פוסט זה זמין גם ב:
עברית
Findings This cohort study was a secondary analysis of prospectively collected multicenter data for 2565 patients with hip and femur fractures enrolled in 2 randomized trials in the US and Canada. After controlling for patient-level characteristics, there was an association between missing the 24-hour benchmark for surgery and the proportion of insured patients cared for by the hospital, and a separate association between proportion of insured patients and the racial and ethnic minority patients cared for by hospitals.
Meaning These results suggest that patients seeking care from institutions with more diverse and more uninsured patients are more likely to experience delays greater than the 24-hour benchmarks, regardless of the individual patient race, implying structural health systems biases.
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.