Global trends of depressive disorders among women of reproductive age from 1990 to 2021: a systematic analysis of burden, sociodemographic disparities, and health workforce correlations

Abstract

Background

Depressive disorders significantly impact women of reproductive age (15–49 years), who face unique biological and social pressures—such as hormonal changes and caregiving responsibilities—that elevate their mental health risks. Despite rising prevalence, regional disparities in burden remain poorly understood. Moreover, no studies to date have examined the relationship between depressive disorders in women of reproductive age and different categories of health workforce. This study examines global, regional, and national trends from 1990 to 2021, highlighting socio-demographic disparities and exploring correlations with health workforce distribution.

Methods

Using data from the Global Burden of Disease Study, we examined the prevalence, incidence, and disability-adjusted life years (DALYs) of depression in women of childbearing age (15–49 years) across 204 countries and territories from 1990 to 2021. Long-term trends were assessed through estimated annual percentage change (EAPC), while decomposition analyses identified drivers of disease burden changes. We also analyzed correlations between depressive disorder burden and various health workforce categories using data from the GBD 2019 Health Workforce Collaborators.

Results

From 1990 to 2021, the global prevalence of depression in women of reproductive age rose by 67.58%, incidence by 71.44%, and DALYs by 69.08%. Notably, this burden increased dramatically during the COVID-19 pandemic, with prevalence rising by 17.86%, incidence by 24.51%, and DALYs by 20.80% between 2019–2021 alone. Regions with low sociodemographic index (SDI) saw the largest increase in absolute cases (157.80% for prevalence), while high SDI regions experienced the fastest rise in age-standardized rates (32.45% for prevalence). Among all SDI levels, the 15–19 age group exhibited the greatest increase. Decomposition analyses indicated that population growth primarily drove the increased disease burden, though epidemiological changes played a larger role in high SDI regions. Our analysis revealed significant correlations between depressive disorder burden and health workforce distribution. Notably, countries with high depressive disorder burden, such as Georgia, might benefit from increasing the number of Audiologists and Counsellors while optimizing the role of Medical Assistants and Community Health Workers in detection and referral.

Conclusion

The global burden of depression among women of childbearing age is increasing significantly, with a marked acceleration during the COVID-19 pandemic period. Notable disparities exist across regions and age groups, with differential impacts in pre-pandemic versus pandemic timeframes. The correlations between health workforce categories and depressive disorder burden underscore the need for targeted interventions and resource allocation. These findings highlight the urgent need for strengthened prevention and intervention efforts, particularly those tailored to socioeconomic differences and focused on this vulnerable population, with special attention to pandemic-related mental health challenges.

Introduction

Depressive disorders have become a pressing global health concern [1], particularly affecting women of reproductive age (15–49 years) [2]. This demographic faces unique vulnerabilities due to a combination of biological factors, such as hormonal fluctuations associated with menstruation, pregnancy, and postpartum stages, and societal roles, including caregiving responsibilities [3, 4]. The importance of addressing these distinctive risks cannot be overstated [5], as the heightened burden of depression among women in this age group not only affects their personal well-being but also has far-reaching implications for family dynamics and community health [6, 7].

While substantial progress has been made in understanding the risk factors associated with depression, significant knowledge gaps persist regarding its impact on women of reproductive age across various sociodemographic contexts. Previous research has often been limited in scope, focusing on specific countries or narrow age ranges, thus providing only fragmented insights into the global and regional prevalence, incidence, and disability-adjusted life years (DALYs) associated with depressive disorders in this population. As the global burden of depression continues to rise, particularly in regions experiencing rapid population growth [8] and socioeconomic disparities [9], there is an urgent need for comprehensive research to inform targeted interventions [10].

Moreover, the relationship between mental health outcomes and the distribution of healthcare resources, particularly human resources for health (HRH), remains understudied. The World Health Organization has emphasized the critical role of health workforce in achieving universal health coverage and addressing global health challenges [11]. However, to date, no studies have specifically examined the correlation between depressive disorders in women of reproductive age and different categories of health workforce. This gap in knowledge is particularly concerning given the potential impact of healthcare professional availability on mental health outcomes [12].

The health workforce, comprising various categories such as physicians, nurses, midwives, community health workers, and mental health specialists, plays a crucial role in the prevention, detection, and treatment of mental health disorders [13]. Understanding the relationship between the distribution of these different health worker categories and the burden of depression among women of reproductive age could provide valuable insights for health system strengthening and resource allocation16. This is especially pertinent in the context of global efforts to improve mental health services and reduce the treatment gap for depression [14].

This study aims to address these knowledge gaps by conducting a comprehensive analysis of global trends in depression among women of reproductive age from 1990 to 2021, utilizing data from the Global Burden of Disease Study. We assess prevalence, incidence, and DALYs across diverse sociodemographic regions9,10, and for the first time, explore the correlations between these mental health outcomes and various categories of health workforce. By examining these relationships, we seek to provide novel insights into the potential impact of health workforce distribution on depressive disorders in this vulnerable population [15, 16]. Our findings have the potential to guide targeted efforts in mental health service delivery, resource allocation, and workforce development, ultimately contributing to the global effort to address the growing burden of depression among women of reproductive age.

Methods

Data source

This study utilized data from the Global Burden of Disease Study 2021 (GBD 2021) database (https://ghdx.healthdata.org/gbd-2021). The GBD 2021 is a comprehensive, systematic effort to quantify health loss from hundreds of diseases, injuries, and risk factors worldwide. It provides estimates for 204 countries and territories, 371 diseases and injuries, and 88 risk factors [17–19]. We extracted data on incidence (new cases per year), prevalence (total existing cases), and disability-adjusted life years (DALYs)( a measure of overall disease burden, expressed as the number of years lost due to ill-health, disability, or early death) [17] For each of these measures, we obtained both absolute numbers and rates per 100,000 population. The GBD 2021 uses various data sources, including vital registration systems, sample registration systems, household surveys, censuses, and other demographic surveillance to compile these estimates.

Human Resources for Health (HRH) data were obtained from the GBD 2019 Health Workforce Collaborators, covering the period from 1990 to 2019. The dataset encompassed a wide range of health worker categories, including physicians, dentistry personnel, pharmaceutical personnel, nursing and midwifery professionals, and various allied health professionals. Specifically, it included data on physicians, clinical officers, community health workers, dentists, pharmacists, emergency medical workers, audiologists, counsellors, dieticians, nutritionists, environmental health officers, medical lab technicians, optometrists, personal care workers, psychologists, radiographers, physical therapists, and traditional or complementary practitioners. The density values for each category represent workers per 10,000 population [11].

Disease definition

Depressive disorders, as defined in the GBD 2021 study, fall under the category of mental disorders within non-communicable diseases. According to the International Classification of Diseases 10th revision (ICD-10), depressive disorders are coded as F32 (depressive episode) and F33 (recurrent depressive disorder) under mood [affective] disorders (F30-F39). In ICD-11, they are classified as 6A70 (single episode depressive disorder) and 6A71 (recurrent depressive disorder) under mood disorders.

Statistical analysis

All data processing, statistical analyses, and visualizations were performed using R version 4.4.1. The following methods were employed:

• Health Inequality Analysis: We assessed health inequalities using the Slope Index of Inequality (SII) and the Concentration Index (CI). The SII measures absolute inequality across socioeconomic groups, while the CI quantifies relative inequality, ranging from −1 to 1, with 0 indicating perfect equality. Both indices were calculated using the 'health disparities' package in R [20].

• Decomposition Analysis: To investigate factors contributing to changes in disease burden over time, we performed a decomposition analysis. This method partitioned the total change in DALYs into contributions from population growth, population aging, and epidemiological changes, following the approach described by Das Gupta [21].

• Trend Analysis: We used LOESS smoothing to visualize trends and conducted Spearman's correlation tests to quantify relationships between disease burden indicators and both SDI and Human Resources for Health (HRH) [22].

Age-standardized rates (ASRs) were calculated using the GBD 2021 global age-standard population. To quantify temporal trends, we employed the estimated annual percentage change (EAPC) model, fitting a linear regression of the natural logarithm of ASRs against time. The EAPC and its 95% confidence interval were derived from the regression coefficient and its standard error [23].

We calculated 95% uncertainty intervals (UIs) for all estimates using the 2.5th and 97.5th percentiles of 1000 draws from the posterior distribution of each estimate. To assess differences between groups, we used two-tailed t-tests for continuous variables and chi-square tests for categorical variables. All statistical tests were two-sided, and p-values less than 0.05 were considered statistically significant.

