Prevalence of childhood mental disorders in high-income countries: a systematic review and meta-analysis to inform policymaking
Bibliographic record
Abstract
QUESTION: Mental disorders typically start in childhood and persist, causing high individual and collective burdens. To inform policymaking to address children's mental health in high-income countries we aimed to identify updated data on disorder prevalence. METHODS: We identified epidemiological studies reporting mental disorder prevalence in representative samples of children aged 18 years or younger-including a range of disorders and ages and assessing impairment (searching January 1990 through February 2021). We extracted associated service-use data where studies assessed this. We conducted meta-analyses using a random effects logistic model (using R metafor package). FINDINGS: =99.1%). Significant heterogeneity pertained to diagnostic measurement and study location. Anxiety (5.2%), attention-deficit/hyperactivity (3.7%), oppositional defiant (3.3%), substance use (2.3%), conduct (1.3%) and depressive (1.3%) disorders were the most common. Among children with mental disorders, only 44.2% (95% CI 37.6% to 50.9%) received any services for these conditions. CONCLUSIONS: An estimated one in eight children have mental disorders at any given time, causing symptoms and impairment, therefore requiring treatment. Yet even in high-income countries, most children with mental disorders are not receiving services for these conditions. We discuss the implications, particularly the need to substantially increase public investments in effective interventions. We also discuss the policy urgency, given the emerging increases in childhood mental health problems since the onset of the COVID-19 pandemic (PROSPERO CRD42020157262).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".