Abstract
Objective
Despite an increase in the depressive disorders burden during the coronavirus disease-2019 (COVID-19) pandemic, research remains insufficient. This study aims to advance the understanding of the global depressive disorders burden, focusing on the COVID-19 pandemic.
Methods
Global and regional estimates of incidence, prevalence, and disability-adjusted life years (DALYs) for depressive disorders from 1990 to 2021 were analyzed using the Global Burden of Disease Study 2021. Depressive disorders were defined as the aggregate of major depressive disorder and dysthymia. Estimates were stratified by age, sex, Socio-demographic Index (SDI), and region, and trends were assessed by annual percent change. Attributable risk factors and projections to 2050 were assessed by modeling risk factors.
Results
In 2021, depressive disorders accounted for 56,330.36 (95% uncertainty interval [UI], 39,339.99 to 76,538.17) DALYs. The age-standardized DALYs rate (ASDR) remained stable until 2019, followed by an increase during the COVID-19 pandemic (ASDR in 1990: 600.51 [95% UI, 420.94 to 818.45] per 100,000; in 2019: 593.50 [413.34 to 810.07]; in 2021: 681.14 [475.19 to 923.83]). The highest ASDRs were observed in low (837.53 [95% UI, 569.85 to 1,140.07] per 100,000) and low-middle (784.07 [542.47 to 1,059.21]) SDI regions. The burden increased rapidly among adolescents and remained higher in females than in males. The burden attributable to intimate partner violence increased during the COVID-19 pandemic. Projections suggest a modest decline in global ASDR, reaching 622.30 (95% UI, 430.96 to 847.82) per 100,000 by 2050.
Conclusion
The global burden of depressive disorders increased sharply during the COVID-19 pandemic. It is essential to address regional disparities in mental health care and promote access to tailored treatment.
Keywords: COVID-19 pandemic, Depressive disorders, Global burden of disease, Mental health
INTRODUCTION
Depressive disorders are a major global public health concern, contributing substantially to the overall burden of disease and disability. Characterized by persistent sadness, loss of interest or pleasure, and various cognitive and physical symptoms, these conditions cause substantial social and economic losses [1]. It ranked as the second leading cause of years lived with disability among mental disorders according to the Global Burden of Diseases Study (GBD) 2021 [1]. Additionally, depressive disorders are characterized by a high risk of chronicity or recurrence and influence individuals across the lifespan [2]. The prevalence of depressive disorders increases sharply during adolescence [3], and is associated with a higher recurrence rate in adulthood, the development of other mental health conditions, and an increased risk of suicide attempts [4]. Therefore, early intervention and targeted strategies are crucial to reducing the burden of depressive disorders.
The World Health Organization (WHO) set a target to increase mental health coverage by 50% by 2030 as part of the Comprehensive Mental Health Action Plan 2013–2030 [5]. To achieve this goal, WHO implemented policies such as the Special Initiative for Mental Health (2019–2023) before the coronavirus disease-2019 (COVID-19) pandemic [6]. However, the COVID-19 pandemic led to a significant increase in the burden of depressive disorders [7]. Compounding the issue, the disruptions in mental health services during this period exacerbated regional disparities in healthcare access. Therefore, a comprehensive understanding of the trend of depressive disorders across ages, sexes, and regions, including the COVID-19 pandemic period, is essential.
This study assessed the global, regional, and national burden of depressive disorders by using the GBD 2021. Previous studies have examined the burden of depressive disorders, including the impact of the COVID-19 pandemic, but were limited to specific countries or age groups [8,9]. Therefore, this study aims to analyze the global and regional burden of depressive disorders, considering Socio-demographic Index (SDI), age, and sex, to observe the most up-to-date data. Additionally, we examined the attributable risk factors and their influence on the onset of depressive disorders, while investigating their association with sociocultural changes resulting from the COVID-19 pandemic. Furthermore, we projected the global and regional burden of depressive disorders up to 2050. Ultimately, this study aims to advance the understanding of the burden of depressive disorders and to guide management and prevention.
METHODS
GBD 2021 overview
This study analyzed the global, regional, and national burden of depressive disorders based on estimates from the GBD 2021. The GBD used de-identified data, approved by the University of Washington IRB (Study Number 9060). The GBD provides a comprehensive assessment of 371 diseases and conditions across 204 countries and territories from 1990 to 2021 (Supplementary Table 1) [10]. The burden of depressive disorders was assessed based on key health metrics, including incidence, prevalence, mortality, years of life lost, years lived with disability, and disability-adjusted life years (DALYs). Analyses were conducted at the country level and aggregated by GBD regions and SDI levels. The SDI is a composite indicator of social and economic factors affecting health outcomes across regions. It is calculated as the geometric mean of three indices: total fertility rate under 25 years of age, average educational attainment, and lagged distributive income per capita [11]. Additionally, future projections of the depressive disorders burden through 2050 were obtained. The current GBD estimation is based on the methodology described in the latest GBD study, with additional detailed information available elsewhere [10]. The GBD 2021 follows the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) to ensure methodological transparency. All statistical analyses were conducted using Python (version 3.10.4; Python Software Foundation) and R software (version 4.3.2; R Foundation).
Case definition
In the GBD 2021, the burden of depressive disorders was assessed as the aggregation of two diagnostic categories, major depressive disorder (MDD) and dysthymia [3]. MDD and dysthymia were both defined according to the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the equivalent diagnosis in the International Classification of Diseases (ICD) codes [7]. Specifically, MDD was classified using DSM-IV-TR codes 296.21-24 and 296.31-34, aligning with ICD-10 codes F32.0-9 and F33.0-9. Dysthymia was classified under DSM-IV-TR code 300.4, aligning with ICD-10 code F34.1. In both diagnoses, cases due to a general medical condition or substance-induced cases were excluded.
According to DSM-IV-TR criteria, MDD involves the presence of at least one major depressive episode, which is the experience of either depressed mood or loss of interest and pleasure, for most of every day, for at least 2 weeks. In addition, four out of the seven criteria (e.g., change in eating, appetite, or weight; excessive sleeping or insomnia; agitated or slow motor activity; fatigue; feeling worthless or inappropriately guilty; trouble concentrating; repeated thoughts about death) must be met to make a diagnosis. Dysthymia involves the experience of chronically depressed mood for most of the day, more days than not, for at least 2 years or a least 1 year in adolescents. During this period, at least two of the six symptoms (e.g., poor appetite or overeating; insomnia or hypersomnia; low energy or fatigue; low self-esteem; poor concentration or indecisiveness; feelings of hopelessness) must also be experienced [7].
Input data and preprocessing
The epidemiological systematic literature review for MDD and dysthymia followed three stages, involving electronic searches of the peer-reviewed literatures, the grey literature, and expert consultation. Studies were included based on four criteria: 1) publication year of 1980 or later; 2) use of clinical threshold established by the DSM or ICD; 3) sufficient methodological and sample information; 4) representativeness of the study population to the general population. No language restrictions were applied during the study selection. Although input data include studies published from 1980 onward, the GBD 2021 provides consistent estimates for depressive disorders from 1990 to 2021, and therefore, the present analysis covers the time period from 1990 to 2021.
