Operative management of rotator cuff tears: identifying disparities in access on a national level.

Abstract

The purpose of this study was to identify nationwide disparities in the rates of operative management of rotator cuff tears based on race, ethnicity, insurance type, and socioeconomic status. Patients diagnosed with a full or partial rotator cuff tear from 2006 to 2014 were identified in the Healthcare Cost and Utilization Project's National Inpatient Sample database using International Classification of Diseases, Ninth Revision diagnosis codes. Bivariate analysis using chi-square tests and adjusted, multivariable logistic regression models were used to evaluate differences in the rates of operative vs. nonoperative management for rotator cuff tears. This study included 46,167 patients. When compared with white patients, adjusted analysis showed that minority race and ethnicity were associated with lower rates of operative management for Black (adjusted odds ratio [AOR]: 0.31, 95% confidence interval [CI]: 0.29-0.33; P < .001), Hispanic (AOR: 0.49, 95% CI: 0.45-0.52; P < .001), Asian or Pacific Islander (AOR: 0.72, 95% CI: 0.61-0.84; P < .001), and Native American patients (AOR: 0.65, 95% CI: 0.50-0.86; P = .002). In comparison to privately insured patients, our analysis also found that self-payers (AOR: 0.08, 95% CI: 0.07-0.10; P < .001), Medicare beneficiaries (AOR: 0.76, 95% CI: 0.72-0.81; P < .001), and Medicaid beneficiaries (AOR: 0.33, 95% CI: 0.30-0.36; P < .001) had lower odds of receiving surgical intervention. Additionally, relative to those in the bottom income quartile, patients in all other quartiles experienced nominally higher rates of operative repair; these differences were statistically significant for the second quartile (AOR: 1.09, 95% CI: 1.03-1.16; P = .004). There are significant nationwide disparities in the likelihood of receiving operative management for rotator cuff tear patients of differing race/ethnicity, payer status, and socioeconomic status. Further investigation is needed to fully understand and address causes of these discrepancies to optimize care pathways.