Results

Global burden of depressive disorders among women of reproductive age from 1990 to 2021

Between 1990 and 2021, the global burden of depression among women of childbearing age showed a significant upward trend (Fig. 1A–F). According to the Global Burden of Disease Study, depression prevalence rose from 72,345,829 cases (95% UI: 57,791,969–89,932,616) in 1990 to 121,237,941 cases (95% UI: 96,047,914–152,522,485) in 2021, marking a 67.58% increase (Table 1). This increase occurred in two distinct periods: a 42.18% increase from 1990 to 2019, followed by a sharper 17.86% rise from 2019 to 2021 during the COVID-19 pandemic. The age-standardized prevalence rate showed an overall increase of 11.33% from 5,545.28 to 6,173.45 cases per 100,000 people, with a notable pattern of declining by 4.48% between 1990–2019 but then increasing by 16.55% during 2019–2021.

Fig. 1.

Global and SDI-specific trends in depressive disorders among women of reproductive age, 1990–2021. A Percentage change in cases of prevalence, incidence, and DALYs for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. B Percentage change in age-standardized rates (ASR) of prevalence, incidence, and DALYs for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. C Estimated Annual Percentage Change (EAPC) in prevalence, incidence, and DALYs for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. D Trends in age-standardized prevalence rates of depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. E Trends in age-standardized incidence rates of depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. F Trends in age-standardized DALY rates of depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021

Table 1.

Global prevalence of depressive disorders among women of reproductive age in 1990 and 2021, with trends from 1990 to 2021

Location	prevalence cases						prevalence rates
	1990_cases(95% UI)	2019_cases(95% UI)	2021_cases(95% UI)	Percentage change in case(1990–2019)	Percentage change in case(2019–2021)	Percentage change in case(1990–2021)	1990_per 100 000(95% UI)	2019_per 100 000(95% UI)	2021_per 100 000(95% UI)	Percentage change in ASRs(1990–2019)	Percentage change in ASRs(2019–2021)	Percentage change in ASRs(1990–2021)	EAPC(1990–2021)
Global	72,345,829 (57,791,969,89,932,616)	102,864,601 (82,235,712,128,085,493)	121,237,941 (96,047,914,152,522,485)	42.18	17.86	67.58	5545.28 (4447.47,6858.89)	5296.68 (4228.2,6605.37)	6173.45 (4883.27,7781.73)	−4.48	16.55	11.33	−0.12 (−0.27 to 0.03)
Low SDI	6,947,491 (5,356,401,8,940,837)	15,162,392 (11,675,728,19,497,035)	17,910,681 (13,611,880,23,067,289)	118.24	18.13	157.80	6575.38 (5096.98,8389.51)	6191.02 (4798.87,7901.86)	6848.6 (5241.12,8747.39)	−5.85	10.62	4.16	−0.25 (−0.37 to −0.13)
Low-middle SDI	16,217,075 (12,627,529,20,522,420)	27,368,024 (21,530,812,34,595,841)	33,062,302 (25,812,931,42,318,827)	68.76	20.81	103.87	6257.67 (4900.48,7860.3)	5679.7 (4477.4,7156.23)	6628.44 (5185.01,8458.5)	−9.24	16.70	5.93	−0.42 (−0.6 to −0.23)
Middle SDI	21,577,106 (17,180,991,26,782,965)	29,406,041 (23,645,891,36,312,272)	34,248,000 (27,294,081,42,579,971)	36.28	16.47	58.72	5051.06 (4048.65,6224.12)	4653.04 (3729.44,5765.15)	5421.62 (4305.2,6772.18)	−7.88	16.52	7.34	−0.19 (−0.35 to −0.03)
High-middle SDI	14,308,992 (11,526,669,17,540,610)	15,463,566 (12,475,157,19,026,485)	17,375,912 (13,800,963,21,678,707)	8.07	12.37	21.43	5218.03 (4212.45,6381.87)	4742.1 (3796.97,5883.52)	5475.98 (4303.49,6906.98)	−9.12	15.48	4.94	−0.24 (−0.41 to −0.07)
High SDI	13,233,096 (10,916,855,15,950,096)	15,389,302 (12,379,940,18,956,630)	18,551,854 (14,883,922,22,977,921)	16.29	20.55	40.19	5796.66 (4770.5,7004.28)	6266.41 (5011.33,7761.03)	7677.86 (6120.77,9579.04)	8.10	22.52	32.45	0.36 (0.18 to 0.53)
High-income Asia Pacific	1,597,489 (1,307,983,1,938,617)	1,395,825 (1,143,567,1,698,974)	1,605,269 (1,294,426,1,981,401)	−12.62	15.01	0.49	3508.02 (2868.29,4262.88)	3580.05 (2907.95,4382.12)	4293.05 (3428.65,5331.89)	2.05	19.92	22.38	0.27 (0.11 to 0.44)
High-income North America	5,238,893 (4,277,201,6,352,283)	6,882,616 (5,626,079,8,382,922)	8,696,420 (7,044,881,10,654,971)	31.38	26.35	66.00	7007.87 (5700.86,8531.26)	8253.11 (6721.74,10,085.03)	10,443.59 (8434.35,12,835.72)	17.77	26.54	49.03	0.49 (0.26 to 0.71)
Western Europe	6,682,642 (5,518,996,8,062,327)	6,531,480 (5,157,989,8,205,490)	7,757,610 (6,002,388,9,979,256)	−2.26	18.77	16.09	6951.88 (5732.39,8402.04)	6807.94 (5326.62,8627.05)	8254.89 (6326.11,10,733.02)	−2.07	21.25	18.74	0.16 (−0.01 to 0.33)
Australasia	407,826 (325,398,503,080)	547,725 (417,799,707,803)	604,265 (449,022,803,272)	34.30	10.32	48.17	7599.59 (6055.54,9386.46)	7849.92 (5940.77,10,180.83)	8462.14 (6257.29,11,300.83)	3.29	7.80	11.35	0.2 (0.07 to 0.33)
Andean Latin America	394,891 (295,653,523,282)	678,996 (511,046,893,738)	939,180 (689,169,1,261,821)	71.95	38.32	137.83	4327.74 (3255.28,5700.01)	4023.77 (3028.59,5289)	5361.92 (3933.81,7199.44)	−7.02	33.26	23.90	0.05 (−0.24 to 0.34)
Tropical Latin America	2,589,371 (2,039,979,3,260,975)	3,619,233 (2,949,063,4,408,518)	4,726,756 (3,693,421,5,948,772)	39.77	30.60	82.54	6672.9 (5284.8,8352.26)	5903.82 (4801.73,7209.36)	7633.97 (5948.15,9648.14)	−11.53	29.31	14.40	−0.37 (−0.68 to −0.05)
Central Latin America	1,775,285 (1,366,312,2,301,496)	3,468,221 (2,667,736,4,420,713)	4,314,393 (3,286,064,5,582,291)	95.36	24.40	143.03	4465.04 (3447.45,5739.42)	5114.19 (3933.29,6518.52)	6293.78 (4790.95,8147.72)	14.54	23.07	40.96	0.8 (0.62 to 0.98)
Southern Latin America	711,119 (555,055,908,562)	872,822 (679,042,1,111,505)	1,128,264 (850,784,1,473,212)	22.74	29.27	58.66	5733.18 (4480.59,7315.73)	5077.73 (3945.12,6470.62)	6509.67 (4896.55,8513.81)	−11.43	28.20	13.54	−0.22 (−0.45 to 0.01)
Caribbean	576,590 (435,398,752,330)	644,894 (479,367,859,575)	786,812 (570,492,1,071,005)	11.85	22.01	36.46	6320.15 (4798.37,8195.97)	5350.23 (3970.45,7138.74)	6502.68 (4706.8,8863.19)	−15.35	21.54	2.89	−0.42 (−0.63 to −0.21)
Central Europe	1,319,933 (1,039,066,1,659,223)	1,062,096 (836,065,1,344,044)	1,266,677 (987,756,1,614,912)	−19.53	19.26	−4.03	4241.09 (3332.5,5343.85)	3789.46 (2960,4828.19)	4677.25 (3608.8,6022.43)	−10.65	23.43	10.28	−0.3 (−0.5 to −0.09)
Eastern Europe	2,856,734 (2,252,291,3,606,395)	2,528,850 (2,005,339,3,150,119)	3,118,657 (2,437,254,3,945,269)	−11.48	23.32	9.17	5063.54 (3985.84,6407.4)	4794.97 (3780.67,6029.17)	6116.21 (4742.5,7829.81)	−5.30	27.55	20.79	−0.08 (−0.28 to 0.12)
Central Asia	745,428 (571,381,964,005)	1,088,245 (836,377,1,408,354)	1,283,739 (965,341,1,682,020)	45.99	17.96	72.22	4607.34 (3546.52,5930.09)	4480.46 (3435.94,5808.49)	5231.16 (3920.92,6874.97)	−2.75	16.75	13.54	0.07 (−0.06 to 0.21)
North Africa and Middle East	11,127,458 (8,462,334,14,576,477)	22,667,975 (16,938,646,30,147,463)	26,592,449 (19,625,519,35,520,625)	103.71	17.31	138.98	7378.92 (5652.37,9576.45)	7291.26 (5445.17,9699.37)	8318.57 (6135.59,11,121.61)	−1.19	14.09	12.73	0.17 (0.05 to 0.3)
South Asia	30,335,899 (23,908,513,37,968,637)	50,952,135 (40,653,956,63,391,548)	62,397,219 (49,237,763,78,530,813)	67.96	22.46	105.69	6252.32 (4948,7775.32)	5405.65 (4320.55,6710.52)	6390.39 (5049.98,8025.78)	−13.54	18.22	2.21	−0.7 (−0.93 to −0.46)
Southeast Asia	4,437,172 (3,476,631,5,619,220)	6,850,804 (5,416,277,8,614,904)	7,958,996 (6,238,354,10,098,326)	54.40	16.18	79.37	3875.31 (3052.48,4886.27)	3738.77 (2953.17,4705.18)	4294.28 (3358.87,5457.74)	−3.52	14.86	10.81	−0.02 (−0.13 to 0.1)
East Asia	15,021,789 (12,122,428,18,400,594)	13,562,794 (11,061,519,16,456,903)	13,471,029 (10,977,067,16,340,997)	−9.71	−0.68	−10.32	4689.83 (3804.73,5717.47)	3616.48 (2923.66,4414.4)	3700 (2994.89,4515.91)	−22.89	2.31	−21.11	−0.93 (−1.08 to −0.78)
Oceania	65,837 (49,257,87,079)	139,981 (105,028,186,007)	154,056 (114,111,205,084)	112.62	10.05	134.00	4395.87 (3303.6,5788.5)	4272.44 (3213.93,5667.94)	4495.59 (3337.06,5970.94)	−2.81	5.22	2.27	−0.08 (−0.12 to −0.04)
Western Sub-Saharan Africa	2,471,385 (1,907,759,3,177,806)	6,031,916 (4,665,612,7,741,586)	6,736,694 (5,165,592,8,681,684)	144.07	11.68	172.59	6101.55 (4749.55,7767.06)	5724.55 (4462.28,7289.93)	5969.48 (4614.72,7618.2)	−6.18	4.28	−2.16	−0.21 (−0.29 to −0.14)
Eastern Sub-Saharan Africa	2,861,716 (2,214,635,3,671,494)	6,300,723 (4,841,253,8,087,356)	7,582,726 (5,752,884,9,816,098)	120.17	20.35	164.97	7087.75 (5520.1,9011.64)	6633.62 (5136.59,8450.74)	7444.18 (5696.51,9554.62)	−6.41	12.22	5.03	−0.21 (−0.31 to −0.1)
Central Sub-Saharan Africa	1,062,099 (780,556,1,423,343)	2,536,250 (1,862,292,3,387,786)	2,949,079 (2,121,735,4,037,268)	138.80	16.28	177.67	8977.33 (6650.3,11,919.06)	8663.78 (6410.22,11,491.61)	9386.69 (6800.91,12,768.75)	−3.49	8.34	4.56	−0.01 (−0.1 to 0.07)
Southern Sub-Saharan Africa	797,950 (635,515,988,606)	1,311,074 (1,046,974,1,626,210)	1,662,484 (1,314,155,2,086,075)	64.31	26.80	108.34	6388.08 (5117.99,7859.14)	6209.43 (4961.96,7694.14)	7672.13 (6067.56,9620.79)	−2.80	23.56	20.10	0.26 (0.07 to 0.46)

