Abstract
Background
The purpose of this study was to evaluate racial differences in total knee arthroplasty (TKA) vs unicompartmental knee arthroplasty (UKA) utilization within the Military Health System (MHS). We hypothesized that there would be no racial differences in utilization rates of TKA and UKA within the MHS.
Methods
We identified patients who underwent primary knee arthroplasty in the MHS between 2009 and 2024. Patients were matched 1:1 (TKA:UKA) by demographic factors other than race. Osteoarthritis severity was measured on preoperative radiographs using Kellgren-Lawrence grading. Matched pairs were eliminated after radiographic review if the TKA would not have been a radiographic candidate for the corresponding UKA based on Kellgren-Lawrence grade. Conditional logistic regressions were performed to examine the association between race and TKA vs UKA utilization in matched cohorts.
Results
We identified 8640 arthroplasties. After demographic matching, minority race was not associated with UKA utilization compared to White race (Black: odds ratio [OR] = 0.86; 95% confidence interval [CI]: 0.63-1.18; non-Black: OR = 0.97; 95% CI: 0.69-1.36). The radiographically matched cohort included 152 TKA patients matched with 152 UKA patients. In this cohort, odds of UKA vs TKA were quantitatively lower for minority race groups vs White race (Black: OR = 0.77; 95% CI: 0.40-1.49; non-Black: OR = 0.61; 95% CI: 0.30-1.26); however, these differences were not statistically significant.
Conclusions
There were no statistically significant racial differences in TKA vs UKA utilization among our matched cohort within the universal military healthcare system. Although not significant, there were quantitatively lower odds of UKA in minority race groups, suggesting a need for continued evaluation of utilization trends.
Keywords: Arthroplasty, Knee, Disparities, Unicompartmental, Matched
Introduction
Health disparities among racial and ethnic groups have been well described in medical literature and garnered considerable attention by medical professionals and journalists alike [1,2]. These disparities have been demonstrated throughout healthcare to include preventative care, cancer diagnosis and treatment, obstetric care, and surgical outcomes [[2], [3], [4], [5], [6]]. Orthopaedic care, treatments, and outcomes are not exempt from these disparities and their associated consequences [5,[7], [8], [9], [10], [11], [12]].
The most common joint arthroplasty performed in the United States is total knee arthroplasty (TKA), while unicompartmental knee arthroplasty (UKA) makes up a smaller proportion of joint arthroplasties at 4.2% of all arthroplasty procedures in 2022 [13,14]. However, UKA may have unique benefits in appropriately indicated patients, such as lower morbidity, faster recovery, and greater postoperative function when compared with TKA [[15], [16], [17], [18], [19]]. Despite these potential advantages, UKA utilization rates in minority groups, particularly Black patients, are lower compared to their White counterparts in civilian settings [20]. The reason for this disparity in treatment selection is unclear. Several hypotheses have been presented including underrepresentation of minority providers, socioeconomic factors, and financial barriers. For example, preoperative counseling for choosing UKA vs TKA is more involved in comparison to counseling for TKA alone and may be more prone to cultural barriers in communication.
Universal healthcare systems may help alleviate access to care issues and narrow this gap in utilization of UKA based on race. Several such systems exist within the United States already, with one of the largest being the Military Health System (MHS). Across multiple specialties, some evidence suggests a reduction in racial disparities in several domains of healthcare within the MHS, while other evidence demonstrates persistence of these trends [[21], [22], [23], [24]]. In comparison, civilian universal healthcare systems have continued to show racial disparities in joint arthroplasty utilization [8,10].
Regarding knee arthroplasty specifically, the potential racial disparity in treatment selection for UKA has yet to be studied within the MHS. Therefore, the purpose of this study was to evaluate potential racial differences in total vs unicompartmental knee arthroplasty utilization within the MHS. We hypothesized that there would be no racial differences in utilization rates of TKA and UKA within the MHS.
