Cross-Sectoral Data Linkage: Tracking Mental Health Service Utilization from Childhood into Adulthood
Bibliographic record
Abstract
IntroductionOf the 15-18% of children and youth in Canada with a mental health disorder, some receive specialized mental health (MH) services and need additional treatment as young adults. Lack of a shared database across child and adult sectors has prevented examining predictors of future MH health service use. Objectives and ApproachWe examined predictors of mental health service utilization in adulthood, and compared a sample of youth who received specialized MH treatment and age-, sex-, and region- matched controls. Patient-level administrative data from five MH agencies funded by the Ministry of Children and Youth Services (MCYS) in Ontario, with population health sector datasets held at the Institute for Clinical Evaluative Sciences (ICES). We expanded previous definitions of coding a MH visit by including codes specific to long-lasting childhood MH diagnoses (e.g., Attention Deficit-Hyperactivity Disorder). ResultsOur match rate for linking the MCYS treated youth with their population health data was 77%. Youth who received MH treatment (N= 2957) were twice as likely as matched controls (N= 8891) to have a MH visit in the medical system in adulthood (i.e., after age 18). The most common diagnostic codes for the first visit were anxiety, depressive disorders, and ADHD. The median survival time (when 50% had a visit) from age 18 to first MH visit was 3.3 years. In adjusted Cox regressions, significant predictors of having an adult MH visit included service use history in both medical and MH systems during childhood and adolescence (e.g., ongoing pattern of children’s MH service use). Conclusion/ImplicationsThis study represents the first longitudinal, case-control cohort study in Canada to examine MH service utilization in the medical sector by youth treated for MH problems. The linkage of information from multiple datasets allowed for a broader understanding of MH service utilization across sectors of care, specific to children and youth.
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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 | 0.032 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".