Xu YX, Niu XX, Jia WC, Wen J, Cheng XL, Han Y, Peng MH, Zhou J, Liu Y, Jiang SF, Li XP. Burden of mental disorders and risk factors in the Western Pacific region from 1990 to 2021. World J Psychiatry 2025; 15(1): 101750 [DOI: 10.5498/wjp.v15.i1.101750]
Corresponding Author of This Article
Xiao-Pan Li, PhD, Associate Chief Doctor, Department of Health Management Centre, Zhongshan Hospital, Fudan University, No. 179 Fenglin Road, Xuhui District, Shanghai 200030, China. zsjkglzx@163.com
Research Domain of This Article
Psychiatry
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Ya-Xin Xu, Wen-Chang Jia, Jing Wen, Xue-Lin Cheng, Yan Han, Ming-Hui Peng, Jing Zhou, Sun-Fang Jiang, Xiao-Pan Li, Department of Health Management Centre, Zhongshan Hospital, Fudan University, Shanghai 200030, China
Ya-Xin Xu, Jing Wen, Xue-Lin Cheng, Yao Liu, Sun-Fang Jiang, Department of General Practice, Zhongshan Hospital, Fudan University, Shanghai 200030, China
Xiao-Xuan Niu, Department of Nutrition, Zhongshan Hospital, Fudan University, Shanghai 200032, China
Wen-Chang Jia, School of Public Health, Fudan University, Shanghai 200032, China
Co-corresponding authors: Xiao-Pan Li and Sun-Fang Jiang.
Author contributions: Xu YX and Niu XX contributed equally to this work as co-first authors. They were jointly responsible for the overall study design, including formulating the research question, developing the methodology, and coordinating the project execution. They also led the primary data analysis and interpretation, ensuring the robustness and accuracy of the results. Additionally, they played a key role in drafting substantial portions of the manuscript and integrating feedback from all authors. Both authors have made crucial and indispensable contributions towards the completion of the project and thus qualified as the co-first authors of the paper. Jia WC made significant contributions to the data analysis and visualization, including creating detailed plots and figures that were crucial for illustrating the study's findings. Wen J, Chen XL, and Han Y were involved in the meticulous collection and organization of data, as well as drafting the initial sections of the manuscript. Peng MH, Zhou J and Liu Y provided crucial assistance with advanced data analysis techniques and contributed significantly to the discussion and interpretation of the findings. Li XP and Jiang SF are co-corresponding authors of this paper. They were responsible for supervising the research process, providing strategic guidance, and ensuring the scientific rigor of the study. They also made significant contributions to revising the manuscript, addressing critical feedback, and enhancing the overall clarity and impact of the paper. Their collaboration was essential in shaping the final version of the manuscript. All authors have read and approved the final manuscript.
Supported by National Key Research and Development Program of China, No. 2022YFC3600903; and Key Discipline Project under Shanghai's Three-Year Action Plan for Strengthening the Public Health System (2023-2025), No. GWVI-11.1-44.
Institutional review board statement: This study utilized secondary data from the Global Burden of Disease (GBD) database, involving no direct contact with participants. Therefore, approval from an Institutional Review Board was not required.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: All data utilized in this study are publicly accessible through the Global Health Data Exchange at the Global Burden of Disease 2021 portal. The specific datasets and results can be accessed via the following link: https://ghdx.healthdata.org/gbd-2021.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xiao-Pan Li, PhD, Associate Chief Doctor, Department of Health Management Centre, Zhongshan Hospital, Fudan University, No. 179 Fenglin Road, Xuhui District, Shanghai 200030, China. zsjkglzx@163.com
Received: September 25, 2024 Revised: November 4, 2024 Accepted: December 3, 2024 Published online: January 19, 2025 Processing time: 84 Days and 2 Hours
Abstract
BACKGROUND
The burden of mental disorders (MD) in the Western Pacific Region (WPR) remains a critical public health concern, with substantial variations across demographics and countries.
AIM
To analyze the burden of MD in the WPR from 1990 to 2021, along with associated risk factors, to reveal changing trends and emerging challenges.
METHODS
We used data from the Global Burden of Disease 2021, analyzing prevalence, incidence, and disability-adjusted life years (DALYs) of MD from 1990 to 2021. Statistical methods included age-standardisation and uncertainty analysis to address variations in population structure and data completeness.
RESULTS
Between 1990 and 2021, the prevalence of MD rose from 174.40 million cases [95% uncertainty interval (UI): 160.17-189.84] to 234.90 million cases (95%UI: 219.04-252.50), with corresponding DALYs increasing from 22.8 million (95%UI: 17.22-28.79) to 32.07 million (95%UI: 24.50-40.68). During this period, the burden of MD shifted towards older age groups. Depressive and anxiety disorders were predominant, with females showing higher DALYs for depressive and anxiety disorders, and males more affected by conduct disorders, attention-deficit hyperactivity disorder, and autism spectrum disorders. Australia, New Zealand, and Malaysia reported the highest burdens, whereas Vietnam, China, and Brunei Darussalam reported the lowest. Additionally, childhood sexual abuse and bullying, and intimate partner violence emerged as significant risk factors.
CONCLUSION
This study highlights the significant burden of MD in the WPR, with variations by age, gender, and nation. The coronavirus disease 2019 pandemic has exacerbated the situation, emphasizing the need for a coordinated response.
