Abstract
Background
Mental disorders are a major public health issue, causing 4.9% of global disability-adjusted life years (DALYs). In Jordan, factors like regional conflicts, economic changes, and population growth contribute to this burden. This study examines the trends and risk factors of mental disorders in Jordan from 1990 to 2021 using Global Burden of Disease (GBD) data.
Methods
We analyzed prevalence, deaths, and DALYs of mental disorders from the GBD 2021 dataset, reporting both all-age numbers and age-standardized rates. Key risk factors, including behavioral risks, bullying, childhood sexual abuse, and substance use, were assessed.
Results
Mental disorder cases in Jordan rose by 279.8%, from 514,234 in 1990 to 1,953,087 in 2021. Anxiety and depression were the most common in 2021. All-age DALYs increased by 649.6%, while age-standardized DALY rates showed a slight 4.3% rise. Females had higher mental disorder prevalence and DALY rates, while males had higher substance use disorder rates. Behavioral risks, bullying, and childhood sexual abuse were major contributors.
Conclusion
The burden of mental disorders in Jordan has grown significantly over three decades. Limited resources, stigma, and regional instability worsen the issue. Policies focusing on stigma reduction, mental health integration, and prevention are essential.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12888-025-06658-x.
Keywords: Mental disorders, Jordan, Global burden of disease, Prevalence, Disability-adjusted life years, Risk factors, Public health
Introduction
Mental disorders represent a substantial and growing global public health challenge. The Global Burden of Disease (GBD) 2019 study estimated that mental disorders accounted for 4.9% of global disability-adjusted life years (DALYs), highlighting their persistent and significant impact over the past three decades [1]. Despite this, a large proportion of individuals with mental disorders do not seek treatment [2], exacerbating the burden on individuals and society. In the Middle East and North Africa (MENA) region, which includes countries in North Africa (e.g., Egypt, Libya, and Morocco) and the Middle East (e.g., Jordan, Iraq, and Lebanon), reports high rates of mental health disorders, with further increases predicted, particularly among populations affected by conflict and displacement [3, 4, 5]. Additionally, institutionalization, challenges in service delivery [6], and the pervasive stigma surrounding mental health [7] further impede effective mental health care in the region. Jordan, located in the heart of this region, faces unique challenges due to its role as a host country for large refugee populations, ongoing economic strains, and the cultural stigma surrounding mental health.
While Jordan has made strides in healthcare, its mental health system remains underdeveloped. Limited resources, workforce shortages, and fragmented service delivery impede effective mental health care, further exacerbated by socioeconomic factors such as unemployment and poverty [8]. Approximately 3 million refugees, including over 600,000 from Syria, reside in Jordan, many of whom have experienced significant trauma, such as violence, displacement, and loss of loved ones [9]. Studies have reported high rates of depression (65.5%), anxiety (64%), and stress (61%) among Syrian refugees in Jordan [10, 11]. However, mental health issues also extend to the general population, reflecting broader systemic challenges rather than being confined to displaced groups. Additionally, cultural stigma surrounding mental health and the long-standing impact of regional conflicts have likely shaped mental health trends in Jordan, setting them apart from global patterns. The healthcare system in Jordan is a dual-sector system, encompassing both public and private healthcare structures [12]. This structure has had to adapt to the country’s evolving needs, particularly in mental health care, where there is a significant gap in population-level research. Studies among the Jordanian population have shown that socioeconomic factors, such as educational level and income, significantly impact access to healthcare services, including mental health care [13, 14]. Historical events, such as regional conflicts and economic changes, have also shaped the mental health landscape in Jordan. For instance, the country’s role as a haven for refugees has placed additional strain on its healthcare resources, complicating efforts to address mental health needs [15]. Despite these challenges, there is a paucity of comprehensive studies on mental disorders in Jordan, particularly those utilizing long-term data. Existing research often focuses on specific issues like drug misuse [16] or the impact of the COVID-19 pandemic on mental health [17, 18, 19]. Furthermore, mental health stigma, influenced by cultural and religious factors, remains a significant barrier to seeking care [20], highlighting the need to understand the multifaceted nature of mental disorders burden and risk factors in the country.
The GBD study is a comprehensive initiative that systematically quantifies the prevalence, morbidity, and mortality associated with various diseases, injuries, and risk factors on a global scale. For Jordan, where national mental health surveillance is limited, GBD data provides a rare opportunity to assess long-term trends in mental disorders using standardized metrics. Previous studies utilizing GBD data have offered valuable insights into mental health burdens at global and regional levels [1], yet Jordan-specific analyses remain scarce. By leveraging GBD 2021 data, this study addresses this gap, offering the first long-term evaluation of mental disorders in Jordan.
Building upon the extensive capabilities of the GBD dataset, this study represents a novel effort to quantify the burden of mental disorders in Jordan from 1990 to 2021 using the 2021 iteration of the dataset. Unlike existing studies that primarily focus on specific populations or short-term impacts, such as those related to the COVID-19 pandemic or drug misuse, this study provides a comprehensive and longitudinal perspective on mental health in Jordan. Additionally, it investigates the unique risk factors shaping mental health trends in the Jordanian population, offering unprecedented insights into the interplay between socioeconomic and cultural determinants. These findings aim to fill critical gaps in the literature and serve as a foundation for evidence-based mental health policies and interventions tailored to Jordan’s unique context.
Methodology
Data source
This study used data from the GBD dataset, an ongoing global collaboration that provides comparable and reliable estimates of population health over time [21, 22]. A variety of data sources, including reports and published systematic evaluations, as well as websites of national and international organizations, were used for GBD 2019. Moreover, the project received dataset contributions from GBD collaborators.
Definitions
In our study, we analyzed the mental disorders in 2021, main risk factors of disease burden in 2021, changes in disease burden from 1990 to 2021 among the Jordanians. We measured the disease burden by using three outcomes including prevalence, deaths, and DALYs. We employed two units to express the results: All-age number and age-standardized rate. Mental disorders causes are detailed with standard definitions in Table S1, as outlined by the GBD study. Prevalence is a measure of the proportion of people in a population who have a particular condition or disease at a specific time. It is commonly used in epidemiology to characterize the disease burden within a population. For Jordan, the GBD dataset involves data from a variety of sources, including vital registry data, hospital records, survey data, and administrative data. A set of statistical models and methods that take uncertainty and missing data into account are utilized to analyze the data. Death estimates are produced using vital registration data categorized to the International Classification of Disease (ICD) system or household mortality surveys referred to as verbal autopsy. DALYs were also defined as years of healthy life lost and were calculated by adding YLLs and YLDs together. YLDs were years lived with either short-term or long-term health loss weighted for severity by the disability weights, and YLLs were years of life lost due to premature death. YLLs and YLDs were included to generate DALYs, however we did not include or examine these two outcomes independently in our study.
Risk factors
The GBD 2021 methods for evaluating the burden of disease attributed to risk factors has been described in other published studies [21, 23]. We have extracted all available risk factors from the 2021 GBD data and used them in this study: (1) Alcohol use; (2) Behavioral risks (e.g., category of health risks within the broader risk factor hierarchy, encompassing behaviors like tobacco use,, poor diet, lack of physical activity, and unsafe sexual practices); (3) Bullying victimization; (4) Childhood sexual abuse; (5) Childhood sexual abuse and bullying; (6) Drug use; (7) Environmental/occupational risks (e.g., unsafe water, sanitation, and handwashing; non-optimal temperature, occupational carcinogens, noise, and injures); (8) Intimate partner violence; (9) Lead exposure; (10) Other environmental risks (e.g., residential radon and both acute and chronic exposure to lead).
