CORR Synthesis: How Might the Preoperative Management of Risk Factors Influence Healthcare Disparities in Total Joint Arthroplasty?

Abstract

Both authors certify that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request. The opinions expressed are those of the writers, and do not reflect the opinion or policy of CORR® or The Association of Bone and Joint Surgeons®.

Metrics for Monitoring Preoperative Optimization Programs for Total Joint Arthroplasty

“…However, these studies were only focused on the initial implementation of these programs and they did not include stratified analyses to determine the impact of these programs on patients from racial and ethnic minority backgrounds, patients with lower incomes, and patients with Medicaid insurance (Dlott & Wiznia, 2022). In our research group's systematic review, we proposed a set of metrics that could be used to monitor preoperative optimization programs to assess whether they were truly beneficial for all patients (Dlott & Wiznia, 2022).…”

“…On the basis of our survey responses, we have developed an updated version of the metrics reported by Dlott and Wiznia (2022) to include a third tier of analysis (see Table 5). Each tier represents a more detailed level of analysis, with Tier 3 describing the most robust program analysis.…”

“…In addition, preoperative optimization programs may contribute to healthcare disparities (Dlott & Wiznia, 2022). Patients from racial and ethnic minority backgrounds, patients with lower incomes, and patients with Medicaid insurance may be considered less eligible to receive TJA (Wang et al, 2018).…”

“…The use of hard cutoffs for hemoglobin A 1c and body mass index (BMI) may also disproportionately exclude patients of color and patients with public insurance (Dlott et al, 2022; Leopold, 2019). In a systematic review of preoperative optimization programs, none of the programs performed stratified analyses by race, gender, or income level to assess the differential impact these programs may have on vulnerable patient populations (Dlott & Wiznia, 2022).…”

Characterizing the lack of diversity in musculoskeletal urgent care website content

“…We collected the following yes/no variables for each prominently featured website graphic (above the fold and visible without scrolling) that included human figures (either full models or partial models with identifiable characteristics) on the website to portray a patient (model)(s): only white model(s), multiracial presence, presence of women, presence of obese/overweight model, white feature model, and female feature model. We selected race/ethnicity, gender, and body habitus as our primary variables given that prior research in orthopedic surgery has demonstrated that patients from racial or ethnic minority backgrounds, women, and obese patients may have difficulty accessing orthopedic care and may have worse outcomes following orthopedic surgery [ 17 – 19 ]. To minimize bias, two authors reviewed each graphic to collect the above variables and a third author was included if any disagreement regarding the variables arose, which occurred infrequently.…”