Abstract
Objective
This study aimed to provide an updated and comprehensive estimate of the non-fatal burden of anxiety disorders in Ethiopia from 1990 to 2023.
Methods
This study analyzed the burden of anxiety disorders in Ethiopia using the Global Burden of Diseases (GBD) 2023 estimates. Incidence, prevalence, and years lived with disability (YLDs) were estimated using a Bayesian meta-regression tool for disease modeling (DisMod-MR 2.1).
Results
There were a total of 589,076 new cases (95% UI: 408,183-856,726) and 5,222,731 prevalent cases (95% UI: 3,863,241-7,156,038) of anxiety disorders in Ethiopia in 2023, which resulted in 627,465 YLDs (95% UI: 391,935-951,877). The age-standardized rates of anxiety disorders were 512 new cases per 100,000 population (95% UI: 362-689), 4763 prevalent cases per 100,000 population (95% UI: 3535-6068), and 564 YLDs per 100,000 population (95% UI: 367-831). Sub-nationally, the rates were uniformly distributed by sex. The highest rates were estimated among adolescents. The rates remained similar from 1990 to 2019 and then experienced an upward increase thereafter. Compared to the rates in 1990, there was an increase in the ASPR and ASYLDs in 2023.
Conclusion
This study revealed that anxiety disorders posed a significant and escalating public health burden in Ethiopia, with adolescents being disproportionately impacted. Policies to prevent anxiety disorders should emphasize improving early detection and care, strengthening adolescent-focused services, and implementing uniform interventions across the country.
Keywords: anxiety disorders, incidence, prevalence, years lived with disability, Ethiopia
Introduction
Anxiety disorders are among the most common mental disorders associated with generalized or situation-specific responses to perceived threats.1,2 Excessive worries, counterproductive and debilitating fears, and related behavioral disturbances manifest anxiety disorders. 3 If they remain untreated, they could impose considerable economic costs, compromise quality of life and psychosocial functioning, and cause premature mortalities. 4 In 2019, there were an estimated 301 million prevalent cases of anxiety disorders globally, with significant variations by demographic, socioeconomic, geographic, and lifestyle conditions. 5
Ethiopia has adopted the World Health Organization’s (WHO’s) mental health gap action program (mhGAP) initiatives and has endorsed the national mental health strategic plan for the years 2021 to 2025 to respond to mental disorders in general. 6 Furthermore, the country has also integrated the mental healthcare services into its health extension program at the community level to ensure no one is left untreated. 4 Despite these initiatives, there is a paucity of comprehensive and comparable data on the distribution of mental conditions, particularly anxiety disorders, in the country. Moreover, the available evidence is limited in scope, covering restricted age ranges, specific population groups, diagnostic categories, or limited time periods.4,5 Lack of such epidemiological information may contribute to the partial effectiveness of policies and interventions.
The study has aimed to determine non-fatal health outcomes of anxiety disorders by sex and age at national and subnational levels in Ethiopia from 1990 to 2023, using the data, methods, and tools of the Global Burden of Diseases (GBD) 2023 study. The study identified high-risk populations across demographic groups, regions, and years for targeted interventions to improve mental health services and resource allocation.
Methods
Study setting
This study was conducted in Ethiopia, which is subnationally divided into Tigray, Afar, Amhara, Benishangul-Gumuz, Gambella, Oromia, Sidama, Southern Nations, Nationalities, and Peoples (SNNP), Harari, Somali, Dire Dawa, South West, and Addis Ababa. About 80% of the population lives in rural areas, where access to mental health services is limited. 7
Study nature
The GBD 2023 estimates were used to determine the prevalence and incidence of anxiety disorders and the years lived with disability (YLDs). From 1990 to 2023, the GBD study conducted a thorough evaluation of the magnitude and burden of 375 diseases or injuries and 88 risk factors by age and sex in 204 nations and territories. It was the most detailed scientific effort coordinated by the Institute for Health Metrics and Evaluation (IHME, University of Washington) with over 20,000 global researchers. Detailed methodological descriptions of the GBD framework are publicly accessible at: https://www.healthdata.org/research-analysis/gbd.