Core Tip: This study is the first to use the Global Burden of Disease database to analyze mental disorder (MD) burden in the Western Pacific Region from 1990 to 2021, examining differences by country, age, and gender. Results reveal a disproportionate MD burden in Australia, New Zealand, and Malaysia, while other countries fall below global average burdens for similar Sociodemographic Index levels. Notably, MD burden surged during the coronavirus disease 2019 pandemic (2019-2021), emphasizing the urgent need for mental health support. Additionally, findings highlight childhood abuse and bullying as prevalent risk factors, offering insights for targeted prevention and resource allocation.
Citation: Xu YX, Niu XX, Jia WC, Wen J, Cheng XL, Han Y, Peng MH, Zhou J, Liu Y, Jiang SF, Li XP. Burden of mental disorders and risk factors in the Western Pacific region from 1990 to 2021. World J Psychiatry 2025; 15(1): 101750
Mental disorders (MD) pose a significant and escalating challenge to global health, impacting individual well-being and broader societal healthcare systems[1]. The Global Burden of Disease (GBD) study indicates a substantial increase in the impact of MD on global health, with disability-adjusted life years (DALYs) rising from 80.8 million in 1990 to 125.3 million in 2019[2]. This growth signifies a heightened contribution of MD to the overall disease burden, increasing from 3.1% to 4.9%[2]. Notably, in 2019, depressive and anxiety disorders were significant contributors, ranking 13th and 24th among global DALYs, respectively[2]. The rising prevalence of MD underscores the urgent need for a deeper understanding of their public health impact and necessitates targeted interventions to mitigate their global burden.
The Western Pacific Region (WPR), defined by the World Health Organization (WHO) as one of six global regions, encompasses a diverse array of countries and territories, each facing unique challenges related to MD[3]. Comprising roughly one-fourth of the world’s population, the WPR has seen a significant increase in the prevalence of MD, now affecting over 215 million individuals[4,5]. According to the 2019 GBD study, MD incidence and DALYs increased by about 40% from 1990 to 2019[2]. Although MD burden in the WPR remains slightly below the global average, substantial disparities exist: High-income countries like Australia and New Zealand bear a significantly higher MD burden, ranking among the world’s highest, compared to low- and middle-income countries like the Philippines and Vietnam[2].
Previous studies have explored the epidemiology of MD in parts of the WPR, often focusing on specific disorders, regions, or limited time frames, leaving gaps in understanding the overall MD burden[6-8]. Research on depression and anxiety in Southeast Asia, for example, often lacks a broader WPR context and excludes comprehensive age coverage[6,8,9]. Over the past three decades, global shifts - including geopolitical changes, demographic trends, and the coronavirus disease 2019 (COVID-19) pandemic - have impacted health systems worldwide[10]. This study examines the MD burden in the WPR from 1990 to 2021, assessing incidence, prevalence, DALYs, and risk factors, to guide targeted mental health interventions addressing the region's complex challenges.
MATERIALS AND METHODS
Data source
This study utilized the latest iteration of the GBD 2021, a comprehensive database spanning communicable diseases, non-communicable diseases, and injuries from 1990 to 2021 across 204 countries[11]. The GBD 2021 dataset encompasses information on 371 diseases and injuries, alongside 87 risk factors stratified by age groups and gender[11,12]. Data for this analysis were sourced from the Institute for Health Metrics and Evaluation website (https://ghdx.healthdata.org/gbd-2021). The detailed methodology has been previously published, and adherence to the Guidelines for Accurate and Transparent Health Estimates Reporting ensures transparency and reproducibility (Supplementary Table 1)[12,13].
MD classification
MD included in the GBD 2021 study comprise depressive disorders, anxiety disorders, bipolar disorder, schizophrenia, autism spectrum disorders, conduct disorder, attention-deficit hyperactivity disorder (ADHD), eating disorders, idiopathic developmental intellectual disability, and a residual category of other MD[2]. To ensure comparability, case definitions predominantly adhere to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision criteria, which are widely utilized across mental health surveys[2]. The classifications and definitions of MD in the GBD study have been extensively detailed previously[2].
Classification of countries
For this investigation, 27 Western Pacific countries were included. These countries are segmented into East Asia (China, Mongolia, Japan, and South Korea), Southeast Asia (Cambodia, Laos, Vietnam, the Philippines, Malaysia, Brunei, and Singapore), and Oceania (Australia, New Zealand, Papua New Guinea, Fiji, Kiribati, Marshall Islands, the Federated States of Micronesia, Samoa, Solomon Islands, Tonga, Vanuatu, Niue, Nauru, Palau, Cook Islands, and Tuvalu)[3]. To demonstrate the relationship between disease burden and development level, we calculated the Sociodemographic Index (SDI) of WPR countries and categorized them into five SDI levels: Low SDI (0-0.2), low-middle SDI (0.2-0.4), middle SDI (0.4-0.6), high-middle SDI (0.6-0.8), and high SDI (0.8-1.0).
Risk factors estimation
The GBD 2021 comparative risk assessment framework categorizes 87 risk factors into behavioral, environmental/occupational, or metabolic domains, with 28 identified as relevant to MD[12]. Major risk factors for MD include childhood sexual abuse and bullying (childhood sexual abuse, bullying victimization), intimate partner violence, and other environmental factors (lead exposure). Detailed information regarding the definition, prevalence, relative risk, and methods for quantifying the burden attributable to these risk factors is extensively documented elsewhere[14].
Measures of burden
The burden of MD was assessed using various measures, including incidence, prevalence, Years lived with disability, years of life lost (YLLs), and DALYs. Notably, eating disorders were the only MD for which YLLs could be estimated. Age-standardised rates (ASR) were calculated using the global age structure from 2021, corrected by the direct method and the world standard population to account for population age structure disparities.
