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. 2020 Sep 14;479(3):575–585. doi: 10.1097/CORR.0000000000001493

Is Geographic Socioeconomic Disadvantage Associated with the Rate of THA in Medicare-aged Patients?

Rafa Rahman 1,2,3,4,5,6,7,8, Joseph K Canner 1,2,3,4,5,6,7,8, Elliott R Haut 1,2,3,4,5,6,7,8, Casey J Humbyrd 1,2,3,4,5,6,7,8,✉
PMCID: PMC7899604  PMID: 32947286

Abstract

Background

Disparities in THA use may lead to inequitable care. Prior research has focused on disparities based on individual-level and isolated socioeconomic and demographic variables. To our knowledge, the role of composite, community-level geographic socioeconomic disadvantage has not been studied in the United States. As disparities persist, exploring the potential underlying drivers of these inequities may help in developing more targeted recommendations on how to achieve equitable THA use.

Questions/purposes

(1) Is geographic socioeconomic disadvantage associated with decreased THA rates in Medicare-aged patients? (2) Do these associations persist after adjusting for differences in gender, race, ethnicity, and proximity to hospitals performing THA?

Methods

In a study with a cross-sectional design, using population-based data from five-digit ZIP codes in Maryland, USA, from July 1, 2012 to March 31, 2019, we included all inpatient and outpatient primary THAs performed in individuals 65 years of age or older at acute-care hospitals in Maryland, as reported in the Health Services Cost Review Commission database. This database was selected because it provided the five-digit ZIP code data necessary to answer our study question. We excluded THAs performed for nonelective indications. We examined the annual rate of THA in our study population for each Maryland ZIP code, adjusted for differences across areas in distributions of gender, race, ethnicity, and distance to the nearest hospital performing THAs. Four hundred fourteen ZIP codes were included, with an overall mean ± SD THA rate of 371 ± 243 per 100,000 persons 65 years or older, a rate similar to that previously reported in individuals aged 65 to 84 in the United States. Statistical significance was assessed at α = 0.05.

Results

THA rates were higher in more affluent areas, with the following mean rates per 100,000 persons 65 years or older: 422 ± 259 in the least socioeconomically disadvantaged quartile, 339 ± 223 in the second-least disadvantaged, 277 ± 179 in the second-most disadvantaged, and 214 ± 179 in the most-disadvantaged quartile (p < 0.001). After adjustment for distributions in gender, race, ethnicity, and hospital proximity, we found that geographic socioeconomic disadvantage was still associated with THA rate. Compared with the least-disadvantaged quartile, the second-least disadvantaged quartile had 63 fewer THAs per 100,000 people (95% confidence interval 12 to 114), the second-most disadvantaged quartile had 136 fewer THAs (95% CI 62 to 211), and the most-disadvantaged quartile had 183 fewer THAs (95% CI 41 to 325).

Conclusion

Geographic socioeconomic disadvantage may be the underlying driver of disparities in THA use. Although our study does not determine the “correct” rate of THA, our findings support increasing access to elective orthopaedic surgery in disadvantaged geographic communities, compared with prior research and efforts that have studied and intervened on the basis of isolated factors such as race and gender. Increasing access to orthopaedic surgeons in disadvantaged neighborhoods, educating physicians about when surgical referral is appropriate, and educating patients from these geographic communities about the risks and benefits of THA may improve equitable orthopaedic care across neighborhoods. Future studies should explore disparities in rates of appropriate THA and the role of density of orthopaedic surgeons in an area.

Level of Evidence

Level III, therapeutic study.

