Trends in annual and lifetime prevalence of child and adolescent mental health service use in the UK between 1991 and 2023: Welsh healthcare register linkage study
Bibliographic record
Abstract
BACKGROUND: The prevalence of mental ill health is increasing in young people worldwide, with rising referrals to child and adolescent mental health services (CAMHS). The numbers and proportions of the youth population who present to CAMHS, however, including how those figures are changing over time, are unclear. Understanding trends in mental health service contacts for young people over time is crucial mental health surveillance data. AIMS: Our aim was to calculate both the lifetime and annual prevalence of CAMHS contact in Wales for young people up to age 18 years. METHOD: Using linked Welsh administrative healthcare records, we calculated the annual prevalence of CAMHS contacts between 2004 and 2023. We also calculated the lifetime prevalence of CAMHS contacts for sequential annual birth cohorts born between 1991 and 2005 and followed to age 18 (between 2009 and 2023). RESULTS: = 19 870) of the total child and adolescent population. Among the 1991 birth cohort who turned 18 in 2009, 5.8% had contact with CAMHS at some stage in childhood or adolescence. For individuals born in 2005 who turned 18 in 2023, this figure had risen to 20.2%. CONCLUSIONS: The number of the young people in contact with CAMHS has increased dramatically over the past 15 years, from 1 in 17 young people who turned 18 in 2009 to 1 in 5 young people who turned 18 in 2023.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".