GBD data analysis
The Global Burden of Disease (GBD) study draws on data from diverse sources, including vital registration systems, censuses, sample registration systems, surveys, medical facilities, and death certificates. These data are processed, adjusted for confounders, and modeled using standardized techniques, notably the Cause of Death Ensemble Model (CODEm), spatiotemporal Gaussian process regression (ST-GPR), and DisMod-MR. Each tool undergoes multiple rounds of internal validation within the GBD framework—such as out-of-sample predictive checks and cross-validation—to refine model specifications and ensure robust estimates. Also, the GBD study team considers multiple methods to address missing data, depending on the nature of the missingness, prioritizes data quality and consistency, and thoroughly describes its processes for handling missing data throughout the research. However, in our current study, we did not perform any additional validation because we used estimates already vetted and validated by the GBD team. This reliance on prevalidated data streamlines our analysis while preserving the methodological rigor established by the GBD initiative. Additional information regarding modeling and processing methodologies can be obtained in the literature [21, 23, 24]. The data were analysed using Microsoft Excel version 2411, and the images were created using Microsoft PowerPoint version 2411. All percentage changes in DALYs, prevalence or death, were calculated using the following formula
 |
Results
Prevalence
Table 1 presents all-age and age-standardized prevalence figures for 1990 and 2021. From 1990 to 2021, the overall number of mental disorder cases in Jordan increased by 279.8% (from 514,234 to 1,953,087). Notable relative increases occurred in other mental disorders (376.3%), other drug use disorders (374.5%), schizophrenia (358.6%), depressive disorders (327.0%), and anxiety disorders (326.2%). By 2021, anxiety disorders (737,435 cases), depressive disorders (555,445), and other mental disorders (171,445) were most prevalent.
Table 1.
The all-age and age-standardized prevalence number and rate in 1990 and 2021 in Jordan
| Subcategory | All-age prevalence number 1990 | All-age prevalence number 2021 | Change % | Age-standardized rate 1990 | Age-standardized rate 2021 | Change % | |
|---|---|---|---|---|---|---|---|
| Mental disorders | Male | 255,570 (285962, 228247) | 958,353 (1081086, 839121) | 274.987 | 13,688 (12408, 15146) | 13,992 (12272, 15695) | 2.221 |
| Female | 258,663 (291591, 232200) | 994,734 (1149944, 843417) | 284.568 | 16,402 (14837, 18289) | 17,135 (14594, 19754) | 4.469 | |
| Total | 514,234 (576599, 462415) | 1,953,087 (2212015, 1686410) | 279.805 | 14,971 (13706, 16468) | 15,412 (13313, 17392) | 2.946 | |
| Schizophrenia | Male | 3716 (4877, 2834) | 17,544 (22364, 13405) | 372.121 | 261 (203, 332) | 258 (199, 326) | -1.149 |
| Female | 2974 (3908, 2220) | 13,135 (17083, 10019) | 341.661 | 236 (184, 306) | 237 (182, 306) | 0.424 | |
| Total | 6690 (8784, 5008) | 30,679 (39264, 23551) | 358.580 | 249 (194, 317) | 248 (194, 314) | -0.402 | |
| Depressive disorders | Male | 48,735 (58816, 41217) | 216,890 (269966, 172390) | 345.039 | 3170 (2746, 3709) | 3254 (2642, 4010) | 2.650 |
| Female | 81,338 (99881, 67615) | 338,554 (439612, 267605) | 316.231 | 6011 (5088, 7181) | 6052 (4882, 7719) | 0.682 | |
| Total | 130,073 (159687, 109314) | 555,445 (707039, 447867) | 327.026 | 4522 (3895, 5363) | 4533 (3694, 5675) | 0.243 | |
| Major depressive disorder | Male | 34,698 (43957, 27797) | 153,548 (205282, 113965) | 342.527 | 2199 (1812, 2694) | 2283 (1709, 3017) | 3.820 |
| Female | 64,394 (81702, 51863) | 267,329 (365185, 194030) | 315.146 | 4708 (3864, 5779) | 4748 (3575, 6368) | 0.850 | |
| Total | 99,093 (125710, 80100) | 420,877 (566868, 309993) | 324.729 | 3393 (2812, 4126) | 3411 (2559, 4496) | 0.531 | |
| Dysthymia | Male | 14,446 (19305, 10967) | 65,320 (86840, 49805) | 352.167 | 1000 (778, 1299) | 1000 (778, 1299) | 0.000 |
| Female | 18,025 (23570, 13714) | 76,023 (99214, 58690) | 321.764 | 1392 (1094, 1805) | 1392 (1094, 1805) | 0.000 | |
| Total | 32,472 (42431, 24780) | 141,343 (184663, 109727) | 335.277 | 1186 (937, 1526) | 1178 (930, 1514) | -0.675 | |
| Bipolar disorder | Male | 11,048 (15043, 8067) | 46,702 (61897, 34318) | 322.719 | 683 (516, 895) | 683 (516, 895) | 0.000 |
| Female | 11,568 (16093, 8287) | 45,207 (61257, 33186) | 290.794 | 784 (589, 1040) | 784 (589, 1040) | 0.000 | |
| Total | 22,616 (30864, 16364) | 91,909 (123484, 68586) | 306.389 | 731 (553, 959) | 730 (553, 958) | -0.137 | |
| Anxiety disorders | Male | 69,738 (90768, 54207) | 299,473 (409172, 208943) | 329.426 | 3683 (2967, 4594) | 4284 (2992, 5790) | 16.318 |
| Female | 103,299 (132598, 80106) | 437,961 (586482, 300449) | 323.974 | 6256 (4992, 7820) | 7357 (5059, 9853) | 17.599 | |
| Total | 173,038 (221038, 135205) | 737,435 (978697, 513268) | 326.169 | 4902 (3986, 6058) | 5694 (4016, 7541) | 16.157 | |
| Eating disorders | Male | 3018 (4128, 2197) | 11,873 (16353, 8600) | 293.406 | 139 (102, 189) | 154 (112, 213) | 10.791 |
| Female | 4579 (6346, 3296) | 17,648 (24779, 12651) | 285.412 | 239 (174, 323) | 272 (195, 380) | 13.808 | |
| Total | 7597 (10490, 5520) | 29,521 (40030, 21645) | 288.588 | 186 (136, 250) | 208 (152, 282) | 11.828 | |
| Anorexia nervosa | Male | 642 (964, 423) | 2047 (3036, 1375) | 218.847 | 25 (17, 36) | 26 (17, 38) | 4.000 |
| Female | 1069 (1593, 714) | 3527 (5228, 2374) | 229.935 | 49 (34, 72) | 52 (35, 77) | 6.122 | |