Material and methods
After exemption by the institutional review board for this retrospective study, we identified patients who had received primary knee arthroplasty for osteoarthritis (OA) in military treatment facilities using the Military Health System Data Repository (MDR). The MDR is a centralized data source that manages electronic health record information for U.S. military service members, retirees, and their family members receiving care in MHS. Deidentified patients were selected for analysis using International Classification of Diseases-10 codes for primary OA (M17.0, M17.1, M17.10, M17.11, M17.12) and Current Procedural Terminology codes for knee arthroplasty to include: 27447 (total knee arthroplasty), 27446 (unicompartmental knee arthroplasty), and 27438 (patellofemoral arthroplasty [PFA]). Patients were included if they were >18 years of age, had documented primary OA (using International Classification of Diseases-10 codes), and documented knee arthroplasty (using Current Procedural Terminology codes) between 2009 and 2024. Patients were excluded if they underwent arthroplasty for reasons other than degenerative joint disease (post-traumatic arthritis, oncologic resection, acute trauma, pathologic fracture, inflammatory arthritis, infection, or avascular necrosis) or had undergone prior knee arthroplasty or high tibial osteotomy. Patients were also excluded if they lacked radiographs to adequately evaluate severity of OA in all 3 compartments of the knee.
Information on comorbidities including cardiovascular, cerebrovascular, pulmonary, connective tissue, rheumatologic, renal, and hematologic disease was collected along with information regarding diabetes, paralysis, and cancer using MHS health risk algorithm chronic condition indicators. Demographic information including age, sex, body mass index (BMI), race, smoking status, beneficiary category (military status), treating facility, and military sponsor rank (enlisted vs officer) was also collected. Race is defined by the Office of Management and Budget as 5 minimum categories: White, Black or African American, American Indian or Alaska Native, Asian, and Native Hawaiian or other Pacific Islander (“American Indian” as classified by Office of Management and Budget is synonymous with Native American). For the purposes of this study, non-White race was categorized as Black and non-Black since Black race was the largest racial minority in the U.S. military during the study period.
Once the cohort was identified, a UKA subgroup and a TKA subgroup were created. Patients were individually matched 1:1 (TKA:UKA) based on age, sex, beneficiary category, sponsor rank, BMI, number of comorbidities, treating facility, and surgery year using nearest-neighbor matching with the Mahalanobis distance metric. Distance-based matching was chosen with the goal to identify and retain individually matched pairs for further radiographic review, rather than to only achieve overall balance in covariates between TKA and UKA groups in the initial cohort. For clarity, this initial matching process will be referred to as the “demographic match.”
Tricompartmental Kellgren-Lawrence (KL) grading of preoperative radiographs was then performed on this demographically matched cohort using standing anteroposterior and sunrise views of the knee. Verification of correctly documented surgery and application of radiographic exclusion criteria was performed during radiographic review. Examples of KL grading are shown in Figures 1 and 2.
Figure 1.
Preoperative and postoperative radiographs of a patient undergoing TKA. Preoperative radiograph (a) of a left knee, graded as KL grade 3 in the medial compartment and KL grade 2 in the lateral compartment preoperatively. Postoperative radiograph (b) of the same knee after TKA.
Figure 2.
Preoperative and postoperative radiographs of a patient undergoing medial UKA. Preoperative radiograph (a) of a left knee, graded as KL grade 3 in the medial compartment and KL grade 2 in the lateral compartment preoperatively. Postoperative radiograph (b) of the same knee after medial UKA.
Following KL grading and radiographic exclusions, demographically matched pairs were evaluated for radiographic eligibility. Pairs were eligible only if the TKA patient would have been considered a radiographic “candidate” for UKA. TKA candidacy was determined by the KL grade of the compartment of interest (ie, the compartment replaced in the matched UKA) and by the KL grades of the remaining compartments. TKAs with a KL grade ≥3 in the compartment of interest and KL grades ≤2 in the other compartments were considered UKA candidates. If the matched UKA met “stretched” radiographic criteria, defined as a KL grade ≤2 in the compartment of interest and/or KL grades ≥3 in one or more non-index compartments, the corresponding TKA was permitted to meet the same KL criteria without exclusion. Any demographically matched pair not meeting these criteria was excluded from analysis. This process will be referred to as the “radiographic match” throughout the manuscript.
