Rising global burden of anxiety disorders among adolescents and young adults: trends, risk factors, and the impact of socioeconomic disparities and COVID-19 from 1990 to 2021
Bibliographic record
Abstract
Background: Anxiety disorders are among the most prevalent mental health conditions globally, particularly affecting adolescents and young adults (10-24 years), and causing substantial psychological and social impairments. This study analyzed changes in the incidence, prevalence, and disability-adjusted life years (DALYs) of anxiety disorders in this age group from 1990 to 2021, emphasizing the impact of socioeconomic disparities and the COVID-19 pandemic, particularly post-2019. Methods: Utilizing the Global Burden of Disease(GBD) 2021 data from 204 countries, this study evaluated global trends in anxiety disorders among adolescents and young adults. Conducted between May 16 and August 1, 2024, it assessed prevalence, incidence, DALYs, and estimated annual percentage changes (EAPCs) from 1990 to 2021. Joinpoint regression identified significant shifts in incidence rates, with key risk factors, especially bullying victimization,examined. The analysis was stratified by region, country, age group, sex, and Socio-Demographic Index (SDI). Results: From 1990 to 2021, the global incidence of anxiety disorders among those aged 10-24 years increased by 52%, particularly in the 10-14 age group and post-2019. Females showed higher prevalence rates than males, and DALYs rose notably among the 20-24-year-olds. Regions with middle SDI reported the highest incidence and prevalence, whereas high SDI regions experienced the largest increases. India had the highest number of cases, while Mexico saw the greatest rise. A gradual decline in incidence was noted until 2001, followed by a slow increase, with a sharp rise from 2019 to 2021. Bullying victimization was a significant risk factor, especially in regions with a high anxiety disorder burden. Conclusion: The rising incidence of anxiety disorders among adolescents and young adults over the past 30 years reflects the increasing global mental health burden. Socioeconomic factors, particularly in middle SDI regions, and the impact of the COVID-19 pandemic have exacerbated this trend. Effective, targeted interventions focusing on early prevention and community-based mental health management are urgently needed to mitigate the long-term impact on young populations globally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".