| Total | 1712 (2539, 1125) | 5575 (8108, 3746) | 225.643 | 36 (25, 52) | 38 (25, 55) | 5.556 | |
| Bulimia nervosa | Male | 2376 (3482, 1557) | 9830 (14161, 6646) | 313.721 | 114 (76, 165) | 128 (87, 186) | 12.281 |
| Female | 3513 (5250, 2229) | 14,136 (20504, 9207) | 302.391 | 190 (123, 272) | 220 (145, 315) | 15.789 | |
| Total | 5890 (8730, 3777) | 23,966 (34150, 16119) | 306.893 | 149 (100, 212) | 170 (114, 241) | 14.094 | |
| Autism spectrum disorders | Male | 20,820 (24872, 17315) | 70,188 (84274, 57856) | 237.118 | 1020 (847, 1218) | 1044 (859, 1253) | 2.353 |
| Female | 10,115 (12094, 8311) | 31,709 (38101, 26031) | 213.485 | 540 (444, 646) | 541 (444, 650) | 0.185 | |
| Total | 30,935 (36772, 25821) | 101,897 (120872, 84711) | 229.391 | 791 (661, 943) | 809 (672, 960) | 2.276 | |
| Attention-deficit/hyperactivity disorder | Male | 34,406 (47890, 24585) | 104,892 (145131, 76856) | 204.865 | 1427 (1054, 1973) | 1427 (1054, 1973) | 0.000 |
| Female | 12,173 (17170, 8657) | 36,426 (50173, 26317) | 199.236 | 563 (408, 773) | 563 (408, 773) | 0.000 | |
| Total | 46,580 (64571, 33373) | 141,319 (194925, 103694) | 203.390 | 1016 (748, 1400) | 1021 (753, 1407) | 0.492 | |
| Conduct disorder | Male | 19,992 (25041, 14590) | 52,494 (65823, 38290) | 162.575 | 698 (509, 875) | 698 (509, 875) | 0.000 |
| Female | 9104 (12369, 6226) | 24,073 (32796, 16526) | 164.422 | 343 (234, 465) | 343 (234, 465) | 0.000 | |
| Total | 29,096 (37016, 20905) | 76,567 (97837, 54808) | 163.153 | 527 (379, 671) | 527 (378, 670) | 0.000 | |
| Idiopathic developmental intellectual disability | Male | 38,698 (56998, 19086) | 95,899 (147726, 43910) | 147.814 | 1765 (861, 2611) | 1388 (630, 2143) | -21.360 |
| Female | 28,758 (41788, 15253) | 66,291 (101954, 31351) | 130.513 | 1453 (762, 2127) | 1104 (522, 1702) | -24.019 | |
| Total | 67,457 (99091, 34018) | 162,190 (250472, 76457) | 140.435 | 1616 (804, 2392) | 1255 (589, 1942) | -22.339 | |
| Other mental disorders | Male | 22,399 (29429, 17602) | 109,390 (140327, 86070) | 388.370 | 1748 (1387, 2225) | 1748 (1387, 2225) | 0.000 |
| Female | 13,598 (17687, 10455) | 62,055 (79870, 47988) | 356.354 | 1212 (948, 1545) | 1212 (948, 1545) | 0.000 | |
| Total | 35,997 (46522, 28159) | 171,445 (219198, 133852) | 376.276 | 1493 (1172, 1891) | 1505 (1182, 1905) | 0.804 | |
| Substance use disorders | Male | 16,539 (19775, 13676) | 62,439 (73074, 52313) | 277.526 | 946 (796, 1096) | 874 (735, 1017) | -7.611 |
| Female | 8899 (10474, 7396) | 32,997 (38517, 28176) | 270.794 | 558 (473, 641) | 550 (471, 637) | -1.434 | |
| Total | 25,438 (30023, 21258) | 95,436 (111157, 81352) | 275.171 | 762 (649, 873) | 727 (623, 838) | -4.593 | |
| Alcohol use disorders | Male | 8597 (11006, 6659) | 33,768 (42830, 26679) | 292.788 | 557 (436, 687) | 493 (393, 620) | -11.490 |
| Female | 3218 (4215, 2466) | 11,784 (15291, 9158) | 266.190 | 220 (172, 279) | 205 (161, 263) | -6.818 | |
| Total | 11,816 (15051, 9206) | 45,553 (58046, 35959) | 285.520 | 397 (313, 491) | 363 (290, 455) | -8.564 | |
| Drug use disorders | Male | 8002 (10360, 6140) | 28,879 (36100, 23058) | 260.897 | 392 (317, 480) | 384 (311, 474) | -2.041 |
| Female | 5701 (6989, 4564) | 21,285 (25377, 17399) | 273.356 | 339 (281, 404) | 346 (285, 412) | 2.065 | |
| Total | 13,704 (17055, 10801) | 50,165 (60860, 41238) | 266.061 | 367 (305, 440) | 366 (304, 439) | -0.272 | |
| Opioid use disorders | Male | 2609 (3319, 1953) | 10,249 (12890, 8078) | 292.833 | 145 (112, 179) | 140 (112, 175) | -3.448 |
| Female | 3586 (4651, 2651) | 13,862 (17856, 10462) | 286.559 | 219 (167, 274) | 228 (173, 288) | 4.110 | |
| Total | 6196 (7914, 4608) | 24,111 (30104, 18504) | 289.138 | 179 (139, 221) | 179 (138, 222) | 0.000 | |
| Cocaine use disorders | Male | 296 (430, 190) | 1050 (1513, 686) | 254.730 | 15 (10, 21) | 14 (9, 20) | -6.667 |
| Female | 184 (282, 112) | 599 (892, 379) | 225.543 | 10 (6, 14) | 9 (6, 13) | -10.000 | |
| Total | 480 (707, 308) | 1649 (2351, 1062) | 243.542 | 12 (8, 18) | 12 (7, 17) | 0.000 | |
| Amphetamine use disorders | Male | 877 (1254, 575) | 3042 (4233, 2035) | 246.864 | 41 (27, 57) | 39 (26, 54) | -4.878 |
| Female | 410 (600, 256) | 1391 (1993, 921) | 239.268 | 22 (14, 32) | 22 (14, 30) | 0.000 | |
| Total | 1287 (1864, 836) | 4434 (6213, 2962) | 244.522 | 32 (22, 45) | 31 (21, 44) | -3.125 | |
| Cannabis use disorders | Male | 4100 (6425, 2446) | 13,908 (20869, 8949) | 239.220 | 181 (119, 267) | 181 (119, 267) | 0.000 |
| Female | 1383 (2148, 865) | 4805 (7270, 3191) | 247.433 | 75 (51, 112) | 75 (51, 112) | 0.000 | |
| Total | 5483 (8569, 3336) | 18,714 (27901, 12368) | 241.310 | 131 (88, 193) | 132 (89, 194) | 0.763 | |
| Other drug use disorders | Male | 138 (188, 99) | 694 (958, 497) | 402.899 | 9 (7, 13) | 9 (7, 13) | 0.000 |
| Female | 147 (203, 107) | 663 (899, 480) | 351.020 | 11 (8, 16) | 11 (8, 15) | 0.000 | |
| Total | 286 (388, 207) | 1357 (1838, 972) | 374.476 | 10 (7, 14) | 10 (7, 14) | 0.000 |
The age-standardized prevalence rose slightly (2.9%), from 14,971 to 15,412 per 100,000. Anxiety disorders (16.2%) and eating disorders (11.8%) experienced marked increases, while idiopathic developmental intellectual disability decreased by 22.3%. Women had a higher age-standardized prevalence rate for mental disorders (17,135 per 100,000) than men (13,992 per 100,000). Females showed higher rates of depressive, anxiety, and eating disorders; males were higher in autism spectrum disorders, ADHD, conduct disorder, and substance use (except opioid use, which was higher in females).
