Abstract
Purpose:
Anxiety and depressive disorders constitute major contributors to global mental health burdens, disproportionately affecting 10-24-year-olds during critical neurodevelopmental windows. Despite their substantial disease burden, persistent treatment gaps and inequitable resource allocation remain worldwide. This study aims to systematically examine the evolving disease burden, inequalities, and achievable frontiers for anxiety and depressive disorders among 10–24 years from 1990 to 2021, providing critical epidemiological evidence to inform integrated adolescent mental health and injury prevention strategies.
Methods:
Employed the data from the Global Burden of Disease Study (GBD) 2021, this study analyzed anxiety and depressive indicators across 953 locations at global, regional, national, and subnational levels, with stratification by sociodemographic index (SDI) quintiles to examine socioeconomic inequalities and identify potentially achievable frontiers. Data analysis occurred from August 2024 to January 2025.
Results:
Key findings reveal a marginal increase in incidence rates over three decades, driven by a marked surge during the pandemic. Significant inter-regional disparities emerged, with high SDI regions such as Western Europe and High-income North America bearing disproportionately greater burdens. Notably, while some countries like Pakistan and Nigeria demonstrated relatively low burdens, subnational analyses uncovered accelerated disease trajectories and persistent health inequities, underscoring emerging challenges in resource-constrained settings. Among the top 100 affected subnational locations in 2021, Iran (31%), Brazil (27%), and Italy (19%) recorded the highest anxiety DALY rates, while the United States of America (51%), the United Kingdom (25%), and Iran (19%) dominated depressive DALYs. Socioeconomic inequalities in disease burdens intensified globally from 1990 to 2021, most pronounced in high SDI countries. In 2021, 89.2% and 92.2% of 204 countries and territories fell behind the anxiety and depressive DALYs frontiers respectively, with 99.0% and 97.1% experiencing an increasing frontier deviation between 1990 and 2021. Among 494 subnational locations, 49.2% fell behind the anxiety DALYs frontiers, and 35.8% fell behind the depressive DALYs.
Conclusion:
These findings emphasize the urgency of implementing targeted interventions addressing developmental-stage vulnerabilities, and integrating equity-focused strategies into mental health prevention and promotion policy frameworks, with direct implications for adolescent self-harm prevention and child safety promotion.
Introduction
Anxiety and depressive disorders represent a leading global health burden, comprising >20% of mental health-related disability (1). Their epidemiological significance arises from both high lifetime prevalence and neurodevelopmental impacts during the critical 10–24 age window (1, 2). This age demarcation captures the neurobiological confluence of adolescence and early adulthood, characterized by prefrontal cortical maturation, hormonal flux, and identity formation processes that heighten susceptibility to psychopathology (3, 4). Beyond their direct disease burden, these disorders are also well-established risk factors for adolescent injury, including self-harm, suicidal behavior, and risk-taking behaviors that elevate unintentional injury risk. Adolescents with untreated depression face substantially increased risks of suicide attempt, while anxiety disorders are linked to engagement in hazardous behaviors. These disorders also substantially impair educational attainment and social relationship formation (5, 6), and demonstrate strong associations with long-term health risks, including metabolic syndrome, cardiovascular diseases, and premature mortality (7–9). Notably, depressive disorders encompass clinically heterogeneous subtypes: major depressive disorder (MDD) and dysthymia are classified as distinct entities in the GBD framework, differing in episode duration, severity, and associations with functional impairment, and may therefore carry different implications for injury risk and long-term health outcomes. The convergence of mental health vulnerability and injury risk during this developmental window highlights the need for integrated prevention frameworks that address both burdens simultaneously.
From 1990 to 2019, the age-standardized incidence rate (ASIR) of anxiety and depressive disorders among adolescents globally both remained relatively stable (2), with a slight decrease in depressive disorders (Estimated annual percentage change, EAPC = −0.23) (11). This relatively stable pattern was disrupted by the COVID-19 pandemic, which substantially increased the global burden of anxiety and depressive disorders (12). As these disorders are closely linked to self-harm, suicidal behavior, risk-taking, and unintentional injury during adolescence, their changing epidemiological patterns are relevant to injury prevention and underscore the need for population-specific strategies that integrate mental health promotion with injury risk reduction.
Despite substantial progress in psychiatric epidemiology, important knowledge gaps remain in understanding how mental health burdens contribute to injury risks. Evidence specific to individuals aged 10–24 years is still limited, although this life stage covers major developmental transitions during which anxiety, depression, risk-taking behaviors, self-harm, and interpersonal violence may emerge or intensify (13). Furthermore, inequalities in mental disorders across and within countries have not been adequately quantified, making it difficult to determine whether the mental health burden associated with injury risk is concentrated in particular demographic or socioeconomic groups. National estimates may obscure subnational variation in disease burden, which is especially relevant for large and heterogeneous countries where prevention resources are planned and allocated locally. In China, for example, provincial-level data have shown higher age-standardized depression incidence in more developed regions than in less-developed regions (14), suggesting that administrative-level estimates may reveal patterns that are not visible in national averages. However, current child and adolescent injury prevention frameworks rarely incorporate mental health burden data. A clearer epidemiological characterization of anxiety and depressive disorders among people aged 10–24 years, including their inequalities and spatial heterogeneity, is needed to support integrated injury prevention strategies.
This study aims to address these limitations through multilevel analysis of GBD 2021 datasets, encompassing 953 locations at regional, national, and subnational levels. We presented temporal trends in incidence, prevalence, and disability-adjusted life years (DALYs) for anxiety and depressive disorders among 10–24 years from 1990 to 2021; MDD and dysthymia were examined separately, alongside the combined depressive disorders category, to determine whether their distinct clinical profiles correspond to divergent burden trajectories and demographic distributions. By integrating these disease burden metrics with SDI stratification, we described the demographic and epidemiological changes in disease burden. Utilizing inequality analysis and frontier analysis, we systematically evaluated spatial heterogeneities in youth mental health burdens, thereby providing an evidence base for integrated mental health and injury prevention strategies, including targeted self-harm prevention, suicide prevention, and child safety promotion across varying resource settings.
Methods
The GBD is a comprehensive epidemiological initiative designed to evaluate the impact of diseases, injuries, and risk factors at global, regional, and national levels. The dataset provides detailed breakdowns by sex, age, and cause, across 953 locations (15), encompassing 1 global entity, 7 GBD super regions (e.g., Latin America and Caribbean), 77 specific international regions (e.g., World Bank income levels), 204 countries and territories (e.g., Brazil), 20 subnational regions (e.g., Brazil North), and 652 local units (e.g., Amazonas). In addition to the global, regional, and national estimates, we also utilized GBD 2021 data sources to ensure robust local-level estimates for 18 countries and territories, including Brazil, China, Ethiopia, Indonesia, Italy, Iran, Japan, Kenya, Mexico, Nigeria, Norway, Pakistan, the Philippines, Poland, South Africa, Sweden, the United Kingdom (UK) and the United States of America (USA). GBD provides full methodological protocols and interactive data visualizations accessible via the Global Health Data Exchange (GHDx) platform http://ghdx.healthdata.org/gbd-results-tool. This study adhered to the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) to ensure methodological rigor throughout the research process. Supplementary materials provide complete geographic hierarchies from global to local (Supplementary Table S1).
GBD uses a variety of indicators to measure population health losses, including prevalence and number of cases, years lost due to premature death (YLLs), years lived with disability (YLDs) and DALYs. DALYs are defined as the sum of YLLs and YLDs, with one DALY representing the loss of 1 year of full health (16). Given the absence of mortality data directly attributable to anxiety and depressive disorders in GBD 2021, YLL estimates for these conditions are constrained to zero. This reflects that anxiety and depressive disorders are not coded as underlying causes of death, and deaths causally related to them, including suicide, are instead attributed to the self-harm cause category. DALYs for these disorders are therefore numerically equivalent to YLDs, capturing morbidity alone rather than combined fatal and non-fatal burden. Consequently, this study primarily utilized the estimates of incidence, prevalence, and DALYs, along with their corresponding 95% uncertainty intervals (UIs), as metrics to assess anxiety and depressive burden and their relevance to injury-related outcomes.
Case definition
Anxiety disorders are characterized by intense and persistent fear and distress, accompanied by physiological symptoms such as increased heart rate, sweating, and trembling. These disorders include panic disorder, agoraphobia, and specific phobia, as classified by DSM-IV-TR codes 300.0–300.3, 208.3, 309.21, 309.81 and ICD-10 classifications F40–F42, F43.0, F43.1, F93.0–F93.2, F93.8 (17). In contrast, the GBD stratifies depressive disorders according to DSM-IV-TR into two categories: major depressive disorders (MDDs) (DSM-IV-TR: 296.21–24, 296.31–34; ICD-10: F32.0–9, F33.0–9) and dysthymia (DSM-IV-TR: 300.4, ICD-10: F34.1), excluding mood disorders secondary to medical comorbidities or substance use (18). MDD is defined by marked depressed mood, anhedonia, and a range of physical and cognitive symptoms. It requires at least one episode of these symptoms daily for at least 2 weeks, significantly affecting functioning (19, 20). Dysthymia presents as a chronically and persistently depressed mood for most of the day, lasting at least 2 years in adults or 1 year in children and adolescents (21). Given these differences in duration, severity, and age-of-onset criteria, MDD and dysthymia may exhibit distinct epidemiological trajectories and demographic distributions, which motivated their separate examination alongside the combined depressive disorders category in this study.
