Long-term trends in co-occurring medical and psychiatric hospitalizations among children and adolescents in Ontario, Canada.
Bibliographic record
Abstract
BackgroundPsychiatric conditions are common amongst hospitalized children. Co-occurring psychiatric conditions for medical hospitalizations contribute to length of stay, costs, and readmissions. We sought to measure trends over 20 years in pediatric hospitalizations for co-occurring medical and psychiatric conditions and compare with those without psychiatric comorbidity, overall and in free-standing children’s hospitals. MethodsWe identified all 3- to 17-year-olds hospitalized in Ontario, Canada between April 1, 2003 and March 31, 2022. Using health record discharge diagnoses, hospitalizations were assigned to 1 of 4 groups: 1) medical-diagnosis-only, 2) psychiatric-diagnosis-only, 3) primary medical diagnosis with psychiatric comorbidity, and 4) primary psychiatric diagnosis with medical comorbidity. Hospitalization trends for 1) all hospitals, and 2) free-standing children’s hospitals were described and compared. ResultsFrom 2003 to 2022, medical-diagnosis-only hospitalizations declined 39% (41,909 to 25,486 hospitalizations), psychiatric-diagnosis-only hospitalizations increased 96% (3227 to 6337), medical hospitalizations with psychiatric comorbidity increased 127% (977 to 2221) and psychiatric hospitalizations with medical comorbidity increased 100% (2051 to 4096). Among pediatric hospitals, medical-diagnosis-only hospitalizations increased 23% (12,430 to 15,318), psychiatric-diagnosis-only hospitalizations increased 420% (271 to 1408), psychiatric hospitalizations with medical comorbidity increased 172% (539 to 1468) and medical hospitalizations with psychiatric comorbidity increased 235% (478 to 1599). ConclusionsHospitals have experienced large absolute and relative increases in volumes for psychiatric conditions both with and without co-occurring medical conditions, particularly among free-standing children’s hospitals. Healthcare provider training, hospital resourcing, and health system planning must consider how best to accommodate the increasing acute psychiatric care needs of hospitalized children and adolescents.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".