Mortality
Table 2 presents all-age and age-standardized death figures for 1990 and 2021. Only anorexia nervosa and substance use disorders had attributable death estimates. All-age anorexia nervosa deaths rose from 0.001 in 1990 to 0.003 in 2021 (200% increase), while substance use disorder deaths increased by 171.3%, from 6.6 to 18. Among specific substances, amphetamine use had the largest relative growth in deaths (498.6%). Age-standardized mental disorder mortality overall showed little change. However, for substance use disorders, age-standardized mortality fell by 40.9%, with a notable gender difference for opioid use: women’s rate rose by 3.2%, while men’s dropped by 37.0%.
Table 2.
The all-age and age-standardized death number and rate in 1990 and 2021 in Jordan†
| Subcategory | All-age death number 1990 | All-age death number 2021 | Change % | Age-standardized rate 1990 | Age-standardized rate 2021 | Change % | |
|---|---|---|---|---|---|---|---|
| Mental disorders | Male | 0.000 (0.000, 0.000) | 0.000 (0.000, 0.000) | 0.000 | 0.000 (0.000, 0.000) | 0.000 (0.000, 0.000) | 0.000 |
| Female | 0.000 (0.001, 0.000) | 0.003 (0.007, 0.000) | 0.000 | 0.000 (0.000, 0.000) | 0.000 (0.000, 0.000) | 0.000 | |
| Total | 0.001 (0.001, 0.000) | 0.003 (0.009, 0.000) | 200.000 | 0.000 (0.000, 0.000) | 0.000 (0.000, 0.000) | 0.000 | |
| Eating disorders | Male | 0.000 (0.000, 0.000) | 0.000 (0.000, 0.000) | 0.000 | 0.000 (0.000, 0.000) | 0.000 (0.000, 0.000) | 0.000 |
| Female | 0.000 (0.001, 0.000) | 0.003 (0.007, 0.000) | 0.000 | 0.000 (0.000, 0.000) | 0.000 (0.000, 0.000) | 0.000 | |
| Total | 0.001 (0.001, 0.000) | 0.003 (0.009, 0.000) | 200.000 | 0.000 (0.000, 0.000) | 0.000 (0.000, 0.000) | 0.000 | |
| Anorexia nervosa | Male | 0.000 (0.000, 0.000) | 0.000 (0.000, 0.000) | 0.000 | 0.000 (0.000, 0.000) | 0.000 (0.000, 0.000) | 0.000 |
| Female | 0.000 (0.001, 0.000) | 0.003 (0.007, 0.000) | 0.000 | 0.000 (0.000, 0.000) | 0.000 (0.000, 0.000) | 0.000 | |
| Total | 0.001 (0.001, 0.000) | 0.003 (0.009, 0.000) | 200.000 | 0.000 (0.000, 0.000) | 0.000 (0.000, 0.000) | 0.000 | |
| Substance use disorders | Male | 4.488 (5.326, 3.635) | 13.652 (17.147, 10.637) | 204.189 | 0.353 (0.282, 0.421) | 0.217 (0.169, 0.272) | -38.527 |
| Female | 2.129 (2.626, 1.712) | 4.298 (5.448, 3.344) | 101.879 | 0.196 (0.163, 0.233) | 0.101 (0.080, 0.125) | -48.469 | |
| Total | 6.617 (7.713, 5.479) | 17.950 (22.164, 14.157) | 171.271 | 0.279 (0.230, 0.324) | 0.165 (0.132, 0.202) | -40.860 | |
| Alcohol use disorders | Male | 1.748 (2.252, 1.081) | 4.576 (6.043, 2.876) | 161.785 | 0.145 (0.089, 0.185) | 0.074 (0.046, 0.098) | -48.966 |
| Female | 0.077 (0.093, 0.057) | 0.123 (0.160, 0.095) | 59.740 | 0.008 (0.006, 0.009) | 0.003 (0.002, 0.004) | -62.500 | |
| Total | 1.825 (2.333, 1.156) | 4.699 (6.206, 2.999) | 157.479 | 0.079 (0.050, 0.101) | 0.042 (0.027, 0.055) | -46.835 | |
| Drug use disorders | Male | 2.740 (3.385, 2.150) | 9.076 (11.570, 6.910) | 231.241 | 0.208 (0.169, 0.252) | 0.143 (0.109, 0.182) | -31.250 |
| Female | 2.052 (2.542, 1.646) | 4.175 (5.301, 3.241) | 103.460 | 0.188 (0.156, 0.225) | 0.098 (0.077, 0.121) | -47.872 | |
| Total | 4.792 (5.698, 3.959) | 13.250 (16.458, 10.291) | 176.503 | 0.199 (0.167, 0.236) | 0.123 (0.097, 0.152) | -38.191 | |
| Opioid use disorders | Male | 1.938 (2.458, 1.465) | 5.903 (7.761, 4.474) | 204.592 | 0.146 (0.114, 0.182) | 0.092 (0.070, 0.119) | -36.986 |
| Female | 0.686 (0.977, 0.505) | 2.734 (3.521, 2.051) | 298.542 | 0.063 (0.049, 0.088) | 0.065 (0.050, 0.082) | 3.175 | |
| Total | 2.624 (3.179, 2.050) | 8.637 (10.926, 6.812) | 229.154 | 0.107 (0.086, 0.131) | 0.080 (0.064, 0.100) | -25.234 | |
| Cocaine use disorders | Male | 0.249 (0.402, 0.155) | 1.028 (1.465, 0.635) | 312.851 | 0.021 (0.012, 0.035) | 0.018 (0.011, 0.025) | -14.286 |
| Female | 0.323 (0.450, 0.217) | 0.326 (0.482, 0.215) | 0.929 | 0.041 (0.029, 0.054) | 0.008 (0.005, 0.011) | -80.488 | |
| Total | 0.572 (0.804, 0.400) | 1.354 (1.890, 0.915) | 136.713 | 0.031 (0.022, 0.041) | 0.013 (0.009, 0.018) | -58.065 | |
| Amphetamine use disorders | Male | 0.093 (0.177, 0.053) | 0.709 (1.087, 0.349) | 662.366 | 0.007 (0.004, 0.014) | 0.011 (0.006, 0.017) | 57.143 |
| Female | 0.046 (0.076, 0.031) | 0.124 (0.176, 0.084) | 169.565 | 0.004 (0.003, 0.007) | 0.002 (0.002, 0.003) | -50.000 | |
| Total | 0.139 (0.222, 0.096) | 0.832 (1.233, 0.463) | 498.561 | 0.006 (0.004, 0.010) | 0.007 (0.004, 0.011) | 16.667 | |
| Other drug use disorders | Male | 0.460 (0.709, 0.293) | 1.436 (2.097, 0.872) | 212.174 | 0.034 (0.023, 0.052) | 0.022 (0.014, 0.032) | -35.294 |
| Female | 0.997 (1.295, 0.768) | 0.991 (1.291, 0.656) | -0.602 | 0.080 (0.063, 0.103) | 0.023 (0.015, 0.029) | -71.250 | |
| Total | 1.457 (1.885, 1.144) | 2.427 (3.216, 1.715) | 66.575 | 0.056 (0.044, 0.071) | 0.023 (0.016, 0.030) | -58.929 |
†Note: The death rates for most mental health categories are minimal as these disorders primarily contribute to morbidity and disability rather than direct mortality. Only specific conditions, such as severe substance use disorders, show notable mortality rates. Also, deaths and Years of Life Lost (YLL) estimates do not fully capture all instances of premature mortality among individuals with mental disorders when the direct cause of death is attributed to another disease or injury. For example, suicide is categorized separately under injuries and is not included within the mental disorders group [1]
DALYs
Table 3 presents all-age and age-standardized DALY figures for 1990 and 2021. In 2021, mental disorders accounted for 283,387 DALYs (2,230 per 100,000), reflecting a 649.6% increase in absolute DALYs since 1990. Despite this, the overall age-standardized rate remained relatively stable, rising by 4.3% (from 2,139 to 2,230). Anxiety (16.3%) and eating disorders (12.8%) saw the greatest increases in age-standardized DALYs, while idiopathic developmental intellectual disability (− 17.5%) and substance use disorders (− 6.6%) decreased. Women’s DALYs increased more than men’s overall (5.3% vs. 4.3%) and specifically for opioid use disorder (2.2% increase). Figure 1 visualizes the mental disorders burden in Jordan by plotting age-standardized DALYs and prevalence rates per 100,000 for both genders from 1990 to 2021.