Statistical analysis
Results including incidence, prevalence, and DALYs were presented in absolute numbers and ASR per 100000 population, along with their respective 95% uncertainty intervals (UIs). Percentage changes between 1990 and 2021 were reported to illustrate temporal variations. All data analyses and calculations were performed using R software (version 4.3.2).
RESULTS
Regional analysis
The global prevalence of MD increased by nearly 63.36% from 670.53 million cases (95%UI: 616.00-734.71 million) in 1990 to 1095.40 million cases (95%UI: 1016.48-1184.47 million) in 2021, with the WPR accounting for 21.44% of the total. In the WPR, the estimated total prevalence of all MD increased by approximately 24.08% from 174.40 million cases (95%UI: 160.17-189.84 million) in 1990 to 220.18 million cases (95%UI: 205.57-235.18 million) by 2019. By 2021, it further rose by nearly 6.68% to approximately 234.90 million cases (95%UI: 219.04-252.50 million). The corresponding ASR per 100000 population changed from an unspecified value in 1990 to 11245.56 (95%UI: 10378.24-12175.34) in 2019, increasing to 11479.42 (95%UI: 10651.52-12422.04) by 2021 (Table 1). Regarding the incidence of MD, our findings reveal a similar trend. In 1990, the estimated incidence was 54.50 million cases (95%UI: 49.38-61.53), with an ASR of 3528.01 per 100000 population (95%UI: 3202.83-3942.15). This number increased to approximately 71.54 million cases (95%UI: 65.12-79.64) in 2019, with an ASR per 100000 population of 3414.98 (95%UI: 3113.25-3782.82). By 2021, the incidence reached around 75.67 million cases (95%UI: 68.01-84.56), with an ASR of 3574.89 per 100000 population (95%UI: 3236.54-4018.22).
Table 1 Comparison of prevalence, incidence, and disability-adjusted life years for mental disorders over the specified years in the Western Pacific region (1990, 2019, and 2021).
1990
2019
2021
Number, in millions (95%UI)
Age-standardised rate per 100000 (95%UI)
Number, in millions (95%UI)
Age-standardised rate per 100000 (95%UI)
Number, in millions (95%UI)
Age-standardised rate per 100000 (95%UI)
All mental disorders
Prevalence
174.40 (160.17, 189.84)
11245.56 (10378.24, 12175.34)
220.18 (205.57, 235.18)
10907.04 (10122.23, 11735.19)
234.90 (219.04, 252.50)
11479.42 (10651.52, 12422.04)
Incidence
54.50 (49.38, 61.53)
3528.01 (3202.83, 3942.15)
71.54 (65.12, 79.64)
3414.98 (3113.25, 3782.82)
75.67 (68.01, 84.56)
3574.89 (3236.54, 4018.22)
DALYs
22.8 (17.22, 28.79)
1471.73 (1112.56, 1854.35)
30.16 (22.94, 38.14)
1440.00 (1092.79, 1821.63)
32.07 (24.50, 40.68)
1517.45 (1159.48, 1910.43)
Depressive disorders
Prevalence
43.71 (39.52, 48.73)
2929.95 (2655.17, 3247.59)
65.15 (58.73, 72.48)
2793.41 (2517.00, 3108.16)
69.26 (62.30, 76.93)
2923.94 (2625.80, 3251.37)
Incidence
39.77 (35, 46.1)
2598.99 (2290.13, 2990.86)
55.79 (49.68, 63.34)
2454.73 (2185.42, 2800.35)
58.50 (51.22, 67.61)
2547.84 (2238.25, 2950.88)
DALYs
6.96 (4.81, 9.40)
458.71 (319.61, 620.71)
9.97 (7.06, 13.45)
433.33 (305.24, 587.40)
10.53 (7.47, 14.20)
452.26 (317.33, 611.00)
Anxiety disorders
Prevalence
51.86 (44.47, 60.53)
3323.29 (2869.51, 3842.55)
62.72 (54.8, 71.59)
3108.83 (2682.97, 3581.50)
72.93 (62.75, 84.9)
3568.44 (3039.14, 4146.63)
Incidence
8.20 (6.75, 10.01)
521.81 (437.49, 626.19)
9.48 (8.05, 11.26)
504.11 (428.16, 598.12)
10.74 (8.97, 12.84)
566.45 (472.32, 689.88)
DALYs
6.26 (4.33, 8.66)
398.43 (277.88, 545.80)
7.47 (5.18, 10.18)
374.16 (261.67, 512.11)
8.67 (6, 11.8)
429.03 (297.18, 586.77)
Bipolar disorder
Prevalence
3.73 (3.11, 4.45)
240.96 (202.85, 284.04)
5.15 (4.36, 6.06)
237.06 (198.68, 281.15)