Statistical analysis
UKA and TKA cohorts were comparatively analyzed following both demographic and radiographic matching. Descriptive statistics (means with standard deviations or frequencies with percentages) were reported for patient characteristics in each matched cohort and compared by UKA vs TKA with two-sample t-tests for continuous and Fisher’s exact or chi-square tests for categorical characteristics. Standardized mean differences were calculated to evaluate covariate balance in each cohort before and after matching and to identify risk of residual confounding, which may be present when standardized mean differences are >0.1. Finally, associations of race with utilization of UKA vs TKA were analyzed using conditional logistic regression as appropriate to the non-independence of the individually matched pairs, and with adjustment for BMI as a covariate to address residual confounding in the matched cohorts. White race was used as the reference group when calculating odds ratios. Statistical significance was determined by a two-sided P value of <0.05. Analyses were performed using the “matchit” and “survival” packages in R version 4.0.5 [25,26]. An a priori analysis was not performed for this retrospectively collected cohort, the sample size was determined by the available eligible patients and their inclusion was determined by the criteria stated above.
Results
We identified 8640 arthroplasties performed between 2009 and 2024. This included 8032 TKAs, 543 UKAs (medial and lateral), and 65 PFAs. Before matching, patients receiving UKA vs TKA were younger (mean age 57.9 vs 62.4 years), had fewer chronic conditions (mean 2.6 vs 3.0), and were more likely to be male (59.4% vs 52.3%) and an active-duty service member (22.5% vs 6.5%) (P < .05 for each). Full comparative analysis is shown in Tables 1 and 2. Initial matching based on demographic criteria (other than race) was performed and resulted in 608 matched pairs (1216 total patients). Comparative analysis of these cohorts is shown in Tables 3 and 4. Radiographic review and application of radiologic exclusion criteria was performed, resulting in 152 matched pairs (304 total patients) for final analysis (Tables 5 and 6). The final UKA subgroup included 146 medial UKAs, 5 PFA, and 1 lateral UKA. Details of matching process are presented in Figure 3.
Table 7.
Comparison of patient characteristics between TKA and UKA cohorts following demographic match.
| Race | TKA cohort (n = 608) (n, %) | UKA cohort (n = 608) (n, %) | OR (95% CI) |
|---|---|---|---|
| Black | 125 (20.6%) | 108 (17.8%) | 0.86 (95% CI: 0.63-1.18) |
| Non-Black | 105 (17.3%) | 110 (18.1%) | 0.97 (95% CI: 0.69-1.36) |
| White | 369 (60.7%) | 376 (61.8%) | Reference |
OR, odds ratio; CI, confidence interval.
Odds ratios represent the odds of receiving UKA compared to TKA based on race. White race was used as the reference group when calculating odds ratios.
Table 8.
Comparison of patient characteristics between TKA and UKA cohorts following radiographic match.
| Race | TKA cohort (n = 152) (n, %) | UKA cohort (n = 152) (n, %) | OR (95% CI) |
|---|---|---|---|
| Black | 27 (17.8%) | 23 (15.1%) | 0.77 (95% CI: 0.40-1.49) |
| Non-Black | 31 (20.4%) | 26 (17.1%) | 0.61 (95% CI: 0.30-1.26) |
| White | 94 (61.8%) | 103 (67.8%) | Reference |
OR, odds ratio; CI, confidence interval.
Odds ratios represent the odds of receiving UKA compared to TKA based on race. White race was used as the reference group when calculating odds ratios.
Table 1.
TKA and UKA groups prior to demographic matching.
| Variable | TKA cohort (n = 8032) | UKA cohort (n = 608) | P value | SMD |
|---|---|---|---|---|
| Average age (y) | 62.4 ± 9.2 | 57.9 ± 10.8 | <.001 | 0.452 |
| Female (%) | 47.7 | 40.6 | .001 | 0.143 |
| Active-duty or retiree (%) | 51.4 | 63.3 | <.001 | 0.489 |
| Officer (%) | 11.8 | 16.8 | <.001 | 0.237 |
| BMI (kg/m2) | 32.2 ± 5.7 | 31.0 ± 5.1 | <.001 | 0.231 |
| Average year of surgery | 2018.2 ± 3.3 | 2018.4 ± 3.3 | .050 | 0.085 |
SMD, standard mean difference.
Continuous data are specified as mean ± standard deviation. Categorical data are specified as proportions (%). Average year of surgery is the calendar year surgery was performed (eg, 2018.5 = mid-2018). Statistical significance identified as P value <.05.