Introduction

Disparities in orthopaedic surgery have been well-documented, especially in total joint arthroplasty (TJA) [1, 4, 36], and they should be addressed to ensure equitable care among patients. Disparities are evident in multiple aspects of TJA, including use of surgery and perioperative metrics such as length of hospital stay, readmission, and complications [1, 2, 4, 11, 18, 23, 32, 33, 36, 46, 49]. Use and rates of TJA, in particular, are affected by disparities across various demographic and socioeconomic categories. For example, Black [6, 10, 20] and Hispanic individuals [10] are at a higher risk of osteoarthritis than people of other races and ethnicities, but these racial and ethnic minorities undergo TJA at lower rates [4, 11, 18, 23, 32, 46, 49]. THA recipients are also less likely to be Hispanic than to be non-Hispanic [12]. Gender may also play a role, as women underutilize TJA compared with men, and are less likely to be recommended surgery [32, 33]. Multiple socioeconomic factors are associated with TJA use, with lower education levels, lower income, less insurance coverage, and lack of paid employment associated with decreased TJA rates [4, 32]. Research in knee arthroplasty suggests that some of these disparities, namely racial and ethnic, may be related to residential segregation and area income levels [42], and that higher total knee arthroplasty rates are seen in higher income areas [43].

Despite efforts to reduce disparities and promote health equity in musculoskeletal care, disparities in TJA use have persisted, and for THA specifically, disparities have worsened [1]. Part of this persistence may be that the current evidence on disparities in TJA utilization tends to focus on demographics at the individual level or isolated socioeconomic factors, as demonstrated by a systematic review on the topic [32]. However, socioeconomic disadvantage involves not only the characteristics of individuals but also of communities [25]. Community-level, geographic socioeconomic disadvantage affects various health outcomes, independent of individual socioeconomic status [17, 24, 28]. To our knowledge, the association between composite geographic socioeconomic disadvantage and THA rates have not been evaluated in the United States. This work may help us understand whether geographic socioeconomic disadvantage may account for some of the known demographic disparities in THA use. Exploring whether THA rates vary by one’s neighborhood may help direct future disparities research and inform how we design and target interventions.

We therefore asked (1) Is geographic socioeconomic disadvantage associated with decreased THA rates in Medicare-aged patients? (2) Do these associations persist after adjusting for differences in gender, race, ethnicity, and proximity to hospitals performing THA?

Patients and Methods

Data Sources and Study Population

This study was approved by our institutional review board. We used the inpatient and outpatient Maryland Health Services Cost Review Commission datasets from July 1, 2012 to March 31, 2019 to determine the number of THAs performed in individuals 65 years or older in each five-digit Maryland ZIP code. The Health Services Cost Review Commission dataset was selected because, unlike national surgical databases, it provides the necessary five-digit ZIP code geographic data for each record. Only ZIP codes with one or more individuals 65 years or older were included. The Health Services Cost Review Commission data reflect all procedures performed at acute-care hospitals in the state. Our inclusion and exclusion criteria were based on Current Procedural Terminology codes and ICD-9 and ICD-10 codes. To capture only primary elective THA, we excluded patients with hip dislocation; hip or pelvic fractures; joint revision, removal, replacement, or resurfacing; tenotomy; partial hip arthroplasties; and selected other procedures (see Appendix Table 1; Supplemental Digital Content 1, http://links.lww.com/CORR/A423). Our exclusion criteria were similar to those of previous studies that included only elective THA [21, 22, 31].

Table 1.

Demographic characteristics and THA rates by socioeconomic disadvantage quartilea in Maryland, USA, 2012 to 2019

Parameter Mean percentage ± SD p valueb
Overall National socioeconomic disadvantage quartile
First Second Third Fourth
Number of ZIP codes 414 212 142 49 11
Residents aged ≥ 65 years 2039 ± 2402 1739 ± 2121 2457 ± 2608 2301 ± 2864 1235 ± 1517 0.02
Women 51 ± 4.0 51 ± 3.8 51 ± 3.9 52 ± 4.7 50 ± 5.3 0.33
Race or ethnicity
 White 73 ± 25 76 ± 22 70 ± 27 69 ± 29 55 ± 38 0.005
 Black 18 ± 22 14 ± 18 21 ± 22 24 ± 27 39 ± 37 < 0.001
 Hispanic or Latino 6.3 ± 8.5 5.4 ± 6.5 8.1 ± 11 5.4 ± 8.1 6.0 ± 5.8 0.03
Distance to nearest hospital performing THA in kmc 21 ± 16 20 ± 11 20 ± 16 29 ± 24 26 ± 29 0.002
Annual THA proportion per 100,000 persons aged ≥ 65 years 371 ± 243 422 ± 259 339 ± 223 277 ± 179 214 ± 179 < 0.001
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a

First quartile is the least disadvantaged and fourth quartile is the most disadvantaged.

b

From analysis of variance evaluating differences between ZIP codes in different socioeconomic disadvantage quartiles; alpha = 0.05.

c

Nearest hospital to ZIP code by linear distance in kilometers. Hospitals are acute care hospitals performing THA for patients in the Maryland Health Services Cost Review Commission dataset, July 1, 2012 to March 31, 2019.

