findings. Firstly, it leverages the comprehensive and globally recognized GBD 2021 dataset, ensuring extensive coverage across 204 countries and territories over 32 years. The use of standardized case definitions and methodologies enhances the comparability and reliability of the findings (17). Secondly, the study’s multi-level analysis, encompassing global, regional, and national perspectives, provides a detailed and nuanced understanding of the burden of depressive disorders among WCBA. This comprehensive approach allows for the identification of specific regional trends and disparities, facilitating targeted policy interventions. Thirdly, the inclusion of age-specific trends enriches the analysis, allowing for targeted insights into different subgroups within the WCBA population. This granularity is crucial for developing age-appropriate mental health strategies and interventions. Finally, the study’s robust statistical modeling and use of the SDI provide a sophisticated framework for understanding the interplay between socioeconomic factors and mental health outcomes (41).

Despite its strengths, this study has several limitations that should be acknowledged. Firstly, the reliance on GBD-modelled estimates introduces potential uncertainties, especially in regions with limited primary data. This reliance may lead to underestimation or overestimation of the true burden of depressive disorders in certain areas, particularly in low and low-middle SDI regions where data collection infrastructure is weaker (41). Secondly, the aggregation of depressive disorders under a single category may obscure important distinctions between different types of depression, such as major depressive disorder and dysthymia. These subtypes can have varying implications for treatment and burden, and their aggregated analysis may overlook nuanced trends and specific needs within the WCBA population (19). Thirdly, the cross-sectional nature of the data limits the ability to draw causal inferences regarding the factors contributing to the observed trends. Longitudinal data would provide more robust insights into the causal relationships between socioeconomic factors, environmental stressors, and the burden of depressive disorders. Additionally, cultural differences in the perception and reporting of depressive symptoms may affect the comparability of data across different regions. The stigma associated with mental health issues varies significantly across cultures, potentially leading to underreporting in some regions and overreporting in others (42). Finally, the study did not account for all possible confounding factors, such as genetic predispositions, individual lifestyle choices, and specific environmental stressors, which could influence the burden of depressive disorders independently of socio-demographic factors.

Future research should aim to address the limitations identified in this study by incorporating more granular and high-quality data, particularly from low and low-middle SDI regions where data scarcity is prevalent. Enhanced data collection efforts, including the implementation of standardized mental health surveys and improved diagnostic practices, can improve the accuracy of burden estimates (43). Longitudinal studies that track individuals over time would provide deeper insights into the causal factors and progression of depressive disorders among WCBA. Such studies could elucidate the temporal relationships between socioeconomic changes, life events, and the onset of depressive symptoms, thereby informing more effective intervention strategies (29). Disaggregating depressive disorders into specific subtypes would allow for more targeted interventions and a better understanding of the nuanced impacts of different forms of depression. Future studies should aim to differentiate between major depressive disorder, dysthymia, and other depressive conditions to tailor treatment and support mechanisms appropriately (19).

Moreover, qualitative studies exploring cultural attitudes towards mental health in various regions can complement quantitative findings, offering a more holistic view of the factors influencing depressive disorder burden. Understanding cultural perceptions and stigma associated with mental health can inform culturally sensitive interventions and reduce barriers to care (44). Integrating environmental and societal variables, such as economic instability, social support networks, and access to mental health services, into future analyses can enhance the explanatory power of models assessing depressive disorder trends (45). Additionally, exploring the impact of global crises, such as the COVID-19 pandemic, on mental health should be a focal point for future studies to inform resilience-building strategies within mental health systems (46). On a policy level, the findings advocate for a global commitment to enhancing mental health infrastructure, particularly in underserved regions. International collaborations aimed at sharing best practices, resources, and research can facilitate more effective mental health interventions. Furthermore, policies should prioritize mental health as a critical component of public health, ensuring adequate funding, resource allocation, and integration of mental health services into primary care (19).