Data sources
The GBD 2023 estimates were used for this study. 8 The incidence, prevalence, and YLDs estimates were retrieved from the GBD Results Tool following the official release of the estimates on 12 October 2025, which is available at: https://vizhub.healthdata.org/gbd-results/. The input data sources included systematic literature reviews, gray literature, surveys, and medical records. Moreover, the estimation process was not only limited to these data sources. The GBD study uses Bayesian spatiotemporal methods, including the Bayesian meta-regression tool for disease modeling (DisMod-MR 2.1), to generate updated estimates annually by using new and existing data sources and improved statistical models for the entire GBD geography and time series. Through this modeling framework, it borrows strengths and, at the same time, redistributes limitations in data quality and models so that the new estimates are robust, complete, and comparable.
Data quality was ensured by applying data-vetting protocols to assess internal consistency, completeness, and plausibility; using tools, such as MR-BRT (meta-regression—Bayesian, regularized, trimmed), to adjust for known biases; and cross-validating new sources against existing datasets. 9 The Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) were also applied to ensure credibility, reproducibility, and applicability of estimates.
Inclusion and exclusion criteria
This study included all available data sources from 1990 to 2023. Data sources lacking age, sex, year, or geographic location metadata were excluded from the study to ensure the validity, comparability, and representativeness of the findings. Moreover, data tied to unclassifiable codes (commonly referred to as “garbage codes”) that could not be systematically redistributed into valid underlying causes of disease were also excluded from the analysis.
Data analyses
This study used the GBD 2023 study methods, tools, and protocol to estimate the incidence, prevalence, and YLDs by age, sex, and location in Ethiopia, from 1990 to 2023 (https://www.healthdata.org/research-analysis/gbd). The DisMod-MR 2.1 was used to estimate each of these metrics by starting from the global fit through super-region, region, national, and subnational levels. In each step, prior information from the immediate upper geography was used together with the covariate data to generate updated estimates at the next lower level. This structure improves estimation in data-limited locations and ensures spatial consistency. Moreover, spatiotemporal Gaussian process regression (ST-GPR) was used to generate stable, comparable estimates across different locations and years. It is a three-stage statistical process, involving first) data adjustment to remove systematic biases across different data sources; second) spatial-temporal smoothing, which borrows statistical strength from neighboring locations and adjacent years to fill data gaps and prevent sudden, unrealistic spikes in estimates; and third) Gaussian process regression, which is applied to the residuals (the differences between the smoothed estimates and the observed data) to capture localized variations and produce precise final estimates along with their associated 95% uncertainty intervals (UIs). The 95% UIs were estimated from the 2.5th and 97.5th ordered values by using 1000 draws from the posterior distribution. Rates were age-standardized to adjust for demographic variability across locations over time. All rates were estimated per 100,000 population. Percentage changes in estimates between 1990 and 2023 were computed, with the 95% UIs crossing zero indicating the absence of change. The following formula was used:
Definition of anxiety disorders
Anxiety disorders were defined as a group of mental conditions characterized by persistent and impairing worry, fear, and avoidance behaviors that considerably disrupt daily functioning. To ensure measurement consistency, the definition complies with the standardized diagnostic thresholds set forth in the 10th edition of the International Classification of Diseases (ICD-10) [Codes F40-F42, F43.0, F43.1, F93.0-F93.2, and F93.8] and the fourth edition text revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) [Codes 300.0-300.3, 308.3, 309.21, and 309.81].
