Editor’s Spotlight/Take 5: Postacute Care Readmission and Resource Utilization in Patients From Socioeconomically Distressed Communities After Total Joint Arthroplasty : Clinical Orthopaedics and Related Research®

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Editor’s Spotlight/Take 5: Postacute Care Readmission and Resource Utilization in Patients From Socioeconomically Distressed Communities After Total Joint Arthroplasty

Leopold, Seth S. MD1

Author Information
Clinical Orthopaedics and Related Research 481(2):p 198-201, February 2023. | DOI: 10.1097/CORR.0000000000002539

It’s safe to say that a central cultural theme in the United States over the last few years has been that of fairness and its absence. Certainly, its absence has manifested in brutal ways: powerful men visiting atrocities on vulnerable women [3, 13], individuals vested with the responsibility to preserve life instead unjustly taking it because of racial animus [16]; open any day’s newspaper for more of the dreadful same. Attempts by people in historically marginalized groups to rectify longstanding repression—movements like #MeToo, Black Lives Matter, and others—have seemed both appropriate and long overdue, no mere swings of the pendulum. By contrast, responses to those responses, whether perverse unintended consequences following #MeToo that limited women’s opportunities [9] or exaggerated expressions of White grievance that seek to maintain the status quo [14, 15], indeed do strike me as reactive, if not frankly retaliatory.

In light of that, it’s been gratifying to see journals in our specialty express a strong and growing interest in these themes by publishing more research about them, including greater coverage of discrimination in professional opportunities for women and physicians of color, and more articles about health disparities arising from issues of sex and gender or race. I’m proud to say that Clinical Orthopaedics and Related Research® is no come-lately to these topics, having run five symposia covering disparities in the last 10 years. All the same, it’s easy to be uneasy about research on these topics, if for no other reasons than the risk of arriving at incorrect conclusions, misunderstanding what we’re seeing, or even asking biased questions [7, 8]. The concern, of course, is that we might cause harm to the very groups we’re trying to help, despite having good intentions [10].

To avoid this, we need to go deeper, to see past the surface. For this reason, I am so excited about this month’s symposium on Diversity and Disparities, guest edited by Kimberly J. Templeton MD and Kris Radcliff MD. In it, there are several papers that help us go further on this critically important topic [4, 6, 12]. Papers like these have changed how the editors at CORR® evaluate research where race is a factor, and they’re worth your attention. I hope other journals will follow this lead in looking past simple categories of race or ethnicity; as one recent interviewee in this space put it: “The outlook for a first-generation Spanish speaker from El Salvador in this regard may be different from that of a Mayan speaker from Central America, a Mexican-American from Texas, and a third-generation native English speaker with African-Caribbean ancestry from New York. All of these individuals likely would have different takes on the challenges to accessing the healthcare system” [11].

Among the many good lessons in this month’s CORR symposium is that when we consider race as a covariate in research about access, utilization, or healthcare outcomes without considering community context, affluence, or other elements contained in markers like the Area Deprivation Index [1] or the Social Vulnerability Index [2], we risk attributing to race what more likely should be ascribed to something else altogether. Looking back 50 or 60 years, the overlap between race and those other elements was substantial; this is less true today, and ignoring that fact is likely to send physicians and healthcare policymakers down blind alleys.

This does not relieve us of the burden to identify and eradicate racism, both systemic and personal. Racism in medicine is real, persistent, and destructive. But if the independent association between some finding—let’s say, postacute care readmission—is mistakenly attributed to race, then the solutions we’re likely to look for will be different than if it is attributable to other kinds of socioeconomic disparities. Missing that point will keep us from solving the problem, and, not incidentally, it won’t help us diminish racism’s harms, either.

This month’s spotlight paper [12] shows how easy it would be to get this wrong by getting it right. Its exploratory analyses, if left unfollowed-up on, would’ve identified race as a key associated factor—perhaps the key factor. But a more nuanced approach, as performed by Chad A. Krueger’s team from Thomas Jefferson University in Philadelphia, PA, USA, found that the factor that mattered was whether patients lived in neighborhoods with higher Distressed Communities Index scores; those who did had higher odds of readmission (and more resource consumption), and that after controlling for relevant confounders including community distress, race had no association with readmission. Another paper on a related theme this month found that although Black patients were more likely than White patients to experience safety-related complications of spine surgery, a substantial part of this finding—nearly half of it, for some endpoints—was attributable to elements of social disadvantage other than race, as measured by the Social Vulnerability Index [4]. This finding seemed consistent across the analyses published in this symposium that asked related questions in this way [6].

This idea is not new [5], but as someone who sees a lot of papers go by, I will say it is much overlooked, especially if one considers papers published about race-associated disparities in our specialty in the last 5 years or so. Look for yourself. What proportion of the papers you’ve read that explored race as a variable in the context of hospital readmissions, complications, or infections also adjusted for things like the Social Vulnerability Index, Area Deprivation Index, or Distressed Communities Index? Did the fact that most did not do so keep those authors from attributing findings to race? That failure points us in the wrong direction for solutions, and it props up some of the most odious prejudices you can imagine—including the idea that patients of a particular race (or patients who lack financial means) must be somehow unclean or weak, because some study associated those “traits” with infections or complicating illnesses without adequately controlling for relevant confounding variables [7]. I’d add that attributing complications or poor clinical outcomes to race in this way is an unproductive, almost nihilistic, approach. When it comes to musculoskeletal care, there are few if any situations in which considering race in this way would point us to actionable interventions that can help our patients.