Data underwent age-sex splitting and bias corrections prior to modeling burden estimates. Data underwent three types of age-sex splitting processes. First, where possible, estimates were further split by sex and age based on the available data using the reported sex-ratio and bounds of uncertainty. Second, a meta-regression–Bayesian, regularized, trimmed model (MR-BRT) analysis was used to split the remaining both-sex estimates in the dataset. Finally, studies reporting prevalence estimates across age groups spanning 25 years or more were split into 5-year age groups using the prevalence age pattern estimated by DisMod-MR 2.1 [12]. For bias corrections, estimates with known biases were cross-walked accordingly prior to DisMod-MR 2.1. For each crosswalk of interest, pairs of the reference and the alternative estimates were matched by age, sex, location, and year.
Estimating the burden of depressive disorders
DisMod-MR 2.1 was used to model the epidemiological data for MDD and dysthymia [3]. Where outliers were identified in the data, we reassessed the study methodology and quality before a decision was made to exclude or include the data. Data across all epidemiological parameters were initially included in the modeling process. The incidence studies reported estimates that were very low relative to the prevalence data. As prevalence studies contributed much greater world coverage than incidence studies, we excluded the incidence data, relying instead on data from the other parameters. We assumed no incidence and prevalence before age 3 [3]. This minimum age of onset was corroborated with expert feedback and was consistent with the available data. Excess-mortality was set to 0 as there is no epidemiological evidence to suggest that dysthymia is associated with a statistically significant risk of mortality.
Annual percentage change
Temporal trends in the burden of depressive disorders were analyzed using the annual percentage change (APC). APC of age-standardized incidence rate (ASIR), prevalence rate (ASPR), and DALYs rate (ASDR) were assessed to study the trend. The formula for the regression model is as follows [13].
y represents the natural logarithm of the burden of depressive disorders, x is the year, and β is the regression coefficient. APC was calculated using the following formula. A p-value≤0.05 was considered statistically significant using a two-sided test.
To assess the APC in depressive disorders from 1990 to 2021, the study period was divided into three intervals: 1990–2010, 2010–2019, and 2019–2021. The year 2019 was selected to differentiate the pre-pandemic and post-pandemic periods. To enable a more precise comparison of recent trends, the interval 2010–2019 was considered as a reference period [14].
Risk factors
The GBD 2021 incorporated risk-outcome pairs supported by strong evidence to quantify the proportion of disease burden attributable to specific risk factors [15]. The relative risk of the outcome was based on exposure levels, the prevalence of exposures, and the theoretical minimum risk exposure level. The risk-deleted burden of depressive disorders is calculated by subtracting the population attributable fraction for a specific risk factor from the observed burden. Through this, the contribution of each risk factor and the number of deaths that could be prevented if exposure to these factors were minimized were estimated. In accordance with the GBD comparative risk assessment framework, which applies the burden-of-proof methodology to determine eligible risk-outcome pairs, only three risk factors—childhood sexual abuse (CSA), bullying victimization (BV), and intimate partner violence (IPV)—met the evidence threshold for inclusion as quantified risk factors for depressive disorders in GBD 2021 [16]. Broader categories, such as “all risk factors,” “behavioral risks,” and “childhood sexual abuse and bullying,” are aggregates of these specific risks; therefore, the present analysis focused exclusively on the most specific risk factors available.
IPV was defined as having ever experienced one or more instances of physical or sexual violence by a current or former intimate partner since the age of 15 years. IPV was estimated only in females. BV was defined as bullying victimization of children and adolescents attending school by peers. This definition includes combined estimates of subtypes such as physical, verbal, relational, and cyberbullying victimization. However, it excludes abuse by siblings, intimate partners, and adults. CSA was defined as exposure to sexual intercourse or other forms of sexual contact at age 15 years or younger, wherein the contact was either unwanted or involved a perpetrator who was at least 5 years older than the victim.
Projections to 2050
DALYs of depressive disorders were forecasted up to 2050 by integrating independent drivers of health, sociodemographic determinants, and key interventions [16]. SDI, vaccine coverages, and anti-retroviral therapy were included. The forecasting framework incorporated historical trends, recency-weighted changes, and external shocks. First, future risk factor trajectories were modeled using summary exposure values to assess population attributable fractions and cause-specific mortality scalars. A generalized ensemble modeling approach was utilized, incorporating 12 sub-models based on two primary methodologies: annualized rate of change-based models and a two-stage spline model employing a MR-BRT [16]. An ensemble modeling approach was adopted for mediation in mediator summary exposure value computation to prevent overestimation of risk contributions. Sub-model weights were determined through out-of-sample predictive validity experiments, training models on 1990–2009 data, and validating on 2010–2019 data using root mean squared error. Final forecasts up to 2050 were generated using 500 draws per model. Cause-specific mortality estimates were obtained using a log-linear model, modeled as a function of the SDI, temporal factors, other cause-specific covariates, and the risk factor scalars.
For sensitivity analysis to consider the impact of the COVID-19 pandemic, projections for DALYs were further estimated using a logit mixed-effects model. The equation for the model is as follows. The location-specific random intercept, αl, represents the random effect, while the SDI coefficient indicates the fixed effect. The error term in the model is represented by ϵ [13].
To assess the potential long-term impact of the COVID-19 pandemic on future projections, a sensitivity analysis was performed by fitting the model using two different training periods. The first period included the COVID-19 pandemic by utilizing data from 2010–2021 to forecast 2022–2050. The second period excluded the COVID-19 pandemic by utilizing data from 2010–2019 to forecast 2020–2050.
RESULTS
Global and regional trends in the burden of depressive disorders
Globally, the number of DALYs due to depressive disorders increased steadily by 29,674,67 (95% uncertainty interval [UI], 20,748.74 to 40,205.91) in 1990 to 56,330.36 (39,339.99 to 76,538.17) in 2021. Specifically, a large increase was reported from 47,982.47 (33,465.57 to 65,436.81) in 2019 (Figure 1, Table 1, Supplementary Tables 2 and 3). Similarly, global ASDR per 100,000 population increased from 600.51 (420.94 to 818.45) in 1990 to 681.14 (475.19 to 923.83) in 2021. Specifically, the ASDR showed a decreasing trend between 2004 and 2010, followed by a significant increase from 2019 onwards (2004: 622.18 [436.97 to 848.02]; 2010: 584.98 [411.55 to 793.87]; 2019: 593.50 [413.34 to 810.07]). The global ASIR per 100,000 population exhibited a similar trend, being stable from 1990 to 2019 (ASIR, 1990: 4,634.85 [4,075.96 to 5,384.21]; 2019: 4,496.81 [3,937.50 to 5,277.23]), then increasing significantly through 2021 (3,748.49 [3,292.73 to 4,353.00]) ( Supplementary Tables 4-6).
Figure 1.