Results
Incidence
There were a total of 589,076 incident cases of anxiety disorders (95% UI: 408,183 - 856,726) in Ethiopia in 2023, with 279,327 new cases among males (95% UI: 191,907-395,905) and 309,749 new cases among females (95% UI: 216,217-446,082). This figure corresponds to an age-standardized incidence rate (ASIR) of 512 new cases per 100,000 population (95% UI: 362-689), with 542 new cases per 100,000 females (95% UI: 385-737) and 483 new cases per 100,000 males (95% UI: 339-644). Sub-nationally, the rate was estimated to be similar by sex since the 95% UIs overlapped, indicating no clear evidence of large differences. The highest incidence rates were estimated among early adolescents. The rate was steady in the years between 1990 and 2019, and then a significant sharp increase was estimated in the years 2020 and 2021, followed by a sharp decline afterwards. The lowest incidence rate was estimated in 2009 [390 new cases per 100,000 population (95% UI: 292-489)], and the highest rate was estimated in 2021 [1353 new cases per 100,000 population (95% UI: 956-1821)] (Table 1, Figures 1 and 2).
Table 1.
Incidence and prevalence of anxiety disorders in Ethiopia, 1990 to 2023.
| Location | Sex | No. of incident cases | ASIR | % change | No. of prevalent cases | ASPR | % change |
|---|---|---|---|---|---|---|---|
| 2023 | 2023 | 1990-2023 | 2023 | 2023 | 1990-2023 | ||
| Ethiopia | Both | 589076(408183-856726) | 512(362-689) | 0.20(-0.13-0.85) | 5222731(3863241-7156038) | 4763(3535-6068) | 0.65(0.19-1.35) |
| Female | 309749(216217-446082) | 542(385-737) | 0.16(-0.18-0.88) | 2809464(2096921-3787093) | 5147(3853-6628) | 0.59(0.12-1.25) | |
| Male | 279327(191907-395905) | 483(339-644) | 0.25(-0.08-0.83) | 2413267(1763912-3373132) | 4382(3214-5661) | 0.73(0.27-1.46) | |
| Addis Ababa | Both | 17719(12788-23726) | 514(363-692) | 0.20(-0.14-0.86) | 178581(134874-224326) | 4988(3697-6388) | 0.72(0.24-1.44) |
| Female | 9891(7215-13390) | 542(385-737) | 0.16(-0.18-0.88) | 102322(77317-129628) | 5361(3999-6902) | 0.65(0.17-1.34) | |
| Male | 7828(5573-10342) | 483(339-644) | 0.25(-0.08-0.83) | 76259(57519-95646) | 4569(3346-5830) | 0.81(0.33-1.56) | |
| Afar | Both | 9728(6487-14651) | 510(360-685) | 0.21(-0.13-0.86) | 87913(62580-120528) | 4929(3649-6306) | 0.73(0.25-1.46) |
| Female | 4735(3172-7082) | 542(385-737) | 0.16(-0.18-0.88) | 43590(31244-59473) | 5365(4002-6907) | 0.66(0.17-1.34) | |
| Male | 4993(3314-7291) | 483(339-644) | 0.25(-0.08-0.83) | 44323(31329-61776) | 4573(3349-5838) | 0.81(0.33-1.57) | |
| Amhara | Both | 123959(87019-174095) | 512(362-689) | 0.20(-0.13-0.86) | 1113685(835896-1490494) | 4708(3498-6002) | 0.63(0.18-1.32) |
| Female | 65287(46277-91022) | 542(385-737) | 0.16(-0.18-0.88) | 599758(452186-790590) | 5090(3813-6551) | 0.57(0.11-1.22) | |
| Male | 58672(40742-80321) | 483(339-644) | 0.25(-0.08-0.83) | 513927(379560-702697) | 4330(3179-5595) | 0.71(0.25-1.43) | |
