You are here

Article Text

Article menu
Clinical and epidemiological research
Extended report
Racial disparities in knee and hip total joint arthroplasty: an 18-year analysis of national medicare data
  1. Jasvinder A Singh1,2,
  2. Xin Lu3,4,
  3. Gary E Rosenthal3,4,
  4. Said Ibrahim5,
  5. Peter Cram3,4
  1. 1Birmingham Veterans Affairs Medical Center, AL and the Departments of Medicine and Epidemiology, Medicine Service, University of Alabama at Birmingham, Birmingham, Alabama, USA
  2. 2Department of Orthopedic Surgery, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
  3. 3Division of General Internal Medicine, Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
  4. 4CADRE, Iowa City Veterans Administration Medical Center, Iowa City, Iowa, USA
  5. 5Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center, and the Perelman University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA

Abstract

Objective To examine whether racial disparities in usage and outcomes of total knee and total hip arthroplasty (TKA and THA) have declined over time.

Methods We used data from the US Medicare Program (MedPAR data) for years 1991–2008 to identify four separate cohorts of patients (primary TKA, revision TKA, primary THA, revision THA). For each cohort, we calculated standardised arthroplasty usage rates for Caucasian and African–American Medicare beneficiaries for each calendar year, and examined changes in disparities over time. We examined unadjusted and adjusted outcomes (30-day readmission rate, discharge disposition etc.) for Caucasians and African–Americans, and whether disparities decreased over time.

Results In 1991, the use of primary TKA was 36% lower for African–Americans compared with Caucasians (20.6 per 10 000 for African–Americans; 32.1 per 10 000 for Caucasians; p<0.0001); in 2008, usage of primary TKA was 40% lower for African–Americans (41.5 per 10 000 for African–Americans; 68.8 per 10 000 for Caucasians; p<0.0001) with similar findings for the other cohorts. Black–White disparities in 30-day hospital readmission increased significantly from 1991–2008 among three patient cohorts. For example in 1991 30-day readmission rates for African–Americans receiving primary TKA were 6% higher than for Caucasians; by 2008 readmission rates for African–Americans were 24% higher (p<0.05 for change in disparity). Similarly, black–white disparities in the proportion of patients discharged to home after surgery increased across the study period for all cohorts (p<0.05).

Conclusions In an 18-year analysis of US Medicare data, we found little evidence of declines in racial disparities for joint arthroplasty usage or outcomes.

  • Orthopedic Surgery
  • Osteoarthritis
  • Epidemiology

https://doi.org/10.1136/annrheumdis-2013-203494

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Introduction

    Total knee arthroplasty (TKA) and total hip arthroplasty (THA) are two of the most common major surgical procedures performed in the USA.1 ,2 In the UK, rates of THA doubled and TKA trebled from 1991 to 2006.3 Similar upward trends have been reported in registries from Denmark, Sweden and Norway.4 In the USA, 670 000 TKA and 427 000 THA procedures were performed in 2009.5 Moreover, the usage of these procedures has increased by 10–20% per year over the past decade, garnering attention from both researchers and policy makers.6–10

    Prior analyses have demonstrated racial disparities for both TKA and THA with African–Americans having reduced usage and higher complication rates as compared with Caucasians.11–13 However, many of these studies were conducted within discrete healthcare systems at a single point in time,11 ,12 and few have examined whether disparities have declined over time. In a recent UK study, disparities by race were noted for TKA, but not THA.14 In a US study in 2005, Jha et al15 found that racial disparities in joint arthroplasty usage had increased between 1992 and 2001. However, these analyses did not examine disparities in arthroplasty outcomes, and updated analyses using more contemporary data have not been conducted.

    The lack of contemporary data evaluating trends in racial disparities for joint arthroplasty presents a challenge and precludes researchers and policy makers from knowing whether efforts to reduce disparities are having the intended effects.13 ,16–19

    The overarching objective of our study was to examine longitudinal trends in racial disparities in primary and revision TKA and THA. We hypothesised that racial disparities in joint arthroplasty usage rates and outcomes have narrowed over time.

