Assessment of Ethno-racial and Insurance-based Disparities in Pediatric Forearm and Tibial Fracture Care in the United States
Bibliographic record
Abstract
INTRODUCTION: Despite growing attention to healthcare disparities and interventions to improve inequalities, additional identification of disparities is needed, particularly in the pediatric population. We used state and nationwide databases to identify factors associated with the surgical treatment of pediatric forearm and tibial fractures. METHODS: The Healthcare Cost and Utilization Project State Inpatient, Emergency Department, and Ambulatory Surgery and Services Databases from four US states and the Nationwide Emergency Department Sample database were quarried using International Classification of Diseases codes to identify patients from 2006 to 2015. Multivariable regression models were used to determine factors associated with surgical treatment. RESULTS: State databases identified 130,006 forearm (1575 open) and 51,979 tibial fractures (1339 open). Surgical treatment was done in 2.6% of closed and 37.5% of open forearm fractures and 7.9% of closed and 60.5% of open tibial fractures. A national estimated total of 3,312,807 closed and 46,569 open forearm fractures were included, 59,024 (1.8%) of which were treated surgically. A total of 719,374 closed and 26,144 open tibial fractures were identified; 52,506 (7.0%) were treated surgically. Multivariable regression revealed that race and/or insurance status were independent predictors for the lower likelihood of surgery in 3 of 4 groups: Black patients were 43% and 35% less likely to have surgery after closed and open forearm fractures, respectively, and patients with Medicaid were less often treated surgically for open tibial fractures in state (17%) and nationwide (20%) databases. CONCLUSIONS: Disparities in pediatric forearm and tibial fracture care persist, especially for Black patients and those with Medicaid; identification of influencing factors and interventions to address them are important in improving equality and value of care.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".