Incidence followed a similar trend, rising 71.44% from 77,722,841 cases (95% UI: 58,860,491–102,539,344) in 1990 to 133,248,593 cases (95% UI: 99,032,450–177,876,463) in 2021 (Table S1). This comprised a 37.69% increase from 1990 to 2019 and an accelerated 24.51% increase during the pandemic period of 2019–2021. The age-standardized incidence rate increased by 15.42% overall, showing a 6.28% decrease from 1990–2019 followed by a substantial 23.16% increase from 2019–2021. Disability-adjusted life years (DALYs) due to depression also rose substantially, from 12,445,336 (95% UI: 8,084,219–18,023,437) in 1990 to 21,042,424 (95% UI: 13,468,194–30,593,481) in 2021–a 69.08% increase (Table S2). This increase occurred as a 39.96% rise from 1990–2019 and a 20.80% rise from 2019–2021. Age-standardized DALY rates increased by 13.14% overall, with a 5.32% decrease from 1990–2019 but a sharp 19.49% increase during the pandemic period.

Sociodemographic disparities in the global burden of depressive disorders among women of reproductive age from 1990–2021

Between 1990 and 2021, the burden of depression among women of childbearing age varied significantly across sociodemographic index (SDI) regions (Fig. 1A–F). Prevalence showed the largest increase in low SDI regions, rising from 6,947,491 cases in 1990 to 17,910,681 in 2021 (157.8%) (Table 1). This increase comprised a substantial 118.24% rise from 1990–2019, followed by an additional 18.13% increase from 2019–2021. In contrast, high-middle SDI areas had the smallest rise in cases (21.43%), with only 8.07% growth from 1990–2019 and 12.37% from 2019–2021.

The high SDI region experienced the most substantial growth in age-standardized prevalence rates (ASRs) at 32.45%. Notably, this included an 8.10% increase from 1990–2019 followed by a sharper 22.52% rise during the pandemic period. Other SDI regions saw ASR increases of less than 8% over the entire period, with most regions showing declining ASRs from 1990–2019 but increases from 2019–2021. Estimated Annual Percentage Change (EAPC) data indicated that only the high SDI region had a positive trend (0.36, 95% CI: 0.18–0.53), with the sharpest decline in low-to-medium SDI regions (−0.42, 95% CI: −0.6 to −0.23) (Table 1).

Incidence patterns reflected similar trends, with low SDI regions experiencing a 162.13% increase in cases (112.45% from 1990–2019 and 23.39% from 2019–2021), while high-middle SDI areas showed the smallest rise at 19.31% (1.25% from 1990–2019 and 17.85% from 2019–2021) (Table S1). The high SDI region also saw the most significant change in age-standardized incidence rates, increasing by 46.31% overall (13.18% from 1990–2019 and 29.26% from 2019–2021). The highest EAPC increase in incidence was in the high SDI region (0.54, 95% CI: 0.31–0.77), while low-intermediate SDI areas showed the steepest decline (−0.6, 95% CI: −0.86 to −0.34) (Table S1).

Disability-adjusted life years (DALYs) data mirrored these findings. The number of DALYs rose most in low SDI regions (161.85%), with increases of 117.45% from 1990–2019 and 20.42% from 2019–2021. The smallest increase was in high-middle SDI areas (20.00%), with only 4.74% from 1990–2019 and 14.57% from 2019–2021 (Table S2). Age-standardized DALY rates saw a 38.92% increase in high SDI regions (10.53% from 1990–2019 and 25.69% from 2019–2021), while EAPC data revealed positive growth only in the high SDI region (0.44, 95% CI: 0.25–0.64), with the fastest decline in low-to-medium SDI areas (−0.49, 95% CI: −0.71 to −0.27) (Table S2).

Regional disparities in the burden of depressive disorders among women of reproductive age from 1990 to 2021

From 1990 to 2021, the global burden of depression among women of reproductive age varied significantly across 21 regions (Fig. 2A–F). Prevalence saw the largest increase in Central Sub-Saharan Africa (177.67%), comprising a 138.80% rise from 1990–2019 and a 16.28% increase from 2019–2021. In contrast, East Asia experienced a 10.32% decrease, with a 9.71% decline from 1990–2019 and a further 0.68% reduction from 2019–2021 (Table 1).

Fig. 2.

