1 Introduction

Unicompartmental knee arthroplasty (UKA) is an orthopedic surgery procedure in which only one compartment of the knee, either medial or lateral, is replaced, rather than the total knee in total knee arthroplasty (TKA).¹ UKA has multiple benefits over TKA, including improved range of motion (ROM), better functional outcomes, quicker return of muscle function and strength, less opioid use, and reduced bone loss.^2,3 Patient selection is crucial in UKA as it tends to have a 2-fold higher revision rate compared to TKA, usually due to progression of the osteoarthritis of the knee.^1,4 However, it provides an excellent medium to long-term option for a select patient population.^1,4 UKA utilization has been predominantly observed among younger patients with lower body mass index (BMI), fewer comorbidities, and generally more favorable prognostic profiles.^5–7

Utilization of UKA has always been less frequent than TKA. However, from 2010 to 2019, the number of UKA procedures performed by early career surgeons increased, with 8% of them reporting they performed at least one UKA.⁸ This increase in the rate of UKA utilization is also accompanied by a relative increase in the utilization of computer-assisted and robotic UKAs.^8,9 Recent studies indicated that UKA utilization reached 15% of all primary knee arthroplasties throughout 2021-2023; however, the rate has dropped since 2021, while the percentage of robotic-assisted UKA has risen.¹⁰ This trend was also present internationally, with a similar trend in the rates of utilization in Europe as well as Asian countries^6,7,10,11

While these trends show an evolving surgical practice and technological integration, significant variation in UKA utilization across the US has been reported.¹² Patients in the northeast had the highest odds of receiving UKA, while patients in rural areas were 60% less likely to receive UKA.¹² Prior research has shown that neighborhood-level socioeconomic status, as measured by the Area Deprivation Index (ADI), is a valid tool to understand disparities in health outcomes and surgical care access.^13,14 Given the selective patient criteria for UKA and its increasing reliance on computer and robotic technology, there is an increasing concern that it may be disproportionately accessible to patients from more affluent communities. In this study, we analyze national claims data answer the following questions: (1) What are the temporal trends in UKA utilization for communities stratified by the Area Deprivation Index? (2) Are there differences in UKA utilization when comparing communities with high and low socioeconomic distress? (3) Are there regional differences in UKA utilization for communities with high socioeconomic distress? We hypothesized that patients from high deprivation communities would have lower odds of receiving UKA as compared to patients in low deprivation communities.

2 Methods

Area Deprivation Index scores for zip codes nationwide were grouped into 3 cohorts, with an ADI score of 1-25 indicating low distress communities (LD) and 76-100 indicating high distress communities (HD), and the last cohort consisting of patients from communities with an ADI score of 26-75. These ADI cohorts were used with a national insurance claims database (PearlDiver, Colorado Springs, CO) to evaluate primary UKA utilization from 2010 to 2023. De-identified patient data was analyzed for this study and therefore institutional review board approval was not obtained. Regional differences in utilization were evaluated based on the 4 different regions, West, Midwest, Northeast, South. The change in surgical procedure volume was evaluated with linear regression using a log-transformation of surgical procedure frequency. This provided the percentage growth in surgical procedure volume and variation by subgroup was evaluated using mixed effects models and the interaction of model terms. Wald chi-square testing was used to evaluate the statistical significance of the model terms with a p-value of 0.05 to determine statistical significance.

3 Results

This study evaluated a total of 122,486 patients that underwent UKA between 2010 and 2023. All patients had a zip code and could therefore be stratified appropriately by ADI. Overall, the utilization of UKA increased over time for patients from the LD cohort and the utilization decreased over time for patients in the HD cohort (LD: 4.798%; HD: 3.875%; p < 0.001) (Table 1) (Fig. 1). The UKA utilization trends stratified by ADI and by region are visualized in Figs. 1–5. The UKA utilization between LD and HD communities was significantly different in all regions, (West: p < 0.001; Midwest: p < 0.006; Northeast: p < 0.019; South: p < 0.001) (Table 1).

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Fig. 1 Utilization of UKA by area deprivation index across the United States.

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Fig. 2 Utilization of UKA by area deprivation index across the western region of the United States.

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Fig. 3 Utilization of UKA by area deprivation index across the southern region of the United States.

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Fig. 4 Utilization of UKA by area deprivation index across the northeast region of the United States.

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Fig. 5 Utilization of UKA by area deprivation index across the midwestern region of the United States.