Conclusions

This study utilized GBD 2021 data to examine depressive disorders in WCBA from 1990 to 2021, uncovering a marked global increase with significant regional and age-related variations. Compared to 1990, the low SDI and low-middle SDI regions experienced the greatest increase in disease burden in 2021. The high SDI regions showed an overall upward trend during the 32 years. The rise in disease burden was particularly pronounced in the 45-49 age group globally and the 15-19 age group in high SDI regions. The absence of an association between SDI and depressive disorder burden suggests that multifaceted determinants, including cultural norms and social support systems, are critical in shaping mental health outcomes. These results underscore the necessity for targeted mental health interventions and strengthened healthcare infrastructure, especially in underserved areas. Indeed, it provides policymakers with actionable insights, enabling them to prioritize mental health resources and develop strategies tailored to specific age groups and regions. Future research should address data limitations and explore the long-term impact of unequal access to prevention, diagnosis, and treatment resources on mental health in different parts of the world, aiming to establish resilient mental health systems.

Abbreviations

WCBA: Women of Childbearing Age
GBD: Global Burden of Disease
DALYs: Disability-Adjusted Life Years
YLLs: Years of Life Lost
YLDs: Years Lived with Disability
UI: Uncertainty Intervals
SDI: Socio-Demographic Index
EAPC: Estimated Annual Percentage Change
CI: Confidence Intervals
GHDx: Global Health Data Exchange

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contribution

RYZ and XMJ: Writing – original draft, Conceptualization, Methodology, Formal Analysis, Visualization; BLF, JHZ and YXW: Writing – review & editing, Project administration, Validation; ZLW and GCY: Writing – review & editing, Funding acquisition, Supervision. All authors have read and approved the final version of the manuscript and agreed to be accountable for all aspects of the work.

Funding

The study was funded by National Natural Science Foundation of China (82374556), Shandong Province Traditional Chinese Medicine Science and Technology project (M-2022253), Shandong Provincial Medical and Health Science and Technology Project (202412040294), the Integration Development Strategy Project of Jinan City and College (JNSX2024046), and the clinical-basic joint innovation team project of Shandong First Medical University (No. CX202408).

Acknowledgments

The authors are deeply grateful to the collaborators of the GBD study 2021 for their work and data sharing.

Supplementary Material

Supplementary Material templates can be found in the Frontiers Word Templates file.

Data Availability Statement

The data utilized in this study were sourced from the GBD Study 2021, which provides comprehensive estimates of the global burden of diseases, injuries, and risk factors. The datasets supporting the findings of this study are publicly available and can be accessed through the GHDx platform (http://ghdx.healthdata.org/). All data used in this manuscript are consistent with the GBD 2021 methodology and reporting standards, ensuring transparency and reproducibility. No primary data collection was conducted, and all analyses were performed on secondary data provided by the GBD Study. The data used are disaggregated by sex, age, and geographical regions to support the stratified analyses. For further inquiries, please contact the GBD Collaborators or visit the GHDx platform for detailed instructions on data access.

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Not applicable.

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Fig. 1 Temporal trend of depressive disorders burden in WCBA in global and 5 territories. A. Percentage change in the prevalence, incidence, and DALYs of depressive disorders between 1990 and 2021. B. The EAPC in the prevalence, incidence, and DALY rates of depressive disorders from 1990 to 2021. C. Change in the prevalent rates of depressive disorders from 1990 to 2021. WCBA = Women of childbearing age, EAPC = Estimated annual percentage change, DALYs = Disability-adjusted life years, SDI = Socio-Demographic Index.

Fig. 2 Temporal trend of depressive disorders burden in WCBA in regions. A. Prevalent rates per 100,000 population in 1990 and 2021. B. Percentage change in the number of prevalent, incident, and DALY between 1990 and 2021. C. The EAPC for prevalent, incident, and DALY rates from 1990 to 2021.WCBA = Women of Childbearing Age, EAPC = Estimated Annual Percentage Change, DALY = Disability-Adjusted Life Year, SDI = Socio-Demographic Index.

