Contributions of Children With Multiple Chronic Conditions to Pediatric Hospitalizations in the United States: A Retrospective Cohort Analysis
Bibliographic record
Abstract
BACKGROUND: Children with multiple chronic conditions (CMCC) are increasingly using hospital care. We assessed how much of US pediatric inpatient care is used by CMCC and which chronic conditions are the key drivers of hospital use. METHODS: A retrospective analysis of all 2.3 million US acute-care hospital discharges in 2012 for children age 0 to 18 years in the Kids' Inpatient Database. The ∼4.5 million US hospitalizations for pregnancy, childbirth, and newborn and neonatal care were not assessed. We adapted the Agency for Healthcare Research and Quality's Chronic Condition Indicators to classify hospitalizations for children with no, 1, or multiple chronic conditions, and to determine which specific chronic conditions of CMCC are associated with high hospital resource use. RESULTS: Of all pediatric acute-care hospitalizations, 34.3% were of children with no chronic conditions, 36.5% were of those with 1 condition, and 29.3% were of CMCC. Of the $23.6 billion in total hospital costs, 19.7%, 27.4%, and 53.9% were for children with 0, 1, and multiple conditions, respectively, and similar proportions were observed for hospital days. The three populations accounted for the most hospital days were as follows: children with no chronic condition (20.9%), children with a mental health condition and at least 1 additional chronic condition (20.2%), and children with a mental health condition without an additional chronic condition (13.3%). The most common mental health conditions were substance abuse disorders and depression. CONCLUSIONS: CMCC accounted for over one-fourth of acute-care hospitalizations and one-half of all hospital dollars for US pediatric care in 2012. Substantial CMCC hospital resource use involves children with mental health-related conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".