Young people
This study particularly focuses on anxiety and depressive disorders in young people aged 10–24 years. In the presentation of partial results, the 10–24 years age range is divided into three 5-year groups: early adolescence (10–14 years), late adolescence (15–19 years), and young adulthood (20–24 years) (4).
Inequality analysis
To evaluate the inequality in the distribution of anxiety and depressive disorder burdens, we utilized the Slope Index of Inequality (SII) and the Concentration Index of Inequality (CII). The SII is calculated through regression analysis, linking the nationwide disease burden due to anxiety and depressive disorders among the population aged 10–24 years to an income-related relative social position scale, which is defined as the midpoint of the cumulative population strata ranked by gross domestic product (GDP) per capita. The SII quantifies the disparity between the most advantaged and the most disadvantaged groups. When the slope of the regression line is flat, indicating no difference in disease burden across socio-economic strata, the SII equals zero. For positive indicators, where groups are ranked from the most disadvantaged to the most advantaged, a positive SII points to a greater concentration of the burden in the most advantaged subgroup, whereas a negative SII means the burden is more prevalent in the most disadvantaged group. For negative indicators, the interpretation is reversed (22). The CII is computed by fitting a Lorenz concentration curve to the observed cumulative relative distributions of the population ranked by income and the disease burden of anxiety and depressive disorders, and numerically calculating the area under the curve (23). The CII ranges from −1 to +1, with zero value representing no inequality. Higher absolute values of the CII denote more severe inequality. Although the theoretical maximum absolute value for the CII is ±1, practical applications rarely exceed 0.5. A value of 0.2 to 0.3 is considered to represent a reasonably high level of relative inequality (22).
In this study, subnational-level analyses were restricted to 14 nations with reliable subnational SDI data, encompassing Brazil, China, Ethiopia, Indonesia, Iran, Japan, Kenya, Mexico, Norway, Pakistan, South Africa, Sweden, the UK and the USA, with a total of 494 provinces. Nigeria, the Philippines, Italy, and Poland were excluded from subnational assessments due to incomplete province-level SDI data, which precluded inequality and frontier analyses for these regions. The investigation employed World Health Organization (WHO) protocols for health equity assessment to maintain methodological uniformity, enabling cross-regional and cross-indicator comparisons (22). This standardized approach ensured data harmonization throughout spatial analyses and between measurement indicators.
Frontier analysis
To evaluate the relationship between the burden of anxiety and depressive disorders and socio-demographic development, we employed frontier analysis, a quantitative method used to determine the lowest achievable burden of disease at different levels of the SDI (24, 25). The frontiers represent the minimum achievable values for each country or region, taking into account its current healthcare resources and socio-economic development as reflected by the SDI. The distance from the frontier, referred to as the effective difference (EF), quantifies the gap between the actual disease burden and the theoretically optimal health outcome. A larger difference indicates greater potential for improvement.
Using data from 1990 to 2021, we constructed SDI-based frontiers for the prevalence, incidence, and DALY rates of anxiety and depressive disorders. To address data uncertainty, we performed 1,000 bootstrapping by randomly sampling from all regions, countries, and subnations. The mean burden for each SDI value was then computed. A locally weighted regression (LOESS) approach, with a polynomial degree of 1 and a span of 0.2, was applied to smooth the frontier curve (24, 25). To mitigate the influence of outliers, super-efficient countries (those with DALYs significantly below the frontier) were excluded from the frontier construction. Finally, we calculated the effective difference, defined as the absolute distance between the observed values and the frontier for each SDI value. Countries or regions with lower prevalence, incidence, and DALY rates than the frontiers were assigned a distance score of zero. This methodology was applied both at the country level and within countries. Similar to the inequality analysis, subnational-level analyses were restricted to 14 nations with reliable subnational SDI data, encompassing 494 provinces. Through the above method, we were able to quantify the potential for improvement in the burden of anxiety and depressive disorders, highlighting opportunities for optimizing health outcomes within the context of socio-demographic development.
Results
Overview of the global burden
From 1990 to 2021, young people aged 10–24 experienced notable increases in mental health burdens, with the incidence rate of anxiety disorders rising by 24.7% (95% UI: −25.6 to 109.1) and that of depressive disorders by 26.7% (−21.6 to 104.9). The growth in depressive disorders was predominantly driven by MDD, which surged by 27.9% (−23.1 to 113.0), contrasting with the stable incidence pattern observed in dysthymia (Figures 1A–D).
Figure 1
By 2021, global prevalence estimates reached alarming levels, with the number of cases reaching 93,947,157 (68,842,790 to 125,013,836) for anxiety disorders and 57,488,802 (42,231,934 to 78,083,149) for depressive disorders (Table 1). Among age groups, the 10–14 group had the highest anxiety disorder incidence [925.1 (704.1 to 1,213.4)], reflecting the early onset of anxiety disorder. In contrast, depressive disorders incidence peaking in the 20–24 group [5,448.6 (3,987.8 to 7,808.0)] (Figures 1A,B). The elevated burden observed in early adolescence and young adulthood, identifying distinct developmental windows for targeted injury prevention and mental health screening.
Table 1
| Disease category | Metric | Number | Rate | Change in rate | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1990 | 2019 | 2021 | 1990 | 2019 | 2021 | 1990–2019 | 1990–2021 | 2019–2021 | ||
| Anxiety disorders | Incidence | 10,954,492 (7,253,757 to 15,154,940) | 13,099,180 (8,687,298 to 18,143,718) | 16,670,880 (11,045,571 to 23,136,222) | 708.0 (468.8 to 979.5) | 704.5 (467.2 to 975.8) | 883.1 (585.1 to 1225.6) | −0.5% (−40.6 to 66.6) | 24.7% (−25.6 to 109.1) | 25.3% (−25.2 to 110.1) |
| Prevalence | 63,753,648 (47,253,662 to 84,038,633) | 74,673,492 (54,848,339 to 99,255,273) | 93,947,157 (68,842,790 to 125,013,836) | 4120.6 (3054.2 to 5431.7) | 4016.1 (2949.9 to 5338.2) | 4976.6 (3646.8 to 6622.3) | −2.5% (−35.2 to 46.7) | 20.8% (−19.9 to 82.1) | 23.9% (−18.3 to 87.9) | |
| DALY | 7,822,522 (4,876,384 to 11,645,915) | 9,178,143 (5,671,541 to 13,668,048) | 11,523,716 (7,139,551 to 17,187,559) | 505.6 (315.2 to 752.7) | 493.6 (305.0 to 735.1) | 610.4 (378.2 to 910.5) | −2.4% (−47.1 to 80.2) | 20.7% (−34.5 to 122.8) | 23.7% (−33.1 to 128.7) | |
| Depressive disorders | Incidence | 47,739,042 (33,980,523 to 65,973,101) | 56,664,428 (39,577,777 to 79,921,077) | 73,809,681 (51,385,998 to 103,861,167) | 3085.5 (2196.3 to 4264.0) | 3047.6 (2128.6 to 4298.4) | 3909.9 (2722.0 to 5501.8) | −1.2% (−38.8 to 59.7) | 26.7% (−21.6 to 104.9) | 28.3% (−21.5 to 109.8) |
| Prevalence | 38,476,683 (28,826,280 to 51,169,859) | 46,544,779 (34,670,404 to 62,380,324) | 57,488,802 (42,231,934 to 78,083,149) | 2486.9 (1863.1 to 3307.3) | 2503.3 (1864.7 to 3355.0) | 3045.3 (2237.1 to 4136.3) | 0.7% (−32.9 to 51.2) | 22.5% (−19.3 to 85.9) | 21.7% (−20.2 to 85.5) | |
| DALY | 7,025,089 (4,440,486 to 10,480,388) | 8,448,036 (5,299,323 to 12,676,544) | 10,718,195 (6,669,859 to 16,195,136) | 454.1 (287.0 to 677.4) | 454.4 (285.0 to 681.8) | 567.8 (353.3 to 857.9) | 0.1% (−45.4 to 83.5) | 25.0% (−32.1 to 130.5) | 25.0% (−32.5 to 131.5) | |
| MDD | Incidence | 45,093,043 (31,383,990 to 63,718,689) | 53,312,931 (36,410,183 to 76,595,551) | 70,396,953 (47,901,653 to 100,687,703) | 2914.5 (2028.4 to 4118.3) | 2867.3 (1958.2 to 4119.5) | 3729.1 (2537.5 to 5333.7) | −1.6% (−40.9 to 63.9) | 27.9% (−23.1 to 113.0) | 30.1% (−22.7 to 118.7) |
| Prevalence | 28,802,707 (20,020,329 to 40,620,556) | 34,195,108 (23,295,247 to 49,151,273) | 45,037,002 (30,674,802 to 64,522,321) | 1861.6 (1294.0 to 2625.4) | 1839.1 (1252.9 to 2643.5) | 2385.7 (1624.9 to 3417.9) | −1.2% (−40.7 to 64.7) | 28.2% (−23.0 to 113.4) | 29.7% (−23.0 to 118.4) | |
| DALY | 6,039,568 (3,698,961 to 9,293,396) | 7,188,643 (4,311,308 to 11,101,662) | 9,440,939 (5,668,847 to 14,519,152) | 390.4 (239.1 to 600.7) | 386.6 (231.9 to 597.1) | 500.1 (300.3 to 769.1) | −1.0% (−48.5 to 90.4) | 28.1% (−33.2 to 146.0) | 29.4% (−33.1 to 150.2) | |
| Dysthymia | Incidence | 2,645,999 (1,630,857 to 3,906,225) | 3,351,497 (2,072,248 to 4,944,674) | 3,412,728 (2,111,894 to 5,040,907) | 171.0 (105.4 to 252.5) | 180.3 (111.5 to 265.9) | 180.8 (111.9 to 267.0) | 5.4% (−42.7 to 93.9) | 5.7% (−42.5 to 94.5) | 0.3% (−45.4 to 84.3) |
| Prevalence | 9,937,619 (6,780,909 to 13,920,335) | 12,669,693 (8,633,422 to 17,808,524) | 12,872,480 (8,753,348 to 18,081,877) | 642.3 (438.3 to 899.7) | 681.4 (464.3 to 957.8) | 681.9 (463.7 to 957.8) | 6.1% (−36.0 to 75.9) | 6.2% (−36.0 to 76.1) | 0.1% (−39.7 to 66.2) | |
| DALY | 985,521 (570,632 to 1,542,592) | 1,259,393 (727,919 to 1,996,099) | 1,277,256 (740,271 to 2,023,838) | 63.7 (36.9 to 99.7) | 67.7 (39.1 to 107.4) | 67.7 (39.2 to 107.2) | 6.3% (−47.3 to 114.6) | 6.2% (−47.2 to 114.1) | −0.1% (−50.6 to 102.0) | |
Incidence, prevalence, and DALYs rate of anxiety and depressive disorders among young people age 10–24 years globally, 1990–2021.