 Fig. 3 Temporal trend of depressive disorders prevalent burden in WCBA globally. A. Percentage change in prevalent cases across 204 countries in 1990 and 2021. B. EAPC in prevalent rates across 204 countries from 1990 to 2021. WCBA = Women of Childbearing Age, EAPC = Estimated Annual Percentage Change.

Fig. 4 Temporal trend of depressive disorders prevalent burden in WCBA by age pattern in different regions. A. Distribution of prevalent cases across 7 age groups as percentages globally, in 5 SDI territories, and 21 GBD regions in 1990 and 2021. B. Percentage change in prevalent cases of 7 age groups globally and in 5 SDI territories in 1990 and 2021. C. EAPC of prevalent rates of 7 age groups globally and in 5 SDI territories from 1990 to 2021.WCBA = Women of Childbearing Age, EAPC = Estimated Annual Percentage Change, SDI = Socio-Demographic Index, GBD = Global Burden of Disease.

Fig. 5 Associations between SDI and the prevalence of depressive disorders in WCBA across global regions and countries. A. Temporal trend in the relationship between SDI and prevalent rates of depressive disorders in WCBA from 1990 to 2021 across 21 GBD regions. B. The relationship between SDI and prevalent rates of depressive disorders in WCBA across 204 GBD countries and territories in 2021.SDI = Socio-Demographic Index, GBD = Global Burden of Disease.

Table 1 The prevalent cases and rates of depressive disorders among WCBA in 1990 and 2021, and the trends from 1990 to 2021.