| Benishangul-Gumuz | Both | 8384(5670-12336) | 511(361-688) | 0.20(-0.14-0.85) | 75752(54674-103543) | 4888(3615-6269) | 0.70(0.22-1.41) |
| Female | 4357(2954-6366) | 542(385-737) | 0.16(-0.18-0.88) | 40192(29155-55024) | 5294(3953-6836) | 0.63(0.15-1.31) | |
| Male | 4028(2716-5759) | 483(339-644) | 0.25(-0.08-0.83) | 35560(25520-48557) | 4511(3294-5752) | 0.78(0.31-1.53) | |
| Dire Dawa | Both | 2676(1900-3672) | 512(362-689) | 0.20(-0.13-0.85) | 25389(18941-32991) | 4875(3603-6248) | 0.69(0.22-1.40) |
| Female | 1414(1012-1943) | 542(385-737) | 0.16(-0.18-0.88) | 13744(10319-17862) | 5263(3930-6795) | 0.62(0.15-1.30) | |
| Male | 1262(888-1710) | 483(339-644) | 0.25(-0.08-0.83) | 11645(8621-15099) | 4483(3273-5717) | 0.77(0.30-1.52) | |
| Gambella | Both | 2843(1983-3930) | 513(362-690) | 0.19(-0.15-0.83) | 26906(19736-35418) | 4880(3608-6247) | 0.66(0.19-1.36) |
| Female | 1510(1075-2088) | 542(385-737) | 0.16(-0.18-0.88) | 14692(10892-19122) | 5257(3927-6787) | 0.62(0.15-1.30) | |
| Male | 1333(905-1864) | 483(339-644) | 0.25(-0.08-0.83) | 12213(8751-16262) | 4478(3269-5711) | 0.77(0.30-1.51) | |
| Harari | Both | 1417(1001-1982) | 512(361-688) | 0.20(-0.14-0.85) | 13400(9972 -17559) | 4915(3639-6293) | 0.69(0.22-1.40) |
| Female | 737(524-1022) | 542(385-737) | 0.16(-0.18-0.88) | 7129(5336-9345) | 5314(3967-6831) | 0.64(0.16-1.32) | |
| Male | 679(477-927) | 483(339-644) | 0.25(-0.08-0.83) | 6271(4633-8223) | 4528(3317-5773) | 0.79(0.32-1.54) | |
| Oromia | Both | 230372(155258-343526) | 512(362-689) | 0.20(-0.14-0.85) | 1991762(1438836-2797569) | 4732(3513-6046) | 0.64(0.18-1.33) |
| Female | 121143(82337-179075) | 542(385-737) | 0.16(-0.18-0.88) | 1070868(782931-1479467) | 5114(3830-6583) | 0.58(0.12-1.23) | |
| Male | 109229(72896-158465) | 483(339-644) | 0.25(-0.08-0.83) | 920894(655905-1322753) | 4352(3195-5636) | 0.72(0.26-1.44) | |
| Sidama | Both | 24240(16426-35653) | 512(362-689) | 0.20(-0.13-0.86) | 209907(152459-290939) | 4693(3488-5980) | 0.63(0.18-1.31) |
| Female | 12793(8716-18681) | 542(385-737) | 0.16(-0.18-0.88) | 113210(82969-154559) | 5075(3803-6524) | 0.57(0.11-1.22) | |
| Male | 11448(7707-16371) | 483(339-644) | 0.25(-0.08-0.83) | 96697(69490-136491) | 4318(3169-5578) | 0.71(0.25-1.42) | |
| Somali | Both | 38183(25735-57848) | 509(359-684) | 0.20(-0.13-0.84) | 336705(242551-468769) | 4841(3577-6203) | 0.68(0.21-1.40) |
| Female | 18427(12443-27838) | 542(385-737) | 0.16(-0.18-0.88) | 164979(119178-231713) | 5275(3940-6812) | 0.63(0.15-1.30) | |
| Male | 19755(13288-28897) | 483(339-644) | 0.25(-0.08-0.83) | 171726(123373-239088) | 4494(3282-5731) | 0.78(0.31-1.52) | |
| Southwest | Both | 14718(9969-21722) | 512(362-689) | 0.20(-0.14-0.85) | 127702(92406-177924) | 4685(3483-5968) | 0.62(0.18-1.31) |
| Female | 7799(5322-11413) | 542(385-737) | 0.16(-0.18-0.88) | 69177(50578-95001) | 5064(3801-6510) | 0.56(0.11-1.21) | |
| Male | 6919(4645-9927) | 483(339-644) | 0.25(-0.08-0.83) | 58525(41808-83209) | 4308(3162-5564) | 0.70(0.25-1.42) | |