This isn’t easy stuff, but it’s important. Read the study “Postacute Care Readmission and Resource Utilization in Patients From Socioeconomically Distressed Communities After Total Joint Arthroplasty” in this month’s CORR and join me in the Take 5 interview that follows to learn from someone who has given these issues a great deal of thought, and who has the analytic chops to show us the right way to think about these thorny problems: Dr. Chad Krueger, senior author of that important paper.

Take 5 Interview with Chad A. Krueger MD, senior author of “Postacute Care Readmission and Resource Utilization in Patients From Socioeconomically Distressed Communities After Total Joint Arthroplasty”

Seth S. Leopold MD:Congratulations on this important study, which (as I mentioned in the Spotlight column before this interview) is one of several in this issue of CORR that looks in a more nuanced way at the important nexus of race and demography. Why does it seem like we’re seeing studies going deeper on this topic, when so many in the last few years did not?

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Chad A. Krueger MD

Chad A. Krueger MD: It often goes this way when we first start evaluating a healthcare topic. I feel that we are finally recognizing the depth associated within healthcare disparities and how they impact patient care and outcome. Many researchers before me have worked hard to develop a foundation on which further research on this topic can be conducted, and it is up to all of us to build upon it going forward. To me, this is no different than much of the work we have done on infection or tribology: The initial investigations demonstrate that there are problems, but it is the subsequent research efforts that aim to figure out the reasoning behind the issues. We hope that by looking at confounding variables in a more nuanced way, we will eventually arrive at solutions to decrease healthcare disparities. This is all the more important as healthcare disparities have a lot of ties into health policy and regulation that are slow moving and complicated. We need reliable information, as well as the right context and perspective, to make good decisions in those domains.

Dr. Leopold:How might the solutions differ if studies like yours identified race as the key independently associated factor versus if—as was the case—social covariates like affluence and community-level distress may be the drivers? In other words, what might we get out of getting this more right, as your study has, compared with other studies that stopped after just analyzing things by race?

Dr. Krueger: We can all agree that people’s social support system and community influence their health. It is difficult if not impossible to evaluate the impact of topics like race, gender, or ethnicity in a vacuum. Worse than that is when variables such as race or ethnicity are used as a surrogate for variables like income level or affluence. As we continue to study and evaluate healthcare disparities we need to make sure that we are not coming to conclusions that are inaccurate or based on conjecture. Obviously, these studies can be hard to do, and obtaining all the information necessary to have a full picture of the data being analyzed is a challenge. Still, it is important that we all recognize what is and what is not being analyzed in any specific study. Information like community distress and affluence in addition to race will provide a much better perspective.

Dr. Leopold:Nothing about your findings diminishes the fact that racism persists and continues to harm professionals in their careers and the patients we’re all charged with caring for. How should we keep readers from missing this important fact?

Dr. Krueger: That is critically important. We need to understand that discrimination and racism do not simply disappear if a study shows that patients of different genders, races, or ethnicities have comparable outcomes or receive similar treatments. We all need to recognize that we are inherently biased at baseline, not only with regard to how we interact with patients or colleagues but also with the treatment decisions we make and discussions that we have with others. And when these biases develop into discrimination or racism, patients and communities suffer. I know that many hospitals and institutions have worked hard to develop diversity, equity, and inclusion initiatives in recent years, and that many of us, myself included, have spent considerable time and energy trying to learn about our own biases and how they may shape our thinking. To mitigate the negative effects of these biases, we first need to recognize that they exist.

Dr. Leopold:You and I both are White guys. People talk about unconscious bias; I find that a complicated term, but there’s no doubt that a lot of important stuff must sit in our blind spots. What approaches do you take to interrupt or dampen bias when developing your research study designs on these topics and when analyzing your findings?

Dr. Krueger: You are absolutely correct! Honestly, one of my biggest fears with research like this is that I come off sounding like an idiot because I didn’t consider something so basic because of the blind spots you mention. I certainly rely on reading other articles for perspective, and I try to keep my perspective as broad as possible in the process, but it is by no means perfect. Having open discussions with coauthors of various backgrounds is another important and incredibly useful strategy. Lastly, I will also say that I’ve become more open to criticism in this regard. I would never want to publish anything that could put a patient population at risk or that could make me sound short-sighted. That said, if I ever publish anything that seems wrong or seems to have arisen from my own misunderstandings on these themes, I hope the audience would reach out to me to help me grow as a person as well.

Dr. Leopold:What should journals do differently when evaluating research about race and healthcare disparities if those studies do not also consider potentially confounding community-level phenomena or other related variables like affluence, educational attainment, food insecurity, insurance, and the like?

Dr. Krueger: As you mention, this type of community-level information is critical in providing context to articles of this nature. I would encourage journals to continue to ask for such information and, more importantly, provide strategies for authors as to how to obtain such information. This may be different within each community, but as this type of research is published more often, my hope is that other authors learn about how best to collect these data.

Without good information about confounding variables, it is even harder to get a balanced picture about healthcare disparities. Local and federal government sources are good places to look for community-level information, but these can be difficult to access and to pair with institutional data. There are other data sources such as the Function and Outcomes Research for Comparative Effectiveness in Total Joint Replacement (FORCE TJR) dataset that have access to some of these data as well. We hope that some of the methodologies used in our research paper can be used and built upon in the future.

Acknowledgments

The author thanks Kris Radcliff MD, Kimberly J. Templeton MD, and Clare M. Rimnac PhD. Their suggestions greatly improved this essay.

References

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