Location	Prevalent cases				Prevalent rates
	1990_millions (95% UI)	2021_millions (95%UI)	Percentage change(100%)		1990_per 100,000 (95% UI)	2021_per 100,000 (95% UI)	EAPC (95% CI)
Global	72.35 (63.63,84.07)	121.24 (104.69,142.43)	0.68		5409.59 (4757.83,6285.95)	6220.98 (5371.96,7308.26)	-0.02 (-0.17,0.14)
Low SDI	6.95 (5.92,8.39)	17.91 (15.15,21.73)	1.58		6220.59 (5303.39,7508.24)	6529.33 (5523.71,7920.21)	-0.22 (-0.34,-0.10)
Low-middle SDI	16.22 (14.01,19.22)	33.06 (28.24,39.58)	1.04		5942.14 (5133.65,7041.56)	6530.56 (5578.11,7818.00)	-0.29 (-0.49,-0.10)
Middle SDI	21.58 (18.93,25.06)	34.25 (29.81,39.83)	0.59		4826.40 (4235.27,5606.44)	5537.56 (4819.56,6439.61)	0.03 (-0.13,0.19)
High-middle SDI	14.31 (12.64,16.52)	17.38 (15.04,20.51)	0.21		5151.68 (4550.17,5948.93)	5694.63 (4928.95,6722.86)	-0.06 (-0.23,0.11)
High SDI	13.23 (11.70,14.99)	18.55 (16.17,21.44)	0.40		5837.10 (5158.74,6612.15)	7629.55 (6650.03,8819.17)	0.33 (0.15,0.50)
High-income Asia Pacific	1.60 (1.41,1.81)	1.61 (1.39,1.87)	0.01		3492.53 (3084.54,3950.92)	4220.09 (3652.65,4924.04)	0.22 (0.04,0.40)
High-income North America	5.24 (4.58,5.96)	8.70 (7.62,9.90)	0.67		7044.62 (6159.75,8014.74)	10350.16 (9070.74,11785.55)	0.45 (0.22,0.68)
Western Europe	6.68 (5.95,7.61)	7.76 (6.61,9.33)	0.16		6993.98 (6225.99,7960.00)	8325.44 (7095.16,10015.26)	0.19 (0.02,0.36)
Australasia	0.41 (0.36,0.47)	0.60 (0.49,0.75)	0.48		7598.14 (6621.33,8749.73)	8370.39 (6744.14,10337.36)	0.17 (0.03,0.30)
Andean Latin America	0.39 (0.33,0.48)	0.94 (0.76,1.17)	1.38		4163.65 (3453.48,5104.23)	5381.39 (4343.22,6721.89)	0.18 (-0.12,0.48)
Tropical Latin America	2.59 (2.24,3.04)	4.73 (4.03,5.53)	0.83		6491.11 (5604.95,7616.04)	7798.27 (6655.70,9122.37)	-0.22 (-0.55,0.11)
Central Latin America	1.78 (1.51,2.13)	4.31 (3.65,5.17)	1.43		4235.61 (3596.90,5086.17)	6326.59 (5352.16,7575.82)	0.97 (0.79,1.16)
Southern Latin America	0.71 (0.61,0.85)	1.13 (0.92,1.39)	0.59		5738.11 (4936.45,6855.52)	6473.18 (5282.94,7974.35)	-0.23 (-0.46,0.00)
Caribbean	0.58 (0.48,0.70)	0.79 (0.64,1.00)	0.37		6185.61 (5170.89,7460.83)	6541.12 (5292.75,8343.63)	-0.33 (-0.55,-0.12)
Central Europe	1.32 (1.13,1.56)	1.27 (1.07,1.51)	-0.04		4297.78 (3665.45,5065.50)	4918.48 (4152.19,5860.12)	-0.15 (-0.36,0.07)
Eastern Europe	2.86 (2.45,3.37)	3.12 (2.64,3.65)	0.09		5166.42 (4430.05,6101.49)	6464.51 (5471.40,7561.70)	0.07 (-0.15,0.28)
Central Asia	0.75 (0.62,0.90)	1.28 (1.05,1.57)	0.72		4443.18 (3709.54,5363.97)	5290.44 (4324.48,6473.68)	0.23 (0.09,0.37)
North Africa and Middle East	5.56 (4.69,6.78)	13.30 (11.00,16.31)	1.39		7121.58 (6005.00,8674.70)	8344.27 (6906.15,10236.44)	0.31 (0.22,0.41)
South Asia	15.17 (13.13,17.75)	31.20 (26.58,36.77)	1.06		5950.73 (5151.47,6964.10)	6313.76 (5378.48,7440.85)	-0.57 (-0.73,-0.40)
Southeast Asia	4.44 (3.79,5.16)	7.96 (6.82,9.29)	0.79		3691.84 (3153.91,4296.29)	4344.39 (3724.45,5073.59)	0.20 (0.08,0.31)
East Asia	15.02 (13.19,17.28)	13.47 (11.73,15.70)	-0.10		4505.86 (3955.42,5183.76)	4070.97 (3545.24,4745.60)	-0.51 (-0.67,-0.35)
Oceania	0.07 (0.05,0.08)	0.15 (0.12,0.19)	1.34		4236.98 (3517.26,5231.57)	4438.45 (3576.58,5542.11)	-0.01 (-0.06,0.03)
Western Sub-Saharan Africa	2.47 (2.10,3.01)	6.74 (5.71,8.16)	1.77		5667.68 (4815.87,6907.31)	5619.07 (4766.78,6809.00)	-0.16 (-0.24,-0.07)
Eastern Sub-Saharan Africa	2.86 (2.44,3.43)	7.58 (6.38,9.14)	1.65		6631.92 (5650.92,7951.63)	7079.99 (5959.38,8533.45)	-0.17 (-0.29,-0.05)
Central Sub-Saharan Africa	1.06 (0.87,1.33)	2.95 (2.36,3.78)	1.78		8592.02 (7039.79,10799.24)	9031.63 (7215.44,11573.59)	-0.01 (-0.11,0.08)
Southern Sub-Saharan Africa	0.80 (0.69,0.93)	1.66 (1.43,1.95)	1.08		6003.20 (5210.95,7002.01)	7656.95 (6568.26,8965.90)	0.44 (0.22,0.65)

Abbreviations: WCBA = Women of Childbearing Age, EAPC = Estimated Annual Percentage Change, CI = Confidence Intervals, UI = Uncertainty Intervals, SDI = Socio-Demographic Index.