| SNNP | Both | 81092(57216-116939) | 513(362-690) | 0.20(-0.14-0.85) | 728791(546305-987831) | 4819(3575-6157) | 0.67(0.20-1.37) |
| Female | 43567(31059-61934) | 542(385-737) | 0.16(-0.18-0.88) | 401408(301822-533653) | 5196(3888-6703) | 0.60(0.14-1.27) | |
| Male | 37526(26152-52897) | 483(339-644) | 0.25(-0.08-0.83) | 327383(242754-454760) | 4424(3243-5739) | 0.75(0.28-1.49) | |
| Tigray | Both | 33744(23636-47807) | 513(362-690) | 0.20(-0.14-0.85) | 306239(229349-413100) | 4784(3551-6113) | 0.65(0.19-1.35) |
| Female | 18088(12804-25252) | 542(385-737) | 0.16(-0.18-0.88) | 168395(126899-222340) | 5160(3862-6648) | 0.59(0.13-1.25) | |
| Male | 15656(10832-21706) | 483(339-644) | 0.25(-0.08-0.83) | 137844(101610-190577) | 4392(3223-5697) | 0.74(0.27-1.47) |
Figure 1.
Age-sex-specific incidence rate of anxiety disorders in Ethiopia, 2023.
Figure 2.
Trends of ASIR of anxiety disorders in Ethiopia, 1990 to 2023.
Prevalence
There were a total of 5,222,731 prevalent cases of anxiety disorders (95% UI: 3,863,241-7,156,038) in 2023, with 2,809,464 cases among females (95% UI: 2,096,921-3,787,093) and 2,413,267 cases among males (95% UI: 1,763,912-3,373,132). The corresponding age-standardized prevalence rate (ASPR) was estimated to be 4763 cases per 100,000 population (95% UI: 3,535-6,068), with 4382 cases per 100,000 males (95% UI: 3214-5661) and 5147 cases per 100,000 females (95% UI: 3853-6628). Subnationally, the rate was uniformly distributed by sex since the 95% UIs overlapped, indicating no clear evidence of large differences. The highest prevalence rates were estimated among adolescents. The rate remained similar between 1990 and 2019, and then a sharp increase was estimated afterwards. Overall, there was a 65% increase in the ASPR (95% UI: 19-135) nationally in 2023 compared to 1990 (Table 1, Figures 3 and 4).
Figure 3.
Age-sex-specific prevalence rate of anxiety disorders in Ethiopia, 2023.
Figure 4.
ASPR of anxiety disorders in Ethiopia, 1990 to 2023.
Years lived with disability
There were an estimated 627,465 YLDs (95% UI: 391,935-951,877) from anxiety disorders in Ethiopia in 2023, with 291,713 YLDs among males (95% UI: 180,555-443,455) and 335,752 YLDs among females (95% UI: 209,929-505,173). The corresponding age-standardized YLDs (ASYLDs) rate was 564 YLDs per 100,000 population (95% UI: 367-831), with 523 YLDs per 100,000 among males (95% UI: 338-773) and 606 YLDs per 100,000 among females (95% UI: 394-896). The rate was uniformly distributed by sex and region since the 95% UIs overlapped, indicating no clear evidence of large differences. The highest YLDs rates were estimated among adolescents. The rate was unchanged in the years between 1990 and 2019, and then a sharp increase was estimated afterwards. Overall, there was a 67% increase in the ASYLDs rate (95% UI: 19-135) nationally between 1990 and 2023 (Table 2, Figures 5 and 6).
Table 2.