To determine the level of socioeconomic disadvantage for people in each five-digit ZIP code in Maryland, we used the Area Deprivation Index (ADI) national percentile. The ADI is a composite measure of an area’s socioeconomic disadvantage, calculated using 17 measures of education, employment, quality of housing, and income, which are collected through the United States Census Bureau’s American Community Survey [24, 38]. Lower ADI percentiles indicate less disadvantage, and higher ADI percentiles indicate greater disadvantage. The ADI provides a nuanced approach for assessing a community’s socioeconomic disadvantage, as it has previously been validated for evaluating various health outcomes [24] and is associated with 30-day rehospitalization rates [25]; all-cause, cardiovascular, and childhood mortality [38, 39, 40]; and life expectancy [41]. A recent study evaluating the association between community-level disadvantage using ADI and discharge location after THA demonstrated the utility of community-level metrics in this population [30]. For this study, we used the most recent ADI data, calculated using 2011 to 2015 American Community Survey information.

The ADI was accessed through the University of Wisconsin’s School of Medicine and Public Health Neighborhood Atlas, which provides ADI national percentiles for nine-digit ZIP code areas. We chose to use national instead of state percentiles to improve generalizability of our findings beyond the state population. Because our analysis was performed for five-digit ZIP codes, the ADI national percentile was calculated as a mean of the ADI national percentiles for the nine-digit ZIP codes located in the five-digit ZIP code of interest [30]. ZIP codes were then grouped into national quartiles of socioeconomic disadvantage according to the ADI percentile. In this grouping, ADI Quartile 1 includes the ZIP codes with the lowest level of disadvantage and ADI Quartile 4 includes the ZIP codes with the highest level of disadvantage.

Additional variables of interest for comparison across ZIP codes included ZIP-code population-level distributions of gender, race, and ethnicity. Data on these variables were collected from the 2013 to 2017 United States Census Bureau American Community Survey dataset. This dataset also provided the proportion of population aged 65 years or older for each ZIP code. We calculated the linear distance from each ZIP code to the nearest acute-care hospital performing THA, based on the hospitals included in the Health Services Cost Review Commission dataset.

Overall, 21,475 THAs performed in Maryland between July 1, 2012 and March 31, 2019 met our inclusion criteria. We excluded 2613 nonelective procedures (Fig. 1). Four hundred twenty-two five-digit Maryland ZIP codes had national ADI percentiles [47]; of these, we included the 414 ZIP codes listed in the American Community Survey that had at least one person aged 65 years or older. According to the national ADI quartiles, 212 ZIP codes were in the least socioeconomically disadvantaged national quartile, 142 were in the second-least disadvantaged, 49 were in the second-most disadvantaged, and 11 were in the most-disadvantaged quartile.

Fig. 1.

Fig. 1

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This flowchart shows the selection of patients who underwent THA based on Maryland Health Services Cost Review Commission 2012 to 2019 data. aSome patients who were excluded met multiple exclusion criteria.

Primary Outcome

The primary outcome was the annual rate of THA per 100,000 individuals 65 years or older. We studied the rate in this age group to avoid confounding by differences in age distribution and insurance coverage [49] across ZIP codes, given the approximately 95% Medicare coverage in this group [5].