Data in parentheses are 95% uncertainty intervals. DALY = disability-adjusted life-year.
Epidemiological trends in anxiety and depressive disorders across GBD regions
Across GBD regions, the annual variation in the incidence of anxiety and depressive disorders among young people aged 10–24 from 1990 to 2021 largely mirrored global temporal trends. The disease burden in most SDI regions exhibited an initial downward or stable trajectory, followed by a marked surge. By 2021, anxiety disorders exhibited the highest burden metrics in high SDI regions. Regions with the highest DALY rates included Western Europe, Tropical Latin America, Andean Latin America, and North Africa and Middle East, all exceeding 1,000 per 100,000 (Figures 1A, 2C; Supplementary Table S1).
Figure 2
The epidemiological pattern of depressive disorders differed slightly. There was a subtle turning point of disease burden in 2005, primarily driven by low-middle and low SDI regions. The highest DALY rates in 2021 were observed in High-income North America, with estimates of 1,387.2 (907.2 to 2,047.5). Australasia, Western Europe and North Africa and the Middle East also exhibited a substantial burden. East Asia was the only region where the incidence rate declined (Figures 1B, 2D; Supplementary Table S2). MDD demonstrated concordant temporal trajectories with depressive disorders throughout the study period, while dysthymia exhibited stable incidence rates with minimal inter-annual variation (Figures 1C,D).
Mapping anxiety and depressive burden across national and subnational levels
During the study period, the trends in anxiety prevalence mirrored those of incidence (Figure 1E). The incidence rates increased in 203 out of 204 countries and territories, including 14 that exceeded 50% increase. Mexico experienced the largest increase in both incidence (75.8% [6.7 to 189.9]) and prevalence rates (73.3% [19.9 to 150.2]). By 2021, the highest incidence rates occurred in Portugal (1880.1 [1132.8 to 2828.3]) followed by Lebanon, Iran, and Brazil (Figure 2A; Supplementary Figures S1, S2; Supplementary Table S3). Across 652 subnational regions in 2021, Iran accounted for 31 of the top 100 highest-DALY regions, followed by Brazil (27 units) and Italy (19 units). Brazil’s DALY rates mostly driven by Rondônia, Paraná, and Santa Catarina. In contrast, Nigeria, Pakistan, and China exhibited lower DALY rate but significant subnational disparities. Pakistan exhibited 20% interprovincial variation [Khyber Pakhtunkhwa: 417.6 (242.7–662.8) vs. Balochistan: 348.8 (206.0–539.7)]. In Nigeria, Osun state constituted an extreme outlier [1,026.3 (611.5 to 1,630.1)], while others clustered between 442.8 and 460.2. China displayed pronounced heterogeneity, with Hunan province bearing the highest DALY rate [916.0 (530.2 to 1,427.2)] (Supplementary Figures S3, S4; Supplementary Tables S5–S22).
The global depression burden rose in over 95% of studied regions during 1990–2021 (Figure 1F). The USA saw the largest incidence increase (96.4% [30.7 to 195.8]) from 1990 to 2021, with Lebanon, Lesotho, Tunisia, and Ireland also showing notably rises. Greenland recorded the highest depressive DALY rate [2,114.8 (1,222.5 to 3,365.0)] in 2021 (Figure 2D). In contrast, Slovenia, Singapore, Switzerland, Cuba, China, Democratic People’s Republic of Korea, Kiribati, Maldives, Niger, Nigeria showed a declining trend. China showed the most pronounced reduction [−41.0% (−62.7 to −6.8)], achieving the lowest global incidence rates (Figure 2B; Supplementary Figures S5, S6; Supplementary Table S4). Sub-nationally, the USA accounted for 51 of the top 100 subnational regions by DALY rates, particularly in New Mexico, West Virginia, Arizona, and Arkansas. The UK and Iran accounted for 25 and 19 regions, respectively. Western Europe showed considerable variation. Italy’s burden clustered in northern regions [highest in Emilia-Romagna: 1,256.6 (720.5 to 1,939.8)], while Norway predominantly clustered in southern regions Viken and Oslo. Notably, Japan, Poland, and the Philippines maintained lower burdens despite comparable development levels MDD showed spatial patterns largely consistent with those of overall depressive disorders. (Figure 2E; Supplementary Figures S7, S8; Supplementary Tables S5–S22).
Dysthymia presented a different DALY rates distribution, with Eastern Sub-Saharan Africa showing the highest regional burden [91.9 (52.1–147.0)] in 2021. Comoros topped the list at 97.6 (52.8 to 163.5), followed by Rwanda, Eritrea, Djibouti, and United Republic of Tanzania (Figure 2F). Subnationally, Kenya contained 46 of the top 100 highest-burden regions, demonstrating significant southward concentration [Kiambu: 102.1 (59.6–160.5)]. The UK (34 regions) and Ethiopia (10 regions) followed, with urban centers like Addis Ababa and Stockholm also showed high burdens (Supplementary Figure S9; Supplementary Tables S23, S24).
During the study period, females consistently exhibited higher anxiety and depressive disorder burdens across all 21 GBD regions, with most pronounced disparities in Southern Latin America and High-income North America (Figures 3A–F).
Figure 3
Inequalities change within and between countries
We further quantified socioeconomic inequalities both between and within countries. Compared to 1990, the global absolute inequalities in anxiety burdens increased in 2021. The SII quantifying an absolute burden differential of 390.48 (289.65 to 491.31) incidence rate, 2668.65 (1970.40 to 3366.90) prevalence rate, and 329.09 (244.56 to 413.63) DALY rate per 100,000 between the highest and the lowest SDI countries. Geospatial evaluation highlighted Portugal, Lebanon, Iran, Switzerland, and Norway as principal contributors to this inequality (Figure 4A). Additionally, the gaps in anxiety burden across countries were most pronounced in high SDI countries, followed by lower-middle SDI countries (Supplementary Figure S10). Among 14 nations with subnational SDI data, China outside of mainland regions demonstrating the highest disparity in anxiety DALY rate [SII = 179.43 (−480.84 to 839.70)], succeeded by Brazil [138.56 (57.28 to 219.83)] (Supplementary Figures S11, S12).
Figure 4
Our revealed absolute differences of 390.48 incidence rate (289.65 to 491.31), 2668.65 prevalence rate (1970.40 to 3366.90), and 329.09 DALY rate (244.56 to 413.63) between socioeconomic extremes for depressive disorders (Supplementary Figures S13, S15). By 2021, Sweden (−4,639.40), the USA (−1,819.49), and Brazil (881.00) showed marked SII values in incidence rates. Norway achieved the most notable inequality reduction, with DALY rate SII declining from 351.04 (53.05 to 649.02) in 1990 to 54.40 (−16.12 to 124.91) in 2021. Similar downward trends occurred in the UK, Indonesia, South Africa, and China (Figure 4B). Despite these equity improvements, all studied nations except China and Norway showed increased depression burdens during the observation period (Supplementary Figures S16, S17).
Frontier analysis
We found that over 99% of countries widened their gap for anxiety disorder from the frontier during the study period (Table 2). The 10 countries with the highest EF values for anxiety DALY rates in 2021 were Portugal, Lebanon, Iran, Netherlands, Ireland, France, Germany, Switzerland, Greece, and Norway (range: 996.953 to 1,396.556), all high SDI nations. Only 22 countries achieved the theoretical minimum burden level, primarily in low SDI regions. Among 494 subnational locations, the UK constituted 87% of the top 100 places ranked by EF attributed to DALY rates (Table 3). Iran and Norway saw significant regional EF value increases, with Semnan, Qom, Chahar Mahaal, Bakhtiari, Oslo, Rogaland, and Møre og Romsdal showing large deviations from the frontier in 2021 (Figure 5A; Supplementary Figures S18–S20).
