Most costly and prevalent reasons for hospitalization in children with medical complexity in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Children with medical complexity (CMC) have chronic health conditions often associated with functional limitations. CMC comprise 1%-5% of the pediatric population. In Canada, their care accounts for one-third of pediatric health spending. We aim to describe the most costly and prevalent conditions leading to hospitalization in CMC in Ontario, Canada. METHODS: Population-based, cross-sectional study from a universally funded system utilizing health administrative databases. Children (<18 years old) with valid provincial healthcare coverage admitted to a hospital from 2014 to 2019 were included. CMC was defined using validated algorithms. Encounters were classified into clinical conditions using the Pediatric Clinical Classification System. Outcomes included condition-specific prevalence, cost, and cost rank estimated using a costing algorithm in Canadian dollars. RESULTS: There were 627,314 pediatric hospitalizations, costing $4.28 billion. Of these, 141,653 (23%) hospitalizations were for CMC, costing $2.25 billion (52%). Among encounters for CMC, 84,280 (60%) were for children with medical technology. One-third of hospitalizations in CMC were in community hospitals. Nearly half (1.30 million, 46%) of days in hospital were in CMC, along with 60% of intensive care unit (ICU) days (667,497 days). Low birth weight ($555.4 million), prematurity ($70.0 million), and respiratory distress of the newborn ($46.6 million) were the costliest conditions. Low birth weight (88 per 1000 encounters), chemotherapy (42 per 1000 encounters), and pneumonia (29 per 1000 encounters) were the most prevalent conditions. CONCLUSIONS: Understanding the most costly and prevalent inpatient conditions in CMC will help to prioritize more targeted research questions and interventions to improve healthcare utilization and patient outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".