Data gaps in prevalence rates of mental health conditions around the world: a retrospective analysis of nationally representative data
Bibliographic record
Abstract
BACKGROUND: Mental health conditions contribute substantially to the global burden of disease, affecting quality of life and leading to increased health-care expenses and mortality. Accurate data on the prevalence and correlates of these disorders are crucial for policy making, advocacy, and improving population health, but there are notable gaps in the available data on the magnitude of mental health difficulties around the world. This study aims to identify and quantify the data gaps on mental disorders across the lifespan in the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. METHODS: We analysed the nationally representative data sources used by GBD 2021 on 11 mental health conditions, including neurodevelopmental disorders and neurodivergence, general psychiatric disorders, and substance-use disorders. Our analysis focused on the geographical origin of the data sources, the age groups and mental health conditions or neurodivergence covered, and temporal trends on the scientific production of data. FINDINGS: GBD 2021 identified 1241 unique nationally representative data sources for mental health conditions since 1950. Neurodevelopmental disorders and neurodivergence had the least coverage, with less than 13% of countries having prevalence data. Low-income countries had the largest data gap, with no data on neurodevelopmental disorders and neurodivergence, only 29% with any data on general psychiatric disorders, and 21% with data on substance-use disorders. The African and Western Pacific regions had the largest data gaps, and children were the least covered demographic: almost 90% of countries did not have any data for children. Most data (70-80% across disorders) were obtained before 2010. INTERPRETATION: Substantial gaps in prevalence data persist globally, particularly in children and in low-income countries. Despite increased scientific production in the 2000s, most mental disorders remain under-represented. Coordinated global efforts are required to enhance mental health data collection and address these gaps. FUNDING: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil.
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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 | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".