Table 3.
The all-age and age standardized dalys number and rate in 1990 and 2021 in Jordan
| Subcategory | All-age DALYs number 1990 | All-age DALYS number 2021 | Change % | Age-standardized rate 1990 | Age-standardized rate 2021 | Change % | |
|---|---|---|---|---|---|---|---|
| Mental disorders | Male | 32,887 (24570, 42384) | 132,263 (173041, 97901) | 302.171 | 1844 (1387, 2371) | 1923 (1423, 2522) | 4.284 |
| Female | 37,806 (27894, 49392) | 151,123 (206489, 105774) | 299.723 | 2467 (1825, 3207) | 2597 (1829, 3538) | 5.270 | |
| Total | 70,694 (52645, 91920) | 283,387 (380927, 205095) | 300.863 | 2139 (1593, 2751) | 2230 (1620, 2996) | 4.254 | |
| Schizophrenia | Male | 2458 (1696, 3407) | 11,475 (15873, 8108) | 366.819 | 170 (117, 231) | 167 (118, 230) | -1.765 |
| Female | 1879 (1259, 2635) | 8239 (11899, 5657) | 338.329 | 147 (102, 203) | 147 (102, 210) | 0.000 | |
| Total | 4337 (2936, 6060) | 19,714 (27659, 13930) | 354.474 | 159 (113, 216) | 158 (114, 221) | -0.629 | |
| Depressive disorders | Male | 8696 (5819, 12244) | 38,223 (57488, 25091) | 339.537 | 551 (373, 759) | 565 (373, 842) | 2.541 |
| Female | 14,829 (9849, 20693) | 61,042 (92479, 39115) | 311.625 | 1073 (717, 1470) | 1078 (703, 1618) | 0.466 | |
| Total | 23,525 (15699, 32551) | 99,265 (147109, 64529) | 321.942 | 799 (537, 1089) | 800 (530, 1178) | 0.125 | |
| Major depressive disorder | Male | 7270 (4745, 10512) | 31,839 (48554, 19737) | 337.935 | 453 (302, 644) | 468 (291, 715) | 3.311 |
| Female | 13,092 (8489, 18700) | 53,806 (83047, 32837) | 310.965 | 941 (606, 1314) | 946 (589, 1442) | 0.531 | |
| Total | 20,362 (13231, 28929) | 85,646 (131381, 53459) | 320.600 | 685 (449, 957) | 687 (430, 1039) | 0.292 | |
| Dysthymia | Male | 1425 (890, 2096) | 6383 (9601, 4020) | 347.633 | 97 (61, 144) | 96 (62, 143) | -1.031 |
| Female | 1736 (1087, 2571) | 7235 (10787, 4581) | 316.537 | 132 (84, 193) | 131 (83, 192) | -0.758 | |
| Total | 3162 (1991, 4652) | 13,618 (20115, 8565) | 330.556 | 114 (73, 169) | 112 (72, 165) | -1.754 | |
| Bipolar disorder | Male | 2458 (1465, 3795) | 10,261 (15272, 6110) | 317.314 | 150 (90, 224) | 149 (89, 221) | -0.667 |
| Female | 2481 (1489, 3813) | 9595 (14300, 5855) | 286.626 | 165 (100, 245) | 165 (99, 244) | 0.000 | |
| Total | 4940 (3008, 7415) | 19,857 (29439, 11905) | 301.919 | 157 (96, 232) | 156 (94, 229) | -0.637 | |
| Anxiety disorders | Male | 8558 (5558, 12569) | 36,539 (57270, 20974) | 326.954 | 446 (294, 623) | 519 (301, 806) | 16.368 |
| Female | 12,381 (8030, 17874) | 52,035 (80650, 30855) | 320.278 | 737 (489, 1037) | 867 (524, 1347) | 17.639 | |
| Total | 20,939 (13810, 30493) | 88,575 (137121, 52135) | 323.011 | 584 (390, 828) | 679 (404, 1050) | 16.267 | |
| Eating disorders | Male | 653 (385, 1072) | 2547 (3981, 1492) | 289.677 | 30 (18, 48) | 33 (19, 51) | 10.000 |
| Female | 963 (576, 1536) | 3689 (5922, 2142) | 282.953 | 50 (30, 79) | 56 (32, 91) | 12.000 | |
| Total | 1616 (965, 2628) | 6236 (10006, 3676) | 285.671 | 39 (24, 63) | 44 (25, 70) | 12.821 | |
| Anorexia nervosa | Male | 140 (71, 238) | 443 (753, 246) | 215.165 | 5 (2, 9) | 5 (3, 9) | 0.000 |
| Female | 226 (127, 369) | 738 (1199, 414) | 225.506 | 10 (6, 16) | 10 (6, 17) | 0.000 | |
| Total | 367 (209, 591) | 1182 (1921, 700) | 221.821 | 7 (4, 12) | 8 (4, 12) | 14.286 | |
| Bulimia nervosa | Male | 513 (278, 887) | 2103 (3481, 1178) | 309.896 | 24 (13, 41) | 27 (15, 45) | 12.500 |
| Female | 736 (413, 1219) | 2950 (4900, 1596) | 300.499 | 39 (21, 65) | 45 (24, 75) | 15.385 | |
| Total | 1249 (712, 2095) | 5054 (8307, 2817) | 304.437 | 31 (18, 52) | 35 (19, 59) | 12.903 | |
| Autism spectrum disorders | Male | 3972 (2674, 5697) | 13,281 (18684, 9035) | 234.282 | 192 (130, 277) | 196 (133, 276) | 2.083 |
| Female | 1902 (1274, 2718) | 5894 (8232, 3969) | 209.773 | 100 (67, 143) | 100 (67, 139) | 0.000 | |
| Total | 5875 (4019, 8403) | 19,175 (26861, 13069) | 226.346 | 148 (101, 212) | 151 (103, 212) | 2.027 | |
| Attention-deficit/hyperactivity disorder | Male | 420 (222, 721) | 1280 (2139, 678) | 204.487 | 17 (9, 28) | 17 (9, 29) | 0.000 |
| Female | 148 (75, 255) | 440 (747, 235) | 196.321 | 6 (3, 11) | 6 (3, 11) | 0.000 | |
| Total | 568 (302, 962) | 1720 (2907, 928) | 202.356 | 12 (6, 20) | 12 (6, 20) | 0.000 | |
| Conduct disorder | Male | 2443 (1314, 3816) | 6439 (10061, 3513) | 163.465 | 85 (46, 133) | 85 (46, 133) | 0.000 |
| Female | 1105 (583, 1787) | 2917 (4791, 1496) | 163.889 | 41 (22, 67) | 41 (21, 68) | 0.000 | |
| Total | 3549 (1906, 5511) | 9357 (14647, 4983) | 163.625 | 64 (34, 99) | 64 (34, 100) | 0.000 | |
| Idiopathic developmental intellectual disability | Male | 1529 (644, 2726) | 3991 (7329, 1579) | 161.001 | 70 (29, 125) | 57 (22, 106) | -18.571 |
| Female | 1113 (495, 1931) | 2756 (4925, 1153) | 147.429 | 56 (24, 97) | 46 (19, 81) | -17.857 | |
| Total | 2642 (1128, 4664) | 6748 (12194, 2752) | 155.319 | 63 (26, 112) | 52 (21, 94) | -17.460 | |
| Other mental disorders | Male | 1695 (1067, 2532) | 8223 (12224, 5210) | 384.863 | 130 (84, 195) | 129 (83, 191) | -0.769 |
| Female | 1001 (630, 1500) | 4512 (6741, 2883) | 350.643 | 87 (56, 131) | 87 (56, 129) | 0.000 | |
| Total | 2697 (1724, 4045) | 12,736 (18840, 8152) | 372.197 | 110 (71, 163) | 110 (71, 162) | 0.000 | |
| Substance use disorders | Male | 2510 (1798, 3237) | 9460 (12320, 6743) | 276.750 | 147 (105, 188) | 132 (95, 172) | -10.204 |
| Female | 2049 (1377, 2748) | 7493 (10095, 5024) | 265.654 | 127 (86, 168) | 124 (83, 166) | -2.362 | |