5.20 (4.40, 6.10)
237.76 (199.23, 282.11)
Incidence
0.27 (0.23, 0.32)
16.91 (14.29, 19.90)
0.33 (0.27, 0.38)
17.14 (14.47, 20.31)
0.33 (0.28, 0.39)
17.17 (14.49, 20.34)
DALYs
0.81 (0.53, 1.18)
52.08 (34.49, 74.97)
1.11 (0.73, 1.59)
51.48 (33.53, 74.15)
1.12 (0.73, 1.60)
51.56 (33.46, 74.24)
Schizophrenia
Prevalence
4.60 (3.93, 5.32)
298.37 (256.83, 342.64)
6.85 (5.91, 7.87)
304.42 (260.01, 351.49)
6.95 (5.99, 7.96)
307.88 (264.08, 355.09)
Incidence
0.32 (0.27, 0.37)
17.79 (15.24, 20.76)
0.32 (0.28, 0.38)
17.84 (15.23, 20.82)
0.32 (0.27, 0.38)
18.01 (15.35, 21.05)
DALYs
3.00 (2.26, 3.79)
193.79 (145.98, 243.81)
4.44 (3.32, 5.59)
198.56 (148.64, 250.44)
4.50 (3.33, 5.66)
200.62 (149.43, 253.12)
Autism spectrum disorders
Prevalence
11.28 (9.43, 13.32)
723.29 (604.16, 853.80)
14.05 (11.79, 16.64)
748.52 (627.48, 885.53)
14.16 (11.86, 16.79)
750.54 (628.69, 888.95)
Incidence
0.24 (0.20, 0.28)
16.05 (13.43, 18.95)
0.16 (0.14, 0.19)
16.35 (13.73, 19.24)
0.14 (0.12, 0.17)
16.63 (13.99, 19.62)
DALYs
2.13 (1.45, 3.00)
136.28 (92.89, 191.47)
2.63 (1.80, 3.71)
141.41 (96.47, 198.96)
2.65 (1.80, 3.74)
141.71 (96.57, 199.53)
Conduct disorder
Prevalence
7.87 (5.75, 10.13)
482.34 (352.84, 622.11)
6.57 (4.76, 8.42)
504.21 (365.43, 645.59)
6.79 (4.92, 8.70)
503.13 (364.50, 644.47)
Incidence
2.65 (1.91, 3.43)
173.58 (124.85, 224.88)
2.34 (1.68, 3.01)
180.36 (129.20, 231.90)
2.45 (1.75, 3.16)
179.86 (128.78, 231.42)
DALYs
0.96 (0.53, 1.51)
59.11 (32.22, 93.31)
0.81 (0.44, 1.27)
62.00 (33.97, 97.24)
0.84 (0.46, 1.32)
61.86 (33.92, 97.46)
ADHD
Prevalence
29.83 (22.17, 40.79)
1785.18 (1334.76, 2442.93)
28.48 (21.72, 38.41)
1829.75 (1383.51, 2480.11)
29.22 (22.21, 39.65)
1863.86 (1405.63, 2569.10)
Incidence
1.36 (0.93, 2.02)
92.37 (63.46, 137.33)
1.37 (0.95, 1.99)
106.57 (73.41, 154.57)
1.42 (0.97, 2.06)
107.03 (73.19, 155.09)
DALYs
0.36 (0.19, 0.59)
21.78 (11.73, 35.57)
0.35 (0.19, 0.56)
22.38 (12.20, 36.00)
0.36 (0.20, 0.57)
22.79 (12.72, 36.59)
Eating disorders
Prevalence
2.13 (1.58, 2.82)
118.45 (88.81, 156.14)
2.89 (2.19, 3.80)
168.81 (127.36, 222.30)
2.92 (2.21, 3.83)
174.79 (131.16, 231.38)
Incidence
1.70 (1.15, 2.45)
90.52 (62.49, 130.38)
1.74 (1.23, 2.45)
117.87 (81.37, 167.86)
1.77 (1.25, 2.48)
121.91 (84.01, 173.93)
DALYs
0.46 (0.27, 0.72)
25.45 (15.48, 39.70)
0.62 (0.39, 0.97)
36.40 (22.39, 56.83)
0.63 (0.39, 0.98)
37.67 (23.00, 58.73)
Idiopathic developmental intellectual disability
Prevalence
8.01 (3.10, 13.37)
500.34 (192.85, 838.15)
5.62 (1.72, 10.03)
330.38 (105.44, 580.28)
5.49 (1.66, 9.82)
323.19 (100.60, 568.37)
Incidence
NA
NA
NA
NA
NA
NA
DALYs
0.33 (0.11, 0.65)
20.64 (6.60, 40.83)
0.25 (0.07, 0.49)
14.39 (4.25, 28.87)
0.24 (0.07, 0.49)
14.09 (4.10, 28.43)
Other mental disorders
Prevalence
20.4 (15.93, 25.38)
1419.98 (1113.87, 1753.02)
33.79 (26.69, 41.96)
1422.88 (1116.32, 1758.21)
34.41 (27.23, 42.70)
1424.19 (1117.53, 1759.91)
Incidence
0 (0, 0)
0 (0, 0)
0 (0, 0)
0 (0, 0)
0 (0, 0)
0 (0, 0)
DALYs
1.53 (0.97, 2.31)
105.45 (67.29, 160.16)
2.50 (1.62, 3.78)
105.88 (68.05, 160.85)
2.54 (1.64, 3.83)
105.87 (67.79, 160.70)
In the WPR, DALYs attributed to MD were estimated at 22.8 million (95%UI: 17.22-28.79) in 1990, with an ASR of 1471.73 per 100000 population (95%UI: 1112.56-1854.35). By 2019, DALYs had increased to approximately 30.16 million (95%UI: 22.94-38.14), with a slight rise in the ASR to 1440 per 100000 population (95%UI: 1092.79-1821.63). This upward trend persisted into 2021, with DALYs estimated at 32.07 million (95%UI: 24.50-40.68) and the ASR further climbing to 1517.45 per 100000 population (95%UI: 1159.48-1910.43) (Table 1).