Trends in age-standardized DALY rates (per 100,000 population) of depressive disorders by sex and SDI, 1990–2021. DALY, disability-adjusted life year; SDI, Socio-demographic Index.
Table 1.
Global count (thousand) and ASRs (per 100,000 population) of depressive disorders, 2021
| DALYs (95% UI) |
Incidence (95% UI) |
Prevalence (95% UI) |
||||
|---|---|---|---|---|---|---|
| Count, thousand | ASR, per 100,000 | Count, thousand | ASR, per 100,000 | Count, thousand | ASR, per 100,000 | |
| Global | 56,330.36 | 681.14 | 357,438.73 | 3,748.49 | 332,410.33 | 4,006.82 |
| (76,538.17 to 39,339.99) | (475.19 to 923.83) | (311,521.93 to 418,969.49) | (3,292.73 to 4,353.00) | (297,742.04 to 376,102.44) | (3,581.26 to 4,539.01) | |
| Sex | ||||||
| Male | 22,210.94 | 540.51 | 58,103.85 | 3,366.27 | 131,144.08 | 3,186.43 |
| (30,314.53 to 15,498.04) | (377.31 to 735.48) | (66,917.22 to 51,569.38) | (2,922.76 to 3,958.07) | (117,402.03 to 147,879.57) | (2,853.40 to 3,604.27) | |
| Female | 34,119.42 | 821.17 | 57,312.68 | 5,295.22 | 201,266.25 | 4,822.12 |
| (46,219.58 to 23,794.82) | (570.96 to 1,110.43) | (66,066.11 to 49,892.37) | (4,606.32 to 6,227.48) | (179,872.07 to 228,454.65) | (4,316.38 to 5,483.35) | |
| SDI | ||||||
| High SDI | 9,007.86 | 766.07 | 99,549.91 | 5,001.70 | 52,032.87 | 4,312.34 |
| (12,110.19 to 6,263.96) | (1,026.59 to 535.99) | (115,650.44 to 86,856.80) | (5,837.96 to 4,364.28) | (57,986.25 to 46,818.96) | (4,866.01 to 3,857.18) | |
| High-middle SDI | 9,408.26 | 610.08 | 94,566.42 | 3,761.68 | 57,626.00 | 3,666.90 |
| (12,819.96 to 6,631.89) | (831.09 to 424.53) | (112,967.83 to 81,363.19) | (4,405.83 to 3,279.49) | (64,197.87 to 51,357.48) | (4,119.10 to 3,280.98) | |
| Middle SDI | 16,158.23 | 605.91 | 47,632.46 | 3,762.03 | 97,973.96 | 3,654.36 |
| (21,965.27 to 11,308.14) | (824.66 to 424.52) | (57,123.56 to 40,235.63) | (4,388.68 to 3,282.29) | (109,600.95 to 87,784.84) | (4,075.44 to 3,272.50) | |
| Low-middle SDI | 14,440.06 | 784.07 | 20,469.75 | 5,151.93 | 82,860.63 | 4,528.90 |
| (19,450.43 to 9,961.81) | (1,059.21 to 542.47) | (23,726.31 to 17,646.85) | (6,092.24 to 4,437.48) | (95,485.30 to 73,600.48) | (5,176.25 to 4,043.41) | |
| Low SDI | 7,273.08 | 837.53 | 3,862.57 | 5,511.85 | 41,664.86 | 4,849.64 |
| (9,960.88 to 4,936.11) | (1,140.07 to 569.85) | (4,617.61 to 3,218.32) | (6,539.42 to 4,713.36) | (48,528.60 to 36,323.96) | (5,554.66 to 4,260.97) | |
| Central Europe, Eastern Europe, and Central Asia | 3,171.83 | 652.90 | 4,640.54 | 4,205.44 | 18,779.45 | 3,820.12 |
| (4,279.76 to 2,217.70) | (885.76 to 459.52) | (5,388.85 to 3,963.07) | (4,889.07 to 3,597.07) | (21,099.89 to 16,638.67) | (4,324.36 to 3,390.55) | |
| Central Asia | 608.76 | 644.28 | 11,966.64 | 4,131.92 | 3,570.53 | 3,773.71 |
| (841.52 to 412.58) | (883.56 to 441.83) | (13,960.02 to 10,128.48) | (4,952.49 to 3,444.95) | (4,148.91 to 3,079.49) | (4,386.79 to 3,243.38) | |
| Central Europe | 744.58 | 521.75 | 62,565.52 | 3,225.42 | 4,590.37 | 3,171.90 |
| (1,005.87 to 515.78) | (710.10 to 358.80) | (72,214.75 to 55,600.90) | (3,788.21 to 2,763.06) | (5,215.27 to 4,047.16) | (3,611.52 to 2,789.03) | |
| Eastern Europe | 1,818.50 | 735.83 | 1,783.88 | 4,833.94 | 10,618.55 | 4,231.79 |
| (2,495.29 to 1,273.42) | (1,005.60 to 510.58) | (2,244.92 to 1,412.12) | (5,669.69 to 4,115.80) | (11,873.09 to 9,394.24) | (4,771.11 to 3,729.90) | |
| High-income | 9,545.82 | 825.78 | 5,909.14 | 5,455.94 | 54,196.96 | 4,576.15 |
| (12,829.63 to 6,662.02) | (1,108.73 to 577.88) | (6,765.19 to 5,142.22) | (6,388.98 to 4,779.08) | (60,637.51 to 48,776.33) | (5,204.24 to 4,103.73) | |
| Australasia | 274.28 | 849.31 | 24,528.89 | 5,579.70 | 1,542.87 | 4,691.88 |
| (384.94 to 186.32) | (1,201.64 to 569.13) | (27,987.38 to 21,700.17) | (7,067.86 to 4,387.27) | (1,843.63 to 1,274.29) | (5,719.89 to 3,904.49) | |
| High-income Asian Pacific | 930.18 | 447.86 | 3,105.31 | 2,845.86 | 5,462.10 | 2,545.16 |
| (1,248.48 to 646.22) | (606.65 to 307.99) | (3,819.50 to 2,555.39) | (3,326.65 to 2,452.42) | (6,075.18 to 4,882.26) | (2,892.38 to 2,266.73) | |
| High-income North America | 3,701.75 | 982.78 | 27,238.28 | 6,572.24 | 20,786.86 | 5,408.26 |
| (4,938.06 to 2,581.25) | (1,322.41 to 685.33) | (31,895.99 to 23,747.80) | (7,626.11 to 5,787.27) | (23,139.97 to 18,818.66) | (6,049.72 to 4,846.90) | |
| Southern Latin America | 470.32 | 652.62 | 30,441.06 | 4,330.32 | 2,618.52 | 3,605.13 |
| (651.26 to 315.91) | (904.82 to 439.62) | (35,686.11 to 26,361.71) | (5,341.73 to 3,543.25) | (3,072.11 to 2,225.95) | (4,246.28 to 3,048.18) | |
| Western Europe | 4,169.30 | 858.16 | 2,519.15 | 5,634.46 | 23,786.61 | 4,778.95 |