Regional comparisons of depressive disorders burden among women of reproductive age, 1990 vs 2021. A Comparison of age-standardized prevalence rates for depressive disorders among women of reproductive age globally, in 5 SDI regions, and 21 GBD regions in 1990 and 2021. B Comparison of age-standardized incidence rates for depressive disorders among women of reproductive age globally, in 5 SDI regions, and 21 GBD regions in 1990 and 2021. C Comparison of age-standardized DALY rates for depressive disorders among women of reproductive age globally, in 5 SDI regions, and 21 GBD regions in 1990 and 2021. D Percentage change in cases of prevalence, incidence, and DALYs for depressive disorders among women of reproductive age in 21 GBD regions, 1990–2021. E Percentage change in age-standardized rates (ASR) of prevalence, incidence, and DALYs for depressive disorders among women of reproductive age in 21 GBD regions, 1990–2021. F Estimated Annual Percentage Change (EAPC) in prevalence, incidence, and DALYs for depressive disorders among women of reproductive age in 21 GBD regions, 1990–2021

Age-standardized prevalence rates (ASRs) rose most significantly in high-income North America (49.03%), with a 17.77% increase from 1990–2019 and a 26.54% rise from 2019–2021. Conversely, rates fell in East Asia by 21.11% overall, declining by 22.89% from 1990–2019 before slightly increasing by 2.31% from 2019–2021. Estimated Annual Percentage Change (EAPC) data showed Central Latin America with the fastest growth (0.8, 95% CI: 0.62–0.98) and East Asia with the sharpest decline (−0.93, 95% CI: −1.08 to −0.78) (Table 1).

Incidence trends mirrored prevalence patterns: Central Sub-Saharan Africa saw the largest increase in cases (180.76%), with 135.99% growth from 1990–2019 and 18.97% from 2019–2021. East Asia declined by 28.67% overall, with a 24.81% decrease from 1990–2019 and a further 5.13% reduction from 2019–2021 (Table S1). The most pronounced ASR changes occurred in high-income North America (77.59% increase), comprising a 32.35% rise from 1990–2019 and a 34.17% increase from 2019–2021. East Asia showed a 33.01% decrease in ASRs, with a 31.79% decline from 1990–2019 and a further 1.78% reduction from 2019–2021. Central Latin America showed the highest EAPC in incidence (1.02, 95% CI: 0.8–1.24), while East Asia declined the fastest (−1.4, 95% CI: −1.65 to −1.16) (Table S1).

For disability-adjusted life years (DALYs), Central Sub-Saharan Africa recorded the highest increase (181.62%), with 140.18% growth from 1990–2019 and 17.25% from 2019–2021. East Asia decreased by 19.31% overall, with a 17.04% reduction from 1990–2019 and a further 2.73% decline from 2019–2021 (Table S2). High-income North America exhibited the largest ASR increase (61.67%), comprising a 24.28% rise from 1990–2019 and a 30.09% increase from 2019–2021. East Asia saw a 26.86% decrease in ASRs, with a 27.08% decline from 1990–2019 followed by a slight 0.29% increase from 2019–2021. EAPC analysis indicated the most rapid DALY increase in Central Latin America (0.91, 95% CI: 0.71–1.11), with East Asia experiencing the fastest decline (−1.15, 95% CI: −1.34 to −0.95) (Table S2).

Trends in depressive disorders among women of reproductive age across 204 countries from 1990 to 2021

Between 1990 and 2021, the epidemiological profile of depression among women of childbearing age exhibited significant geographic and temporal variations across 204 countries (Fig. 3A–I). In terms of prevalence (Table S3), Georgia showed the largest increase in age-standardized prevalence rate (ASR), rising from 7,195.82 cases per 100,000 in 1990 to 21,948.82 in 2021—a 205.02% increase, with an Estimated Annual Percentage Change (EAPC) of 2.95 (95% CI: 2.75–3.14). Conversely, Singapore’s ASR declined from 5,203.52 in 1990 to 3,606.56 in 2021, a 30.69% decrease, with an EAPC of −1.85 (95% CI: −2.16 to −1.54). Incidence data (Table S4) reflected similar geographic differences. Georgia experienced the highest increase in incidence rate, from 7,302.49 per 100,000 in 1990 to 26,877.97 in 2021—a 268.07% rise, with an EAPC of 3.36 (95% CI: 3.09–3.63). In contrast, Singapore’s incidence decreased from 6,469.95 to 4,201.22, a reduction of 35.07%, with an EAPC of −2.20 (95% CI: −2.57 to −1.83). For disability-adjusted life years (DALYs) (Table S5), Georgia’s DALY rate per 100,000 increased from 1,220.99 in 1990 to 4,078.19 in 2021, representing a 234.01% increase, with an EAPC of 3.15 (95% CI: 2.93–3.38). Conversely, Singapore’s DALY rate declined from 990.39 to 660.40, a decrease of 33.32%, with an EAPC of −2.05 (95% CI: −2.40 to −1.71).

Fig. 3.

Global geographical distribution of changes in depressive disorders burden among women of reproductive age, 1990–2021. A Percentage change in prevalence cases of depressive disorders among women of reproductive age in 204 countries/territories, 1990–2021. B Percentage change in incidence cases of depressive disorders among women of reproductive age in 204 countries/territories, 1990–2021. C Percentage change in DALY cases of depressive disorders among women of reproductive age in 204 countries/territories, 1990–2021. D Percentage change in age-standardized prevalence rates of depressive disorders among women of reproductive age in 204 countries/territories, 1990–2021. E Percentage change in age-standardized incidence rates of depressive disorders among women of reproductive age in 204 countries/territories, 1990–2021. F Percentage change in age-standardized DALY rates of depressive disorders among women of reproductive age in 204 countries/territories, 1990–2021. G EAPC in prevalence of depressive disorders among women of reproductive age in 204 countries/territories, 1990–2021. H EAPC in incidence of depressive disorders among women of reproductive age in 204 countries/territories, 1990–2021. I EAPC in DALYs of depressive disorders among women of reproductive age in 204 countries/territories, 1990–2021

Age-specific trends in depressive disorders among women of reproductive age globally from 1990 to 2021

From 1990 to 2021, the global age-specific pattern of depression in women of reproductive age showed a complex trend (Figs. 4 and 5). Prevalence data (Table 2) indicated a substantial increase in absolute cases across all age groups, ranging from 37.18% in the 20–24 group to 123.68% in the 45–49 group. However, age-standardized rate (ASR) changes were modest, with increases from 7.07% (30–34 years) to 25.66% (15–19 years). Notably, the 15–19 group was the only age group with a positive EAPC (0.26, 95% CI: 0.08–0.43), reflecting the highest growth in prevalence. Morbidity data (Table S6) followed a similar pattern, with absolute incidence increases ranging from 39.86% (20–24 years) to 130.99% (45–49 years). Age-standardized incidence rates rose between 10.89% (30–34 years) and 26.59% (15–19 years). The 15–19 group also had a positive EAPC in incidence (0.13, 95% CI: −0.09–0.35), though confidence intervals included zero. Regarding disease burden (Table S7), the absolute number of disability-adjusted life years (DALYs) increased substantially across all age groups, from 38.32% (20–24 years) to 126.46% (45–49 years). Age-standardized DALY rate increases ranged from 8.39% (30–34 years) to 27.86% (15–19 years). Once again, the 15–19 group showed the most significant increase, with an EAPC of 0.25 (95% CI: 0.06–0.45).

Fig. 4.

Age-specific trends and distributions of depressive disorders burden among women of reproductive age, 1990–2021. A Trends in prevalence across 7 age groups (15–49 years, 5-year intervals) for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. B Percentage distribution of prevalent cases across 7 age groups for depressive disorders among women of reproductive age globally, in 5 SDI regions, and 21 GBD regions in 1990 and 2021. C Trends in incidence across 7 age groups (15–49 years, 5-year intervals) for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. D Percentage distribution of incident cases across 7 age groups for depressive disorders among women of reproductive age globally, in 5 SDI regions, and 21 GBD regions in 1990 and 2021. E Trends in DALYs across 7 age groups (15–49 years, 5-year intervals) for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. F Percentage distribution of DALYs across 7 age groups for depressive disorders among women of reproductive age globally, in 5 SDI regions, and 21 GBD regions in 1990 and 2021

Fig. 5.

Age-specific changes in depressive disorders burden among women of reproductive age, 1990–2021. A Percentage change in prevalence cases across 7 age groups for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. B Percentage change in incidence cases across 7 age groups for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. C Percentage change in DALY cases across 7 age groups for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. D Percentage change in prevalence rates across 7 age groups for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. E Percentage change in incidence rates across 7 age groups for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. F Percentage change in DALY rates across 7 age groups for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. G EAPC in prevalence across 7 age groups for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. H EAPC in incidence across 7 age groups for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021. I EAPC in DALYs across 7 age groups for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021

Table 2.