Table 2
| Metric | Frontier analysis outcome | Both | Female | Male |
|---|---|---|---|---|
| Anxiety disorder | ||||
| Incidence | Countries behind frontier n (%) | 174 (85.3%) | 171 (83.8%) | 183 (89.7%) |
| Countries with largest deviation in 2021 | Portugal | Portugal | Portugal | |
| Number of countries with increasing deviation from frontier n (%) | 203 (99.5%) | 203 (99.5%) | 203 (99.5%) | |
| Countries with largest decrease in deviation from 1990 to 2021 | Taiwan (Province of China) | Taiwan (Province of China) | Taiwan (Province of China) | |
| Countries with largest increase in deviation from 1990 to 2021 | Bolivia (Plurinational State of) | Brazil | Bolivia (Plurinational State of) | |
| Prevalence | Countries behind frontier n (%) | 194 (95.1%) | 193 (94.6%) | 196 (96.1%) |
| Countries with largest deviation in 2021 | Portugal | Portugal | Portugal | |
| Number of countries with increasing deviation from frontier n (%) | 202 (99.0%) | 202 (99.0%) | 203 (99.5%) | |
| Countries with largest decrease in deviation from 1990 to 2021 | Taiwan (Province of China) | Taiwan (Province of China) | Taiwan (Province of China) | |
| Countries with largest increase in deviation from 1990 to 2021 | Lebanon | Brazil | Lebanon | |
| DALY | Countries behind frontier n (%) | 182 (89.2%) | 182 (89.2%) | 183 (89.7%) |
| Countries with largest deviation in 2021 | Portugal | Portugal | Portugal | |
| Number of countries with increasing deviation from frontier n (%) | 202 (99.0%) | 202 (99.0%) | 203 (99.5%) | |
| Countries with largest decrease in deviation from 1990 to 2021 | Taiwan (Province of China) | Taiwan (Province of China) | Taiwan (Province of China) | |
| Countries with largest increase in deviation from 1990 to 2021 | Lebanon | Brazil | Lebanon | |
| Depressive disorders | ||||
| Incidence | Countries behind frontier n (%) | 197 (96.6%) | 199 (97.5%) | 197 (96.6%) |
| Countries with largest deviation in 2021 | Greenland | Greenland | Greenland | |
| Number of countries with increasing deviation from frontier n (%) | 198 (97.1%) | 197 (96.6%) | 199 (97.5%) | |
| Countries with largest decrease in deviation from 1990 to 2021 | Cuba | Cuba | Switzerland | |
| Countries with largest increase in deviation from 1990 to 2021 | United States of America | United States of America | Lesotho | |
| Prevalence | Countries behind frontier n (%) | 195 (95.6%) | 196 (96.1%) | 196 (96.1%) |
| Countries with largest deviation in 2021 | Greenland | Greenland | Greenland | |
| Number of countries with increasing deviation from frontier n (%) | 197 (96.6%) | 196 (96.1%) | 196 (96.1%) | |
| Countries with largest decrease in deviation from 1990 to 2021 | Cuba | Cuba | Switzerland | |
| Countries with largest increase in deviation from 1990 to 2021 | United States of America | United States of America | Lesotho | |
| DALY | Countries behind frontier n (%) | 188 (92.2%) | 193 (94.6%) | 189 (92.6%) |
| Countries with largest deviation in 2021 | Greenland | Greenland | Greenland | |
| Number of countries with increasing deviation from frontier n (%) | 198 (97.1%) | 196 (96.1%) | 199 (97.5%) | |
| Countries with largest decrease in deviation from 1990 to 2021 | Cuba | Cuba | Switzerland | |
| Countries with largest increase in deviation from 1990 to 2021 | United States of America | United States of America | Lesotho | |
| MDD | ||||
| Incidence | Countries behind frontier n (%) | 197 (96.6%) | 198 (97.1%) | 195 (95.6%) |
| Countries with largest deviation in 2021 | Greenland | Greenland | Greenland | |
| Number of countries with increasing deviation from frontier n (%) | 198 (97.1%) | 197 (96.6%) | 199 (97.5%) | |
| Countries with largest decrease in deviation from 1990 to 2021 | Cuba | Cuba | Switzerland | |
| Countries with largest increase in deviation from 1990 to 2021 | United States of America | United States of America | Lesotho | |
| Prevalence | Countries behind frontier n (%) | 197 (96.6%) | 198 (97.1%) | 196 (96.1%) |
| Countries with largest deviation in 2021 | Greenland | Greenland | Greenland | |
| Number of countries with increasing deviation from frontier n (%) | 198 (97.1%) | 197 (96.6%) | 199 (97.5%) | |
| Countries with largest decrease in deviation from 1990 to 2021 | Cuba | Cuba | Switzerland | |
| Countries with largest increase in deviation from 1990 to 2021 | United States of America | United States of America | Lesotho | |
| DALY | Countries behind frontier n (%) | 189 (92.6%) | 193 (94.6%) | 185 (90.7%) |
| Countries with largest deviation in 2021 | Greenland | Greenland | Greenland | |
| Number of countries with increasing deviation from frontier n (%) | 198 (97.1%) | 197 (96.6%) | 199 (97.5%) | |
| Countries with largest decrease in deviation from 1990 to 2021 | Cuba | Cuba | Switzerland | |
| Countries with largest increase in deviation from 1990 to 2021 | United States of America | United States of America | Lesotho | |
| Dysthymia | ||||
| Incidence | Countries behind frontier n (%) | 36 (17.6%) | 69 (33.8%) | 44 (21.6%) |
| Countries with largest deviation in 2021 | Comoros | Comoros | Saudi Arabia | |
| Number of countries with increasing deviation from frontier n (%) | 154 (75.5%) | 151 (74.0%) | 161 (78.9%) | |
| Countries with largest decrease in deviation from 1990 to 2021 | United States of America | United States of America | United States of America | |
| Countries with largest increase in deviation from 1990 to 2021 | Mozambique | Mozambique | Ethiopia | |
| Prevalence | Countries behind frontier n (%) | 95 (46.6%) | 96 (47.1%) | 120 (58.8%) |
| Countries with largest deviation in 2021 | Comoros | Comoros | Saudi Arabia | |
| Number of countries with increasing deviation from frontier n (%) | 153 (75.0%) | 146 (71.6%) | 156 (76.5%) | |
| Countries with largest decrease in deviation from 1990 to 2021 | United States of America | United States of America | United States of America | |
| Countries with largest increase in deviation from 1990 to 2021 | Mozambique | Mozambique | Ethiopia | |
| DALY | Countries behind frontier n (%) | 42 (20.6%) | 69 (33.8%) | 25 (12.3%) |
| Countries with largest deviation in 2021 | Comoros | Comoros | Saudi Arabia | |
| Number of countries with increasing deviation from frontier n (%) | 154 (75.5%) | 148 (72.5%) | 157 (77.0%) | |
| Countries with largest decrease in deviation from 1990 to 2021 | United States of America | United States of America | United States of America | |
| Countries with largest increase in deviation from 1990 to 2021 | Mozambique | Mozambique | Ethiopia | |
Summary of Frontier Analysis of 204 Countries from 1990 to 2021.
DALY, Disability-adjusted life-year.