Statistical Analysis

ADI quartiles and THA rates for each Maryland ZIP code were mapped using ArcGIS Pro, version 2.4.0, software (Esri, Redlands, CA, USA) using ZIP code tabulation data for area geographic information systems [45]. Initial exploratory analysis used an ANOVA test to determine whether there was a difference in crude THA rates across socioeconomic disadvantage quartiles. ANOVA tests were also performed to determine whether there were differences across socioeconomic disadvantage quartiles in ZIP code population-level distributions of gender, race, and ethnicity, as well as distance from the ZIP code to the nearest acute-care hospital performing THA (Table 1). Using a threshold of α = 0.05, we identified race distribution, ethnicity distribution, and distance to the nearest acute-care hospital performing THA as potential confounders to include in our multivariable linear regression model for the association between the ADI quartile of a ZIP code and the annual THA rate in individuals 65 years or older. Although gender distribution did not meet the α = 0.05 threshold, given the prior studies demonstrating higher rates of hip osteoarthritis in women [4, 20, 35] than in men, as well as the gender-based disparities in TJA [32, 33], we used a conservative approach and included gender population distribution in the multivariable model, as well. Thus, our multivariable linear regression model adjusted for percentages of the population in the ZIP code who are women, white, Black, and Hispanic or Latino, as well as the distance to the nearest hospital performing THA.

We attempted a sensitivity analysis for the small portion of THA records that included nine-digit ZIP code data. These data were available in only a portion of inpatient Health Services Cost Review Commission records between 2016 and 2018. We created unadjusted linear regression models to assess associations between ADI data and the absolute number of THAs from 2016 to 2018 in these nine-digit ZIP codes. We did not calculate THA rates because total population data are unavailable from the American Community Survey at the nine-digit ZIP code level. Also, we did not adjust for confounders in this model because the American Community Survey does not include these statistics at the nine-digit ZIP code level. To assess the similarity of the trend between this model and our primary model for the association between ADI and the THA rate for each five-digit ZIP code, we calculated the percentage change from the model intercept. In the 2016 to 2018 inpatient database, 97% (7035 of 7276) of the records for patients 65 years or older undergoing THA had nine-digit ZIP codes. A linear regression model assessing the association between ADI quartile and the number of THAs between 2016 and 2018 showed a lower number of THAs in areas with worsening disadvantage (see Appendix Table 2; Supplemental Digital Content 2, http://links.lww.com/CORR/A424).

Table 2.

Difference in THA ratesa by socioeconomic disadvantage quartile in Maryland, USA, 2012 to 2019

Parameter Difference in THA rate relative to least disadvantaged quartile, per 100,000 population aged ≥ 65 years (95% CI) p valueb
National socioeconomic disadvantage quartilec
 1 (least disadvantaged) Referent
 2 -63 (-114 to -12) 0.02
 3 -136 (-211 to -62) < 0.001
 4 (most disadvantaged) -183 (-325 to -41) 0.01
Female sex, %d -5.0 (-11 to 0.7) 0.09
Race or ethnicity, %
 Whited 3.5 (-0.1 to 7.1) 0.06
 Blackd 1.8 (-2.0 to 5.6) 0.35
 Hispanic or Latinod -2.7 (-6.6 to 1.2) 0.18
Distance to nearest hospital performing THAd 0.2 (-2.5 to 2.9) 0.90
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a

Annual number of THAs per 100,000 population aged ≥ 65 years.

b

From adjusted multivariable linear regression model including all covariates shown in table; alpha = 0.05.

c

Difference in THA rate for each national disadvantage quartile relative to least-disadvantaged. For example, a ZIP code in the most disadvantaged quartile would have a THA rate 183 per 100,000 population aged ≥ 65 years lower than that of a ZIP code in the least-disadvantaged quartile.

d

Difference in THA rate for these variables is the difference for a one-unit increase in the covariate. For example, a one percentage increase in percentage of women is associated with a THA rate decrease of 5 per 100,000 population aged ≥ 65 years (an insignificant finding).

The statistical analysis was performed using Stata, version 15.1, software (StataCorp LLC, College Station, TX, USA) and R, version 3.6.1, software (The R Foundation for Statistical Computing, Vienna, Austria). Significance was assessed at α = 0.05.

