Should hospital readmission rates be adjusted for race/ethnicity and socioeconomic status (SES) when it comes to total joint replacement? Perhaps not, says new research from the RAND Corporation, Truven Health Analytics, and Agency for Healthcare Research and Quality (AHRQ). For this study, which was supported by the AHRQ Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project (HCUP), researchers calculated two sets of readmission rates, both of which were risk-adjusted. One was done using the Centers for Medicare & Medicaid Services standard risk-adjustment algorithm that incorporates patient age, sex, comorbidities, and hospital effects; for the other calculation researchers added race/ethnicity and socioeconomic status to the model. Then they compared the performance of 1, 194 hospitals taking into consideration both of the methods.
Readmission Rates Post Joint Replacement: Adjust for Race, SES?

Grant R. Martsolf, Ph.D. is a policy researcher with the RAND Corporation. Dr. Martsolf, the lead investigator on this study, told OTW, “Many studies have demonstrated that race and socioeconomic status are significantly associated with readmission risk after a joint replacement. This has led many clinicians and policymakers to argue that readmission rates should be risk-adjusted for those factors when the rates are publicly reported or used for payment purposes. We wanted to empirically test if adding these factors to common risk-adjustment algorithms would really make meaningful differences in hospital performance. So, this is fundamentally a question of policy question as opposed to a clinical question.
“Our findings indicated no change in the readmission rate as better, worse, or not different from the population mean at >99% of the hospitals; there was also no change in the excess readmission ratio at >97% of the hospitals.
“Despite the importance of demographic characteristics when considering patients’ readmission risk, adding these factors to common risk adjustment algorithms does not seem to make much of a difference on hospital reported performance.
“I hope that this informs policy debates about whether or not these rates need to be risk-adjusted to ensure fairness in public reporting or payment.”

Discussion
This is a fascinating development. In my practice we've seen similar outcomes with the revised protocol. The key differentiator seems to be patient selection criteria. Has anyone else noticed the correlation with BMI thresholds?
Great point. I'd push back slightly on the conclusion, the sample size in the cited study is too small to draw population-level inferences. That said, the directional signal is compelling and worth a larger RCT.
We implemented a similar approach last year. Early results are promising but we're still gathering 12-month follow-up data. Happy to share our protocol if anyone is interested.
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