YLDs from anxiety disorders in Ethiopia, 1990 to 2023.
| Location | Sex | No. of YLDs | AS YLDs rate | % change |
|---|---|---|---|---|
| 2023 | 2023 | 1990-2023 | ||
| Ethiopia | Both | 627465(391935-951877) | 564(367-831) | 0.67(0.19-1.35) |
| Female | 335752(209929-505173) | 606(394-896) | 0.60(0.12-1.25) | |
| Male | 291713(180555-443455) | 523(338-773) | 0.75(0.28-1.48) | |
| Addis Ababa | Both | 21363(13504-31732) | 594(383-883) | 0.73(0.23-1.44) |
| Female | 12183(7852-18168) | 635(409-951) | 0.66(0.17-1.36) | |
| Male | 9180(5684-13629) | 547(344-816) | 0.81(0.33-1.52) | |
| Afar | Both | 10559(6352-16196) | 582(371-858) | 0.75(0.26-1.48) |
| Female | 5209(3121-8020) | 629(403-944) | 0.67(0.20-1.38) | |
| Male | 5350(3231-8248) | 543(346-798) | 0.82(0.33-1.58) | |
| Amhara | Both | 133539(85296-199632) | 558(366-823) | 0.65(0.18-1.35) |
| Female | 71566(45516-107940) | 600(390-887) | 0.59(0.12-1.24) | |
| Male | 61973(39537-92917) | 517(339-759) | 0.73(0.25-1.48) | |
| Benishangul-Gumuz | Both | 9122(5667-13894) | 580(379-852) | 0.71(0.23-1.43) |
| Female | 4819(2964-7360) | 624(403-916) | 0.65(0.15-1.34) | |
| Male | 4303(2692-6583) | 538(350-794) | 0.80(0.32-1.56) | |
| Dire Dawa | Both | 3036(1910-4560) | 577(372-862) | 0.70(0.21-1.41) |
| Female | 1633(1046-2455) | 619(402-934) | 0.63(0.14-1.32) | |
| Male | 1403(864-2123) | 534(342-789) | 0.79(0.27-1.53) | |
| Gambella | Both | 3229(1985-4844) | 577(371-848) | 0.68(0.20-1.37) |
| Female | 1754(1094-2647) | 619(395-916) | 0.64(0.14-1.36) | |
| Male | 1475(910-2241) | 533(345-795) | 0.79(0.30-1.51) | |
| Harari | Both | 1609(1029-2445) | 583(382-865) | 0.70(0.24-1.41) |
| Female | 852(540-1279) | 627(410-935) | 0.65(0.16-1.32) | |
| Male | 757(474-1154) | 540(351-802) | 0.80(0.31-1.55) | |
| Oromia | Both | 239745(146796-366512) | 560(365-826) | 0.66(0.17-1.33) |
| Female | 128214(78678-195541) | 602(394-889) | 0.59(0.09-1.24) | |
| Male | 111532(67404-173331) | 519(333-763) | 0.74(0.27-1.45) | |
| Sidama | Both | 25299(15677-38430) | 556(362-823) | 0.64(0.20-1.32) |
| Female | 13579(8437-20694) | 598(392-887) | 0.58(0.13-1.21) | |
| Male | 11720(7157-17983) | 516(331-766) | 0.72(0.26-1.44) | |
| Somali | Both | 40380(24419-62332) | 572(369-838) | 0.70(0.20-1.40) |
| Female | 19623(11953-30676) | 616(397-908) | 0.64(0.14-1.32) | |
| Male | 20758(12442-31608) | 536(344-794) | 0.79(0.30-1.55) | |
| Southwest | Both | 15419(9488-23460) | 556(362-821) | 0.64(0.18-1.34) |
| Female | 8323(5068-12611) | 598(386-895) | 0.58(0.10-1.25) | |
| Male | 7095(4331-10666) | 514(332-753) | 0.72(0.24-1.45) | |
| SNNP | Both | 87474(55092-132180) | 572(371-846) | 0.69(0.20-1.37) |
| Female | 47907(30058-72561) | 613(394-920) | 0.62(0.11-1.28) | |
| Male | 39566(24878-59032) | 529(344-788) | 0.77(0.29-1.49) | |
| Tigray | Both | 36690(23176-55691) | 567(368-833) | 0.67(0.20-1.38) |
| Female | 20091(12835-30596) | 608(397-899) | 0.61(0.14-1.30) | |
| Male | 16600(10341-25163) | 523(334-782) | 0.76(0.28-1.49) |
Figure 5.