Within the WPR, depressive and anxiety disorders were the most common mental health conditions in the years 1990, 2019, and 2021 (Table 1). Conversely, bipolar disorder and eating disorders were among the least common. Depressive disorders experienced an increase in prevalence from approximately 43.71 million cases (95%UI: 39.52-48.73) in 1990, with an ASR of 2929.95 per 100000 population (95%UI: 2655.17-3247.59), to about 65.15 million cases (95%UI: 58.73-72.48) in 2019, despite a slight reduction in ASR to 2793.41 per 100000 population (95%UI: 2517.00-3108.16). The trend of increasing prevalence continued into 2021, reaching 69.26 million cases (95%UI: 62.30-76.93 million) and accompanied by a higher ASR of 2923.94 per 100000 population (95%UI: 2625.80-3251.37). Anxiety disorders exhibited a similar trajectory, with an estimated 51.86 million cases (95%UI: 44.47-60.53) in 1990 and an ASR of 3323.29 per 100000 population (95%UI: 2869.51-3842.55). By 2019, the prevalence had increased to around 62.72 million cases (95%UI: 54.8-71.59), with an ASR of 3108.83 per 100000 population (95%UI: 2682.97-3581.50). By 2021, the prevalence surged to approximately 72.93 million cases (95%UI: 62.75-84.9), with an ASR of 3568.44 per 100000 population (95%UI: 3039.14-4146.63), indicating an increasing burden of anxiety disorders. Other MD such as bipolar disorder and schizophrenia also exhibited variations in prevalence and ASR between 1990 and 2021.
Age and gender patterns
The analysis of the MD burden by age and gender in the years 1990 and 2021, as depicted in Figure 1. In 1990, the distribution of DALYs across age groups indicated a significant concentration of mental health disorders in the younger population, particularly among those aged 15-40 years. However, by 2021, the data revealed a shift in the mental health burden towards older age groups, with a noticeable increase among individuals over 40 years old. A noticeable gender disparity was observed, with females exhibiting higher DALYs for depressive and anxiety disorders, while males showed more prevalence of conduct disorders, ADHD, and autism spectrum disorders. MD imposed a significant burden across all age groups. Autism spectrum disorders, conduct disorder, and ADHD predominantly impact individuals under 15 years old. As individuals transitioned into adulthood (20-59 years), anxiety, depression, bipolar disorder, and schizophrenia became more prominent. In the elderly population (60 years and above), depressive and anxiety disorders continued to be major mental health concerns (Supplementary Table 2).
Figure 1 Disability-adjusted life-years for mental disorders in the Western Pacific Region by sex and age, 1990 and 2021.
DALYs: Disability-adjusted life-years.
National trends
In the WPR, Australia, New Zealand, and Malaysia recorded the highest ASR of DALYs for MD in 2021 (Figure 2), with Australia at 2810.18 per 100000 population (95%UI: 2099.45-3593.58), New Zealand at 2676.77 cases per 100000 population (95%UI: 1973.29-3482.92), and Malaysia at 1866.65 per 100000 population (95%UI: 1377.20-2476.70). Conversely, Vietnam, China, and Brunei Darussalam reported the lowest ASR of DALYs, with Vietnam at 1369.69 per 100000 population (95%UI: 1031.59-1763.48), China at 1446.81 per 100000 population (95%UI: 1102.89-1826.67), and Brunei Darussalam at 1553.43 per 100000 population (95%UI: 1186.99-1959.00) (Supplementary Table 3). In most countries, the top four MD conditions contributing to the highest DALYs in 2021 were depressive disorders, anxiety disorders, schizophrenia, and autism spectrum disorders. However, in New Zealand, bipolar disorder was ranked third in DALYs, and in Australia, it was ranked fourth (Figure 3, Supplementary Table 4).
Figure 2 Age-standardised disability-adjusted life-years per 100000 population attributable to mental disorders in the Western Pacific Region, 1990 and 2021.
DALYs: Disability-adjusted life-years; ASR: Age-standardised rates.
Figure 3 Rankings of age-standardised disability-adjusted life-years for mental disorders by country, 2021.
The disorders are sequentially ordered from highest to lowest burden per country. Each distinct color denotes a different type of mental disorder. The numerical labels within each box indicate the regional rank of that particular mental disorder's disability-adjusted life-years within the Western Pacific Region. DALYs: Disability-adjusted life-years.
Globally, the ASR of DALYs for MD was typically concentrated in high SDI countries. In the WPR, except for Australia, New Zealand, and Malaysia, the ASR of DALYs for MD in other countries was generally below the global average for their corresponding SDI categories (Figure 4). From 1990 to 2019, the ASR of DALYs for MD remained relatively stable across most WPR countries, despite minor fluctuations. Specifically, between 2000 and 2004, Australia and Malaysia saw an increase in the ASR of DALYs, which stabilized after 2004. Notably, from 2019 to 2021, a significant upward trend in the ASR of DALYs was observed across all countries in the region, indicating a widespread increase in the burden of MD.
Figure 4 Temporal trends of age-standardised disability-adjusted life-years for mental disorders in the Western Pacific Region by country and Sociodemographic Index, 1990-2021.
DALYs: Disability-adjusted life-years; ASR: Age-standardised rates; SDI: Sociodemographic Index.
Risk factors
Childhood sexual abuse and bullying, intimate partner violence, and other environmental factors are primary risk factors contributing to MD. In 2021, countries such as Australia, New Zealand, and Tonga exhibited higher rates of childhood sexual abuse and bullying, which were primary risk factors contributing to MD. Additionally, Mongolia, Australia, and New Zealand were ranked highest in terms of intimate partner violence, while Cambodia, Solomon Islands, and the Lao People's Democratic Republic emerged as the top countries with the highest prevalence of other environmental factors contributing to MD (Figure 5 and Supplementary Table 5).
