Trends and future burden of schizophrenia in youth across G20 countries: a systematic analysis of the global burden of disease 2021 study
Bibliographic record
Abstract
BACKGROUND: Schizophrenia is a severe mental disorder with an increasing burden among adolescents and young adults (aged 10-24 years). However, age-specific epidemiological data remains limited. This study aims to analyze the incidence, prevalence, and disability-adjusted life years (DALYs) of schizophrenia in youth (aged 10-24 years) across the Group of Twenty (G20) countries and to project future trends from 2022 to 2035. METHODS: Data were sourced from the Global Burden of Disease (GBD) 2021 dataset, encompassing information on 369 diseases across 204 countries and regions from 1990 to 2021. Bayesian age-period-cohort (APC) modeling was employed to estimate future burden. Age-standardized incidence rates (ASIRs), prevalence rates (ASPRs), and DALY rates (ASDRs) were analyzed by country, sex, and sociodemographic index (SDI). RESULT: From 1990 to 2021, the burden of schizophrenia among youth has increased across most G20 countries, with particularly sharp rises observed in China and India. The lowest burdens were reported in Canada, Saudi Arabia, and Australia, which also recorded the lowest DALYs. Russia exhibited marked increases in ASIR, ASPR, and ASDR, while the United States and the United Kingdom showed declines. Substantial variations were observed across gender, regions, and SDI levels. Projections indicate that ASPRs will continue to rise in Australia, China, and Japan through 2035, while declines are anticipated in the United States and Italy, and stability is expected in Argentina and Germany. CONCLUSION: The burden of schizophrenia among youth in G20 countries is increasing, accompanied by substantial regional, gender, and socioeconomic disparities. Strengthening early intervention, enhancing diagnostic capacity, and implementing youth-targeted mental health policies are urgently needed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| 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".