Age-sex-specific YLDs rate from anxiety disorders in Ethiopia, 2023.
Figure 6.
ASYLDs rate of anxiety disorders in Ethiopia, 1990 to 2023.
Discussion
The current study estimated the non-fatal health outcomes of anxiety disorders in Ethiopia from 1990 to 2023, using the GBD 2023 estimates. The age-standardized rate of incidence was 512 new cases per 100,000 population (95% UI: 362-689), prevalence was 4763 cases per 100,000 population (95% UI: 3535-6068), and YLDs was 564 YLDs per 100,000 population (95% UI: 367-831). Sub-nationally, the rates were uniformly distributed by sex. The highest prevalence and YLDs rates were estimated among adolescents. The rates remained similar from 1990 to 2019 and then experienced an upward increase thereafter. Compared to the rates in 1990, there was an increase in the ASPR and ASYLDs in 2023.
The ASIR and ASPR estimated in this study were consistent with those reported by the GBD 2019 study [554 new cases per 100,000 population (95% UI: 447-667) and 3466 prevalent cases per 100,000 population (95% UI: 2967-4084)]. 10 Similar estimates were reported by the GBD 2021 study globally and in the Eastern Sub-Saharan African region. 11 Moreover, the ASYLDs rates estimated in the current study overlap with the estimates in the Middle East and North Africa region. 12 Moreover, consistent estimates of ASYLDs rates were also reported globally by the GBD 2021 and GBD 2023 studies.9,13
The reason for the lower incidence rate estimated by the GBD 2021 study might be the effect of COVID-19, which resulted in increased anxiety and depression disorders.14–16 This pattern was also estimated in our study, which showed a steep increase from 2020 to 2021, reaching an ASIR of more than 1300 per 100,000, followed by a sharp decline after 2021. This spike coincides with the COVID-19 pandemic, which other studies have linked to increased anxiety and depression; however, the present analysis cannot definitively attribute causality.17,18 The pattern is consistent with a chronic course and limited remission, but individual-level data would be needed to confirm this.
This study provides updated estimates on the magnitude and burden of anxiety disorders disaggregated by age, sex, and location in Ethiopia from 1990 to 2023. However, certain limitations should be considered when interpreting the findings. First, the GBD data are systematically analyzed and modeled. Although the inclusion of different data sources by appraising data quality makes it strong, modelling and statistical smoothing to accommodate incomplete and heterogeneous data, especially in resource-scarce settings, might affect estimates. 9 Therefore, the GBD estimates should be validated by using primary data from the study setting. Second, GBD assumes independent comorbidity occurrence—which might not always be true for mental disorders, such as anxiety—that could underestimate the YLDs. Third, disability weight estimated by the severity distribution from Australian and American studies might not truly reflect the severity of anxiety disorders in Ethiopia, which might affect the YLDs estimates. 9 Finally, acknowledging Ethiopia-specific data scarcity, possible under-diagnosis and misclassification of anxiety disorders, cultural differences in symptom expression and help-seeking, and the inability of this ecological analysis to identify individual-level risk factors and the consequent uncertainty in estimates should also be important.
Ethiopia-specific data scarcity, possible under-diagnosis of anxiety disorders, cultural differences in symptom expression and help-seeking, limitations of subnational estimates, and the inability of this ecological analysis to identify individual-level risk factors.