Table 2.
Comorbidities in TKA and UKA groups prior to demographic matching.
| Comorbidity (%) | TKA cohort (n = 8032) | UKA cohort (n = 608) | P value | SMD |
|---|---|---|---|---|
| Cancer (%) | 18.4 | 14.5 | .009 | 0.106 |
| Cardiovascular disease (%) | 69.0 | 55.1 | <.001 | 0.289 |
| Cerebrovascular disease (%) | 2.0 | 1.3 | .174 | 0.052 |
| Diabetes (%) | 22.8 | 15.8 | <.001 | 0.177 |
| Rheumatologic disease (%) | 18.4 | 19.1 | .682 | 0.017 |
| Renal disease (%) | 20.3 | 15.3 | .001 | 0.131 |
| Pulmonary disease (%) | 30.8 | 29.4 | .475 | 0.030 |
| Connective tissue disease (%) | 5.8 | 4.1 | .051 | 0.076 |
| Hematologic disease (%) | 12.5 | 6.7 | <.001 | 0.197 |
SMD, standard mean difference.
Categorical data are specified as proportions (%). Statistical significance identified as P value <.05.
Table 3.
TKA and UKA groups after demographic matching.
| Variable | TKA cohort (n = 608) | UKA cohort (n = 608) | P value | SMD |
|---|---|---|---|---|
| Average age (y) | 59.1 ± 9.8 | 57.9 ± 10.8 | .042 | 0.117 |
| Female (%) | 39.3 | 40.6 | .682 | 0.027 |
| Active-duty or retiree (%) | 62.8 | 63.3 | .656 | 0.026 |
| Officer (%) | 16.4 | 16.8 | .968 | 0.029 |
| BMI (kg/m2) | 32.6 ± 5.6 | 31.0 ± 5.1 | <.001 | 0.300 |
| Average year of surgery | 2020.0 ± 3.0 | 2020.0 ± 3.3 | .890 | 0.008 |
SMD, standard mean difference.
Continuous data are specified as mean ± standard deviation. Categorical data are specified as proportions (%). Average year of surgery is the calendar year surgery was performed (eg, 2018.5 = mid-2018). Statistical significance identified as P value <.05.
Table 4.
Comorbidities in TKA and UKA groups after demographic matching.
| Comorbidity (%) | TKA cohort (n = 608) | UKA cohort (n = 608) | P value | SMD |
|---|---|---|---|---|
| Cancer (%) | 18.1 | 14.5 | .103 | 0.098 |
| Cardiovascular disease (%) | 60.0 | 55.1 | .092 | 0.100 |
| Cerebrovascular disease (%) | 1.0 | 1.3 | .788 | 0.031 |
| Diabetes (%) | 15.0 | 15.8 | .751 | 0.023 |
| Rheumatologic disease (%) | 17.4 | 19.1 | .504 | 0.043 |
| Renal disease (%) | 16.0{Citation} | 15.3 | .813 | 0.018 |
| Pulmonary disease (%) | 25.2 | 29.4 | .108 | 0.096 |
| Connective tissue disease (%) | 3.5 | 4.1 | .652 | 0.034 |
| Hematologic disease (%) | 8.1 | 6.7 | .443 | 0.050 |
SMD, standard mean difference.
Categorical data are specified as proportions (%). Statistical significance identified as P value <.05.
Table 5.
Demographic variables in TKA and UKA groups after demographic and radiographic matching.
| Variable | TKA cohort (n = 152) | UKA cohort (n = 152) | P value | SMD |
|---|---|---|---|---|
| Average age (y) | 59.6 ± 8.9 | 58.8 ± 10.1 | .467 | 0.084 |
| Female (%) | 37.5 | 40.1 | .724 | 0.054 |
| Active-duty or retiree (%) | 63.1 | 62.5 | .987 | 0.043 |
| Officer (%) | 13.2 | 13.2 | 1.000 | 0.000 |
| Average BMI (kg/m2) | 32.8 ± 5.7 | 30.4 ± 5.0 | <.001 | 0.447 |
| Average year of surgery | 20.0 ± 2.9 | 20.0 ± 3.2 | .780 | 0.033 |
SMD, standard mean difference.
