Racial and ethnic disparities in hospital readmissions following total joint arthroplasty present opportunities for reducing cost and improving health equity. Despite efforts to reduce readmissions following total joint arthroplasty in the general population, no studies have documented the impact of these efforts on racial and ethnic disparities in total joint arthroplasty readmissions. The purpose of this study was to determine whether comprehensive efforts to reduce hospital readmissions following total joint arthroplasty have impacted racial and ethnic disparities in readmission rates during the period from 2005 to 2015.
We conducted a retrospective analysis comparing patients readmitted and not readmitted to the hospital within 30 days of a total joint arthroplasty by estimating logistic regression models for clustered data using generalized estimating equations (GEEs) in R. Connecticut hospital discharge data for patients admitted for International Classification of Diseases, Ninth Revision (ICD-9) procedure codes 81.51 and 81.54 (Current Procedural Terminology [CPT] codes 27130 and 27447) during the 2005 to 2015 U.S. Centers for Medicare & Medicaid Services (CMS) fiscal years were analyzed. Models included quadratic terms to capture nonlinear time trends in readmissions, as well as terms for the statistical interaction between race or ethnicity and both the linear and quadratic time trends in predicting the odds of readmission.
There were 102,510 total admissions to Connecticut hospitals for total joint arthroplasty from 2005 to 2015. The 30-day (all-cause) readmission rate declined from 5.1% in 2005 to 3.6% in 2015, with a steeper downward trend observed from 2009 to 2015. The results from logistic models indicated that black patients (odds ratio [OR], 1.68; p < 0.0001) and Hispanic patients (OR, 1.48; p < 0.0001) were significantly more likely to be readmitted within 30 days of discharge following a total joint arthroplasty than white patients over the study period. The significant interaction of black race and the quadratic time trend in models capturing nonlinear trends in readmission over time indicated that the readmission rates for black patients increased compared with those for white patients from 2005 through 2008 and decreased relative to those for white patients from 2009 to 2015 (OR, 0.24; p = 0.030).
Data from Connecticut hospitals show that 30-day readmissions following a total joint arthroplasty declined by 1.5 percentage points from 2005 to 2015, and that this decline was much more pronounced among black patients, resulting in the narrowing of racial disparities in readmission following a surgical procedure.
Racial and ethnic minorities have historically been at increased risk for complications and readmission following hospital-based surgical care. This analysis of readmission following total joint arthroplasty reveals that such disparities are remediable and should foster further research on the primary drivers of and remedies for readmission disparities.
1Division of Behavioral Science and Community Health, UConn Health, Farmington, Connecticut
2Center for Population Health, UConn Health, Farmington, Connecticut
3Department of Statistics, University of Connecticut, Storrs, Connecticut
4Hartford Healthcare Bone & Joint Institute, Hartford, Connecticut
5Connecticut State Medical Society, New Haven, Connecticut
Email address for R.H. Aseltine Jr.: email@example.com
Investigation performed at the Center for Population Health, UConn Health, Farmington, Connecticut
Disclosure: The authors indicated that no external funding was received for any aspect of this work. The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJS/F542).
Disclaimer: Data used in this study were obtained from the Connecticut Department of Public Health. The authors assume full responsibility for the analysis and interpretation of these data. The Connecticut Department of Public Health does not endorse or assume any responsibility for any analyses, interpretations or conclusions based on the data.