Table 2 The prevalence of depressive disorders cases and rates among WCBA in 1990 and 2021, and the trends in age patterns from 1990 to 2021

Location	Age (years)	Prevalent cases				Prevalent rates
		1990_thousands (95% UI)	2021_thousands (95%UI)	percentage change(100%)		1990_per 100,000 (95% UI)	2021_per 100,000 (95% UI)	EAPC (95% CI)
Global	15-19	8599.33 (6529.06,11098.07)	12840.70 (9489.52,16836.97)	0.49		3365.18 (2555.02,4343.01)	4228.78 (3125.15,5544.86)	0.26 (0.08,0.44)
	20-24	12186.73 (9264.41,16382.32)	16718.17 (12607.12,23077.96)	0.37		4991.80 (3794.79,6710.35)	5691.25 (4291.75,7856.27)	-0.15 (-0.36,0.06)
	25-29	12010.97 (9765.94,14816.69)	17500.01 (14180.52,22147.97)	0.46		5457.06 (4437.05,6731.81)	6014.01 (4873.24,7611.32)	-0.19 (-0.38,-0.01)
	30-34	11130.37 (8817.95,13741.86)	18739.69 (14686.00,23575.26)	0.68		5854.73 (4638.37,7228.41)	6268.90 (4912.84,7886.52)	-0.21 (-0.37,-0.06)
	35-39	10993.55 (9079.52,13008.17)	19275.72 (15606.83,23134.15)	0.75		6338.04 (5234.56,7499.52)	6938.64 (5617.95,8327.55)	-0.15 (-0.30,0.00)
	40-44	9492.70 (7595.90,11510.35)	18420.97 (14521.67,22726.53)	0.94		6769.60 (5416.92,8208.46)	7425.12 (5853.39,9160.60)	-0.15 (-0.29,0.00)
	45-49	7932.18 (6739.18,9375.16)	17742.68 (14956.26,21023.65)	1.24		6970.24 (5921.91,8238.23)	7529.49 (6347.01,8921.84)	-0.14 (-0.25,-0.02)
	15-49	72345.83 (63629.46,84065.84)	121237.94 (104691.69,142427.51)	0.68		5409.59 (4757.83,6285.95)	6220.98 (5371.96,7308.26)	-0.02 (-0.17,0.14)
Low SDI	15-19	941.33 (687.82,1253.55)	2589.29 (1847.20,3448.88)	1.75		3746.02 (2737.17,4988.48)	4199.42 (2995.87,5593.55)	-0.05 (-0.19,0.09)
	20-24	1237.30 (922.49,1706.86)	3177.92 (2344.12,4386.63)	1.57		5696.37 (4247.02,7858.17)	6026.51 (4445.33,8318.68)	-0.23 (-0.36,-0.10)
	25-29	1198.15 (949.72,1537.10)	2963.54 (2287.67,3797.94)	1.47		6468.31 (5127.15,8298.14)	6727.83 (5193.45,8622.08)	-0.25 (-0.38,-0.13)
	30-34	1071.47 (819.43,1358.77)	2691.88 (2042.92,3438.49)	1.51		7044.09 (5387.09,8932.85)	7252.14 (5503.79,9263.56)	-0.29 (-0.42,-0.16)
	35-39	980.99 (783.55,1206.89)	2485.85 (1950.55,3049.18)	1.53		7613.67 (6081.30,9366.87)	7801.00 (6121.15,9568.84)	-0.29 (-0.42,-0.17)
	40-44	804.77 (612.03,1017.72)	2175.72 (1652.08,2740.33)	1.70		8116.51 (6172.66,10264.30)	8329.74 (6324.98,10491.32)	-0.28 (-0.41,-0.16)
	45-49	713.49 (581.37,859.96)	1826.48 (1487.34,2205.84)	1.56		8594.49 (7003.00,10358.88)	8794.94 (7161.91,10621.61)	-0.28 (-0.40,-0.15)