Continuous data are specified as mean ± standard deviation. Categorical data are specified as proportions (%). Average year of surgery is the calendar year surgery was performed (eg, 2018.5 = mid-2018). Statistical significance identified as P value <.05.
Table 6.
Comorbidities in TKA and UKA groups after demographic and radiographic matching.
| Comorbidity (%) | TKA cohort (n = 152) | UKA cohort (n = 152) | P value | SMD |
|---|---|---|---|---|
| Cancer (%) | 19.1 | 12.5 | .157 | 0.181 |
| Cardiovascular disease (%) | 65.1 | 53.9 | .062 | 0.229 |
| Cerebrovascular disease (%) | 1.3 | 2.0 | 1.000 | 0.052 |
| Diabetes (%) | 15.1 | 11.8 | .502 | 0.096 |
| Rheumatologic disease (%) | 11.2 | 15.1 | .396 | 0.117 |
| Renal disease (%) | 18.4 | 17.8 | 1.000 | 0.017 |
| Pulmonary disease | 25.0 | 34.2 | .102 | 0.203 |
| Connective tissue disease (%) | 3.3 | 4.0 | 1.000 | 0.035 |
| Hematologic disease (%) | 5.3 | 8.6 | .366 | 0.130 |
SMD, standard mean difference.
Categorical data are specified as proportions (%). Statistical significance identified as P value <.05.
Figure 3.
Strengthening the Reporting of Observational Studies in Epidemiology flow chart. ∗: includes medial UKA, lateral UKA, and patellofemoral arthroplasty (PFA).
Discussion
This study aimed to evaluate the association between race and utilization rates of UKA vs TKA within the MHS using matched pairs to control for demographic and radiographic differences. In this universal healthcare system, we found no significant differences in UKA utilization between Black and non-Black patients when compared to White patients with the numbers available. This finding differs from prior studies demonstrating significant racial disparities in arthroplasty utilization for both UKA and TKA.
Racial disparities in healthcare are well-documented, even when controlling for demographic and socioeconomic factors [[2], [3], [4],6,27,28]. These disparities persist in orthopaedic literature, with several studies demonstrating inequities in surgical utilization and outcomes [5,[7], [8], [9], [10], [11], [12],29]. UKA is a safe and reliable procedure that can adequately address OA in isolated knee compartments and is a less invasive procedure than TKA with potential advantages in appropriately indicated patients [15,17,30,31]. However, appropriate counseling for UKA involves discussing and contrasting the unique risks and benefits of UKA to those of a TKA. This can be significantly more involved and require a strong therapeutic alliance between patient and provider. The presence of personal or structural biases and barriers, which predominantly affect minority groups [32], can lead to patients receiving more invasive procedures (TKA) when less invasive procedures (UKA) may be an option. This is of particular interest in Black patient populations which have been shown to have worse postoperative outcomes following joint arthroplasty [5,8,33].
Prior literature regarding the utilization rates of UKA within civilian healthcare systems shows that UKA utilization rates are significantly lower within minority groups. Kamaraju et al. and Paisner et al. evaluated these rates using large national registries [20,34]. Both found that Black and Hispanic patients had significantly lower utilization rates compared to their White counterparts, with Black patients having the lowest utilization rates among all racial groups. However, these registry studies served as epidemiological investigations of utilization rates, without a detailed analysis of radiographic parameters which may have influenced indications.
Many theories exist on structural causes of racial health inequities to include historical, systemic, and financial barriers. Therefore, universal healthcare programs and the principles behind them (affordability, easily-accessible, and high-quality care) have been suggested as a possible intervention to alleviate many of these contributors [[35], [36], [37]]. As a result, there have been several studies that have evaluated how disparities are impacted by universal healthcare systems within the United States and around the world, with many showing reduction of these disparities but not complete elimination [36,[38], [39], [40]]. This trend is mirrored in the arthroplasty literature with Okike et al. finding that disparities in arthroplasty utilization persist despite the advantages of a universal healthcare system [10].