| Total | 4560 (3167, 5902) | 16,954 (22102, 11975) | 271.786 | 137 (98, 177) | 128 (91, 166) | -6.569 | |
| Alcohol use disorders | Male | 957 (641, 1383) | 3616 (5177, 2448) | 277.736 | 62 (41, 86) | 52 (35, 73) | -16.129 |
| Female | 321 (199, 492) | 1166 (1761, 744) | 262.738 | 21 (13, 32) | 20 (13, 30) | -4.762 | |
| Total | 1278 (833, 1885) | 4783 (6879, 3284) | 274.044 | 43 (28, 61) | 37 (25, 53) | -13.953 | |
| Drug use disorders | Male | 1553 (1080, 2090) | 5843 (7935, 4009) | 276.078 | 85 (59, 113) | 79 (54, 106) | -7.059 |
| Female | 1727 (1141, 2361) | 6327 (8663, 4212) | 266.196 | 105 (71, 141) | 103 (70, 141) | -1.905 | |
| Total | 3281 (2245, 4419) | 12,170 (16214, 8556) | 270.875 | 94 (65, 123) | 90 (63, 119) | -4.255 | |
| Opioid use disorders | Male | 1214 (806, 1667) | 4642 (6447, 3070) | 282.155 | 68 (47, 91) | 63 (42, 87) | -7.353 |
| Female | 1519 (973, 2129) | 5778 (8023, 3741) | 280.343 | 92 (60, 126) | 94 (62, 131) | 2.174 | |
| Total | 2733 (1811, 3766) | 10,420 (14310, 7006) | 281.148 | 79 (53, 106) | 77 (52, 105) | -2.532 | |
| Cocaine use disorders | Male | 55 (36, 88) | 201 (296, 129) | 259.490 | 3 (2, 4) | 2 (1, 4) | -33.333 |
| Female | 39 (26, 60) | 100 (158, 60) | 153.117 | 2 (1, 3) | 1 (1, 2) | -50.000 | |
| Total | 95 (63, 149) | 301 (450, 191) | 215.448 | 2 (2, 4) | 2 (1, 3) | 0.000 | |
| Amphetamine use disorders | Male | 123 (55, 204) | 441 (730, 228) | 257.735 | 5 (2, 9) | 5 (3, 9) | 0.000 |
| Female | 56 (25, 100) | 188 (325, 88) | 235.537 | 3 (1, 5) | 2 (1, 5) | -33.333 | |
| Total | 179 (85, 297) | 630 (1015, 352) | 251.356 | 4 (2, 7) | 4 (2, 7) | 0.000 | |
| Cannabis use disorders | Male | 119 (57, 218) | 407 (699, 207) | 240.067 | 5 (2, 9) | 5 (2, 9) | 0.000 |
| Female | 40 (19, 74) | 139 (239, 68) | 240.198 | 2 (1, 3) | 2 (1, 3) | 0.000 | |
| Total | 160 (76, 284) | 546 (925, 285) | 240.100 | 3 (2, 6) | 3 (2, 6) | 0.000 | |
| Other drug use disorders | Male | 40 (27, 57) | 151 (211, 101) | 276.493 | 2 (1, 3) | 2 (1, 2) | 0.000 |
| Female | 72 (55, 90) | 120 (179, 79) | 66.179 | 4 (3, 6) | 2 (1, 3) | -50.000 | |
| Total | 112 (88, 140) | 271 (375, 190) | 141.279 | 3 (2, 4) | 2 (1, 2) | -33.333 |
Fig. 1.
Mental disorders burden in Jordan: Age-standardized DALYs and prevalence rates per 100,000 for both genders from 1990–2021
Risk factors for mental disorders
Figure 2 illustrates the age-standardized mental and substance use disorders DALYs per 100,000 persons attributed to risk factors in 2021. Behavioral risks, bullying victimization, childhood sexual abuse, and drug use were major contributors to age-standardized DALYs for mental and substance use disorders. Idiopathic developmental intellectual disabilities were uniquely linked to environmental risks and lead exposure.
Fig. 2.
Age-standardized Mental Disorders DALYs per 100,000 persons attributable to risk factors in 2021. The most significant risk factors, highlighted in the figure, are behavioral risk, bullying victimization, childhood sexual abuse, and drug use
Mental disorders by sex and age
Tables 1, 2 and 3 show the findings of this paper by gender. By 2021, women had higher age-standardized prevalence (17,135 per 100,000) and DALY rates (2,597) for mental disorders than men (13,992 and 1,923, respectively), whereas men had a higher prevalence for substance use disorders (874 vs. 550). Across ages, DALY rates for mental disorders rose steadily through childhood, peaking between 30 and 39 years, then declined. Males had higher DALY rates before age 10, whereas from early adolescence onward, females had higher rates (Fig. 3). Also, changes in all-age numbers and standardized rates for prevalence and DALYs in Jordan from 1990 to 2021 are shown in Fig. 4.
Fig. 3.
Gender-Specific Burden of Mental disorders by Age Group in Jordan in 2021
Fig. 4.
Changes in All-Age Numbers and Standardized Rates for Prevalence and DALYs in Jordan from 1990 to 2021. Each column is colored red for higher numbers and blue for lower numbers
Discussion
This study aimed to quantify the burden of mental disorders in Jordan over the past three decades, utilizing the GBD dataset to provide a comprehensive picture of these conditions in the region. This investigation is the first of its kind to use GBD data specifically for Jordan, shedding light on critical trends in mental health and substance use disorders. Our findings reveal a substantial increase in the prevalence of mental disorders from 1990 to 2021, with particularly pronounced growth in other mental disorders (an aggregate category encompassing personality disorders), other drug use disorders, schizophrenia, depressive disorders, and anxiety disorders (Figs. 1 and 4). Gender differences were evident, with prevalence rates of mental disorders consistently higher among females (Figs. 1 and 3), while substance use disorders were more prevalent among males (Table 1). Mortality trends highlighted a marked increase in deaths due to anorexia nervosa and substance use disorders, particularly amphetamine and opioid use disorders (Table 2). The most significant risk factors for age-standardized mental disorders DALYs per 100,000 persons were behavioral risk, bullying victimization, childhood sexual abuse, and drug use (Fig. 2). Additionally, DALY rates indicated notable rises in anxiety and eating disorders, contrasting with a reduction in the burden of idiopathic developmental intellectual disability (Table 3).