| (5,610.06 to 2,951.54) | (1,164.62 to 600.37) | (3,106.82 to 2,069.19) | (6,686.61 to 4,852.74) | (26,711.54 to 21,136.83) | (5,528.96 to 4,207.89) | |
| Latin America and Caribbean | 4,532.38 | 714.86 | 2,507.10 | 4,813.69 | 25,408.49 | 4,005.64 |
| (6,186.23 to 3,136.77) | (974.82 to 495.73) | (3,057.59 to 2,075.07) | (5,648.30 to 4,188.38) | (28,937.35 to 22,597.11) | (4,567.12 to 3,565.90) | |
| Andean Latin America | 388.29 | 578.74 | 12,122.53 | 3,764.83 | 2,225.11 | 3,325.81 |
| (542.32 to 260.79) | (804.97 to 389.32) | (14,363.52 to 10,447.50) | (4,617.21 to 3,107.00) | (2,603.46 to 1,905.54) | (3,880.45 to 2,851.30) | |
| Caribbean | 373.52 | 737.81 | 13,292.29 | 4,956.53 | 2,090.67 | 4,121.50 |
| (519.75 to 258.02) | (1,028.88 to 507.64) | (15,468.34 to 11,459.05) | (6,075.48 to 4,094.80) | (2,463.66 to 1,781.49) | (4,870.28 to 3,512.09) | |
| Central Latin America | 1,813.26 | 682.93 | 37,496.99 | 4,574.68 | 10,146.45 | 3,825.60 |
| (2,485.86 to 1,242.83) | (935.02 to 468.28) | (45,692.40 to 30,790.57) | (5,407.86 to 3,953.81) | (11,609.22 to 9,008.03) | (4,375.35 to 3,399.91) | |
| Tropical Latin America | 1,957.32 | 780.15 | 93,896.45 | 5,317.59 | 10,946.25 | 4,352.09 |
| (2,657.23 to 1,353.65) | (1,062.15 to 539.06) | (110,941.02 to 80,835.46) | (6,168.77 to 4,592.42) | (12,422.30 to 9,714.09) | (4,948.92 to 3,871.00) | |
| North Africa and Middle East | 5,658.55 | 900.68 | 63,372.62 | 5,983.10 | 31,377.64 | 5,024.69 |
| (7,775.92 to 3,733.34) | (1,242.73 to 598.46) | (73,233.83 to 55,085.63) | (7,214.68 to 4,953.30) | (36,911.41 to 26,947.18) | (5,857.38 to 4,346.39) | |
| South Asia | 14,248.39 | 777.80 | 43,675.67 | 5,151.00 | 82,018.20 | 4,500.48 |
| (19,288.78 to 9,868.95) | (1,049.65 to 542.56) | (50,494.48 to 38,197.25) | (6,036.89 to 4,461.45) | (93,536.16 to 73,144.79) | (5,106.63 to 4,034.93) | |
| Southeast Asia, East Asia, and Oceania | 11,633.15 | 453.36 | 373.88 | 2,514.30 | 77,370.34 | 2,972.37 |
| (15,787.88 to 8,214.66) | (615.48 to 317.36) | (481.77 to 286.58) | (2,922.23 to 2,191.68) | (86,818.95 to 69,413.16) | (3,323.26 to 2,663.06) | |
| East Asia | 8,120.34 | 429.67 | 19,323.07 | 2,337.70 | 54,868.50 | 2,870.61 |
| (11,045.63 to 5,749.11) | (585.42 to 304.25) | (23,013.28 to 16,461.72) | (2,718.38 to 2,058.01) | (61,336.15 to 48,917.57) | (3,205.35 to 2,583.73) | |
| Oceania | 63.40 | 507.90 | 49,196.34 | 2,956.63 | 389.99 | 3,201.99 |
| (88.56 to 40.94) | (704.04 to 329.20) | (58,891.94 to 41,562.99) | (3,768.08 to 2,307.72) | (473.26 to 320.66) | (3,811.00 to 2,654.70) | |
| Southeast Asia | 3,449.41 | 467.63 | 8,218.96 | 2,646.51 | 22,111.84 | 2,991.56 |
| (4,708.30 to 2,383.62) | (634.88 to 323.74) | (10,553.81 to 6,492.78) | (3,152.37 to 2,262.28) | (25,067.94 to 19,531.32) | (3,401.76 to 2,647.86) | |
| Sub-Saharan Africa | 7,540.23 | 883.05 | 20,196.52 | 5,817.09 | 43,259.26 | 5,108.10 |
| (10,312.69 to 5,115.22) | (1,197.94 to 607.96) | (24,091.92 to 16,968.24) | (6,836.31 to 5,014.76) | (50,572.95 to 37,893.05) | (5,808.85 to 4,544.07) | |
| Central Sub-Saharan Africa | 1,214.01 | 1,136.91 | 4,540.16 | 7,703.41 | 6,665.88 | 6,337.03 |
| (1,686.81 to 802.67) | (1,588.38 to 756.88) | (5,397.70 to 3,867.47) | (9,565.94 to 6,194.22) | (8,282.57 to 5,446.86) | (7,669.98 to 5,236.44) | |
| Eastern Sub-Saharan Africa | 3,080.93 | 974.65 | 16,240.71 | 6,468.12 | 17,512.84 | 5,576.42 |
| (4,196.82 to 2,080.51) | (1,308.67 to 668.67) | (19,363.27 to 13,742.96) | (7,580.31 to 5,519.80) | (20,493.23 to 15,227.90) | (6,372.72 to 4,939.64) | |
| Southern Sub-Saharan Africa | 685.19 | 880.69 | 58,103.85 | 5,878.91 | 3,957.60 | 5,113.03 |
| (947.28 to 471.34) | (1,219.63 to 609.09) | (66,917.22 to 51,569.38) | (6,920.42 to 5,041.12) | (4,539.43 to 3,510.15) | (5,818.20 to 4,540.53) | |
| Western Sub-Saharan Africa | 2,560.09 | 736.53 | 57,312.68 | 4,739.64 | 15,122.95 | 4,372.19 |
| (3,511.33 to 1,763.81) | (1,002.77 to 502.61) | (66,066.11 to 49,892.37) | (5,556.83 to 4,046.24) | (17,550.34 to 13,269.76) | (4,963.05 to 3,880.29) | |
ASR, age-standardized rate; DALY, disability-adjusted life year; SDI, Socio-demographic Index; UI, uncertainty interval.
At the regional level, low SDI region had the highest ASDR in 2021 (837.53 [569.85 to 1,140.07]), followed by low-middle SDI region (784.07 [542.47 to 1,059.21]) and high SDI region (766.07 [535.99 to 1,026.59]). Low-middle SDI region had the most notable decreasing trend from 2005 to 2012 (ASDR, 2005: 773.50 [532.79 to 1,051.02]; 2012: 678.77 [470.71 to 922.01]), and high SDI region increased the most from 2019 to 2021 (2019: 630.36 [440.17 to 858.66]; 2021: 766.07 [535.99 to 1,026.59]). Overall, the burden of depressive disorders was higher in females than in males (Figure 1, Supplementary Figures 1 and 2).