Age-specific prevalence of depressive disorders among women of reproductive age in 1990 and 2021, with trends in age patterns from 1990 to 2021

Location	Age(years)	Prevalence cases			Prevalence rates
		1990_cases(95% UI)	2021_cases(95% UI)	Percentage change in case(100%)	1990_per 100 000(95% UI)	2021_per 100 000(95% UI)	Percentage change in ASRs(100%)	EAPC(95% CI)
Global	45 to 49	7932.18(6739.18–9375.16)	17,742.68(14,956.26–21,023.65)	123.68	6970.24(5921.91–8238.23)	7529.49(6347.01–8921.84)	8.02	−0.14 (−0.25 to −0.03)
Global	40 to 44	9492.7(7595.9–11,510.35)	18,420.97(14,521.67–22,726.53)	94.05	6769.6(5416.92–8208.46)	7425.12(5853.39–9160.6)	9.68	−0.15 (−0.28 to −0.01)
Global	35 to 39	10,993.55(9079.52–13,008.17)	19,275.72(15,606.83–23,134.15)	75.34	6338.04(5234.56–7499.52)	6938.64(5617.95–8327.55)	9.48	−0.15 (−0.3 to 0)
Global	30 to 34	11,130.37(8817.95–13,741.86)	18,739.69(14,686–23,575.26)	68.37	5854.73(4638.37–7228.41)	6268.9(4912.84–7886.52)	7.07	−0.21 (−0.36 to −0.07)
Global	25 to 29	12,010.97(9765.94–14,816.69)	17,500.01(14,180.52–22,147.97)	45.70	5457.06(4437.05–6731.81)	6014.01(4873.24–7611.32)	10.21	−0.19 (−0.37 to −0.01)
Global	20 to 24	12,186.73(9264.41–16,382.32)	16,718.17(12,607.12–23,077.96)	37.18	4991.8(3794.79–6710.35)	5691.25(4291.75–7856.27)	14.01	−0.15 (−0.35 to 0.06)
Global	15 to 19	8599.33(6529.06–11098.07)	12,840.7(9489.52–16,836.97)	49.32	3365.18(2555.02–4343.01)	4228.78(3125.15–5544.86)	25.66	0.26 (0.08 to 0.43)
High SDI	45 to 49	1482.73(1274.55–1729.31)	2550.51(2161.27–2985.68)	72.01	5868.32(5044.41–6844.24)	7103.29(6019.24–8315.25)	21.04	0.3 (0.19 to 0.41)
High SDI	40 to 44	1914.92(1609.65–2282.25)	2802.15(2261.19–3383.33)	46.33	6133.02(5155.32–7309.49)	7632.89(6159.34–9215.97)	24.46	0.3 (0.16 to 0.44)
High SDI	35 to 39	2049.1(1757.11–2368.09)	2899.41(2433.26–3430.75)	41.50	6127.83(5254.65–7081.8)	7643.65(6414.73–9044.41)	24.74	0.19 (0.04 to 0.35)
High SDI	30 to 34	2134.41(1757.03–2542.83)	2767.46(2162.5–3447.64)	29.66	6015.04(4951.52–7166.02)	7393.05(5776.95–9210.1)	22.91	0.02 (−0.17 to 0.2)
High SDI	25 to 29	2166.17(1791.75–2555.06)	2651.35(2147.43–3283.98)	22.40	6042.99(4998.45–7127.88)	7671.92(6213.78–9502.5)	26.96	0.09 (−0.12 to 0.3)
High SDI	20 to 24	2012.09(1584.83–2619.94)	2596.8(1957.64–3583.08)	29.06	5992.24(4719.82–7802.5)	8237.62(6210.07–11366.33)	37.47	0.37 (0.16 to 0.58)
High SDI	15 to 19	1473.69(1141.94–1852.61)	2284.17(1760.64–2863.46)	55.00	4624.21(3583.23–5813.2)	7851.58(6052–9842.84)	69.79	1.22 (1 to 1.45)
High-middle SDI	45 to 49	1657.16(1414.14–1948.15)	3382.91(2846.11–4005.19)	104.14	6759.12(5767.89–7946)	7014.42(5901.36–8304.7)	3.78	−0.21 (−0.3 to −0.12)
High-middle SDI	40 to 44	1984.34(1587.49–2400.57)	3036.72(2406.84–3773.87)	53.03	6454.34(5163.55–7808.2)	6671.28(5287.51–8290.7)	3.36	−0.26 (−0.39 to −0.12)
High-middle SDI	35 to 39	2341.31(1951.25–2749.68)	2981.75(2407.15–3571.05)	27.35	5927.84(4940.27–6961.76)	6017.54(4857.94–7206.84)	1.51	−0.26 (−0.41 to −0.11)
High-middle SDI	30 to 34	2254.01(1799.32–2742.21)	2738.53(2183.99–3414.17)	21.50	5399.04(4309.92–6568.42)	5318.49(4241.51–6630.65)	−1.49	−0.34 (−0.48 to −0.2)
High-middle SDI	25 to 29	2291.49(1871.37–2790.73)	2078.08(1660.47–2638.85)	−9.31	5009.19(4090.8–6100.53)	5155.36(4119.34–6546.54)	2.92	−0.4 (−0.6 to −0.19)
High-middle SDI	20 to 24	2227.99(1703.45–2940.56)	1783.56(1310.16–2451.01)	−19.95	4628.06(3538.46–6108.23)	5010.6(3680.68–6885.69)	8.27	−0.4 (−0.69 to −0.1)
High-middle SDI	15 to 19	1552.69(1199.65–1968.7)	1374.36(986.24–1824.56)	−11.48	3278.45(2533.02–4156.86)	3991.28(2864.15–5298.69)	21.74	0.28 (0.06 to 0.51)
Low SDI	45 to 49	713.49(581.37–859.96)	1826.48(1487.34–2205.84)	155.99	8594.49(7003–10358.88)	8794.94(7161.91–10,621.61)	2.33	−0.28 (−0.4 to −0.16)
Low SDI	40 to 44	804.77(612.03–1017.72)	2175.72(1652.08–2740.33)	170.35	8116.51(6172.66–10,264.3)	8329.74(6324.98–10,491.32)	2.63	−0.28 (−0.4 to −0.16)
Low SDI	35 to 39	980.99(783.55–1206.89)	2485.85(1950.55–3049.18)	153.40	7613.67(6081.3–9366.87)	7801(6121.15–9568.84)	2.46	−0.29 (−0.41 to −0.17)
Low SDI	30 to 34	1071.47(819.43–1358.77)	2691.88(2042.92–3438.49)	151.23	7044.09(5387.09–8932.85)	7252.14(5503.79–9263.56)	2.95	−0.29 (−0.41 to −0.16)
Low SDI	25 to 29	1198.15(949.72–1537.1)	2963.54(2287.67–3797.94)	147.34	6468.31(5127.15–8298.14)	6727.83(5193.45–8622.08)	4.01	−0.25 (−0.37 to −0.13)
Low SDI	20 to 24	1237.3(922.49–1706.86)	3177.92(2344.12–4386.63)	156.84	5696.37(4247.02–7858.17)	6026.51(4445.33–8318.68)	5.80	−0.23 (−0.35 to −0.11)
Low SDI	15 to 19	941.33(687.82–1253.55)	2589.29(1847.2–3448.88)	175.07	3746.02(2737.17–4988.48)	4199.42(2995.87–5593.55)	12.10	−0.05 (−0.19 to 0.08)
Low-middle SDI	45 to 49	1807.39(1500.69–2140.83)	4276.36(3534.58–5147.11)	136.60	8327.03(6913.97–9863.24)	8650.11(7149.66–10,411.44)	3.88	−0.44 (−0.61 to −0.27)
Low-middle SDI	40 to 44	2074.42(1603.55–2590.02)	4821.87(3735–6077.13)	132.44	7995.6(6180.68–9982.93)	8359.44(6475.18–10,535.62)	4.55	−0.44 (−0.63 to −0.26)
Low-middle SDI	35 to 39	2369.02(1904.55–2873.98)	5158.97(4137.02–6311.42)	117.77	7395.31(5945.38–8971.62)	7747.74(6212.97–9478.47)	4.77	−0.46 (−0.65 to −0.27)
Low-middle SDI	30 to 34	2489.31(1934.68–3114.15)	5111.06(3974.4–6453.25)	105.32	6648.13(5166.89–8316.89)	6915.44(5377.5–8731.47)	4.02	−0.49 (−0.7 to −0.29)
Low-middle SDI	25 to 29	2712.37(2149.85–3419.08)	5126.95(4071.1–6649.94)	89.02	6043.22(4789.93–7617.81)	6310.33(5010.78–8184.84)	4.42	−0.48 (−0.67 to −0.28)
Low-middle SDI	20 to 24	2801.46(2089.27–3794.46)	4980.15(3739–6957.18)	77.77	5371.47(4005.93–7275.43)	5723.72(4297.26–7995.93)	6.56	−0.43 (−0.62 to −0.23)
Low-middle SDI	15 to 19	1963.11(1444.95–2589.89)	3586.93(2621.82–4722.8)	82.72	3341.36(2459.41–4408.19)	3967.8(2900.21–5224.27)	18.75	−0.02 (−0.21 to 0.18)
Middle SDI	45 to 49	2263.9(1925.92–2682.12)	5692.92(4797.07–6698.95)	151.46	6679.54(5682.35–7913.5)	7018.66(5914.2–8258.99)	5.08	−0.22 (−0.32 to −0.11)
Middle SDI	40 to 44	2705.74(2163.6–3290.22)	5570.73(4394.06–6843.39)	105.89	6403.2(5120.2–7786.38)	6805.96(5368.38–8360.81)	6.29	−0.23 (−0.37 to −0.09)
Middle SDI	35 to 39	3243.87(2678.1–3829.66)	5735.88(4680.76–6820.36)	76.82	5852.03(4831.37–6908.81)	6258.62(5107.34–7441.94)	6.95	−0.18 (−0.34 to −0.02)
Middle SDI	30 to 34	3171.73(2519.31–3881.75)	5417.3(4329.19–6714.34)	70.80	5282.78(4196.12–6465.39)	5485.7(4383.84–6799.12)	3.84	−0.21 (−0.37 to −0.05)
Middle SDI	25 to 29	3632.91(2937.55–4460.99)	4667.26(3730.72–5825.03)	28.47	4849.84(3921.55–5955.3)	5151.58(4117.86–6429.5)	6.22	−0.23 (−0.41 to −0.04)
Middle SDI	20 to 24	3897.76(2939.78–5208.52)	4167.32(3133.95–5719.25)	6.92	4412.62(3328.1–5896.52)	4808.39(3616.06–6599.07)	8.97	−0.24 (−0.46 to −0.02)
Middle SDI	15 to 19	2661.2(2016.73–3429.71)	2996.6(2228.33–3958.64)	12.60	2886.2(2187.24–3719.69)	3411.5(2536.86–4506.75)	18.20	0.02 (−0.17 to 0.21)