The MHS is distinct among other universal healthcare systems as it serves a large and diverse patient population extending across the country. Unique to the MHS is its overarching mission: the care of the nation’s service members, retirees, and military dependents. This responsibility, and the ethos that accompany it, may assist in eliminating barriers to care. The military also carries a reputation of egalitarianism which may alleviate biases or tendencies toward preferential treatment that may be present outside of the military. This hypothesis has been supported by previous studies which have shown that racial disparities in postsurgical complications and acute poststroke care are eliminated in patients treated in the MHS, as compared to those treated in civilian centers [[41], [42], [43], [44]]. Unfortunately, this finding is not ubiquitous in all aspects of military health care with other studies demonstrating persistence of racial inequities despite the perceived egalitarian nature of the military [45,46].
As it relates to arthroplasty, Sowa et al. analyzed knee arthroplasty in the MHS over a 4-year time period to evaluate if rates of knee arthroplasty utilization were impacted by racial group and found that minority patients were significantly less likely to receive knee arthroplasty compared to their White counterparts at 3 years following OA diagnosis [47]. Our study focused specifically on whether, once indicated for arthroplasty, disparities exist in treatment selection for TKA vs UKA. However, it is possible that selection for knee arthroplasty vs nonoperative management may still demonstrate differences based on race. The underlying cause for the findings in the present study is likely multifactorial. The universal care given within the MHS in addition to the unique characteristics of military healthcare are possible contributors to these findings.
Ultimately, our results support that the MHS and its providers may be more successful in delivering equal and accessible care to our nations’ service members, veterans, and their families when compared to other healthcare systems. However, these findings warrant additional investigation given the qualitatively lower (although nonsignificant) UKA utilization rates for Black and non-Black individuals.
The primary limitations of this study revolve around its retrospective nature. As a result of this, patient information is reliant on complete and accurate coding by treating providers at the time of care. Variations in provider judgment or facility protocols across the MHS can also influence procedure choice and timing. By incorporating both documented data as well as objective radiographic findings into our matching process, we hope to more completely reflect the factors that influence clinical decisions and control for some of this variability as well. Comparative statistical analysis prior to and after each round of matching suggest that our process was mostly successful in eliminating possible confounders between the cohorts (besides a small, likely clinically insignificant, difference in age and BMI after demographic matching, and BMI after radiographic matching). However, it must be acknowledged that these small differences may act as confounding factors in evaluation of our results and conclusions.
This patient cohort was drawn from military treatment facilities which may limit its generalizability to civilian populations due to the health standards that the military requires of its service members during their service. However, the inclusion of military dependents and family members likely make this study more generalizable. Of note, the race of the treating surgeons was not known for this study, therefore the role in concordance or discordance between patient and surgeon race and its effect on treatment selection is not known. Finally, while the rigorous matching process allowed us to control for as many clinical and radiographic variables as possible, it also reduced the overall sample size, limiting the precision of our confidence intervals. While no significant racial differences were found with the numbers available, it is possible that they may emerge with a larger sample. However, it is important to note that this nonsignificance was similarly found in the larger demographically matched group, supporting the nonsignificant findings in the radiographically matched group.
Conclusions
There were no statistically significant racial differences in TKA vs UKA utilization among our matched cohort within the universal military healthcare system. Although not significant, there were quantitatively lower odds of UKA in minority race groups, suggesting a need for continued evaluation of utilization trends.
Conflicts of interest
C. McCarthy is a member of Orthopaedic Trauma Association Public Relations Committee. S. Feeley has received education payments from Supreme Orthopedic Systems LLC; all other authors declare no potential conflicts of interest.
For full disclosure statements refer to https://doi.org/10.1016/j.artd.2026.101972.
CRediT authorship contribution statement
Aidan M. McQuade: Writing – review & editing, Writing – original draft, Methodology, Investigation, Data curation, Conceptualization. Sarah E. Rabin: Writing – review & editing, Supervision, Data curation. Scott M. Feeley: Writing – review & editing, Supervision, Conceptualization. Jefferson L. Lansford: Writing – review & editing, Supervision, Methodology, Formal analysis, Conceptualization. Conor F. McCarthy: Writing – review & editing, Supervision, Methodology, Conceptualization. Nora L. Watson: Writing – review & editing, Methodology, Formal analysis. Robert W. Tracey: Writing – review & editing, Supervision. John P. Cody: Writing – review & editing, Supervision. Sean E. Slaven: Writing – review & editing, Supervision, Methodology, Conceptualization.
Appendix A. Supplementary data
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