The burden of depressive disorders by age and sex
Overall, the burden of depressive disorders was higher in females compared to males. In both sexes, the prevalence rate raised sharply from 10–14 years age group to 15–19 years age group (10–14 years: 1,264.95 [837.50 to 1,774.73]; 15–19 years: 3,380.02 [2,501.25 to 4,435.82]). The prevalence rate peaked in 55–59 years age group in females (7,682.10 [6,398.23 to 9,063.91]), and in 60–64 years age group in males (5,211.18 [4,297.93 to 6,289.85]), before declining. Similarly, incidence rate peaked in 60–64 years in female and 75–79 years in male, then eventually declined.
In 1990, ASPR and ASIR per 100,000 exhibited similar trends but slightly different peak age groups compared to rates in 2021. For females, the age group with the highest prevalence rate was similar compared to that in 2021, while the peak incidence shifted to those aged 95 years and older. Also, the prevalence rate increased in more than 80 years age group, after having a downward trend in 55–80 years. Males showed similar trends compared to females (Figure 2 and Supplementary Tables 7-12).
Figure 2.
Age-specific disability-adjusted life year (DALY) rates (per 100,000 population) of depressive disorders by sex in 2021.
The burden of depressive disorders by demographic factors
At the regional level, ASPR per 100,000 population was highest in high-income regions accounting for 5,108.10 (4,544.07 to 5,808.85) in 2021. North Africa and Middle East (900.68 [598.46 to 1,242.73]) had the highest ASDR per 100,000 population in 2021 (Supplementary Figure 3).
Globally, ASDR decreased from 600.52 (420.94 to 818.45) in 1990 to 584.98 (411.55 to 793.87) in 2010 (APC, -0.11 [-0.25 to 0.03]). In the same period, most regions showed decreasing ASDR, while high-income regions were observed with an increase (1990: 631.44 [440.24 to 852.37]; 2010: 685.56 [483.47 to 931.16]; APC, 0.44 [0.31 to 0.57]). The largest decrease was found in Southeast Asia, East Asia, and Oceania, decreasing from 457.48 (319.49 to 618.29) to 422.43 (300.32 to 566.83) (APC, -0.58 [-0.71 to -0.46]). From 2010 to 2019, the global ASDR showed an increasing trend (2010: 584.98 [411.55 to 793.87]; 2019: 593.50 [413.34 to 810.07]; APC, 0.17 [0.16 to 0.19]), whereas the trends of ASDR in super-regions varied. Southeast Asia, East Asia, and Oceania had the most increase (2010: 422.43 [300.32 to 566.83]; 2019: 429.29 [302.17 to 581.74]; APC, 0.21 [0.09 to 0.33]), and Latin America and Caribbean decreased the most (2010: 602.08 [424.58 to 811.10]; 2019: 584.36 [409.09 to 793.25]; APC, -0.32 [-0.40 to -0.24]). A significant increase in ASDR was shown from 2019 to 2021 in both global and super-regional levels. The global ASDR increased from 593.50 (413.34 to 810.07) in 2019 to 681.14 (475.19 to 923.83) in 2021. Latin America and Caribbean had the most significant increase (2019: 584.36 [409.09 to 793.25]; 2021: 714.86 [495.73 to 974.82]), and Southeast Asia, East Asia, and Oceania had the smallest increase (2019: 429.29 [302.17 to 581.74]; 2021: 453.36 [317.36 to 615.48]) (Figure 3, Supplementary Tables 13-15).
Figure 3.
Global distribution of (A) age-standardized DALY rates of depressive disorders in 2021 and annual percentage change during (B) 1990–2010, (C) 2010–2019, and (D) 2019–2021. DALY, disability-adjusted life year.
Burden of depressive disorders by risk factors
In 2021, the global ASDR attributable to all risk factors was 91.80 (41.77 to 154.99) per 100,000 population representing 13.42% (7.18% to 20.58%) of all depressive disorders DALYs. Males had 62.71 (30.07 to 104.77) ASDR attributable to all risk factors, accounting for 11.54% (6.79% to 17.82%). Females had 120.70 (45.06 to 213.63) ASDR attributable to all risk factors, accounting for 14.64% (6.36% to 23.53%).
In 2021, the leading risk factor in males was BV accounting for 2.68% (1.51% to 4.14%), and another was CSA (9.17 [4.55 to 15.49]). In females, the leading risk factor was IPV, accounting for 8.13% (0.03% to 17.13%) of all ASDR. Females also had CSA and BV as risk factors, representing 2.63% (1.47% to 4.00%) and 4.70% (2.18% to 8.24%) each of all ASDR. In both males and females, the ASDR attributable to CSA and BV showed a gradual upward trend in all estimated years but had a slight downward trend from 2005 to 2011. Whereas ASDR attributable to IPV in females experienced a stable trend from 1990 to 2005, followed by a decreasing trend until 2010, an increasing trend until 2019, and sharp increase from 2019 to 2021 (Figure 4 and Supplementary Tables 16-21).
Figure 4.
Global trend in age-standardized depressive disorders DALYs rate (per 100,000 population) attributable to risk factors (childhood sexual abuse, bullying victimization, and intimate partner violence) by sex, 1990–2021. DALY, disability-adjusted life year.
The ASDR attributable to CSA was high in low and low-middle SDIs for males (low SDI: 24.61 [11.91 to 42.34]; low-middle SDI: 23.70 [11.09 to 40.08]), and in high SDI for females (41.48 [20.28 to 68.73]). The ASDR attributable to BV was high in low-middle and high SDIs for males (low-middle SDI: 62.00 [26.58 to 110.80]; high SDI: 61.78 [27.05 to 112.63]), and in high SDI for females (61.09 [26.43 to 115.98]). The ASDR attributable to IPV was highest in low SDI (96.31 [0.43 to 212.09]) (Supplementary Tables 22 and 23).
Projected DALYs of depressive disorders to 2050
The estimated global ASDR is forecasted to show little change, decreasing from 681.14 (95% UI, 475.19 to 923.83) per 100,000 population in 2021 to 622.30 (430.96 to 847.82) in 2050. However, notable changes were made in closer inspection, decreasing sharply from 2021 (681.14 [475.19 to 923.83]) to 2022 (599.60 [415.78 to 816.26]). After 2022, it is projected to have a gradual inclination from 2022 to 2050.