Sociodemographic and age-specific patterns of depressive disorders among women of reproductive age: a global perspective from 1990 to 2021

From 1990 to 2021, age-specific patterns in depression among women of childbearing age varied significantly by socio-demographic index (SDI) levels (Figs. 4 and 5). In high SDI countries, depression burden rose across all age groups, with the 15–19 group experiencing a 69.79% increase in prevalence, from 4,624.21 cases per 100,000 in 1990 to 7,851.58 in 2021, with an EAPC of 1.22 (95% CI: 1.00–1.45) (Table 2). This age group also saw a notable rise in DALY rates, increasing 77.52% from 870.21 to 1,544.84, with an EAPC of 1.35 (95% CI: 1.10–1.59) (Table S7). In low SDI countries, the trend was more moderate. For instance, in the 45–49 age group, prevalence rose modestly from 8,594.49 cases per 100,000 in 1990 to 8,794.94 in 2021, a 2.33% increase, with an EAPC of −0.28 (95% CI: −0.40 to −0.16) (Table 2). However, low SDI countries showed a relatively large increase in DALY rates for the 15–19 age group, rising from 668.20 to 767.97 (14.93% increase) (Table S7). Medium SDI countries exhibited trends between high and low SDI levels. For example, in the 45–49 age group, prevalence rose from 6,679.54 to 7,018.66 (5.08% increase), with an EAPC of −0.22 (95% CI: −0.32 to −0.11) (Table 2). Meanwhile, the DALY rate in this group increased by 7.03%, from 1,051.77 to 1,125.72 (Table S7). Notably, the 15–19 age group showed the highest growth rates in prevalence, incidence, and DALY rates across all SDI levels. In medium SDI countries, for instance, DALY rates for this group increased by 19.46%, while increases in other age groups generally remained below 10% (Table S7).

Correlation between depression burden and socio-demographic index among women of reproductive age

We analyzed the relationship between depression burden and sociodemographic index (SDI) in women of reproductive age (Fig. 6). Overall, the burden of depression exhibited a nonlinear relationship with SDI, reaching its lowest point around an SDI of 0.7. Regionally (Fig. 6A, C, E), the disease burden was markedly higher than expected in Central Sub-Saharan Africa, while it was lower than expected in high-income Asia–Pacific (for prevalence) and South-East Asia (for incidence and DALYs). At the country level (Fig. 6B, D, F), higher-than-expected disease burdens were observed in Georgia, Greenland, and Lesotho, whereas lower burdens appeared in Colombia, Myanmar, and Brunei (for prevalence and DALYs) and in China, Myanmar, and North Korea (for prevalence).

Fig. 6.

Relationship between SDI and depressive disorders burden among women of reproductive age. A Association between SDI and age-standardized prevalence rates (per 100,000) of depressive disorders among women of reproductive age in 21 GBD regions. B Association between SDI and age-standardized prevalence rates (per 100,000) of depressive disorders among women of reproductive age in 204 countries/territories. C Association between SDI and age-standardized incidence rates (per 100,000) of depressive disorders among women of reproductive age in 21 GBD regions. D Association between SDI and age-standardized incidence rates (per 100,000) of depressive disorders among women of reproductive age in 204 countries/territories. E Association between SDI and age-standardized DALY rates (per 100,000) of depressive disorders among women of reproductive age in 21 GBD regions. F Association between SDI and age-standardized DALY rates (per 100,000) of depressive disorders among women of reproductive age in 204 countries/territories

The correlation between prevalence and SDI was significant at the regional level (R = −0.14, P = 1e-04) but not at the national level (R = 0.017, P = 0.81). Incidence rate showed no significant correlation with SDI at the district (R = −0.05, P = 0.15) or national (R = 0.085, P = 0.23) levels. The DALY rate was weakly correlated with SDI at the district level (R = −0.08, P = 0.031) but not at the national level (R = 0.069, P = 0.33).

Decomposition analysis of depressive disorder burden among women of reproductive age from 1990 to 2021

From 1990 to 2021, the global burden of depression among women of reproductive age saw a substantial rise (Table S8). Worldwide, disability-adjusted life years (DALYs) due to depression increased by 8.597 million, with 71.53% attributed to population growth, 23.89% to epidemiologic changes, and 4.59% to population aging. Depression prevalence rose by 48.9 million cases, of which 72.76% was due to population growth, 21.17% to epidemiologic changes, and 6.07% to population aging. Morbidity rose by 55.5 million cases, with 69.67% attributed to population growth, 26.94% to epidemiologic changes, and 3.39% to population aging (Fig. 7).

Fig. 7.

Decomposition analysis of changes in incidence, prevalence, and DALYs for depressive disorders among women of reproductive age globally and in 5 SDI regions, 1990–2021

The burden of depression grew differently across sociodemographic index (SDI) regions. In high SDI areas, DALYs increased by 1.059 million years, 83% of which was due to epidemiologic changes, while population aging slightly reduced the burden by 1.59%. Conversely, in low SDI areas, DALYs rose by 1.916 million years, with 92.94% attributed to population growth and 6.8% to epidemiologic changes. In medium SDI areas, DALYs increased by 2.143 million years, with 70.56% due to population growth, 18.66% to epidemiologic changes, and 10.79% to population aging. Similar trends were observed in high-intermediate and low-intermediate SDI regions, where population growth was the primary contributor, accounting for 51.64% and 85.52%, respectively (Fig. 7).These findings highlight the distinct challenges regions face at different levels of development in addressing the burden of depression.

