75 Identifying High Priority Conditions for Research in Hospitalized Children Using a Data-driven Approach: A Population-based Study
Bibliographic record
Abstract
Abstract Background Identifying conditions that should be prioritized for research based on their healthcare system burden is imperative to build a meaningful research agenda for the care of hospitalized children. No previous Canadian prioritization studies have been conducted in this area. Objectives To determine the prevalence, cost, and variation in cost of pediatric hospitalizations at all hospital types, to identify conditions that should be prioritized for future research. Design/Methods Population-based cross-sectional study of children (< 18 years), with an inpatient hospital encounter between April 1, 2014 and March 31, 2019 in Ontario, Canada. Data were obtained from linked health administrative databases. For each encounter, the most responsible ICD-10-CA discharge diagnosis code was classified into clinical categories using the Pediatric Clinical Classification System. The condition-specific prevalence and cost of pediatric hospitalizations, and condition-specific variation in cost per encounter across hospitals were determined. The variation in cost was evaluated using number of outlier hospitals, and intraclass correlation coefficient (ICC). Results There were 627,314 inpatient hospital encounters from 165 hospitals costing $4.3 billion. A total of 408,003 (65.0%) hospitalizations and $1.9 billion (43.8%) in hospital costs occurred at general hospitals. Table 1 presents the 25 most prevalent and 25 most costly conditions (34 in total) ranked by cumulative cost. The top 10 costly conditions accounted for 70.0% of all costs and 59.6% of all encounters. Conditions that were highly prevalent and costly included: low birth weight, preterm newborn, major depressive disorder, pneumonia, other perinatal conditions, bronchiolitis, and neonatal hyperbilirubinemia. Figure 1 illustrates the 25 most costly medical conditions, of which the majority of the most prevalent and costly conditions were newborn conditions. Amongst the most costly conditions, the highest variations in cost across hospitals were observed in two mental health conditions (other mental health disorders [ICC = 0.28]; anxiety disorders [ICC = 0.19]), and three newborn conditions (intrauterine hypoxia and birth asphyxia [ICC = 0.27]; other perinatal conditions [ICC = 0.17]; surfactant deficiency disorder [ICC = 0.17]). Conclusion This study identified several newborn and mental health conditions as the most prevalent, costly, and with high variation in cost across hospitals in hospitalized children. These results can be used to generate a research agenda for the care of hospitalized children in general and children’s hospitals to build a stronger evidence-base and improve patient outcomes.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".