Socioeconomic Factors Are Associated With Trends in Treatment of Pediatric Femoral Shaft Fractures, and Subsequent Implant Removal in New York State
Notice bibliographique
Résumé
BACKGROUND: Disparities exist in access to outpatient pediatric orthopaedic care. The purpose of this study was to assess whether disparities also exist in elective pediatric orthopaedic surgical procedures such as implant removal, and to determine which demographic and socioeconomic factors may be associated with differences in treatment. METHODS: Children aged 7 to 18 inclusive who sustained femoral shaft fractures between the years 1997 and 2010 were identified in the New York State SPARCS database. Patient age, sex, race/ethnicity, insurance status, education, and poverty were identified. Factors associated with the method of fracture treatment were assessed through multivariate regression analysis. The subset of patients that received internal fixation were followed up until 2011 inclusive for implant removal. Factors associated with implant removal were assessed using a Cox proportional hazards survival analysis (time to implant removal). RESULTS: Of the 3220 closed femoral shaft fractures identified, 2609 (81%) were treated with internal fixation, 9 (0.3%) had open treatment without implants, 203 (6.3%) were treated with external fixation, and 399 (12.4%) with closed methods. Patients with No Fault/Accident insurance by No Fault/Accident insurance were more likely to undergo internal fixation compared with patients with private insurance (P<0.001). Of the 3220 patients, 2572 were included in the implant removal subanalysis. Implant removal was performed in 725 (28.2%) patients. In the multivariate model, patients were more likely to undergo removal if they were younger (P<0.001), white [vs. black (P<0.001), vs. Hispanic (P=0.035), vs. other (P=0.001)], and lived in neighborhoods with less poverty (P=0.016). Insurance status was not a statistically significant predictor of implant removal. CONCLUSIONS: There is an association between implant removal and younger age, white race, and higher socioeconomic status in children. Awareness of these disparities should prompt further evaluation of causation, whether it be from lack of evidence-based guidelines for implant removal, surgeon bias, variations in reimbursement, or disparities in access to care. Further study is recommended to better elucidate the indications for implant removal in children and the causes for the disparities identified here. LEVEL OF EVIDENCE: Level III-retrospective cohort study.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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