Health inequality analysis of depression among women of reproductive age

We conducted a comprehensive analysis of health inequalities in depression among women of reproductive age using the Skewness Index (SII) and Concentration Index (CCI) (Fig. 8). The SII results revealed a significant shift in depression prevalence inequality from 1990 to 2021, with SII increasing from −546 to 350 (Fig. 8A), indicating a shift in concentration from low to high SDI areas. Similarly, the SII for morbidity increased from −387 to 839 (Fig. 8C), reflecting a growing morbidity inequality skewed towards high SDI regions. The SII for disease burden (DALYs) showed a comparable trend, rising from −61 to 108 (Fig. 8E).

Fig. 8.

Health inequality analysis of the global burden of depressive disorders among women of reproductive age in 1990 and 2021. A Slope index of inequality for crude prevalence rate of depressive disorders among women of reproductive age in 1990 and 2021. B Concentration index of prevalence of depressive disorders among women of reproductive age in 1990 and 2021. C Slope index of inequality for crude incidence rate of depressive disorders among women of reproductive age in 1990 and 2021. D Concentration index of incidence of depressive disorders among women of reproductive age in 1990 and 2021. E Slope index of inequality for crude DALY rate due to depressive disorders among women of reproductive age in 1990 and 2021. F Concentration index of DALYs due to depressive disorders among women of reproductive age in 1990 and 2021

The Concentration Index (CCI) analysis offered further insight into the distribution of inequality. The CCI for prevalence shifted from −0.01 (95% CI: −0.03, 0.01) in 1990 to 0 (95% CI: −0.03, 0.02) in 2021 (Fig. 8B), suggesting a move toward equalization. The CCI for morbidity remained relatively stable, from −0.01 (95% CI: −0.04, 0.01) to −0.01 (95% CI: −0.04, 0.02) (Fig. 8D), indicating a slight skew toward low SDI areas. The CCI for DALYs showed a similar pattern, moving from −0.01 (95% CI: −0.03, 0.02) in 1990 to 0 (95% CI: −0.03, 0.02) in 2021 (Fig. 8F), reflecting a more balanced distribution of disease burden.

Correlations between health workforce categories and depressive disorder burden among women of reproductive age

Human Resources for Health (HRH) data were obtained from the GBD 2019 Health Workforce Collaborators, covering the period from 1990 to 2019. Our analysis revealed significant correlations between the incidence, prevalence, and DALYs of depressive disorders among women of reproductive age and various categories of health workforce across 204 countries (Fig. 9). For incidence (Fig. 9A), medical assistants and community health workers showed the strongest positive correlation in both 1990 (r = 0.23, p = 0.001) and 2019 (r = 0.21, p = 0.003), indicating a slight decrease but consistently significant relationship. This may reflect their role in improved detection and reporting of depressive disorders. The strongest negative correlation for incidence was observed with audiologists and counsellors, increasing from 1990 (r = −0.23, p = 0.001) to 2019 (r = −0.27, p < 0.0001). For prevalence (Fig. 9B), the distribution of audiologists and counsellors demonstrated the strongest negative correlation, intensifying from 1990 (r = −0.27, p = 0.00011) to 2019 (r = −0.33, p < 0.00001), suggesting that increased availability of these professionals may be increasingly associated with lower depression rates. For DALYs (Fig. 9C), audiologists and counsellors again showed the strongest negative correlation, strengthening from 1990 (r = −0.24, p = 0.00065) to 2019 (r = −0.29, p < 0.0001), while medical assistants and community health workers exhibited the strongest positive correlation, slightly decreasing from 1990 (r = 0.20, p = 0.0044) to 2019 (r = 0.17, p = 0.015). These findings underscore the evolving and complex relationship between health workforce distribution and mental health outcomes, highlighting the growing importance of specialized mental health professionals in potentially reducing the burden of depressive disorders among women of reproductive age.

Fig. 9.

Correlation between health workforce categories and depressive disorder burden among women of reproductive age (1990 and 2019). A Correlation between health workforce categories and incidence of depressive disorders among women of reproductive age globally in 1990 and 2019. B Correlation between health workforce categories and prevalence of depressive disorders among women of reproductive age globally in 1990 and 2019. C Correlation between health workforce categories and disability-adjusted life years (DALYs) due to depressive disorders among women of reproductive age globally in 1990 and 2019. D Scatter plot showing the correlation between Audiologists & Counsellors and incidence, prevalence, and DALYs due to depressive disorders among women of reproductive age globally in 1990 and 2019. E Scatter plot showing the correlation between Medical Assistants & Community Health Workers (MA & CHWs) and incidence, prevalence, and DALYs due to depressive disorders among women of reproductive age globally in 1990 and 2019

Building upon these findings, we conducted a detailed analysis of the two health workforce categories showing the strongest correlations: Medical Assistants & Community Health Workers (MA & CHWs) and Audiologists and Counsellors (Fig. 9D and E). For MA & CHWs, we observed a positive correlation with depressive disorder burden, particularly in countries with high disease rates. In 1990, Greenland had the highest DALYs (2649.15 per 100,000) and incidence (17,571.9 per 100,000) rates, corresponding to 13.833 MA & CHWs per 10,000 population. By 2019, while Greenland's MA & CHWs increased to 21.887 per 10,000, Georgia emerged with the highest burden (DALYs: 2934.933, incidence: 18725.86 per 100,000) despite a lower MA & CHWs ratio (0.369 per 10,000). Conversely, Audiologists and Counsellors demonstrated a negative correlation with depressive disorder burden. In 1990, Myanmar had the lowest DALYs (416.1256 per 100,000) with 0.642 Audiologists and Counsellors per 10,000 population. By 2019, Myanmar's burden remained low (DALYs: 416.3002 per 100,000) while its Audiologists and Counsellors increased to 3.007 per 10,000. These data suggest that while MA & CHWs may be associated with higher reported rates, possibly due to improved detection, the presence of Audiologists and Counsellors correlates with lower depressive disorder burden, potentially indicating their role in prevention and effective treatment. Based on these findings, countries with high depressive disorder burden, such as Georgia, might consider reallocating resources to increase the number of Audiologists and Counsellors while optimizing the role of MA & CHWs in detection and referral, potentially leading to better prevention and management of depressive disorders among women of reproductive age.

Discussion

This study offers an in-depth analysis of the global burden of depressive disorders among women of reproductive age, highlighting a significant increase in prevalence, incidence, and disability-adjusted life years (DALYs) from 1990 to 2021. The findings emphasize depression as an escalating public health challenge [24] within this demographic, marked by substantial regional, sociodemographic, and age-related disparities. Our analysis shows that although global population growth is a key driver, regional differences [10] indicate additional, context-specific factors contributing to these trends.

The distinct temporal patterns observed in our analysis—comparing pre-pandemic (1990–2019) and pandemic (2019–2021) periods—reveal the significant impact of COVID-19 on mental health among women of reproductive age. While the overall burden of depression showed a steady increase from 1990 to 2019, the accelerated growth during 2019–2021 suggests that pandemic-related stressors substantially exacerbated this public health challenge. Age-standardized rates, which had actually decreased by 4.48% from 1990–2019, increased by 16.55% during 2019–2021, indicating that pandemic-related factors affected women of reproductive age independent of demographic shifts. This dramatic reversal likely reflects the multiple challenges women faced during the pandemic, including increased caregiving responsibilities, economic insecurity, social isolation, and reduced access to support services. The pandemic's disproportionate impact across SDI regions further underscores the need for context-specific mental health interventions that address both pre-existing vulnerabilities and the unique stressors introduced by global health crises.

Our results indicate that low-SDI (Socio-demographic Index) regions have experienced the greatest absolute increase in depressive disorder cases [25], likely due to limited mental health services [8, 26], economic instability, and high social stress. In contrast, high-SDI regions have seen the most rapid growth in age-standardized rates, possibly driven by lifestyle and socioeconomic pressures unique to high-income settings [9]. The notable rise in the 15–19 age group across all SDI levels underscores adolescence as a vulnerable period [27], highlighting the urgent need for mental health interventions for young women [28]. And studies have shown that depression has an intergenerational risk [29, 30]. These demonstrate the importance of early prevention [31].

These findings provide critical insights for shaping targeted mental health policies: improving access to basic mental health services in low-SDI regions may significantly reduce the burden, while addressing social and environmental stressors [32]—such as work-related pressure, social isolation, and rapid lifestyle changes—may be crucial in high-SDI regions. The disparities observed underscore the need for tailored approaches that account for the diverse socioeconomic and cultural challenges facing women of reproductive age worldwide.