Table 3
| Metric | Frontier analysis outcome | Both | Female | Male |
|---|---|---|---|---|
| Anxiety disorder | ||||
| Incidence | Locations behind frontier n (%) | 263 (53.2%) | 239 (48.4%) | 252 (51.0%) |
| Locations with largest deviation in 2021 | Northern Ireland | Northern Ireland | Northern Ireland | |
| Number of Locations with increasing deviation from frontier n (%) | 494 (100%) | 494 (100%) | 494 (100%) | |
| Locations with largest decrease in deviation from 1990 to 2021 | Balochistan | Balochistan | Balochistan | |
| Locations with largest increase in deviation from 1990 to 2021 | Northern Ireland | Rondônia | Northern Ireland | |
| Prevalence | Locations behind frontier n (%) | 289 (58.5%) | 289 (58.5%) | 279 (56.5%) |
| Locations with largest deviation in 2021 | Northern Ireland | Northern Ireland | Northern Ireland | |
| Number of Locations with increasing deviation from frontier n (%) | 494 (100%) | 494 (100%) | 494 (100%) | |
| Locations with largest decrease in deviation from 1990 to 2021 | Balochistan | Oslo | Shanghai | |
| Locations with largest increase in deviation from 1990 to 2021 | Northern Ireland | Northern Ireland | Northern Ireland | |
| DALY | Locations behind frontier n (%) | 243 (49.2%) | 230 (46.6%) | 227 (46.0%) |
| Locations with largest deviation in 2021 | Northern Ireland | Northern Ireland | Northern Ireland | |
| Number of Locations with increasing deviation from frontier n (%) | 494 (100%) | 494 (100%) | 494 (100%) | |
| Locations with largest decrease in deviation from 1990 to 2021 | Balochistan | Oslo | Shanghai | |
| Locations with largest increase in deviation from 1990 to 2021 | Northern Ireland | Northern Ireland | Northern Ireland | |
| Depressive disorders | ||||
| Incidence | Locations behind frontier n (%) | 389 (78.7%) | 412 (83.4%) | 360 (72.9%) |
| Locations with largest deviation in 2021 | New Mexico | Arkansas | New Mexico | |
| Number of Locations with increasing deviation from frontier n (%) | 472 (95.5%) | 473 (95.7%) | 461 (93.3%) | |
| Locations with largest decrease in deviation from 1990 to 2021 | Hubei | Hubei | Hunan | |
| Locations with largest increase in deviation from 1990 to 2021 | New Mexico | Arkansas | West Virginia | |
| Prevalence | Locations behind frontier n (%) | 311 (63.0%) | 382 (77.3%) | 274 (55.5%) |
| Locations with largest deviation in 2021 | New Mexico | Arkansas | Fars | |
| Number of Locations with increasing deviation from frontier n (%) | 461 (93.3%) | 469 (94.9%) | 431 (87.2%) | |
| Locations with largest decrease in deviation from 1990 to 2021 | Hunan | Hubei | Westminster | |
| Locations with largest increase in deviation from 1990 to 2021 | New Mexico | Arkansas | West Virginia | |
| DALY | Locations behind frontier n (%) | 177 (35.8%) | 175 (35.4%) | 184 (37.2%) |
| Locations with largest deviation in 2021 | New Mexico | Arkansas | Fars | |
| Number of Locations with increasing deviation from frontier n (%) | 470 (95.1%) | 469 (94.9%) | 448 (90.7%) | |
| Locations with largest decrease in deviation from 1990 to 2021 | Hubei | Hubei | Hunan | |
| Locations with largest increase in deviation from 1990 to 2021 | New Mexico | Arkansas | West Virginia | |
| MDD | ||||
| Incidence | Locations behind frontier n (%) | 391 (79.1%) | 416 (84.2%) | 358 (72.5%) |
| Locations with largest deviation in 2021 | New Mexico | Arkansas | New Mexico | |
| Number of Locations with increasing deviation from frontier n (%) | 473 (95.7%) | 473 (95.7%) | 461 (93.3%) | |
| Locations with largest decrease in deviation from 1990 to 2021 | Hubei | Hubei | Hunan | |
| Locations with largest increase in deviation from 1990 to 2021 | New Mexico | Arkansas | West Virginia | |
| Prevalence | Locations behind frontier n (%) | 391 (79.1%) | 419 (84.8%) | 355 (71.9%) |
| Locations with largest deviation in 2021 | New Mexico | Arkansas | New Mexico | |
| Number of Locations with increasing deviation from frontier n (%) | 472 (95.5%) | 470 (95.1%) | 454 (91.9%) | |
| Locations with largest decrease in deviation from 1990 to 2021 | Hubei | Hubei | Hunan | |
| Locations with largest increase in deviation from 1990 to 2021 | New Mexico | Arkansas | West Virginia | |
| DALY | Locations behind frontier n (%) | 187 (37.9%) | 188 (38.1%) | 189 (38.3%) |
| Locations with largest deviation in 2021 | New Mexico | Arkansas | New Mexico | |
| Number of Locations with increasing deviation from frontier n (%) | 471 (95.3%) | 469 (94.9%) | 456 (92.3%) | |
| Locations with largest decrease in deviation from 1990 to 2021 | Hubei | Hubei | Hunan | |
| Locations with largest increase in deviation from 1990 to 2021 | New Mexico | Arkansas | West Virginia | |
| Dysthymia | ||||
| Incidence | Locations behind frontier n (%) | 1 (0.2%) | 7 (1.4%) | 0 (0%) |
| Locations with largest deviation in 2021 | Tehran | Tehran | Dire Dawa | |
| Number of Locations with increasing deviation from frontier n (%) | 299 (60.5%) | 307 (62.1%) | 300 (60.7%) | |
| Locations with largest decrease in deviation from 1990 to 2021 | District of Columbia | District of Columbia | District of Columbia | |
| Locations with largest increase in deviation from 1990 to 2021 | Kurdistan | Kurdistan | Harari | |
| Prevalence | Locations behind frontier n (%) | 21 (4.3%) | 60 (12.1%) | 26 (5.3%) |
| Locations with largest deviation in 2021 | Tehran | Tehran | Dire Dawa | |
| Number of Locations with increasing deviation from frontier n (%) | 305 (61.7%) | 334 (67.6%) | 308 (62.3%) | |
| Locations with largest decrease in deviation from 1990 to 2021 | District of Columbia | District of Columbia | District of Columbia | |
| Locations with largest increase in deviation from 1990 to 2021 | Kurdistan | Kurdistan | Harari | |
| DALY | Locations behind frontier n (%) | 1 (0.2%) | 1 (0.2%) | 0 (0%) |
| Locations with largest deviation in 2021 | Tehran | Tehran | Dire Dawa | |
| Number of Locations with increasing deviation from frontier n (%) | 305 (61.7%) | 332 (67.2%) | 310 (62.8%) | |
| Locations with largest decrease in deviation from 1990 to 2021 | District of Columbia | District of Columbia | District of Columbia | |
| Locations with largest increase in deviation from 1990 to 2021 | Kurdistan | Kurdistan | Harari | |
Summary of Frontier Analysis of 494 Locations from 1990 to 2021.
DALY, Disability-adjusted life-year.
Figure 5
For depressive disorders, the top 10 countries exhibiting the largest deviations in 2021 being Greenland, the USA, Palestine, Tunisia, Greece, Lebanon, Chile, Portugal, Ireland, and Finland (range: 1896.458 to 877.386) (Table 2). Subnationally, the USA had the highest representation in both the top 100 regions ranked by EF values and those ranked by EF increase, comprising 51% of these locations (Table 3). The largest discrepancies between actual and potential minimum DALY rates were observed in high SDI states of the USA such as New Mexico, Arkansas, West Virginia, Arizona, and Utah. Additionally, significant deviations from the frontier were noted in Fars and Kerman in Iran, and Viken in Norway (Figure 5B; Supplementary Figures S21–S23).
For dysthymia, 42 countries (predominantly high SDI) still did not reach the burden frontier in 2021 (Table 2). Conversely, the gap continued to widen in several low-middle SDI countries, including Comoros, Rwanda, and Eritrea (Supplementary Figures S24, S25).
Discussion
This study, leveraging the GBD 2021 dataset, provides a comprehensive geospatial and temporal analysis of anxiety and depressive disorders among young people aged 10–24 years across 953 locations at regional, national, and subnational levels. This study has five principal findings: (1) Over the past three decades, the anxiety and depressive burdens of young people have increased significantly across over 95% of the 204 countries and territories; (2) High and high-middle SDI areas bore marked burdens in 2021, with Portugal and the USA exhibiting the heaviest incidence rates in anxiety and depressive disorders respectively, while divergent burden levels were observed in low and low-middle SDI areas; (3) Females maintained persistently greater absolute burdens; (4) Socioeconomic inequality in disease burdens has intensified globally, with pronounced impacts in high SDI countries like Norway, and also disproportionately affecting low-middle and low SDI countries (e.g., Pakistan and Nigeria); (5) Frontier analysis revealed substantial and widening frontier deviations in DALY rates, with 89.2 and 92.2% of the countries failed to achieve the anxiety and depressive frontier levels in 2021. All subnational locations experienced increasing deviation from the frontier for anxiety disorders during 1990–2021, while 95.1% of units in depressive disorders. These findings carry direct implications for adolescent injury prevention. The identified high burden and high inequality settings, spanning high SDI countries such as Portugal and the United States, middle SDI regions including Brazil and Mexico, and vulnerable subnational areas in Iran and the United Kingdom, represent priority targets for integrated mental health and safety promotion interventions. The widening frontier gaps further quantify the scale of unmet prevention potential, where addressing mental health burden could simultaneously mitigate associated injury outcomes.
Epidemiological trends and gender disparity in burden of anxiety and depressive disorders
The burden of anxiety and depressive disorders among youth followed population-wide temporal trends (26, 27). The 2005–2010 depression burden inflection in low-middle SDI regions aligned with China’s enormous investment in controlling mental illness, supplemented by India and Sudan’s contributions (26). The COVID-19 pandemic exacerbated adolescent mental vulnerability (28), with MDD’s acute nature rendering it more responsive to pandemic-related stressors than chronic dysthymia (29), explaining its pronounced incidence increase. The post-pandemic surge documented here has potentially serious implications for adolescent injury epidemiology (30), given that population-level increases in depression and anxiety are consistently associated with elevated rates of emergency department visits for self-harm and suicide attempts in the same age group.