Figure 5 Distribution of age-standardised disability-adjusted life-years for mental disorders attributed to various risk factors in the Western Pacific Region in 2021.
DALYs: Disability-adjusted life-years; ASR: Age-standardised rates.
Childhood sexual abuse and bullying substantially contributed to MD across nearly all age groups in 2021, particularly prevalent among individuals aged 10-34. Intimate partner violence and other environmental risks also contributed to MD, albeit less significantly than childhood sexual abuse and bullying. Gender-specific analysis indicated a disproportionate impact on females by intimate partner violence, particularly evident in reproductive and post-reproductive age groups (Figure 6, Supplementary Table 6).
Figure 6 Distribution of disability-adjusted life-years for mental disorders attributed to various risk factors by sex and age in the Western Pacific Region in 2021.
DALYs: Disability-adjusted life-years.
DISCUSSION
This study offers a detailed exploration of the escalating burden of MD in the WPR from 1990 to 2021, highlighting the significant growth in cases. By analyzing gender and age distribution, along with variations across countries, the study offers insights into the intricate dynamics influencing mental health in the region. The research further identifies critical risk factors, including childhood sexual abuse and bullying, and intimate partner violence, emphasizing their persistent impact over time. This comprehensive analysis illuminates not only the current state of mental health in the WPR but also lays the groundwork for future investigations and interventions aimed at addressing this burgeoning public health challenge.
In 2021, the WPR witnessed a surge in MD, experiencing a more than 50% increase since 1990. This upward trend is consistent with findings from previous studies and can be attributed to a variety of interconnected factors[4]. Primary drivers of the increasing burden of MD include global population growth and the rising proportion of elderly individuals[15]. This demographic shift, along with significant advances in medical diagnostics and a reduction in mental health stigma, has led to more individuals being diagnosed and treated[16,17]. Furthermore, with mental health gaining recognition as a critical public health issue, there is an improved understanding of its effects on society at large[18]. Additionally, socioeconomic changes driven by globalization, such as rapid urbanization and the breakdown of traditional support systems, have heightened stress levels, potentially leading to an increase in mental health disorders[19]. The COVID-19 pandemic has exacerbated these issues, as the resulting social isolation and economic pressures have heightened pre-existing conditions and led to a surge in new cases[20,21].
When analyzing the burden of MD in the WPR based on the SDI, a significant disparity becomes apparent. The majority of WPR countries report burdens of MD below the global average for their corresponding SDI levels. Conversely, Australia, New Zealand, and Malaysia exhibit significantly higher burdens, surpassing the averages of their respective SDI categories due to various factors. These factors include advanced healthcare systems, heightened public awareness, and socio-economic factors such as work-related stress, urban isolation, and fast-paced lifestyles, all of which are recognized risk factors for MD[22-24]. These nations have established healthcare systems that guarantee broader access to mental health services and advocate for superior diagnostic and reporting practices[22-24]. This leads to better recognition and treatment of mental health conditions, contributing to the higher reported prevalence of MD. Additionally, reduced stigma and supportive societal attitudes in these nations encourage more people to seek help, further increasing reported cases[25-27]. In contrast, Vietnam, China, and Brunei report the lowest burdens of MD in the region, attributed to cultural stigmas and infrastructural and economic limitations. Cultural stigmas may deter individuals from seeking help to avoid societal judgment, particularly in less developed areas, where healthcare systems may not prioritize mental health[17]. Economic limitations also divert attention away from mental health, focusing on more immediate survival concerns such as infectious diseases or malnutrition[28]. However, the robust family bonds and communal support prevalent in these societies frequently mitigate mental health impacts by furnishing emotional and psychological support, thereby diminishing the observable burden[29]. The disparity in the burden of MD across the WPR highlights the intricate interplay among healthcare systems, societal attitudes, economic circumstances, and cultural norms.
Age plays a critical role in the onset and progression of MD[30]. Adolescents and young adults often encounter distinctive stressors, including academic pressures and the onset of identity exploration, which may contribute to elevated rates of depression[31]. In contrast, older adults may experience MD in the context of different life stressors, such as health issues, loss of loved ones, and social isolation[32]. Comparing data from 1990 to 2021, there is a significant increase in the DALYs of MD among the elderly and individuals over 40 years old. This trend is concerning and underscores the impact of aging populations on mental health care systems. Among these age groups, the rates of depression, anxiety, and schizophrenia are notably higher, with a slightly higher burden observed in females. This increase may reflect the cumulative effects of life stressors, health issues, and social changes affecting older populations[33]. Additionally, the higher burden in females could be attributed to longer life expectancies and the unique challenges women face as they age, including post-menopausal changes and the potential for increased caregiving responsibilities[34,35]. The higher prevalence of conduct disorders, ADHD, and autism spectrum disorders in males may result from a combination of biological, hormonal, social, and diagnostic factors[36,37]. Males may be more susceptible to these neurodevelop MD due to differences in brain development and the influence of sex hormones[36,37]. Genetic studies suggest certain risk factors are more prevalent or impactful in males[36]. Additionally, societal and cultural expectations might lead to underdiagnosis or different presentations in females, contributing to the gender disparity[38]. In light of these findings, it's evident that mental health interventions need to be age and gender-specific, addressing the unique challenges and risk factors each group faces.