When comparing these findings with global trends in mental health, both similarities and unique patterns emerge. Globally, the burden of mental disorders has continued to rise [1]. This increase is driven by a combination of rising prevalence and demographic shifts, such as population aging, which exacerbate the impact of these disorder [1]. In Jordan, the sharp rise in prevalence mirrors global patterns, especially for disorders like depressive and anxiety disorders, which remain strong contributors to DALYs worldwide, particularly among youth populations. The findings align with the broader trend of mental health burden increases across the Middle East, as shown by Mokdad’s research. Mokdad et al. noted that this increase from 1990 to 2013 in the Eastern Mediterranean region is primarily attributed to population growth and aging rather than an intrinsic rise in prevalence rates. They also highlight potential underreporting due to stigma and delays in seeking healthcare, as well as the lack of sufficient epidemiological data to assess the more recent impact of conflicts like the Arab Spring [25].
Recent data emphasizes the heightened demand for mental health services, especially after the COVID-19 pandemic. The pandemic amplified the prevalence of mental health disorders, particularly anxiety and depression. Abazid et al. (2023) [26] found substantial increases in these disorders during the pandemic, emphasizing the urgent mental health needs worldwide and in the Middle East. This trend is consistent with findings from the Global Burden of Disease Study, which reported substantial increases in DALYs related to mental health conditions in the Eastern Mediterranean region [27, 28]. Specifically, the burden of mental disorders in the Middle East and North Africa grew from 80.8 million to 125.3 million DALYs between 1990 and 2019, reflecting an increasing demand for mental health services in this region [27].
Overall, while Jordan’s mental health trends align with global patterns, particularly in the rise of anxiety and depressive disorders, the regional context highlights additional potential pressures, including the effects of rapid population growth, political turmoil [29] and social changes, which may further intensify the mental health burden. Sociopolitical changes, including regional conflicts and the influx of refugees, particularly from Syria, have strained Jordan’s resources and healthcare systems, heightening stress and mental health challenges for both refugees and the host population. Syrian refugees often experience trauma from violence and displacement, intensifying their mental health needs [30], while the general Jordanian population has faced increased anxiety and depression due to the instability and uncertainty characterizing the region [9, 26]. Economic challenges further compound these issues, with high unemployment rates, inflation, and economic setbacks from the COVID-19 pandemic leading to heightened stress, anxiety, and financial barriers to accessing mental healthcare [9, 18, 31, 32]. Additionally, the COVID-19 pandemic has shown profound negative effects on the sleep health of populations, including in Jordan, which is in turn linked to negative mental health outcomes [18, 33]. Rapid urbanization has also contributed to social isolation and increased competition for resources, which may exacerbate mental health issues, particularly for adolescents who report diminished social support in urban settings [34]. Meanwhile, cultural shifts have influenced mental health perceptions, with many individuals continuing to seek faith-based or traditional remedies rather than professional mental health care due to stigma and limited mental health literacy [35, 36]. Stigma remains a major barrier among the Jordanian population, discouraging individuals, especially women, from seeking help [37], further compounding the burden of untreated mental health issues.
The interplay of various risk factors drastically influences mental health outcomes among adolescents in Jordan (Fig. 2). For the three most prevalent mental disorders—anxiety, depression, and others—leading risk factors include behavioral risks, bullying victimization, and childhood sexual abuse, respectively. Childhood sexual abuse is associated with long-term adverse outcomes, including increased risks for post-traumatic stress disorder, depression, anxiety, substance abuse, and suicidal behavior [38]. Research by Itani et al. revealed that marijuana use history and recent bullying were strongly linked to higher odds of suicidal ideation and planning among Jordanian youth, with bullied students having nearly twice the odds of experiencing suicidal thoughts compared to their non-bullied peers [39]. Additionally, Mohannad et al. found that Frequent Mental Distress (FMD) is associated with chronic conditions and adverse health behaviors. Individuals with hypertension, high cholesterol, diabetes, and asthma were more likely to experience FMD, while smokers had higher odds of FMD. Conversely, those engaging in vigorous physical activity were less likely to experience mental distress [40].
Furthermore, gender differences in the mental health burden in Jordan reveal higher prevalence rates of mental disorders among females and greater prevalence of substance use disorders among males, likely driven by a combination of biological, psychosocial, and cultural factors (Figs. 1 and 3). For females, hormonal fluctuations related to menstruation, pregnancy, and menopause have been shown to influence mood and emotional regulation, contributing to increased susceptibility to mood disorders [41]. Additionally, women often face unique psychosocial stressors, including gender-based violence, societal expectations, and caregiving responsibilities, which elevate their risk of conditions like depression and anxiety. Abuhammad et al. reported that Jordanian women experienced heightened levels of despair and anxiety during the COVID-19 pandemic, a period that intensified domestic responsibilities and social isolation [42]. Cultural stigma may also affect these gender differences in mental health reporting; while women may feel somewhat less restrained in seeking help, societal expectations may lead men to underreport mental health issues, masking the true prevalence of certain conditions among males [34].
In contrast, the higher prevalence of substance use disorders among males can be attributed to sociocultural norms that often promote risk-taking behaviors (Table 1). Behavioral tendencies toward externalizing disorders, such as impulsivity and aggression, are more common in males and further contribute to substance use risk [43]. Social networks that normalize or even encourage drug use may provide males with easier access to substances, perpetuating higher rates of substance use [44]. These findings emphasize the need for gender-sensitive mental health interventions that account for the unique stressors, societal pressures, and behavioral patterns influencing mental health and substance use disorders among men and women in Jordan.
The burden of mental disorders in Jordan exhibits distinct patterns across different age groups, particularly during childhood, adolescence, and early adulthood (Fig. 3). Studies indicate that mental health issues often emerge early in life, with a national school-based survey revealing that approximately 24.5% of children experience anxiety symptoms and 16.6% show signs of major depressive disorder, with higher prevalence rates among refugee populations. As children transition into adolescence, the prevalence of mental health problems tends to increase, with about 19.7% of adolescents displaying abnormal levels of emotional and behavioral difficulties [45]. Adolescents, who make up 21% of the total Jordanian population, are particularly vulnerable to discrimination, stigma, social exclusion, educational difficulties, risk-taking behaviors, physical ill-health, and human rights violations, all of which exacerbate their mental well-being [46]. In our study, we found that from the age of 10 onwards, females show a higher rate of DALYs (Fig. 3). This could be attributed to factors like financial dependence, exposure to sexual violence, adherence to traditional customs, and a higher likelihood of women reporting depressive symptoms to healthcare providers. Furthermore, depression tends to be more prevalent among displaced women compared to men [47]. The impact of these disorders during childhood can have long-lasting effects. The peak burden of mental disorders is observed between the ages of 35–39 [25], which coincides with crucial years of productivity. This places a significant strain on healthcare systems and public resources, contributing to both high treatment costs and lost productivity. Individuals with these conditions often face greater financial instability due to increased healthcare expenses. Moreover, the long-term effects of childhood mental disorders can perpetuate cycles of poverty and disadvantage, underscoring the importance of early intervention to mitigate these broader socioeconomic consequences [48].