	15-49	6947.49 (5923.11,8385.60)	17910.68 (15152.15,21726.00)	1.58		6220.59 (5303.39,7508.24)	6529.33 (5523.71,7920.21)	-0.22 (-0.34,-0.10)
Low-middle SDI	15-19	1963.11 (1444.95,2589.89)	3586.93 (2621.82,4722.80)	0.83		3341.36 (2459.41,4408.19)	3967.80 (2900.21,5224.27)	-0.02 (-0.22,0.19)
	20-24	2801.46 (2089.27,3794.46)	4980.15 (3739.00,6957.18)	0.78		5371.47 (4005.93,7275.43)	5723.72 (4297.26,7995.93)	-0.43 (-0.63,-0.22)
	25-29	2712.37 (2149.85,3419.08)	5126.95 (4071.10,6649.94)	0.89		6043.22 (4789.93,7617.81)	6310.33 (5010.78,8184.84)	-0.48 (-0.68,-0.27)
	30-34	2489.31 (1934.68,3114.15)	5111.06 (3974.40,6453.25)	1.05		6648.13 (5166.89,8316.89)	6915.44 (5377.50,8731.47)	-0.49 (-0.70,-0.29)
	35-39	2369.02 (1904.55,2873.98)	5158.97 (4137.02,6311.42)	1.18		7395.31 (5945.38,8971.62)	7747.74 (6212.97,9478.47)	-0.46 (-0.66,-0.26)
	40-44	2074.42 (1603.55,2590.02)	4821.87 (3735.00,6077.13)	1.32		7995.60 (6180.68,9982.93)	8359.44 (6475.18,10535.62)	-0.44 (-0.63,-0.25)
	45-49	1807.39 (1500.69,2140.83)	4276.36 (3534.58,5147.11)	1.37		8327.03 (6913.97,9863.24)	8650.11 (7149.66,10411.44)	-0.44 (-0.62,-0.26)
	15-49	16217.07 (14010.59,19217.57)	33062.30 (28240.33,39580.27)	1.04		5942.14 (5133.65,7041.56)	6530.56 (5578.11,7818.00)	-0.29 (-0.49,-0.10)
Middle SDI	15-19	2661.20 (2016.73,3429.71)	2996.60 (2228.33,3958.64)	0.13		2886.20 (2187.24,3719.69)	3411.50 (2536.86,4506.75)	0.02 (-0.18,0.22)
	20-24	3897.76 (2939.78,5208.52)	4167.32 (3133.95,5719.25)	0.07		4412.62 (3328.10,5896.52)	4808.39 (3616.06,6599.07)	-0.24 (-0.47,-0.01)
	25-29	3632.91 (2937.55,4460.99)	4667.26 (3730.72,5825.03)	0.29		4849.84 (3921.55,5955.30)	5151.58 (4117.86,6429.50)	-0.23 (-0.42,-0.03)
	30-34	3171.73 (2519.31,3881.75)	5417.30 (4329.19,6714.34)	0.71		5282.78 (4196.12,6465.39)	5485.70 (4383.84,6799.12)	-0.21 (-0.38,-0.04)
	35-39	3243.87 (2678.10,3829.66)	5735.88 (4680.76,6820.36)	0.77		5852.03 (4831.37,6908.81)	6258.62 (5107.34,7441.94)	-0.18 (-0.35,-0.01)
	40-44	2705.74 (2163.60,3290.22)	5570.73 (4394.06,6843.39)	1.06		6403.20 (5120.20,7786.38)	6805.96 (5368.38,8360.81)	-0.23 (-0.38,-0.08)
	45-49	2263.90 (1925.92,2682.12)	5692.92 (4797.07,6698.95)	1.52		6679.54 (5682.35,7913.50)	7018.66 (5914.20,8258.99)	-0.22 (-0.33,-0.11)
	15-49	21577.11 (18934.36,25064.37)	34248.00 (29807.36,39826.86)	0.59		4826.40 (4235.27,5606.44)	5537.56 (4819.56,6439.61)	0.03 (-0.13,0.19)