Childhood sexual abuse and bullying are significant risk factors for mental health issues across all age groups, reflecting a pervasive societal challenge[39]. Traumatic experiences from child abuse, including physical, emotional, and sexual abuse, can lead to psychological problems ranging from anxiety and depression to severe disorders like post-traumatic stress disorder (PTSD)[40]. Similarly, bullying during childhood or adolescence can have lasting effects on mental health, damaging self-esteem and increasing the risk of mental health disorders. Intimate partner violence, particularly severe for women during their reproductive years, includes physical, sexual, and psychological abuse by a current or former partner[41]. Women experiencing intimate partner violence face a higher risk of mental health conditions such as depression, anxiety, and PTSD. The reproductive years, with the added responsibilities of childbearing and child-rearing, can exacerbate the impact of such violence on mental health. In conclusion, childhood sexual abuse, bullying, and intimate partner violence are critical risk factors for mental health issues, affecting various age groups and demographics, especially women in their reproductive years. Addressing these risks requires a comprehensive approach, including prevention, early intervention, and targeted support for those affected[41]. Additionally, environmental factors like lead exposure also pose significant risks to mental health[42]. Lead, known for its neurotoxic properties, can disrupt brain development and function, leading to cognitive impairments and increasing the likelihood of MD such as depression, anxiety, and ADHD[42,43]. Children are particularly vulnerable to lead's effects, as their developing brains are more susceptible to damage[44]. Long-term exposure can result in lasting neurological and psychological effects, emphasizing the importance of minimizing lead exposure to protect mental health[44].
The COVID-19 pandemic, spanning from 2019 to 2021, has profoundly affected global mental health, significantly increasing the burden of MD[21]. The COVID-19 MD collaborators documented the extensive psychological effects of the pandemic, attributing the increased burden of MD to factors such as social isolation, economic instability, and stress resulting from uncertainty and fear related to the virus[45]. Social isolation, resulting from lockdowns and social distancing measures, has been a key contributor to the rise in MD[46]. The lack of physical contact with friends, family, and community networks disrupted usual support systems, leading to increased feelings of loneliness and depression[46]. The human need for social interaction was severely hampered, exacerbating existing mental health conditions and leading to new cases of MD. Economic instability, with widespread job losses, business closures, and financial insecurity, added another layer of stress and anxiety for many individuals[33]. The fear of economic ruin and the struggle to meet basic needs have been significant stressors, directly impacting mental well-being and contributing to the increased prevalence of MD. The constant uncertainty and fear surrounding the virus itself also played a critical role. Concerns about contracting the virus, the health of loved ones, and the overall impact on society have led to heightened anxiety and stress levels[47]. This ongoing state of hyperarousal and worry has been detrimental to mental health, further fueling the surge in MD cases. Addressing this increased burden requires a multifaceted approach, including enhanced mental health services, social support systems, and economic interventions to mitigate the pandemic's lasting effects on mental health.
Over the past 30 years, the global health system, including the WHO, has taken various steps to reduce the risk factors associated with MD[48]. Despite these efforts, the WPR continues to face significant challenges due to the burden of MD. In response, WHO launched the "Regional Framework for the Future of Mental Health in the Western Pacific 2023-2030", which aims to improve mental health outcomes by addressing issues such as inadequate resources, persistent stigma, inequality in access, and lack of service integration[49]. However, challenges like insufficient funding, limited infrastructure, and cultural barriers still hinder progress[48]. To tackle these issues, we recommend enhancing MD screening in primary healthcare, training professionals to recognize early symptoms, and combating stigma through public awareness campaigns. Expanding access to mental health services, especially in underserved areas, and utilizing telemedicine can bridge care gaps. Collaboration among healthcare providers, policymakers, and communities is vital for sustainable strategies. By implementing these measures, WHO and the WPR can significantly improve mental health outcomes in the region.
There were several limitations to the study. At first, the study was based on data from the GBD to generate the estimates provided. While the GBD methodology is generally considered reliable, it is important to acknowledge its limitations due to the variability in data quality, which can differ across countries and over time. The GBD relies on mathematical models to estimate the burden in countries with limited direct data, which can affect the accuracy of the results. Furthermore, this research focuses on countries in the WPR, which exhibit a wide range of economic, cultural, and geographic diversity. While we strive to provide a comprehensive analysis, there may be subtle differences at the national or sub-regional level that our study might not fully capture. Variations in local disease prevalence, healthcare infrastructure, and public health policies could affect the observed overall trends. Despite these limitations, we believe this study offers valuable insights into the burden of MD in the WPR, providing useful information for future public health policies and interventions.
CONCLUSION
In conclusion, this study underscores that the burden of MD in the WPR is considerable and exhibits significant variations across age groups, genders, and nations. Notably, Australia, New Zealand, and Malaysia face the most severe impacts. Moreover, the ongoing COVID-19 pandemic has further intensified the global mental health burden, emphasizing the critical need for a coordinated response. This research emphasizes the necessity for region-specific interventions, enhanced mental health services, and cross-sectoral collaborations to effectively address the escalating challenges of MD and improve outcomes while considering the unique risk factors and demographic characteristics of the WPR.