Results

Socioeconomic Disadvantage is Associated with Lower THA Rates

THA rates were higher in more affluent communities. The mean THA rates per 100,000 Medicare-aged individuals were 422 ± 259 in the least socioeconomically disadvantaged quartile, 339 ± 223 in the second-least disadvantaged, 277 ± 179 in the second-most disadvantaged, and 214 ± 179 in the most-disadvantaged (p < 0.001) (Table 1). Thus, those in the most-disadvantaged ZIP codes had a THA rate approximately half that of the least-disadvantaged. Qualitative visualization of Maryland ZIP codes were consistent with these findings, showing that areas of less disadvantage also demonstrated higher rates of THA (Fig. 2). The mean ± SD annual THA rate across all ZIP codes in Maryland was 371 ± 243 per 100,000 Medicare-aged individuals.

Fig. 2.

Fig. 2

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A-B These maps show variation in (A) socioeconomic disadvantage and (B) THA rates across Maryland ZIP codes. (B) In this figure, THA rates are divided into quartiles. A color image accompanies the online version of this article.

Disparities Persist After Adjusting Confounding Variables

After controlling for potentially confounding variables including ZIP code population distributions of gender, race, ethnicity, and distance to the nearest hospital performing THA, compared with the least-disadvantaged socioeconomic quartile, the second-least disadvantaged quartile ZIP codes had 63 fewer THAs per 100,000 persons (95% CI 12 to 114; p = 0.02), the second-most disadvantaged quartile had 136 fewer THAs (95% CI 62 to 211; p < 0.001), and the most-disadvantaged quartile had 183 fewer THAs (95% CI 41 to 325; p = 0.01) (Fig. 3). Furthermore, in this adjusted model, ZIP code population distributions of gender, race, and ethnicity, and distance to the nearest hospital were not associated with THA rate (Table 2).

Fig. 3.

Fig. 3

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This bar graph shows the difference in THA rates by quartile of socioeconomic disadvantage compared with the least disadvantaged quartile. We adjusted for gender, race, ethnicity, and distance to the nearest hospital performing THA. Quartile 1 was based on a model prediction in which gender, race, ethnicity, and distance were the mean values for ZIP code in Maryland. aIndicates difference at α = 0.05.

Discussion

Disparities are well-documented in orthopaedic surgery and have been studied extensively in arthroplasty [1, 2, 4, 11, 18, 23, 32, 33, 36, 46, 49]. Utilization of TJA has specifically been shown to vary based on individual-level demographic and isolated socioeconomic variables [4, 11, 12, 18, 23, 32, 33, 46, 49]. Despite efforts to promote health equity, disparities in THA utilization have worsened in recent years [1]. Exploration of how composite, community-level socioeconomic disadvantage is related to joint arthroplasty use in the United States, and specifically THA, may elucidate how to study and mitigate these disparities. We found that in Maryland, THA rates were higher in more affluent communities. These disparities persisted after adjusting for confounding variables, including ZIP-code population distributions of gender, race, and ethnicity, and the distance to the nearest hospital performing THA. The findings suggest that the neighborhood one lives in may have more to do with ability to receive THA than individual-level characteristics, and that resources should be devoted to access to and education about joint replacement surgery in more-disadvantaged communities.

Limitations

Our study has several limitations. Because we used the Health Services Cost Review Commission’s data, we were able to study geographic areas no more granular than five-digit ZIP codes. To assess this limitation, we used sensitivity analysis to compare our findings with the results from a smaller subset of our dataset that did have nine-digit ZIP codes. We found that with this nine-digit ZIP code data, areas with worse socioeconomic disadvantage had lower THA rates compared with more affluent areas. This is consistent with the trend in our study’s overall findings, suggesting that although our geographic granularity remains a limitation, the findings are consistent with results when more granular data are available.

