Trends in Hospital Resource Use for Children With Complex Chronic Conditions
Bibliographic record
Abstract
Importance: Despite a small prevalence, children with complex chronic conditions (CCCs) use substantial inpatient resources. Objective: To assess national trends in hospital discharges, bed days, and hospital charges for children with and without CCCs in the US from 2000 to 2022. Design, Setting, and Participants: This retrospective, repeated cross-sectional study used hospital discharge data from the Kids' Inpatient Database (KID) from the years 2000, 2003, 2006, 2009, 2012, 2016, 2019, and 2022 for US children aged 0 to 18 years, excluding uncomplicated newborn discharges. Exposure: Presence of 0, 1, 2, or 3 or more CCCs. Main Outcomes and Measures: Trends in the hospital discharge rate per 100 000 children and percentage of total hospital discharges, bed days, and charges attributable to children with CCCs, identified with International Classification of Diseases, 9th Revision, Clinical Modification and International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Clinical Modification codes using Feudtner's diagnosis code classification system, version 3. Survey weights were applied to estimate hospital discharges, bed days, and charges. Sociodemographic (eg, primary payer) and clinical (eg, technology dependence, mental health comorbidity) characteristics for each hospital discharge were also assessed. Results: Across all years, there were an estimated 26 342 497 hospital discharges, of which 54.1% (95% CI, 54.0%-54.2%) were among males and 55.4% (95% CI, 54.4%-55.8%) were for infants. From 2000 to 2022, the discharge rate per 100 000 US children increased by 24.3% (95% CI, 22.7%-26.3%), from 779 to 968, for children with 1 or more CCCs and decreased by 9.7% (95% CI, 9.4%-10.0%), from 3831 to 3459, for children with no CCCs. From 2000 to 2022, the percentage change in the hospital discharge rate varied by number of CCCs: a 3.8% (95% CI, 0.9%-6.0%) decrease was found for 1 CCC, a 60.9% (95% CI, 57.7%-65.5%) increase for 2 CCCs, and a 340.0% (95% CI, 332.6%-351.1%) increase for 3 or more CCCs. In 2000 and 2022, children with 1 or more CCCs accounted for 16.9% (95% CI, 15.7%-17.9%) and 21.9% (95% CI, 20.7%-22.9%) of hospital discharges, 32.0% (95% CI, 30.8%-33.1%) and 44.1% (95% CI, 42.6%-45.4%) of bed days, and 44.2% (95% CI, 42.6%-45.5%) and 59.5% (95% CI, 57.8%-60.9%) of hospital charges, respectively. From 2000 to 2022, the percentage of hospital discharges in children with 1 or more CCCs increased with gastroenterologic technology dependence (7.0% [95% CI, 6.0%-8.0%] to 14.4% [95% CI, 12.4%-16.4%]), neurodevelopmental or neurocognitive disorders (5.7% [95% CI, 4.8%-6.5%] to 13.5% [95% CI, 11.7%-15.2%]), and public insurance (40.9% [95% CI, 38.8%-42.9%] to 52.1% [95% CI, 50.2%-54.1%]). Conclusions and Relevance: In this national, repeated cross-sectional study, the hospital discharge rate and the percentage of hospital resource use attributable to children with CCCs increased from 2000 to 2022, and these trends were mainly attributable to children with multiple CCCs. It is critical that health systems are equipped with the resources, staff, and payments to sustainably meet the increasing needs for inpatient care among children with CCCs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".