At the regional level, all super-regions are forecasted to experience a rapid reduction from 2021 to 2022. However, no significant changes are projected to be made in all super-regions from 2022 to 2050. Sub-Saharan Africa is expected to have the highest ASDR throughout the forecast period, reaching 789.99 (543.21 to 1,070.21) DALYs rate per 100,000 in 2050. Subsequently, North Africa and Middle East and high-income regions exhibited the highest ASDR. Whereas the super-region maintaining the lowest rate will be Southeast Asia, East Asia, and Oceania (426.49 [299.62 to 575.87]), followed by Central Europe, Eastern Europe, and Central Asia, and Latin America and Caribbean. In most regions, ASDR are forecasted to have a modest reduction from 2022 to 2050, whereas high-income regions are forecasted to have a slight increase (2022: 678.09 [476.13 to 918.74]; 2050: 682.89 [479.82 to 926.16]) (Figure 5 and Supplementary Table 24). Sensitivity analysis examining the potential impact of the COVID-19 pandemic showed that overall trajectories remained stable regardless of differences in baseline training data. Although projected burdens would have been lower without the peak during the COVID-19 pandemic, the trends are expected to remain relatively stable (Supplementary Figures 4, 5 and Supplementary Tables 25, 26).
Figure 5.
Projection of depressive disorders age-standardized DALYs rate (per 100,000 population) to 2050, globally and by region. The dashed line marks the start of the forecast period in 2022. DALY, disability-adjusted life year.
DISCUSSION
Key findings
This study presents an updated global burden of depressive disorders from 1990 to 2021, analyzing by region, sex, and age, and projects trends through 2050. Before the COVID-19 pandemic, ASDR remained relatively stable, but during the COVID-19 pandemic a sharp increase was observed. The most significant increase during the COVID-19 pandemic was in Latin America and Caribbean. Low-middle and low SDI regions had a high burden of depressive disorders with North Africa and Middle East showing the highest ASDR. The burden increased most rapidly among adolescents and remained consistently higher in females than males. Among females, IPV was the leading risk factor, especially increasing sharply from 2019 to 2021. CSA contributed most significantly to depressive disorders among males in low and low-middle SDI regions and among females in low-middle and high SDI regions. ASDR is projected to show a steady trend until 2050 globally, which implies the burden of depressive disorders has not decreased, highlighting the need for appropriate implications.
Plausible underlying mechanisms
Growing recognition of mental health issues, alongside rising psychosocial and environmental stressors, has intensified concern over depressive disorders [17]. The decline in incidence during the 2000s is partly attributed to the World Mental Health Survey by the WHO, which advanced research and informed national mental health policies [18]. The COVID-19 pandemic introduced acute stressors, including social distancing, economic disruptions, and widespread unemployment [7,19]. Individuals with COVID-19 were vulnerable, possibly due to immune-mediated mechanisms, such as excessive release of pro-inflammatory cytokines associated with the Th1 response [20]. Regional variations in ASDR during the COVID-19 pandemic reflect differences in socioeconomic context and public health infrastructure. In Latin America and the Caribbean, societal disruptions, such as violence, inflation, and political unrest, may have diminished the quality of life [21]. In low and low-middle SDI regions, poverty-related stressors, such as financial instability and inadequate housing led to higher ASDR [22], while cultural stigma and reliance on traditional medicines further increased vulnerability [23]. In high SDI regions, stressors related to advanced economies, including educational pressures, social competition, and intensified social comparisons may explain the elevated ASDR [22].
Females exhibited a higher ASDR than males in all age groups, potentially due to factors like limited social engagement and heightened risk of domestic violence [7]. Low sex equality, including factors such as sexual harassment predominantly targeting females, job insecurity, and high caregiving burdens, increases the risk of depression [24]. Also, estrogen enhances glucocorticoid secretion thereby increasing stress reactivity [25]. The genetic contribution to disparities in sex differences remains unclear; however, recent studies have shown that variations in chromosomes such as phosphodiesterase 4A and ferredoxin 1-like, which are potentially linked to depression, are more frequent in females than in males [26]. These differences are attributed to a combination of sociocultural, physiological, and genetic factors that help explain the sex-based disparities in depression. Age-specific analyses revealed a significant increase in burden during adolescence, due to pubertal hormonal changes, identity development, and emotional volatility [27]. Academic stress and social media exposure further increase vulnerability during this period [17]. The prevalence of depressive disorders peaks in middle and late adulthood. In middle-aged adults, elevated risk is associated with socioeconomic adversity and marital strain, whereas in older adults, cognitive decline and physical comorbidities are key contributors [28].
The ASDR of depressive disorders attributable to BV has shown a continuous increase, reflecting rising bullying incidence [29]. While the ASDR attributable to IPV declined until 2019—likely due to legal measures and increased awareness through mass media [30], a sharp rise occurred in 2020, coinciding with COVID-19 lockdowns which heightened IPV risk through social isolation [31]. Across SDI levels, IPV-related burden was high in low and low-middle SDI regions, where females face limited economic autonomy and weak social support [30]. CSA and BV are linked to reduced social mobility, parental neglect, and exposure to domestic violence [32]. In high SDI regions, increased exposure to online sexual abuse, greater awareness of children’s rights, and improved reporting systems may lead to higher burden attributable to CSA and BV [33].
Clinical and policy implications
In the 2000s, growing awareness of depressive disorders and increased mental health budgets in various countries contributed to a reduction in its incidence [18]. However, since COVID-19 has brought a significant increase in the burdens, a mental health care approach distinct from previous strategies is needed. The WHO’s Comprehensive Mental Health Action Plan 2013–30 aims to increase mental health service coverage by at least 50% by 2030; however, treatment accessibility for depressive disorders remains notably low across all income levels [5]. Particularly, low and low-middle SDI countries face significant barriers due to limited healthcare access and financial burdens, hindering early diagnosis and intervention [34]. To address these gaps, collaboration with research institutions in underserved regions is essential for establishing data platforms, and the expansion of digital mental health services could reduce disparities. Strengthening infrastructure to ensure access to psychiatric specialists, rather than reliance on traditional remedies or secondary hospitals, is also critical. Additionally, international organizations must support knowledge dissemination, encourage increased national mental health budgets, and promote community-based mental health networks.
A socio-structural approach is also crucial for developing effective interventions. In particular, the incidence of depressive disorders sharply increases during adolescence, making early intervention crucial. Given that adolescents are heavily influenced by media and spend a significant amount of time in educational institutions, targeting these environements for early intervention is essential. Targeted initiatives through media, community engagement, and educational institutions are needed to reduce stigma and enhance public understanding of depressive disorders [23,30,35]. Governments should implement policies to eliminate social disadvantages linked to mental health histories and address childhood maltreatment, including CSA, BV, and IPV. As early intervention is critical, expanding school-based prevention programs, which have proven effective in high SDI settings, are essential [18]. In addition, reducing social sex inequalities, such as disadvantages in employment, is crucial to alleviating the burden of depressive disorders in female. Additionally, given that IPV is a female-specific risk factor and as its attributable burden increased during the social isolation caused by the COVID-19 pandemic, establishing support structures for the safety of women is an important measure. Furthermore, projections for 2050 suggest that the global burden of depressive disorders will remain stable without showing improvement, highlighting the need for alternative mental health strategies.
Comparison of previous studies
To the best of our knowledge, this study provides the most comprehensive analysis of data on the burden of depressive disorders using the most recent dataset. While several studies have assessed the global burden of depressive disorders, their scope has often been limited to specific countries or age groups [8,9]. Additionally, other previous studies focused on MDD, thereby limiting the generalizability of their findings [18,30]. However, this study analyzes data on depressive disorders as a whole, encompassing not only MDDs but also dysthymia and double depressive disorders, thereby offering a more comprehensive assessment of the disease burden [36].