We could not control for the level of demand for THA because the Health Services Cost Review Commission does not report the severity of hip osteoarthritis. Because of the documented associations between osteoarthritis and age, gender, race, and ethnicity [4, 6, 10, 20, 27, 35], we believe we indirectly controlled for the demand for THA by including a specific age group and controlling for the other factors. Furthermore, it is unlikely that regional variations in the demand for THA are as great as variations in THA rates [21]. We were unable to compare comorbidity burdens by ZIP code because, to our knowledge, this information is unavailable in the United States. In Finland, where a morbidity index is available for populations by municipality, no associations were found between regional morbidity and THA rates [29], suggesting that this should not substantially limit interpretation of our findings. We also did not adjust for the number of orthopaedic surgeons per capita in each ZIP code because these data are unavailable in the Health Services Cost Review Commission database. This is a limitation of our study given that prior work demonstrated that higher surgeon density is associated with higher THA rates [4]. However, we posit that we indirectly and partially controlled for this access-related variable by adjusting our model for the distance to the nearest acute care hospital performing THA. Further research should adjust for level of demand, community-level comorbidities, and geographic density of orthopaedic surgeons when possible. Using a large database has inherent limitations, including inconsistency of data entry across institutions [7]. However, using International Classification of Diseases codes for identifying THA has demonstrated sensitivity of 99% [9]. Finally, these data are only from Maryland. It is unknown whether these disparities would be larger or smaller in other states.

Socioeconomic Disadvantage is Associated with Lower THA Rates

In this cross-sectional analysis of inpatient and outpatient acute-care hospital data in Maryland, areas with worse socioeconomic disadvantage had lower THA rates in the Medicare-aged population than did the least disadvantaged areas. Although, to our knowledge, this topic has not been explored in the United States, our results are consistent with a prior study in England in which residents of the most disadvantaged areas had lower rates of THA relative to demand compared with those in the least disadvantaged areas [22]. This consistency between our findings and prior work supports the generalizability of the association between community-level socioeconomic disadvantage and THA rates, and the idea that worse disadvantage is associated with lower THA rates. This is especially true considering the consistent results even in light of the vast differences in health care delivery and insurance coverage in the United Kingdom’s National Health Service compared with the mixed public and private provider and insurance system in the United States [22, 34].

It is important to note that our findings do not promote a “correct” rate of THA. Our overall rate across the state was 371 per 100,000 Medicare-aged individuals. This rate is also likely more representative of the less disadvantaged areas of Maryland, given that 86% of ZIP codes in our study were part of the least or second-least disadvantaged quartiles. However, this does not change our conclusions about disparities between more and less disadvantaged areas, given that there were ZIP codes present in every national quartile. For context, the THA rate among Medicare enrollees in 2001 was 290 per 100,000 persons [4]. Although our mean rate was higher, the 2001 rate was a 34% increase from that reported in 1992 [4], and studies suggest that the rate continues to increase [8, 26, 32]. Additionally, in individuals aged 65 to 84 years in 2014, the annual rate of THR per 100,000 in the United States ranged between 418 and 487 [44]. The THA rate in our least-disadvantaged quartile was also markedly higher than the overall mean, at 422 per 100,000 people, and while similar to national rates [44], may represent overuse of THA in these geographic areas. Indeed, a previous study reported that a higher number of surgeons in an area is associated with higher THA rates [4], and increased access in more affluent areas may be driving higher rates. It is possible that patients in more advantaged areas in our study may have received excess surgical intervention. This is particularly concerning, as there is evidence that osteoarthritis deemed less severe radiographically is associated with worse patient outcomes after TJA compared with outcomes in those with more severe disease [48]. However, an exploration of the appropriateness of surgical indications was beyond the scope of this study. A decision to proceed with surgery is based on bedside ethical principles, including autonomy, beneficence, and non-maleficence, whereas access to surgery is related to social justice, which is a societal-level principle [3]. The benefits of THA are well-known [8, 16, 27, 29, 32], and having controlled for other possible causes of disparate rates, it is important to explore why, in some areas, access to THA appears to be less than in others. Our study focuses on this question, and future studies should evaluate appropriateness of surgical intervention and disparities in appropriate TJA use.