Globally, young females exhibited consistently higher burdens than males, reflecting both biological susceptibility and sociocultural factors (31). Estrogen fluctuations may affect limbic emotional regulation and enhance hypothalamic–pituitary–adrenal (HPA) axis responses, increasing sensitivity to stress during adolescence and early adulthood (32). Notably, the female excess varied substantially by sociocultural context, suggesting that social and cultural stressors may amplify this biological vulnerability (33). In Southern Latin America, the larger sex disparity may be related to persistent gender-based violence, unequal power relations, and sociocultural norms that normalize female self-silencing or constrain help-seeking, all linked to depression and anxiety among girls (34). In High-income North America, the wider gap may reflect a different constellation of pressures, including high exposure to social media, online harassment, appearance-based comparison, sexual violence, and rising psychological distress among adolescent girls. The US Youth Risk Behavior Survey reported that female students experienced higher levels of violence, poor mental health, and suicidal thoughts and behaviors than male students, while the US Surgeon General’s Advisory highlighted stronger associations of social media use with depressive symptoms, poor body image, and online harassment among girls than boys (35, 36). In regions with large female-to-male ratios, self-harm and injury prevention programs should integrate mental health screening with interventions addressing gender-based violence, digital safety, body image, school connectedness, and accessible counseling.
Variations in disease burden by SDI levels and regions
Our analyses identified high and high-middle SDI regions exhibiting elevated burdens despite superior healthcare infrastructure, alongside the most pronounced intra-regional disparities. In post-industrial societies such as Western Europe and High-income North America, pressures from compulsory education and cultural emphases on achievement at school amplify performance anxiety (37), which interacts with wealth inequality to create chronic stress and intensify mental health disparities (38). Environmental factors significantly modulate disease burdens, with Greenland exhibiting extreme depression rates attributable to climatic extremes and seasonal affective disorder (39). Mediterranean regions like Italy demonstrate climate-related psychiatric impacts, where increasing flood events elevate trauma-related disorders (40). Socioeconomic and structural determinants further drive inequalities. In the USA, depression burdens concentrate in southwestern and eastern states (Arizona, Nevada, West Virginia, South Carolina), reflecting industrial decline, economic disparities, and substance use (41). Norway’s restrictive mental health services criteria on adolescents similarly create treatment gaps (42). The high anxiety and depressive burden in these high SDI settings coexists with elevated adolescent self-harm and suicide rates. In the United States, suicide is now the second leading cause of death among individuals aged 10 to 24 years, a trajectory intensified by untreated depression (43). Our subnational findings provide a spatial evidence base for targeting school-based mental health screening and safety promotion programs to the highest burden states identified here, including New Mexico, West Virginia, and Arkansas.
Notwithstanding comparable development levels, East Asian nations exhibit relatively lower mental health burdens, attributable to unique sociocultural protections. Japan’s declining birthrates have reduced absolute case numbers (44), while its “blue-space” initiatives, which integrate urban water environments such as rivers, lakes, and coastal areas into public health planning, provide ecological stress buffers that may alleviate stress among adolescents in high-density urban settings (45). Similarly, China and South Korea’s nighttime light regulations demonstrate neuroprotective effects by stabilizing circadian rhythms and attenuating cortisol responses (46).
Frontier analysis highlighted inefficiencies in mental healthcare in high and high-middle SDI countries, with Portugal, Lebanon, Greece, and Ireland demonstrating significant improvement potential. Lebanon’s compounded crises [1990 civil war (10) financial collapse (47), and Beirut explosion (48)] created exceptional youth mental health burdens, while Ireland grapples with substance abuse and bullying epidemics (49). Conversely, Cuba and China achieved notable depressive disorder reductions through targeted prevention. China’s “Healthy China 2030” initiative and three-tiered intervention strategy, incorporating digital mental health platforms for high-risk adolescents (50), proved particularly effective. These models provide adaptable frameworks for nations at comparable development stages.
Middle and low-middle SDI regions also faced distinct challenges, particularly in Andean and Tropical Latin America. Brazil’s rapid urbanization alongside traditional structures creates severe disparities, particularly in southern states (e.g., Paraná) where violence, substance use, and educational gaps converge (51). Mexico exemplifies the middle SDI paradox, where economic growth coexists with increasing mental burden. The 2017 Puebla earthquake exposed inadequate preparation in psychosocial emergency response, while subsequent pandemic overwhelmed the fragile mental healthcare system (52). South Asia and Western Sub-Saharan Africa show uneven burdens. Pakistan’s Khyber Pakhtunkhwa demonstrates a high prevalence linked to decades of sociopolitical instability and conflict (53), while Balochistan’s rural areas likely underestimate burdens due to screening limitations (Government of Balochistan (10)). Nigeria reports significant internal variations, predominantly in southwestern states like Osun and Oyo, attributed to socioeconomic conditions, insufficient mental health services, and stigma (54).
Low SDI regions exhibited substantial mental health disparities, characterized by critical shortages of mental health professionals and pervasive stigmatization that contribute to significant underreporting (38). These challenges are exacerbated by conflict and displacement (55). Notably, frontier attainment in some low SDI countries likely reflects diagnostic limitations rather than effective management, underscoring persistent systemic gaps. Future research must employ standardized assessment tools with cross-cultural validation to disentangle these measurement challenges from true epidemiological patterns.
Implications for child injury prevention and safety promotion
Our findings have several implications for adolescent and young adult mental health promotion and injury prevention. The unequal burdens of anxiety and depressive disorders across SDI levels, regions, and subnational units suggest that injury prevention strategies should not rely only on national averages or broad age categories. Mental health indicators should be incorporated into injury surveillance, school health programs, and local priority-setting systems so that areas with overlapping burdens of psychological distress, self-harm, interpersonal violence, or unintentional injury can be identified earlier. This approach is consistent with WHO guidance, which recommends mainstreaming adolescent mental health promotion and prevention across health, education, and community platforms (56), and with the WHO-UNICEF Helping Adolescents Thrive framework, which emphasizes supportive environments, caregiver support, psychosocial interventions, and policies that reduce self-harm and other risk behaviors (57). In high-SDI settings, school-based mental health literacy, early screening, counseling pathways, and safe digital support may be particularly relevant where academic pressure, social isolation, and online exposure contribute to psychological distress. In resource-constrained settings, task-sharing, community-based psychosocial support, and integration with primary care and injury prevention programs may offer more feasible routes to scale-up interventions. For large and heterogeneous countries, subnational estimates can help direct mental health professionals, school counseling resources, and community prevention programs to the areas where young people face the greatest overlapping burden of mental disorders and injury risk (58).
Limitations
With this study represents the most comprehensive analysis to date of anxiety and depressive disorder burdens among young people aged 10–24 years across 204 countries and 652 subnational regions, several constraints warrant careful consideration. First, GBD estimates are derived largely from secondary data sources and statistical modeling rather than direct raw data from various countries, and data availability and quality vary substantially across countries and subnational units. In low- and middle-income settings, limited mental health surveillance capacity, heterogeneous reporting protocols, delayed case ascertainment, and underdiagnosis may lead to underestimated burdens, whereas broader screening and greater service coverage in high-income settings may increase detection and potentially inflate observed differences (39). Cross-country and temporal comparisons may also be affected by cultural variation in symptom recognition, help-seeking behavior, stigma, diagnostic practices, and changes in diagnostic criteria or measurement instruments over time. Second, because YLLs are constrained to zero for these disorders in GBD, our DALY estimates reflect disability alone and are not directly comparable to DALYs for conditions where mortality contributes substantially, such as self-harm or injury. This likely understates their true population impact. Third, although 95% UIs were reported throughout the analysis, they may not fully capture measurement error, selection bias, diagnostic misclassification, or model specification uncertainty. In addition, this study was descriptive and did not apply formal trend testing methods such as EAPC estimation or joinpoint regression; therefore, percentage changes with wide uncertainty intervals should be interpreted cautiously, particularly when the intervals include both decreases and increases. Finally, the multifactorial etiology of anxiety and depressive disorders in the 10–24 age group limit causal interpretation. Observed geographic differences may reflect a mixture of true epidemiological variation and differences in detection, service access, humanitarian response, environmental exposure, socioeconomic conditions, and other unmeasured confounders.
Conclusion
In conclusion, while modest progress has been achieved in mitigating anxiety and depressive disorders among young people aged 10–24 years, the COVID-19 pandemic has underscored the fragility of these gains. Countries with high and high-middle SDI experienced a greater burden of anxiety and depressive disorders, alongside profound health inequalities. Middle and middle-low SDI countries faced accelerating disease trajectories and persistent health inequities across subnational regions, highlighting emerging challenges for resource-limited settings. While a few low SDI countries showed the best performance in reaching the frontier level, significant disparities in mental health burden still remain. These findings emphasize the imperative for coordinated strategies that not only address mental health disparities but also leverage this evidence for child injury prevention and safety promotion. The widening frontier deviations identified here, with 89.2% and 92.2% of countries failing to achieve the anxiety and depressive DALYs frontiers, represent not only mental health treatment gaps but also substantial unrealized potential for reducing adolescent self-harm, suicide, and injury related outcomes. By identifying priority populations and settings through inequality and frontier analyses, this study provides an actionable evidence base for designing region-specific interventions that integrate mental health promotion into child safety frameworks, particularly in resource-limited settings where mental health and injury burdens converge most acutely.
Statements
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found at: https://ghdx.healthdata.org/gbd-2021/sources.
Ethics statement
Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and the institutional requirements.