ACKNOWLEDGEMENTS
We are grateful to the Global Burden of Disease (GBD) database for providing essential data that supported our study on mental health burdens in the Western Pacific Region.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Psychiatry
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade C
Novelty: Grade A
Creativity or Innovation: Grade B
Scientific Significance: Grade A
P-Reviewer: Zhang H S-Editor: Li L L-Editor: A P-Editor: Yu HG
Castelpietra G, Knudsen AKS, Agardh EE, Armocida B, Beghi M, Iburg KM, Logroscino G, Ma R, Starace F, Steel N, Addolorato G, Andrei CL, Andrei T, Ayuso-Mateos JL, Banach M, Bärnighausen TW, Barone-Adesi F, Bhagavathula AS, Carvalho F, Carvalho M, Chandan JS, Chattu VK, Couto RAS, Cruz-Martins N, Dargan PI, Deuba K, da Silva DD, Fagbamigbe AF, Fernandes E, Ferrara P, Fischer F, Gaal PA, Gialluisi A, Haagsma JA, Haro JM, Hasan MT, Hasan SS, Hostiuc S, Iacoviello L, Iavicoli I, Jamshidi E, Jonas JB, Joo T, Jozwiak JJ, Katikireddi SV, Kauppila JH, Khan MAB, Kisa A, Kisa S, Kivimäki M, Koly KN, Koyanagi A, Kumar M, Lallukka T, Langguth B, Ledda C, Lee PH, Lega I, Linehan C, Loureiro JA, Madureira-Carvalho ÁM, Martinez-Raga J, Mathur MR, McGrath JJ, Mechili EA, Mentis AA, Mestrovic T, Miazgowski B, Mirica A, Mirijello A, Moazen B, Mohammed S, Mulita F, Nagel G, Negoi I, Negoi RI, Nwatah VE, Padron-Monedero A, Panda-Jonas S, Pardhan S, Pasovic M, Patel J, Petcu IR, Pinheiro M, Pollok RCG, Postma MJ, Rawaf DL, Rawaf S, Romero-Rodríguez E, Ronfani L, Sagoe D, Sanmarchi F, Schaub MP, Sharew NT, Shiri R, Shokraneh F, Sigfusdottir ID, Silva JP, Silva R, Socea B, Szócska M, Tabarés-Seisdedos R, Torrado M, Tovani-Palone MR, Vasankari TJ, Veroux M, Viner RM, Werdecker A, Winkler AS, Hay SI, Ferrari AJ, Naghavi M, Allebeck P, Monasta L. The burden of mental disorders, substance use disorders and self-harm among young people in Europe, 1990-2019: Findings from the Global Burden of Disease Study 2019.Lancet Reg Health Eur. 2022;16:100341.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 65][Cited by in F6Publishing: 65][Article Influence: 32.5][Reference Citation Analysis (0)]
Gossage L, Narayanan A, Dipnall JF, Iusitini L, Sumich A, Berk M, Wrapson W, Tautolo ES, Siegert R. Risk factors for depression in Pacific adolescents in New Zealand: A network analysis.J Affect Disord. 2022;311:373-382.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Bendall S, Eastwood O, Spelman T, McGorry P, Hickie I, Yung AR, Amminger P, Wood SJ, Pantelis C, Purcell R, Phillips L. Childhood trauma is prevalent and associated with co-occurring depression, anxiety, mania and psychosis in young people attending Australian youth mental health services.Aust N Z J Psychiatry. 2023;57:1518-1526.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 1][Reference Citation Analysis (0)]
GBD 2021 Diseases and Injuries Collaborators. 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;403:2133-2161.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 1][Reference Citation Analysis (0)]
GBD 2021 Demographics Collaborators. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021.Lancet. 2024;403:1989-2056.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 5][Reference Citation Analysis (0)]
Stevens GA, Alkema L, Black RE, Boerma JT, Collins GS, Ezzati M, Grove JT, Hogan DR, Hogan MC, Horton R, Lawn JE, Marušić A, Mathers CD, Murray CJ, Rudan I, Salomon JA, Simpson PJ, Vos T, Welch V; (The GATHER Working Group). Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement.Lancet. 2016;388:e19-e23.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 492][Cited by in F6Publishing: 708][Article Influence: 88.5][Reference Citation Analysis (1)]
Ahmed N, Barnett P, Greenburgh A, Pemovska T, Stefanidou T, Lyons N, Ikhtabi S, Talwar S, Francis ER, Harris SM, Shah P, Machin K, Jeffreys S, Mitchell L, Lynch C, Foye U, Schlief M, Appleton R, Saunders KRK, Baldwin H, Allan SM, Sheridan-Rains L, Kharboutly O, Kular A, Goldblatt P, Stewart R, Kirkbride JB, Lloyd-Evans B, Johnson S. Mental health in Europe during the COVID-19 pandemic: a systematic review.Lancet Psychiatry. 2023;10:537-556.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 13][Cited by in F6Publishing: 38][Article Influence: 38.0][Reference Citation Analysis (0)]
Yang C, Gao H, Li Y, Wang E, Wang N, Wang Q. Analyzing the role of family support, coping strategies and social support in improving the mental health of students: Evidence from post COVID-19.Front Psychol. 2022;13:1064898.
[PubMed] [DOI][Cited in This Article: ][Cited by in F6Publishing: 7][Reference Citation Analysis (0)]
Hooper SC, Marshall VB, Becker CB, LaCroix AZ, Keel PK, Kilpela LS. Mental health and quality of life in postmenopausal women as a function of retrospective menopause symptom severity.Menopause. 2022;29:707-713.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 6][Reference Citation Analysis (0)]
Wakuta M, Nishimura T, Osuka Y, Tsukui N, Takahashi M, Adachi M, Suwa T, Katayama T. Adverse childhood experiences: impacts on adult mental health and social withdrawal.Front Public Health. 2023;11:1277766.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
World Health Organization.
Regional Framework for the Future of Mental Health in the Western Pacific 2023-2030. Manila: World Health Organization Regional Office for the Western Pacific, 2023.
[PubMed] [DOI][Cited in This Article: ]
