Association of Patient-Level and Hospital-Level Factors With Timely Fracture Care by Race
Notice bibliographique
Résumé
Importance: Racial disparities in treatment benchmarks have been documented among older patients with hip fractures. However, these studies were limited to patient-level evaluations. Objective: To assess whether disparities in meeting fracture care time-to-surgery benchmarks exist at the patient level or at the hospital or institutional level using high-quality multicenter prospectively collected data; the study hypothesis was that disparities at the hospital-level reflecting structural health systems issues would be detected. Design, Setting, and Participants: This cohort study was a secondary analysis of prospectively collected data in the PREP-IT (Program of Randomized trials to Evaluate Preoperative antiseptic skin solutions in orthopaedic Trauma) program from 23 sites throughout North America. The PREP-IT trials enrolled patients from 2018 to 2021, and patients were followed for 1-year. All patients with hip and femur fractures enrolled in the PREP-IT program were included in analysis. Data were analyzed April to September 2022. Exposures: Patient-level and hospital-level race, ethnicity, and insurance status. Main Outcomes and Measures: Primary outcome measure was time to surgery based on 24-hour time-to-surgery benchmarks. Multilevel multivariate regression models were used to evaluate the association of race, ethnicity, and insurance status with time to surgery. The reported odds ratios (ORs) were per 10% change in insurance coverage or racial composition at the hospital level. Results: A total of 2565 patients with a mean (SD) age of 64.5 (20.4) years (1129 [44.0%] men; mean [SD] body mass index, 27.3 [14.9]; 83 [3.2%] Asian, 343 [13.4%] Black, 2112 [82.3%] White, 28 [1.1%] other) were included in analysis. Of these patients, 834 (32.5%) were employed and 2367 (92.2%) had insurance; 1015 (39.6%) had sustained a femur fracture, with a mean (SD) injury severity score of 10.4 (5.8). Five hundred ninety-six patients (23.2%) did not meet the 24-hour time-to-operating-room benchmark. After controlling for patient-level characteristics, there was an independent association between missing the 24-hour benchmark and hospital population insurance coverage (OR, 0.94; 95% CI, 0.89-0.98; P = .005) and the interaction term between hospital population insurance coverage and racial composition (OR, 1.03; 95% CI, 1.01-1.05; P = .03). There was no association between patient race and delay beyond 24-hour benchmarks (OR, 0.96; 95% CI, 0.72-1.29; P = .79). Conclusions and Relevance: In this cohort study, patients who sought care from an institution with a greater proportion of patients with racial or ethnic minority status or who were uninsured were more likely to experience delays greater than the 24-hour benchmarks regardless of the individual patient race; institutions that treat a less diverse patient population appeared to be more resilient to the mix of insurance status in their patient population and were more likely to meet time-to-surgery benchmarks, regardless of patient insurance status or population-based insurance mix. While it is unsurprising that increased delays were associated with underfunded institutions, the association between institutional-level racial disparity and surgical delays implies structural health systems bias.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».