Our findings align with prior studies showing higher depression rates in high-income regions, where social pressures and lifestyle factors, such as work-related stress, are prevalent [33]. High-SDI regions exhibit the fastest rise in age-standardized rates, suggesting that mental health burdens are shaped by lifestyle, societal expectations, and economic stressors associated with higher income. Conversely, the larger absolute burden in low-SDI regions aligns with research linking depression to economic instability [25], limited healthcare access, and heightened social stress. This pattern highlights the complex nature of depression [34], driven by both socioeconomic status and access to mental health resources, and underscores the need for context-specific mental health research.

The disproportionate increase in depressive disorders among adolescents aged 15–19, across all SDI levels, likely reflects specific vulnerabilities during this developmental stage. Hormonal changes [35], intensified social pressures, and the transition to adulthood may contribute to these trends as young women encounter heightened societal expectations. This age-specific rise underscores the need for early intervention strategies focused on adolescents, which could reduce long-term impacts and alleviate the future burden [36]. Addressing adolescent depression is especially crucial, as early-onset cases can have lasting effects on both physical and mental health, potentially leading to chronic conditions if untreated [37]. Our analysis of health inequality trends indicates a shift in the depression burden toward high-SDI regions between 1990 and 2021, as evidenced by health inequality indices. This shift may be due to improved diagnostic capabilities and heightened awareness in high-income settings, but it also points to the influence of socioeconomic and lifestyle changes that have made mental health issues more visible and pressing. In contrast, low-SDI regions continue to face significant barriers to mental health access, contributing to a high absolute burden and highlighting the urgent need for expanded mental health infrastructure and services.

The analysis of human resources for health (HRH) distribution reveals complex relationships with the burden of depressive disorders among women of reproductive age across 204 countries from 1990 to 2019. The strongest negative correlation was observed with audiologists and counsellors, intensifying from 1990 (r = −0.27, p = 0.00011) to 2019 (r = −0.33, p < 0.00001) for prevalence rates. This suggests that increased investment in these specialized mental health professionals could significantly reduce the burden of depressive disorders. Conversely, medical assistants and community health workers showed the strongest positive correlation with incidence rates, slightly decreasing from 1990 (r = 0.23, p = 0.001) to 2019 (r = 0.21, p = 0.003). While this correlation might reflect improved detection and reporting, it also indicates a potential misallocation of resources that could be more effectively utilized elsewhere [38]. These findings underscore the need for a strategic reallocation of health workforce investments. Countries should prioritize increasing the number of audiologists and counsellors, particularly in regions with high depressive disorder burden. Simultaneously, the role of medical assistants and community health workers should be optimized, focusing on early detection and referral rather than direct intervention. This could involve reducing their numbers in favor of more specialized mental health professionals or retraining them to better support the work of audiologists and counsellors [39]. Additionally, the weak correlations observed for other health workforce categories, such as dentists and pharmacists, suggest that resources currently allocated to these areas might be more impactful if redirected towards mental health specialties. By implementing these targeted adjustments in health workforce distribution and investment, countries can more effectively address the growing burden of depressive disorders among women of reproductive age and improve overall mental health outcomes [40].

Our comparative analysis of pre-pandemic (1990–2019) and pandemic (2019–2021) periods reveals concerning trends that necessitate immediate attention. The acceleration of depression burden during the pandemic years was observed across all SDI regions, though with varying intensities. High SDI regions, which had shown moderate increases in age-standardized rates from 1990–2019 (8.10% for prevalence), experienced a dramatic rise during 2019–2021 (22.52%). Conversely, low and middle SDI regions, many of which had declining age-standardized rates pre-pandemic, saw these rates increase during 2019–2021. These findings align with emerging research suggesting that the pandemic both exacerbated existing mental health challenges and created new vulnerabilities, particularly among women. The pandemic's disproportionate impact on women's mental health may be attributed to increased gender-based violence, expanded caregiving responsibilities, employment instability in female-dominated sectors, and reduced access to reproductive healthcare. These pandemic-specific stressors, superimposed on pre-existing vulnerabilities, highlight the need for mental health responses that address both immediate crisis needs and long-term structural factors affecting women's mental health globally. While this study provides valuable insights, certain limitations should be acknowledged. First, variations in diagnostic criteria and reporting across countries may affect data comparability, especially in low-SDI regions where healthcare systems and mental health resources are often limited. Additionally, although SDI is a useful composite measure, other unmeasured factors—such as genetic predispositions, lifestyle variables, and specific health behaviors—may influence depression rates but are not fully accounted for in this analysis.

Our correlation analyses between health workforce categories and depressive disorder burden may be influenced by outlier countries with extreme values. While we believe the overall patterns remain informative for global health planning, country-specific contexts should be considered when interpreting these findings. Future research might benefit from regional analyses that can account for these contextual differences. Future studies could build on our findings by incorporating longitudinal and cohort data to track individuals over time, enabling a deeper exploration of the causal relationships between socioeconomic factors and depression. Further research is also needed to assess the roles of specific cultural [41], genetic, and environmental factors in shaping regional and sociodemographic differences in depression burden. Such work would offer a more nuanced perspective and support the development of culturally and contextually tailored interventions.

In conclusion, this study underscores the urgent global mental health challenge posed by the rising burden of depressive disorders among women of reproductive age, with a pronounced impact during adolescence. The disparities observed across regions and sociodemographic indices highlight the need for culturally sensitive, comprehensive policies that address the specific vulnerabilities of this demographic. By adopting age- and region-specific strategies for prevention and intervention, health policymakers and practitioners can work to reduce the growing burden of depression and improve mental health outcomes for women worldwide.

Conclusions

This study provides a comprehensive analysis of the global burden of depression among women of reproductive age from 1990 to 2021, with special attention to the impact of the COVID-19 pandemic. The findings underscore the severity and complexity of depression in this population. First, the global burden of depression among women of reproductive age has risen significantly over the past three decades, with increases in prevalence, incidence, and disability-adjusted life years (DALYs) of 67.58%, 71.44%, and 69.08%, respectively. Particularly concerning is the accelerated growth during the pandemic period (2019–2021), with increases of 17.86% in prevalence, 24.51% in incidence, and 20.80% in DALYs in just two years. These trends vary across socio-demographic index (SDI) regions and age groups: low SDI regions experienced the largest absolute increase in cases, while high SDI regions saw the fastest rise in age-standardized rates. Age-specific analyses revealed the most pronounced increase in the 15–19 age group across all SDI levels.

Second, a complex relationship between depression burden and socioeconomic development emerged. Overall, depression burden showed a nonlinear relationship with SDI, reaching its lowest level around an SDI of 0.7. However, this association varied widely across regions and countries, reflecting the multifactorial nature of depression. Third, decomposition analysis identified population growth as the main driver of the global rise in depression, followed by epidemiologic shifts. In high SDI areas, however, epidemiologic factors were the dominant contributors, likely reflecting the impact of socioeconomic stressors and lifestyle changes on mental health.

Fourth, health inequality analysis revealed shifts in depression burden distribution from low to high SDI regions between 1990 and 2021, with inequality in prevalence and incidence increasing in higher SDI areas and a more balanced distribution of disease burden overall. The pandemic period (2019–2021) further intensified these trends, with age-standardized rates increasing across all SDI regions after having declined in many regions during the pre-pandemic period. This shift underscores the need for targeted interventions based on regional development levels and pandemic-related vulnerabilities.

Lastly, our analysis of health workforce correlations revealed significant relationships between depressive disorder burden and various categories of health professionals. Notably, the distribution of audiologists and counsellors showed the strongest negative correlation with depression prevalence and DALYs, intensifying from 1990 to 2019. Conversely, medical assistants and community health workers exhibited the strongest positive correlation with incidence rates. These findings suggest that countries with high depressive disorder burden, such as Georgia, might benefit from increasing the number of audiologists and counsellors while optimizing the role of medical assistants and community health workers in detection and referral. This nuanced understanding of the relationship between health workforce distribution and mental health outcomes highlights the need for tailored approaches in addressing the growing burden of depression among women of reproductive age, particularly in the context of global health crises like the COVID-19 pandemic.

Authors’ contributions

Conceptualization, F.D., Y.C.; Data management, F.D., M.C.; Methodology, F.D., Y.C.; Software, F.D., Y.C.; Supervision, Y.D.; Writing—original draft, F.D., Y.C.; Writing—criticism and editing, F.D., Y.C.; Visualization, F.D., Y.C. All authors have read and agreed to the published version of the manuscript.

Funding

Not applicable.

Data availability

All GBD data for this study are publicly available and can be found here: Global Burden of Disease (GBD).

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

All participants provided informed consent before enrollment.

Competing interests

The authors declare no competing interests.