Additionally, the utilization of the updated GBD 2021 enables a broader evaluation of the global impact of the COVID-19 pandemic on mental health in this study. While previous studies have investigated the effect of the COVID-19 pandemic on depressive disorders [7], their analyses primarily centered on prevalence trends and the influence of pandemic response policies on depressive disorders, relying on data from 2020. Account for the trends in risk factors during the COVID-19 pandemic nor examine the overall burden of depressive disorders beyond this period. Consequently, these studies were limited in their ability to assess the long-term implications of the pandemic. To address these limitations, this study incorporates updated GBD, further analyzing trends and pathways associated with risk factors to elucidate the socio-structural changes induced by the COVID-19 pandemic and their influence on depressive disorders. Furthermore, this study projected the burden of depressive disorders through 2050 and provided clinical and policy recommendations to mitigate this burden.
Strengths and limitations
This study has several limitations. First, inherent constraints within the GBD framework must be acknowledged. The GBD database lacks original data for certain countries and regions, especially in low SDI regions due to the lack of healthcare and data availability. The presence of missing data introduces the potential for selection bias in estimating disease burden. Moreover, variations in access to mental health services across regions precluded the quantification of interventions for depressive disorders, contributing to measurement bias in assessing prevalence. Second, the study was unable to quantify factors not captured in the analysis, such as behavioral patterns, differences in incidence due to race or genetic factors, and the impact of cultural variations. In some regions, deeply rooted sociocultural stigma surrounding mental disorders may have led to underreporting [23]. To address these limitations, we aimed to overcome them by categorizing similar population groups through SDI and super regions, comparing the data, and explaining factors that may exacerbate depression based on previous studies. Third, while GBD 2021 provides estimates for only three risk factors associated with depressive disorders, the burden of depressive disorders is influenced by a wide range of medical and social determinants that were not captured in the study, limiting a comprehensive assessment of their risk landscape. However, this study aimed to not only observe the trends of risk factors but also quantify their impact on the disease, thereby assessing their relative importance. Despite these limitations, this study provides a comprehensive overview of trends in depressive disorders over a long period, including the period with COVID-19 pandemic, by evaluating the burden and risk factors. Also, by making projections, we offer directions for clinical policies and healthcare systems towards enhancements to optimize global mental health outcomes.
Conclusion
Globally, depressive disorders have shown a consistent trend since 1990, but experienced a notable surge during the COVID-19 pandemic. The burden of depressive disorders was particularly high in low-middle, low, and high SDI regions, with a sharp increase during adolescence and was higher in females than in males. Projections to 2050 suggest that while the impact of the COVID-19 pandemic will eventually subside, no significant downward trend in the burden of depressive disorders is anticipated. Therefore, it is crucial to consider regional disparities in mental health care access and sociocultural factors and to develop policies aimed at the widespread implementation of tailored therapies for depressive disorders. This approach will help reduce the global burden of depressive disorders and improve overall mental health outcomes.
Footnotes
Availability of Data and Material
The findings from this study were produced using data available in public online repositories or in the published literature, data that are publicly available on request from the data provider, and data that are not publicly available due to restrictions by the data provider and which were used under license for the current study. Details on data sources can be found on the GHDx website, including information about the data provider and links to where the data can be accessed or requested (where available). To download the data used in these analyses, please visit the Global Health Data Exchange GBD 2021 website at https://ghdx.healthdata.org/gbd-2021.
Conflicts of Interest
The authors have no potential conflicts of interest to disclose.
Author Contributions
Conceptualization: Eunchae Lee, Jinyoung Jeong, Seohyun Hong, Dong Keon Yon. Data curation: Eunchae Lee, Jinyoung Jeong, Seohyun Hong, Dong Keon Yon. Formal analysis: Eunchae Lee, Jinyoung Jeong, Seohyun Hong, Dong Keon Yon. Funding acquisition: Dong Keon Yon. Investigation: Eunchae Lee, Jinyoung Jeong, Seohyun Hong, Dong Keon Yon. Methodology: Eunchae Lee, Jinyoung Jeong, Seohyun Hong, Dong Keon Yon. Project administration: Dong Keon Yon. Resources: Dong Keon Yon. Software: Eunchae Lee, Jinyoung Jeong, Seohyun Hong, Dong Keon Yon. Supervision: Dong Keon Yon. Validation: Eunchae Lee, Jinyoung Jeong, Seohyun Hong, Dong Keon Yon. Visualization: Eunchae Lee, Jinyoung Jeong, Seohyun Hong, Dong Keon Yon. Writing—original draft: Eunchae Lee, Jinyoung Jeong, Seohyun Hong, Dong Keon Yon. Writing—review & editing: all authors.
Funding Statement
This research was supported by the Ministry of Science and ICT (RS- 2024-00509257 and IITP-2024-RS-2024-00438239) and the Ministry of Health & Welfare (RS-2025-02220492), Republic of Korea.
Acknowledgments
None
Supplementary Materials
The Supplement is available with this article at https://doi.org/10.30773/pi.2025.0211.
Classification of seven super-regions and 21 regions by GBD 2021.
Global count (thousand) and ASRs (per 100,000 population) of depressive disorders, 2019.
Global count (thousand) and ASRs (per 100,000 population) of depressive disorders, 1990.
in age-standardized incidence rate (per 100,000 population) of depressive disorders by SDI level for both sexes from 1990 to 2021.
Trends in age-standardized prevalence rate (per 100,000 population) of depressive disorders by SDI level for both sexes from 1990 to 2021.
Trends in age-standardized DALYs rate (per 100,000 population) of depressive disorders by SDI level for both sexes from 1990 to 2021.
Global counts (thousand) and ASRs (per 100,000 population) of incidence, prevalence, and DALYS for depressive disorders by age group in 2021, for both sexes.
Global counts (thousand) and ASRs (per 100,000 population) of incidence, prevalence, and DALYS for depressive disorders by age group in 2021, for females.
Global counts (thousand) and ASRs (per 100,000 population) of incidence, prevalence, and DALYS for depressive disorders by age group in 2021, for males.
Global counts (thousand) and ASRs (per 100,000 population) of incidence, prevalence, and DALYS for depressive disorders by age group in 1990, for both sexes.
Global counts (thousand) and ASRs (per 100,000 population) of incidence, prevalence, and DALYS for depressive disorders by age group in 1990, for females.
Global counts (thousand) and ASRs (per 100,000 population) of incidence, prevalence, and DALYS for depressive disorders by age group in 1990, for males.
Age-standardized annual percent change in DALYs, incidence, prevalence of depressive disorders from 1990 to 2010.
Age-standardized annual percent change in DALYs, incidence, prevalence of depressive disorders from 2010 to 2019.
Age-standardized annual percent change in DALYs, incidence, prevalence of depressive disorders from 2019 to 2021.