Disparities Persist After Adjusting Confounding Variables

Geographic socioeconomic disparity persisted after accounting for differences in distributions of gender, race, ethnicity, and distance to the nearest hospital performing THA. Importantly, when considered with geographic disadvantage, demographic differences and hospital proximity were not associated with THA rates. This is consistent with work by Skinner et al. [42], who demonstrated that racial and ethnic disparities in knee arthroplasty are related to residential segregation and median income levels. Later work by Skinner et al. [43] also demonstrated that within regions, higher TKA rates were seen for higher ZIP code income levels. Our study takes these findings a step further, providing a dedicated analysis of composite geographic disadvantage, which is more likely to affect an individual’s health care access than solely the income level of their neighborhood. Thus, our finding is a novel one for our US-based population, in that it shows that composite geographic socioeconomic disadvantage may be the true underlying driver of lower THA rates, especially in contrast with prior research focusing on race, ethnicity, and gender-based disparities [1, 4, 11, 12, 18, 23, 32, 33, 46, 49]. Our findings should promote increased geographic targeting of resources, and namely, to more disadvantaged neighborhoods. Prior work has shown lower TJA rates in areas with more non-surgeons [4]. Another study found substantial variability in physician opinion about indications for TKA among orthopaedic surgeons, rheumatologists, and family physicians, half of which was attributable to individual physician inconsistency [50]. If more disadvantaged areas have less access to orthopaedic surgeons, institutions might provide more orthopaedic clinics in those areas or provide more education about appropriate referral to surgeons for primary care providers. Educational interventions for patients may also increase appropriate utilization, as has been seen in TKA use among Black patients [19], especially given that patient willingness to consider TJA is associated with utilization [14] and is tied to knowledge about the procedure [15]. Decision aids for THA have been shown to provide patients with a better understanding of expectations [13]. They can also be provided without extending visit durations and with high surgeon satisfaction, and a randomized trial showed that a short, interactive version may provide better patient knowledge than a longer version [37]. Although much prior research and national efforts to reduce disparity have focused on individual-level factors such as race and gender, our findings promote the inclusion of geographic-based initiatives. Thus, geographic community-level disadvantage and associated disparities in THA utilization may be addressed through greater access to orthopaedic surgeons, education for non-surgeons on when surgery is appropriate, and education for patients on risks and benefits of THA. All of these efforts should be focused on more disadvantaged communities, rather than only targeting certain demographic groups or patients based on individual-level socioeconomic and demographic factors.

Conclusion

Geographic areas with greater socioeconomic disadvantage, as measured by the ADI, have lower rates of THA in the Medicare-aged population than do areas with the least socioeconomic disadvantage. These differences persisted after controlling for geographic variations in the distributions of gender, race, ethnicity, and distance to the nearest hospital performing THA. Our findings suggest that community-level disadvantage may be the primary underlying driver of disparities in THA rates rather than individual-level demographics or isolated socioeconomic variables. This is a distinct finding given that prior retrospective research, prospective interventions, and national initiatives to address disparities have focused largely on demographic groups such as those defined by race and gender. We posit that the neighborhood one lives in may play a substantial role in the ability to access THA. Thus, in addition to efforts to reduce disparities based on demographic factors, we should further explore interventions that promote more equitable care between our affluent and disadvantaged geographic communities. We recommend institutional efforts to increase access to orthopaedic surgeons in more disadvantaged neighborhoods and educate primary care providers in these areas about when surgical referral is appropriate. Policymakers should also be attuned to supporting such efforts financially. We recommend increased education for patients from disadvantaged neighborhoods on the risks and benefits of THA. Our study does not provide information on what the correct rate of THA should be, and future work should explore the disparities in rates of appropriate utilization between geographic areas, as well as how geographic density of orthopaedic surgeons varies between areas of more versus less disadvantage.

Supplementary Material

SUPPLEMENTARY MATERIAL
abjs-479-575-s001.docx (50.3KB, docx)

Acknowledgments

We thank the Johns Hopkins Institute for Clinical and Translational Research and the Johns Hopkins Surgery Center for Outcomes Research for providing feedback throughout the duration of this research.

Footnotes

The institution of one of the authors (RR) has received, during the study period, funding from the National Center for Advancing Translational Sciences, a component of the National Institutes of Health, and the National Institutes of Health Roadmap for Medical Research, via grant number TL1 TR003100.

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.

Each author certifies that his or her institution approved the human protocol for this investigation and that all investigations were conducted in conformity with ethical principles of research.

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