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The Northeast had the largest growth rate among the LD cohorts (3.389%; p < 0.001), and the Midwest had the lowest (−1.909%; p < 0.001). Additionally, the Midwest region exhibited the lowest growth rate of all the HD cohorts (−5.207%; p = 0.004) and exhibited the exhibited reduced growth in surgical procedure volume across all ADI quartiles (p < 0.001).

4 Discussion

As, indications for and frequency of UKA expand in the United States,¹⁵ ensuring equitable access and utilization is essential. There is well established literature highlighting disparities among the utilization and outcomes of various surgical procedures, such as total knee and hip arthroplasty based on neighborhood-level socioeconomics quantified through ADI.^16,17 However, the relationship between regional ADI and utilization of UKA in the United States has yet to be investigated. This study highlights significant differences in UKA utilization based on the Area Deprivation Index. UKA utilization increased over time in low socioeconomic distress communities, while declining in high socioeconomic distress communities, highlighting a growing disparity in access based on socioeconomic status. These trends were consistent across all U.S. regions, with the greatest divergence seen in the Northeast and the most uniform decline across cohorts observed in the Midwest.

The reasoning behind the decline in UKA utilization is multifaceted. It is well documented in the literature, higher ADIs are associated with comorbidity and multi-morbidity.^18–20 These comorbidities may prevent selection for UKA as surgeons may screen out patients with multi-morbidity due to concerns of surgical complications. A retrospective study by Kandil et al. found patients classified as obese or morbidly obese were twice as likely to experience short-term complications and revision rates as compared to non-obese counterparts. These patients also experienced higher pre-operative comorbidities.²¹ Furthermore, there is evidence to suggest that UKA is associated with higher revision rates.^22,23 Hence, increased scrutiny may be utilized by providers when selecting patients for UKA, which may disqualify patients from higher ADI regions.

Another facet behind our findings may lie in the nature and requirements of the UKA procedure itself. There is evidence to suggest that regions of higher deprivation have lower access to high volume surgical centers where UKA is more likely to be performed. In a 2022 analysis, Wu et al. found that TKA was more frequently conducted at hospitals of higher socioeconomic status.²⁴ Another 2022 study conducted in New York found that higher volume hospitals were clustered in more affluent areas while lower volume facilities acted as a “safety net” for patients of Hispanic, African, and Asian descent; demographics often found in areas of higher deprivation.^25–27 These findings are only further compounded when factors such as lack of insurance coverage for high-volume centers and specialist emigration from high ADI regions due to poor reimbursement are considered.^28–30 This suggests that less UKA utilization in higher ADI regions may also be the multifactorial result of insurance and geographical access barriers. However, these assumptions are drawn from similar areas of focus and that literature specifically considering these factors in UKA are very limited. Therefore, further research is required into UKA and these variables.

Our study is not without limitations. While our study provides valuable insight into geographical access disparities among patients receiving UKA based on area deprivation index, our findings are limited to the US healthcare system. Although, we suspect that the association between higher ADI and decreased UKA utilization is likely to be universal. Furthermore, given that we used a national claims database to base our analysis, we did not have access to insights involving clinical rationale for UKA candidacy such as the severity of osteoarthritis or range of motion limitations. There is also the potential for ICD/CPT coding errors to persist. With respect to ADI, grouping our cohorts into quartiles allowed for easier interpretation of results, however these larger groupings may mask intra-group variability. Deprivation also has the potential to change over time, and it is possible that changes could occur over the course of the 13 years from which we drew our data from, potentially leading to misclassification of community distress levels.

5 Conclusions

The findings suggest that socioeconomic and regional disparities may influence access to surgical care or patient selection, underscoring the need for targeted interventions to ensure equitable access to UKA. However, further research is needed to evaluate the potential causes of variation in the respective regions.

Consent to participate

Not applicable.

Consent to publish

Not applicable.

Ethical approval

Not applicable.

Institutional ethical committee approval

No approval was necessary due to the nature of the study.

Author contribution statement

DDO: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Visualization; Writing – original draft; Writing – review &; editing.

MC: Data curation; Formal analysis; Investigation; Methodology; Visualization; Writing – original draft; Writing –review & editing.

AZ: Data curation; Formal analysis; Investigation; Methodology; Visualization; Writing – original draft; Writing –review & editing.

ST: Investigation; Methodology; Writing –review & editing.

SK: Conceptualization; Methodology; Writing – review & editing.

CD: Conceptualization; Methodology; Writing – review & editing.

JM: Conceptualization; Methodology; Writing – review & editing.

ZL: Conceptualization; Methodology; Supervision; Writing – review & editing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.