High-middle SDI	15-19	1552.69 (1199.65,1968.70)	1374.36 (986.24,1824.56)	-0.12		3278.45 (2533.02,4156.86)	3991.28 (2864.15,5298.69)	0.28 (0.05,0.52)
	20-24	2227.99 (1703.45,2940.56)	1783.56 (1310.16,2451.01)	-0.20		4628.06 (3538.46,6108.23)	5010.60 (3680.68,6885.69)	-0.40 (-0.70,-0.09)
	25-29	2291.49 (1871.37,2790.73)	2078.08 (1660.47,2638.85)	-0.09		5009.19 (4090.80,6100.53)	5155.36 (4119.34,6546.54)	-0.40 (-0.61,-0.18)
	30-34	2254.01 (1799.32,2742.21)	2738.53 (2183.99,3414.17)	0.22		5399.04 (4309.92,6568.42)	5318.49 (4241.51,6630.65)	-0.34 (-0.49,-0.19)
	35-39	2341.31 (1951.25,2749.68)	2981.75 (2407.15,3571.05)	0.27		5927.84 (4940.27,6961.76)	6017.54 (4857.94,7206.84)	-0.26 (-0.42,-0.11)
	40-44	1984.34 (1587.49,2400.57)	3036.72 (2406.84,3773.87)	0.53		6454.34 (5163.55,7808.20)	6671.28 (5287.51,8290.70)	-0.26 (-0.40,-0.11)
	45-49	1657.16 (1414.14,1948.15)	3382.91 (2846.11,4005.19)	1.04		6759.12 (5767.89,7946.00)	7014.42 (5901.36,8304.70)	-0.21 (-0.31,-0.11)
	15-49	14308.99 (12638.28,16523.37)	17375.91 (15039.61,20513.34)	0.21		5151.68 (4550.17,5948.93)	5694.63 (4928.95,6722.86)	-0.06 (-0.23,0.11)
High SDI	15-19	1473.69 (1141.94,1852.61)	2284.17 (1760.64,2863.46)	0.55		4624.21 (3583.23,5813.20)	7851.58 (6052.00,9842.84)	1.22 (0.99,1.46)
	20-24	2012.09 (1584.83,2619.94)	2596.80 (1957.64,3583.08)	0.29		5992.24 (4719.82,7802.50)	8237.62 (6210.07,11366.33)	0.37 (0.15,0.59)
	25-29	2166.17 (1791.75,2555.06)	2651.35 (2147.43,3283.98)	0.22		6042.99 (4998.45,7127.88)	7671.92 (6213.78,9502.50)	0.09 (-0.12,0.31)
	30-34	2134.41 (1757.03,2542.83)	2767.46 (2162.50,3447.64)	0.30		6015.04 (4951.52,7166.02)	7393.05 (5776.95,9210.10)	0.02 (-0.18,0.21)
	35-39	2049.10 (1757.11,2368.09)	2899.41 (2433.26,3430.75)	0.42		6127.83 (5254.65,7081.80)	7643.65 (6414.73,9044.41)	0.19 (0.03,0.35)
	40-44	1914.92 (1609.65,2282.25)	2802.15 (2261.19,3383.33)	0.46		6133.02 (5155.32,7309.49)	7632.89 (6159.34,9215.97)	0.30 (0.15,0.44)
	45-49	1482.73 (1274.55,1729.31)	2550.51 (2161.27,2985.68)	0.72		5868.32 (5044.41,6844.24)	7103.29 (6019.24,8315.25)	0.30 (0.19,0.42)
	15-49	13233.10 (11695.20,14990.19)	18551.85 (16170.07,21444.50)	0.40		5837.10 (5158.74,6612.15)	7629.55 (6650.03,8819.17)	0.33 (0.15,0.50)

Abbreviations: WCBA Women of Childbearing Age, EAPC Estimated Annual Percentage Change, CI=Confidence Intervals, UI=Uncertainty Intervals, SDI = Socio-Demographic Index