Rates of Dual Diagnosis in Child and Adolescent Psychiatric Inpatients: A Scoping Review
Bibliographic record
Abstract
OBJECTIVES: Child and adolescent psychiatric (CAP) inpatient admissions have increased since 2009 and the clinical profile of these patients has become more complex. Unrecognized dual diagnosis, that is, comorbid substance use or substance use disorder (SUD) may contribute to this problem, but the prevalence of dual diagnosis in this population is inadequately understood. The goal of this scoping review was to summarize the range and content of research on this topic. METHODS: MEDLINE, EMBASE, and PsychINFO databases were systematically searched for studies published from 2008 to 2019 containing information on rates of comorbid substance use or SUD in CAP inpatients. RESULTS: A total of 23,326 abstracts were located. After removing duplicates, screening abstracts and full-text papers, and extracting data with full-text reviews, fourteen studies meeting our criteria remained. Rates of substance use or SUD ranged from 0.9% to 54.8%, differing on the basis of: (1) type of outcome; (2) type of data source; and (3) whether samples had a specific diagnostic focus or not. Rates of any type of SUD were reported in approximately 25% of samples from administrative databases, in 17.7% to 38.5% of chart reviews, and in 55% of studies with data from clinical research examinations. The highest rates of substance-specific substance use or SUD were for alcohol, cannabis, and nicotine. CONCLUSIONS: We located 14 studies, but methodologic heterogeneity precluded quantitative calculation of a single estimate for the prevalence of dual diagnosis. However, most of the rates suggest that this is an important problem in CAP inpatients, meriting further research. We suggest ways to improve future studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".