Notwithstanding all the limitations, the study findings imply that anxiety disorders continue to be the major public health problem in Ethiopia, with an increasing trend in recent years. This underscores that there is a need to revise the national mental health strategy (2020-2025) 6 and existing mental health and psychosocial support initiatives. Furthermore, the estimated discrepancy between incidence and prevalence rates implies that emphasis should also be directed towards clinical response.
Policy implications
The significant and growing prevalence of anxiety disorders and the YLDs in Ethiopia emphasizes the need to strengthen mental health systems through early detection, adolescent-focused therapies, and community-based preventive initiatives, in accordance with WHO guidelines for youth mental health promotion. 19 The study also suggests that there is a potential need for a uniform implementation of mental health services across the country since there is no clear evidence of large regional variations in the distribution of anxiety disorders. Integrating routine screening and basic management of anxiety disorders into primary care, particularly through the health extension program, is consistent with WHO’s mhGAP recommendations for low-resource settings. 20 The growing disability burden emphasizes the need for increased mental health workforce capacity, domestic investment, and improved multi-sectoral coordination to address social determinants of mental health, in accordance with global mental health frameworks. 21 National mental health information systems must also be strengthened to promote surveillance and evidence-based planning, in accordance with WHO standards. 22
Conclusion
This study revealed that anxiety disorders posed a significant and escalating public health burden in Ethiopia, with adolescents being disproportionately impacted. Policies to prevent anxiety disorders should emphasize improving early detection and care, strengthening adolescent-focused services, and implementing uniform interventions across the country.
Acknowledgments
The GBD study is a global effort to quantify and compare the burden of diseases and injuries. We are grateful for this global initiative.
Appendix.
List of Abbreviations
- ASIR
Age-Standardized Incidence Rate
- ASPR
Age-Standardized Prevalence Rate
- ASYLDs
Age-Standardized Years Lived with Disability
- COVID-19
Coronavirus Disease 2019
- DisMod-MR 2.1
Disease Modeling with Bayesian Meta-Regression Tool Version 2.1
- DSM-IV-TR
Diagnostic and Statistical Manual of Mental Disorders 4th Edition Text Version
- GATHER
Guidelines for Accurate and Transparent Health Estimates Reporting
- GBD
Global Burden of Diseases
- ICD-10
International Classification of Diseases Version 10
- mhGAP
Mental Health Gap Action Program
- MR-BRT
Meta-Regression—Bayesian, Regularized, Trimmed
- SNNP
Southern Nations, Nationalities, and Peoples
- ST-GPR
Spatio-Temporal Gaussian Process Regression
- UI
Uncertainty Interval
- WHO
World Health Organization
- YLDs
Years Lived with Disability.
Footnotes
Author contributions: ST conceived and designed the study. ST, MJ, AS, BKL, SB, AGG, GW, AZ, and GT were involved in the analysis and interpretation of the findings. All authors have approved the final version of the manuscript.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
ORCID iDs
Musa Jemal https://orcid.org/0000-0002-5619-6511
Sewhareg Belay https://orcid.org/0000-0002-9465-8940
Gelgelo Wodessa https://orcid.org/0009-0008-0832-3783
Abinet Zewudie https://orcid.org/0009-0001-1946-779X
Gemechis Tuke https://orcid.org/0000-0001-5199-0428
Ethical considerations
This study used estimates of the GBD 2023 study, which were publicly available online. No identifiable individual-level data were accessed by the authors, and all analyses used aggregated GBD 2023 estimates. Therefore, written informed consent was not needed.
Data Availability Statement
The incidence, prevalence, and YLDs estimates were extracted from the GBD Results Tool following the official release of the estimates on 12 October 2025, which is available at: https://vizhub.healthdata.org/gbd-results/.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The incidence, prevalence, and YLDs estimates were extracted from the GBD Results Tool following the official release of the estimates on 12 October 2025, which is available at: https://vizhub.healthdata.org/gbd-results/.