Percent of age-standardized DALYs for depressive disorders attributable to all risk factors, childhood sexual abuse, bullying victimization, and intimate partner violence, 1990–2021, both sexes.
Percent of age-standardized DALYs for depressive disorders attributable to all risk factors, childhood sexual abuse, bullying victimization, and intimate partner violence, 1990–2021, females.
Percent of age-standardized DALYs for depressive disorders attributable to all risk factors, childhood sexual abuse, bullying victimization, and intimate partner violence, 1990–2021, males.
Age-standardized DALYs rate (per 100,000 population) for depressive disorders attributable to all risk factors, childhood sexual abuse and bullying, and intimate partner violence, 1990–2021, both sexes.
Age-standardized DALYs rate (per 100,000 population) for depressive disorders attributable to all risk factors, childhood sexual abuse and bullying, and intimate partner violence, 1990–2021, females.
Age-standardized DALYs rate (per 100,000 population) for depressive disorders attributable to all risk factors, childhood sexual abuse and bullying, and intimate partner violence, 1990–2021, males.
Age-standardized DALYs rate (per 100,000 population) for depressive disorders attributable to risk factors in 2021, by region.
Age-standardized DALYs rate (per 100,000 population) for depressive disorders attributable to risk factors in 1990, by region.
Projected age-standardized DALY rates of depressive disorder (per 100,000 population) globally and by regions from 2022 to 2050.
Projected age-standardized DALY rates of depressive disorder (per 100,000 population) globally and by regions from 2022 to 2050, using training data from 2010 to 2021.
Projected age-standardized DALY rates of depressive disorder (per 100,000 population) globally and by regions from 2020 to 2050, using training data from 2010 to 2019.
Trends in age-standardized incidence rates (per 100,000 population) of depressive disorders by sex and SDI, 1990-2021. SDI, Socio-demographic index.
Trends in age-standardized prevalence rates (per 100,000 population) of depressive disorders by sex and SDI, 1990-2021. SDI, Socio-demographic index.
Global distribution of (A) age-standardized prevalence of depressive disorders in 2021 and annual percentage change during (B) 1990-2010, (C) 2010-2019, and (D) 2019-2021.
Projection of depressive disorders age-standardized DALYs rate (per 100,000 population) to 2050 using data from 2010 to 2021, globally and by region. The dashed line marks the start of the forecast period in 2022. DALYs, disability-adjusted life years.
Projection of depressive disorders age-standardized DALYs rate (per 100,000 population) to 2050 using data from 2010 to 2019, globally and by region. The dashed line marks the start of the forecast period in 2020. DALYs, disability-adjusted life years.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Classification of seven super-regions and 21 regions by GBD 2021.
Global count (thousand) and ASRs (per 100,000 population) of depressive disorders, 2019.
Global count (thousand) and ASRs (per 100,000 population) of depressive disorders, 1990.
in age-standardized incidence rate (per 100,000 population) of depressive disorders by SDI level for both sexes from 1990 to 2021.
Trends in age-standardized prevalence rate (per 100,000 population) of depressive disorders by SDI level for both sexes from 1990 to 2021.
Trends in age-standardized DALYs rate (per 100,000 population) of depressive disorders by SDI level for both sexes from 1990 to 2021.
Global counts (thousand) and ASRs (per 100,000 population) of incidence, prevalence, and DALYS for depressive disorders by age group in 2021, for both sexes.
Global counts (thousand) and ASRs (per 100,000 population) of incidence, prevalence, and DALYS for depressive disorders by age group in 2021, for females.
Global counts (thousand) and ASRs (per 100,000 population) of incidence, prevalence, and DALYS for depressive disorders by age group in 2021, for males.
Global counts (thousand) and ASRs (per 100,000 population) of incidence, prevalence, and DALYS for depressive disorders by age group in 1990, for both sexes.
Global counts (thousand) and ASRs (per 100,000 population) of incidence, prevalence, and DALYS for depressive disorders by age group in 1990, for females.
Global counts (thousand) and ASRs (per 100,000 population) of incidence, prevalence, and DALYS for depressive disorders by age group in 1990, for males.
Age-standardized annual percent change in DALYs, incidence, prevalence of depressive disorders from 1990 to 2010.
Age-standardized annual percent change in DALYs, incidence, prevalence of depressive disorders from 2010 to 2019.
Age-standardized annual percent change in DALYs, incidence, prevalence of depressive disorders from 2019 to 2021.
Percent of age-standardized DALYs for depressive disorders attributable to all risk factors, childhood sexual abuse, bullying victimization, and intimate partner violence, 1990–2021, both sexes.
Percent of age-standardized DALYs for depressive disorders attributable to all risk factors, childhood sexual abuse, bullying victimization, and intimate partner violence, 1990–2021, females.
Percent of age-standardized DALYs for depressive disorders attributable to all risk factors, childhood sexual abuse, bullying victimization, and intimate partner violence, 1990–2021, males.
Age-standardized DALYs rate (per 100,000 population) for depressive disorders attributable to all risk factors, childhood sexual abuse and bullying, and intimate partner violence, 1990–2021, both sexes.
Age-standardized DALYs rate (per 100,000 population) for depressive disorders attributable to all risk factors, childhood sexual abuse and bullying, and intimate partner violence, 1990–2021, females.
Age-standardized DALYs rate (per 100,000 population) for depressive disorders attributable to all risk factors, childhood sexual abuse and bullying, and intimate partner violence, 1990–2021, males.
Age-standardized DALYs rate (per 100,000 population) for depressive disorders attributable to risk factors in 2021, by region.
Age-standardized DALYs rate (per 100,000 population) for depressive disorders attributable to risk factors in 1990, by region.
Projected age-standardized DALY rates of depressive disorder (per 100,000 population) globally and by regions from 2022 to 2050.
Projected age-standardized DALY rates of depressive disorder (per 100,000 population) globally and by regions from 2022 to 2050, using training data from 2010 to 2021.
Projected age-standardized DALY rates of depressive disorder (per 100,000 population) globally and by regions from 2020 to 2050, using training data from 2010 to 2019.
Trends in age-standardized incidence rates (per 100,000 population) of depressive disorders by sex and SDI, 1990-2021. SDI, Socio-demographic index.
Trends in age-standardized prevalence rates (per 100,000 population) of depressive disorders by sex and SDI, 1990-2021. SDI, Socio-demographic index.
Global distribution of (A) age-standardized prevalence of depressive disorders in 2021 and annual percentage change during (B) 1990-2010, (C) 2010-2019, and (D) 2019-2021.
Projection of depressive disorders age-standardized DALYs rate (per 100,000 population) to 2050 using data from 2010 to 2021, globally and by region. The dashed line marks the start of the forecast period in 2022. DALYs, disability-adjusted life years.
Projection of depressive disorders age-standardized DALYs rate (per 100,000 population) to 2050 using data from 2010 to 2019, globally and by region. The dashed line marks the start of the forecast period in 2020. DALYs, disability-adjusted life years.