Author contributions
MY: Writing – original draft, Formal analysis, Data curation, Methodology, Visualization, Project administration. XL: Methodology, Writing – original draft, Project administration, Formal analysis, Visualization, Data curation. WJ: Methodology, Software, Writing – original draft, Visualization, Data curation, Resources. MZ: Data curation, Methodology, Writing – original draft, Visualization, Formal analysis. SF: Formal analysis, Writing – original draft, Data curation, Visualization, Methodology. JinZ: Investigation, Methodology, Project administration, Writing – review & editing. SL: Formal analysis, Project administration, Data curation, Writing – review & editing. LZ: Software, Funding acquisition, Writing – review & editing, Validation. LT: Investigation, Writing – review & editing, Supervision. YiZ: Writing – review & editing, Methodology, Formal analysis. TS: Project administration, Resources, Writing – review & editing. YY: Writing – review & editing, Investigation, Project administration. GC: Data curation, Writing – review & editing, Project administration. YC: Software, Writing – review & editing, Investigation. YiW: Supervision, Writing – review & editing, Validation. YL: Visualization, Writing – review & editing, Methodology. DW: Supervision, Data curation, Writing – review & editing. YuW: Resources, Writing – review & editing, Investigation. NW: Methodology, Writing – review & editing, Investigation. CL: Validation, Project administration, Supervision, Writing – review & editing. WP: Supervision, Validation, Writing – review & editing, Resources. JiaZ: Writing – review & editing, Resources, Validation. YoZ: Supervision, Validation, Writing – review & editing, Investigation. LS: Writing – review & editing, Resources, Validation, Supervision, Project administration. NH: Validation, Supervision, Writing – review & editing, Resources. YZ: Resources, Writing – review & editing, Software. CZ: Validation, Visualization, Writing – review & editing. QL: Writing – review & editing, Supervision, Resources, Funding acquisition, Validation, Project administration, Conceptualization, Data curation, Investigation. XH: Project administration, Validation, Writing – review & editing, Supervision, Conceptualization. WC: Conceptualization, Validation, Investigation, Funding acquisition, Resources, Supervision, Project administration, Writing – review & editing, Data curation.
Funding
The author(s) declared that financial support was received for this work and/or its publication. Guangdong Basic and Applied Basic Research Foundation (2022B1515020094) The Natural Science Foundation of China (#82303552, Recipient: LZ) Guangdong Clinical Medicine Science Data Center (grant number: 2024B1212070015) Guangdong Provincial Clinical Research Center for Breast Diseases (2023B110005) Shanwei Science Technology Commission (#2023A012).
Acknowledgments
We are sincerely grateful to those many personnel across the globe to collect and collate such valuable data for the GBD study. Wenyi Jin had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was used in the creation of this manuscript. Generative AI tools were only used for English language polishing and sentence optimization. All research design, data analysis, result interpretation and manuscript writing were independently completed by the authors, who take full responsibility for all contents of the manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Correction note
A correction has been made to this article. Details can be found at: 10.3389/fpubh.2026.1966767.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Author disclaimer
The opinions expressed herein are solely those of the authors and do not necessarily reflect the positions of the United States Government, the World Health Organization, or the United Nations. Furthermore, we also pray for those children and adolescents affected by the conflict and terrorism worldwide.
Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2026.1916777/full#supplementary-material
References
1.
GBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet Psychiatry. (2022) 9:137–50. doi: 10.1016/S2215-0366(21)00395-3
2.
YangXFangYChenHZhangTYinXManJet al. Global, regional and national burden of anxiety disorders from 1990 to 2019: results from the global burden of disease study 2019. Epidemiol Psychiatr Sci. (2021) 30:e36. doi: 10.1017/S2045796021000275,
3.
ChiniMHanganu-OpatzIL. Prefrontal cortex development in health and disease: lessons from rodents and humans. Trends Neurosci. (2021) 44:227–40. doi: 10.1016/j.tins.2020.10.017,
4.
SawyerSMAfifiRABearingerLHBlakemoreS-JDickBEzehACet al. Adolescence: a foundation for future health. Lancet. (2012) 379:1630–40. doi: 10.1016/S0140-6736(12)60072-5,
5.
LochnerCMogotsiMdu ToitPLKaminerDNiehausDJSteinDJ. Quality of life in anxiety disorders: a comparison of obsessive-compulsive disorder, social anxiety disorder, and panic disorder. Psychopathology. (2003) 36:255–62. doi: 10.1159/000073451,
6.
MychailyszynMPMendezJLKendallPC. School functioning in youth with and without anxiety disorders: comparisons by diagnosis and comorbidity. Sch Psychol Rev. (2010) 39:106–21. doi: 10.1080/02796015.2010.12087793
7.
BonnetFIrvingKTerraJ-LNonyPBerthezèneFMoulinP. Anxiety and depression are associated with unhealthy lifestyle in patients at risk of cardiovascular disease. Atherosclerosis. (2005) 178:339–44. doi: 10.1016/j.atherosclerosis.2004.08.035,
8.
GarfieldLDScherrerJFHauptmanPJFreedlandKEChruscielTBalasubramanianSet al. Association of Anxiety Disorders and Depression with Incident Heart Failure. Psychosom Med. (2014) 76:128–36. doi: 10.1097/PSY.0000000000000027,
9.
PinquartMDubersteinPR. Depression and cancer mortality: a meta-analysis. Psychol Med. (2010) 40:1797–810. doi: 10.1017/S0033291709992285,
10.
Government of Balochistan. Environmental and Social Management Framework: Balochistan Human Capital Investment Project (BHCIP) (Safeguard Document / Policy Framework No. SFG5366). World Bank (2019). Available online at: https://documents1.worldbank.org/curated/en/890741582188225878/pdf/Resettlement-Policy-Framework.pdf.
11.
ZhaoHLiLZhangXShiJLaiWWangWet al. Global, regional, and national burden of depressive disorders among young people aged 10–24 years, 2010–2019. J Psychiatr Res. (2024) 170:47–57. doi: 10.1016/j.jpsychires.2023.11.047,
12.
SantomauroDFHerreraAMMShadidJZhengPAshbaughCPigottDMet al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet. (2021) 398:1700–12. doi: 10.1016/S0140-6736(21)02143-7,
13.
HuaZWangSYuanX. Trends in age-standardized incidence rates of depression in adolescents aged 10–24 in 204 countries and regions from 1990 to 2019. J Affect Disord. (2024) 350:831–7. doi: 10.1016/j.jad.2024.01.009,
14.
TianWYanGXiongSZhangJPengJZhangXet al. Burden of depressive and anxiety disorders in China and its provinces, 1990–2021: findings from the global burden of disease study 2021. Br J Psychiatry. (2025) 228:18–28. doi: 10.1192/bjp.2024.267,
15.
FerrariAJSantomauroDFAaliAAbateYHAbbafatiCAbbastabarHet al. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the global burden of disease study 2021. Lancet. (2024) 403:2133–61. doi: 10.1016/S0140-6736(24)00757-8,
16.
VosTLimSSAbbafatiCAbbasKMAbbasiMAbbasifardMet al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet. (2020) 396:1204–22. doi: 10.1016/S0140-6736(20)30925-9,
17.
BaxterAJVosTScottKMFerrariAJWhitefordHA. The global burden of anxiety disorders in 2010. Psychol Med. (2014) 44:2363–74. doi: 10.1017/S0033291713003243,
18.
American Psychiatric Association. (2018). DSM-5 Update: Supplement to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (October 2018). Available online at: https://seragpsych.com/wordpress/wp-content/uploads/2019/02/DSM5Update_October2018.pdf (Accessed July 19, 2026).
19.
LiSZhangXCaiYZhengLPangHLouL. Sex difference in incidence of major depressive disorder: an analysis from the global burden of disease study 2019. Ann General Psychiatry. (2023) 22:53. doi: 10.1186/s12991-023-00486-7,
20.
MoitraMSantomauroDCollinsPYVosTWhitefordHSaxenaSet al. The global gap in treatment coverage for major depressive disorder in 84 countries from 2000–2019: a systematic review and Bayesian meta-regression analysis. PLoS Med. (2022) 19:e1003901. doi: 10.1371/journal.pmed.1003901,
21.
ThaparACollishawSPineDSThaparAK. Depression in adolescence. Lancet. (2012) 379:1056–67. doi: 10.1016/S0140-6736(11)60871-4,
22.
World Health Organization. Handbook on Health Inequality Monitoring with a Special Focus on Low- and Middle-Income Countries. Geneva: World Health Organization (2013).
23.
MújicaÓJMorenoCM. De la retórica a la acción: Medir desigualdades en salud para “no dejar a nadie atrás.”. Rev Panam Salud Publica. (2019) 43:1–8. doi: 10.26633/RPSP.2019.12,
24.
RuanRLiuXZhangYTangMHeBZhangQet al. Global, regional, and national advances toward the management of rheumatic heart disease based on the global burden of disease study 2019. J Am Heart Assoc. (2023) 12:e028921. doi: 10.1161/JAHA.122.028921,
25.
XieYBoweBMokdadAHXianHYanYLiTet al. Analysis of the global burden of disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int. (2018) 94:567–81. doi: 10.1016/j.kint.2018.04.011,
26.
ChenXLiFZuoHZhuF. Trends in prevalent cases and disability-adjusted life-years of depressive disorders worldwide: findings from the global burden of disease study from 1990 to 2021. Depress Anxiety. (2025) 2025:5553491. doi: 10.1155/da/5553491,
27.
