Characterization of Birth Hospitalizations in the United States
Bibliographic record
Abstract
OBJECTIVES: A broad understanding of the scope of birth hospitalizations in the United States is lacking. We aimed to describe the demographics and location of birth hospitalizations in the United States and rank the most common and costly conditions documented during birth hospitalizations. METHODS: We conducted a cross-sectional analysis of the 2019 Kids' Inpatient Database, a nationally-representative administrative database of pediatric discharges. All hospitalizations with the indicator "in-hospital birth" and any categorized by the Pediatric Clinical Classification System as "liveborn" were included. Discharge-level survey weights were used to generate nationally-representative estimates. Primary and secondary conditions coded during birth hospitalizations were categorized using the Pediatric Clinical Classification System, rank-ordered by total prevalence and total marginal costs (calculated using design-adjusted lognormal regression). RESULTS: In 2019, there were an estimated 5 299 557 pediatric hospitalizations in the US and 67% (n = 3 551 253) were for births, totaling $18.1 billion in cost. Most occurred in private, nonprofit hospitals (n = 2 646 685; 74.5%). Prevalent conditions associated with birth admissions included specified conditions originating in the perinatal period (eg, pregnancy complications, complex births) (n = 1 021 099; 28.8%), neonatal hyperbilirubinemia (n = 540 112; 15.2%), screening or risk for infectious disease (n = 417 421; 11.8%), and preterm newborn (n = 314 288; 8.9%). Conditions with the highest total marginal costs included specified conditions originating in perinatal period ($168.7 million) and neonatal jaundice with preterm delivery ($136.1 million). CONCLUSIONS: Our study details common and costly areas of focus for future quality improvement and research efforts to improve care during term and preterm infant birth hospitalizations. These include hyperbilirubinemia, infectious disease screening, and perinatal complications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".