Public health implications
The rising prevalence and burden of mental disorders in Jordan highlight the urgent need for enhanced mental health resources and services that are both accessible and culturally sensitive. Given the increasing prevalence of conditions such as anxiety, depression, and substance use disorders, Jordan’s healthcare system must prioritize mental health in its public health agenda. Integrating mental health services into primary care settings could improve access, especially in underserved areas, and reduce stigma by normalizing mental health care as part of routine health services. Targeted interventions addressing stigma and mental health literacy are essential to encourage early help-seeking, particularly among populations that may face cultural barriers to accessing care. Educational campaigns and community-based programs that engage religious leaders, educators, and local influencers could play a pivotal role in shifting societal attitudes and promoting mental health awareness.
Furthermore, gender-specific mental health interventions are crucial in addressing the distinct needs of men and women. For women, interventions should consider the psychosocial stressors they face, such as caregiving responsibilities and the impact of gender-based violence, which are often linked to higher rates of anxiety and depression. Programs that support male mental health could focus on reducing risk-taking behaviors and substance use, which are often influenced by societal expectations of masculinity. Implementing school-based programs that address bullying and other early risk factors, such as substance use, could also help mitigate the onset of mental health issues among adolescents. Overall, a multifaceted public health approach that incorporates gender-sensitive strategies, mental health education, and community engagement will be essential in addressing the growing mental health burden in Jordan.
In addition to gender-specific approaches, interventions should be tailored for high-risk groups, particularly adolescents and refugees. School-based mental health programs can play a crucial role in early identification and prevention of mental health issues among adolescents, addressing risk factors such as bullying and substance use. For refugees, expanding trauma-informed care and ensuring access to culturally competent mental health professionals are essential to addressing the psychological distress caused by displacement and conflict. Strengthening community outreach programs and providing mobile mental health services in refugee-populated areas can help bridge gaps in care.
From a policy perspective, sustained investment in mental health infrastructure is necessary to meet the growing demand for services. Policymakers should consider increasing funding for mental health programs, training more professionals in evidence-based therapies, and integrating digital mental health solutions to expand access. Ensuring mental health coverage in national insurance schemes and developing policies that protect vulnerable populations, including refugees and low-income individuals, can further enhance the reach and effectiveness of mental health services.
Overall, a multifaceted public health approach that incorporates demographic-specific strategies, mental health education, and community engagement will be essential in addressing the growing mental health burden in Jordan.
Strengths and Limitations
One of the primary strengths of this study is its use of the GBD dataset, which provides a comprehensive and standardized measure of mental health outcomes over an extended period. By analyzing data from 1990 to 2021, this study offers valuable insights into long-term trends in the prevalence, mortality, and DALYs associated with mental disorders in Jordan. The inclusion of detailed demographic breakdowns, such as gender differences and age-specific trends, adds depth to our understanding of how mental health burdens vary across population subgroups. This level of detail allows for a more targeted approach to public health interventions and highlights areas where resources may be most urgently needed.
However, several limitations must be acknowledged. First, the GBD dataset relies on modeling and estimates, which may be subject to data quality issues, reporting biases, and potential underestimation or overestimation of burden, particularly in regions where mental health surveillance is inconsistent. The reliance on indirect data sources and extrapolation methods may introduce uncertainties, particularly for subpopulations with limited available data. Additionally, the dataset does not capture certain key social and structural determinants that influence mental health, such as poverty, healthcare access, and stigma, which are critical to understanding the full burden of mental disorders in Jordan. Furthermore, GBD data does not distinguish between Jordanian citizens and refugees, despite significant demographic shifts in the country. Given the unique mental health challenges faced by refugees, including trauma and displacement-related stressors, this limitation restricts the ability to tailor interventions effectively. Unfortunately, there is also a lack of locally generated mental health statistics, further exacerbating reliance on modeled data. A literature search revealed that even the Jordan Ministry of Health data primarily references GBD estimates, underscoring the need for strengthened national mental health data collection efforts. Finally, this study is cross-sectional in design, limiting our ability to establish causality between observed trends and specific risk factors. Despite these limitations, the findings provide a valuable foundation for future research and public health initiatives focused on closing data gaps, improving mental health surveillance, and informing evidence-based policy interventions in Jordan.
Future research
Future research should aim to address the gaps identified in this study by exploring additional factors that may influence mental health outcomes in Jordan, including socioeconomic status, access to mental health services, and cultural attitudes toward mental illness. Longitudinal studies would be particularly valuable to examine causal relationships between specific risk factors—such as exposure to trauma, economic hardship, and social support networks—and the development of mental disorders over time. Further investigation into gender-specific mental health dynamics is also warranted, as understanding the distinct needs and experiences of men and women could inform more tailored interventions. In addition, research on the effectiveness of culturally adapted mental health interventions is essential. Studies evaluating school-based mental health programs, stigma-reduction campaigns, and primary care integration models could provide insight into strategies that are most effective within the Jordanian context. Finally, with the rising prevalence of substance use disorders among adolescents and young adults, targeted research into the social and environmental factors contributing to this trend, as well as preventive interventions, would be beneficial.
Conclusions
This study provides a comprehensive analysis of the burden of mental disorders in Jordan over three decades, revealing a substantial increase in prevalence, mortality, and DALYs. The findings underscore the significant public health challenge posed by mental disorders, driven by sociopolitical factors such as regional conflicts, economic instability, and the influx of refugees, as well as cultural barriers like stigma and limited mental health literacy. The gender and age-specific variations highlight the need for targeted interventions, with females experiencing a greater burden of mental disorders and males disproportionately affected by substance use disorders. Behavioral risks, bullying, and childhood abuse emerged as key determinants, indicating the importance of early prevention efforts. These results call for urgent policy actions, including the integration of mental health services into primary care, stigma reduction initiatives, and gender-sensitive strategies, to alleviate the growing mental health burden and improve outcomes for the Jordanian population.
To mitigate this growing burden, policymakers should focus on integrating mental health services into primary care, expanding access to community-based support, and implementing national anti-stigma campaigns. Strengthening mental health education in schools and workplaces can also promote early intervention, particularly for at-risk youth. Additionally, ensuring equitable access to services, especially for refugees and underserved populations, is essential.
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Supplementary Table S1. Mental disorders and definitions as outlined by the GBD Study
Author contributions
Author Contributions: Y.A.A, O.T, and A. A-Z conceptualized the research question and hypothesis. M.A.Z conducted data analysis and graphical representation. Y.A.A, A.A-Z, A.B.S, D.O, and W.S wrote the main manuscript text. All authors contributed to revise work for important intellectual content, gave the final approval of the version to be published, and agreed on all aspects of the work, especially concerning its design, accuracy and integrity. The corresponding author confirms that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.
Funding
We received no funding for this study.
Data availability
The data is available upon reasonable request from the corresponding author.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Conflict of interest
The authors declare no conflict of interest.
Clinical trial number
Not applicable.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Table S1. Mental disorders and definitions as outlined by the GBD Study
Data Availability Statement
The data is available upon reasonable request from the corresponding author.