WuYLiXJiXRenWZhuYChenZet al. Trends in the epidemiology of anxiety disorders from 1990 to 2021: a global, regional, and national analysis with a focus on the sociodemographic index. J Affect Disord. (2025) 373:166–74. doi: 10.1016/j.jad.2024.12.086,
28.
ReedHC. E-learning fatigue and the cognitive, educational, and emotional impacts on communication sciences and disorders students during COVID-19. Perspect ASHA Spec Interest Groups. (2022) 7:1885–902. doi: 10.1044/2022_PERSP-22-00049
29.
LiuJLiuYMaWTongYZhengJ. Temporal and spatial trend analysis of all-cause depression burden based on global burden of disease (GBD) 2019 study. Sci Rep. (2024) 14:12346. doi: 10.1038/s41598-024-62381-9,
30.
MadiganSKorczakDJVaillancourtTRacineNHopkinsWGPadorPet al. Comparison of paediatric emergency department visits for attempted suicide, self-harm, and suicidal ideation before and during the COVID-19 pandemic: a systematic review and meta-analysis. Lancet Psychiatry. (2023) 10:342–51. doi: 10.1016/S2215-0366(23)00036-6,
31.
KuehnerC. Why is depression more common among women than among men?Lancet Psychiatry. (2017) 4:146–58. doi: 10.1016/S2215-0366(16)30263-2,
32.
KundakovicMRocksD. Sex hormone fluctuation and increased female risk for depression and anxiety disorders: from clinical evidence to molecular mechanisms. Front Neuroendocrinol. (2022) 66:101010. doi: 10.1016/j.yfrne.2022.101010,
33.
PalamarchukISVaillancourtT. Integrative brain dynamics in childhood bullying victimization: cognitive and emotional convergence associated with stress psychopathology. Front Integr Neurosci. (2022) 16:782154. doi: 10.3389/fnint.2022.782154,
34.
NuñezAGonzálezPTalaveraGASanchez-JohnsenLRoeschSCDavisSMet al. Machismo, Marianismo, and negative cognitive-emotional factors: findings from the Hispanic community health study/study of Latinos sociocultural ancillary study. J Latinao Psychol. (2016) 4:202–17. doi: 10.1037/lat0000050,
35.
Centers for Disease Control and Prevention. Youth Risk Behavior Survey Data Summary & Trends Report: 2013-2023. Atlanta, Georgia: Centers for Disease Control and Prevention (2025).
36.
Office of the Surgeon General (OSG). Social Media and Youth Mental Health: The U.S. Surgeon General’s Advisory. US Department of Health and Human Services (2023).
37.
FergussonDMWoodwardLJ. Mental health, educational, and social role outcomes of adolescents with depression. Arch Gen Psychiatry. (2002) 59:225–31. doi: 10.1001/archpsyc.59.3.225,
38.
ShahbaziFShahbaziMPoorolajalJ. Association between socioeconomic inequality and the global prevalence of anxiety and depressive disorders: an ecological study. Gen Psychiatry. (2022) 35:e100735. doi: 10.1136/gpsych-2021-100735,
39.
RongJWangXChengPLiDZhaoD. Global, regional and national burden of depressive disorders and attributable risk factors, from 1990 to 2021: results from the 2021 global burden of disease study. Br J Psychiatry. (2025) 227:688–97. doi: 10.1192/bjp.2024.266,
40.
ClaytonSManningC. Psychology and Climate Change: Human Perceptions, Impacts, and Responses. New York: Academic Press (2018).
41.
WeinbergerAHZhuJLeeJAnastasiouECopelandJGoodwinRD. Cannabis use among youth in the United States, 2004–2016: faster rate of increase among youth with depression. Drug Alcohol Depend. (2020) 209:107894. doi: 10.1016/j.drugalcdep.2020.107894,
42.
SundAMLarssonBWichstrømL. Prevalence and characteristics of depressive disorders in early adolescents in Central Norway. Child Adolesc Psychiatry Ment Health. (2011) 5:28. doi: 10.1186/1753-2000-5-28,
43.
CurtinSGarnettM. Suicide and Homicide Death Rates among Youth and Young Adults Aged 10–24: United States, 2001–2021. National Center for Health Statistics (US). (2023).
44.
MitomaTMakiJOobaHMasuyamaH. Decline in and recovery of fertility rates after COVID-19-related state of emergency in Japan. BMJ Open. (2024) 14:e087657. doi: 10.1136/bmjopen-2024-087657
45.
JoTSatoMMinamotoTUshimaruA. Valuing the cultural services from urban blue-space ecosystems in Japanese megacities during the COVID-19 pandemic. People Nat. (2022) 4:1176–89. doi: 10.1002/pan3.10366
46.
WuGNgarambeJKimG. Comparative study on current outdoor lighting policies in China and Korea: a step toward a sustainable nighttime environment. Sustainability. (2019) 11:14. doi: 10.3390/su11143989
47.
Al-KhalilZMEl SheikhWGLababidiGHShehayebEOGhanimePMTalihFRet al. Impact of socioeconomic and political stressors on mental health: a cross-sectional study on university students in Lebanon. BMC Med Educ. (2025) 25:91. doi: 10.1186/s12909-025-06701-1,
48.
MaaloufFTHaidarRMansourFElbejjaniMKhouryJEKhouryBet al. Anxiety, depression and PTSD in children and adolescents following the Beirut port explosion. J Affect Disord. (2022) 302:58–65. doi: 10.1016/j.jad.2022.01.086,
49.
DooleyBFitzgeraldAGiollabhuiNM. The risk and protective factors associated with depression and anxiety in a national sample of Irish adolescents. Ir J Psychol Med. (2015) 32:93–105. doi: 10.1017/ipm.2014.83,
50.
LiuSYangLZhangCXiangY-TLiuZHuSet al. Online mental health services in China during the COVID-19 outbreak. Lancet Psychiatry. (2020) 7:e17–8. doi: 10.1016/S2215-0366(20)30077-8,
51.
MolinaMRALSpessatoBJansenKPinheiroRSilvaRSouzaLD d M. Prevalence of comorbidities between mood and anxiety disorders: associated factors in a population sample of young adults in southern Brazil. Cad Saude Publica. (2014) 30:2413–22. doi: 10.1590/0102-311X00012614,
52.
The burden of mental disorders in Mexico, 1990-2019: mental and neurological disorders, substance use, suicides, and related somatic disorders. Span J Psychiatry Ment Health. (2024) 17:64–70. doi: 10.1016/j.rpsm.2021.06.005
53.
ShahSMSunTXuWJiangWYuanY. The mental health of China and Pakistan, mental health laws and COVID-19 mental health policies: a comparative review. Gen Psychiatry. (2022) 35:e100885. doi: 10.1136/gpsych-2022-100885,
54.
GurejeOLasebikanVOEphraim-OluwanugaOOlleyBOKolaL. Community study of knowledge of and attitude to mental illness in Nigeria. Br J Psychiatry. (2005) 186:436–41. doi: 10.1192/bjp.186.5.436,
55.
WanbergCRCsillagBDouglassRPZhouLPollardMS. Socioeconomic status and well-being during COVID-19: a resource based examination. J Appl Psychol. (2020) 105:1382–96. doi: 10.1037/apl0000831,
56.
World Health Organization. Guidelines on Mental Health Promotive and Preventive Interventions for Adolescents: Helping Adolescents Thrive. 1st ed. Geneva: World Health Organization (2020).
57.
World Health Organization and the United Nations Children’s Fund (UNICEF). Helping Adolescents Thrive Toolkit: Strategies to Promote and Protect Adolescent Mental Health and Reduce Self-Harm and Other Risk Behaviours. 1st ed. Geneva: World Health Organization (2021).
58.
World Health Organization. Comprehensive Mental Health Action Plan 2013–2030. 1st ed. Geneva: World Health Organization (2021).
Summary
Keywords
anxiety disoders, child and adolescent, depressive disorder, health inequality, injury prevention, self-harm
Citation
Yuan M, Liang X, Jin W, Zheng M, Feng S, Zhao J, Li S, Zhu L, Tan L, Zeng Y, Shang T, Ye Y, Cai G, Chen Y, Wang Y, Lu Y, Wang D, Wan Y, Wu N, Lin C, Pan W, Zhou J, Zeng Y, Szarpak L, Hamdy NM, Zuo Y, Zhong CC, Lin Q, Huang X and Chen W (2026) Anxiety and depressive disorders in adolescent injury prevention and safety promotion: frontier and inequality mapping across 953 locations. Front. Public Health 14:1916777. doi: 10.3389/fpubh.2026.1916777
Received
23 June 2026
Revised
06 July 2026
Accepted
07 July 2026
Published
11 August 2026
Corrected
18 August 2026
Volume
14 - 2026
Edited by
Pengpeng Ye, Chinese Center For Disease Control and Prevention, China
Updates
Copyright
© 2026 Yuan, Liang, Jin, Zheng, Feng, Zhao, Li, Zhu, Tan, Zeng, Shang, Ye, Cai, Chen, Wang, Lu, Wang, Wan, Wu, Lin, Pan, Zhou, Zeng, Szarpak, Hamdy, Zuo, Zhong, Lin, Huang and Chen.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Wen Chen, chenw43@mail.sysu.edu.cn; Xiaoliang Huang, 497946115@qq.com; Queran Lin, queran.lin18@alumni.imperial.ac.uk
† These authors have contributed equally to this work
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.