    Methods

    Data

    We used data from the US Medicare Provider Analysis and Review (MedPAR) Part A data files to identify fee-for-service beneficiaries who underwent primary or revision TKA or THA between 1991 and 2008. MedPAR data includes all Americans enrolled in the government Medicare insurance programme, an insurance programme designed to cover US adults aged 65 years and older. TKA and THA patients were identified using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9-CM) procedure codes: 81.54 for primary TKA; 80.06, 81.55, 00.80, 00.81, 00.82, 00.83, 00.84 for revision TKA; 81.51 for primary THA; and 80.05, 81.53, 00.70, 00.71, 00.72, 00.73 for revision THA.20–23 The Part A files contain data collected from discharge abstracts for all hospitalised fee-for-service Medicare enrolees including: patient demographics (age, sex, race/ethnicity); ICD9-CM codes for primary and secondary diagnoses and procedures (for index admission and subsequent admissions); admission source (eg, emergency department or transfer from outside hospital); admission and discharge dates; discharge disposition (eg, home, nursing home, transfer to another acute-care hospital, dead); death occurring up to 3 years after discharge; each patient's unique Medicare beneficiary number allowing for identification of patient readmissions; and each hospital's unique six digit identification number.

    We limited our cohort to patients whose race was categorised as either non-Hispanic Caucasian or African–American. We excluded patients with other racial categories (eg, Native American, Asian/Pacific Islander and Hispanic) or whose race was coded as missing or unknown. We focused our analysis on African–Americans and Caucasians for several reasons: First, prior studies have shown black–white racial designation to be more valid (less biased) than other ethnic or racial designations.24 Second, most recent studies that examined this disparity have focused on African–American and Caucasian patients. Lastly, the representation of other racial/ethnic groups in our sample was less robust, particularly in the early 1990s.25

    Consistent with prior studies in this area, we applied separate exclusion criteria to the primary and revision populations.26–28 From the primary TKA and THA cohorts, we excluded patients with codes indicating infection at the time of surgery (N=4517 for TKA, 6250 for THA), bone or metastatic cancer (N=3101 for TKA, 13 794 for THA), or fracture (N=4603 for TKA, 127 102 for THA), patients admitted through the emergency department (N=14 343 for TKA, 14 851 for THA), and patients admitted after transfer from another acute-care hospital (N=2590 for TKA, 2248 for THA) (see online supplementary figures S1 and S2). These exclusions were not applied to the revision TKA and THA populations because revision procedures are commonly non-elective, and exclusion of emergent procedures would not make sense from a clinical standpoint.

    For each hospital admission (primary or revision TKA or THA), we identified comorbid conditions using algorithms described by Elixhauser et al.29 ,30 We also calculated the mean number of comorbid conditions for each patient as an aggregate measure of comorbidity. We assessed key outcomes for each admission including: hospital length of stay (LOS); mortality within 30 days of discharge; readmission within 30 days of discharge; discharge disposition (categorised as home vs other); and the occurrence of a composite outcome representing readmission for one or more of six key complications within 30 days of discharge (infection, haemorrhage, myocardial infarction, sepsis, deep vein thrombosis, or pulmonary embolism). For this analysis, we used algorithms that we and others have published previously.26–28

    Statistical analysis

    We compared demographic and clinical characteristics of Caucasian and African–American patients who underwent primary or revision TKA or THA between 1991 and 2008. We used analysis of variance for comparisons of continuous variables and the χ2 test for categorical variables. All analyses were performed separately for primary TKA, revision TKA, primary THA and revision THA. We then calculated age-standardised and sex-standardised usage rates for Caucasians and African–Americans for each of the four cohorts for each calendar year. Usage rates were calculated as the number of each procedure performed for Caucasians and African–Americans divided by the number of fee-for-service Caucasian (or African–American) Medicare enrolees. We used graphical methods to plot the trends in usage over time for Caucasian and African–American Medicare enrolees, and compared black–white differences in usage using logistic regression. Changes in the magnitude of disparities in usage over time were assessed in each cohort using an interaction term between race and calendar year, and p values of <0.05 were construed significant.

    We used similar methods to examine trends in several distinct unadjusted arthroplasty outcomes among Caucasians and African–Americans over the study period. These included hospital LOS, 30-day mortality, hospital readmission within 30 days of discharge, occurrence of the composite outcome and discharge-to-home after hospitalisation. We compared changes in the unadjusted black–white differences for each of these outcomes between 1991 and 2008 using Cochran–Mantel–Haenszel statistics. Finally, we examined trends in risk-adjusted 30-day mortality using logistic regression models with generalised estimating equations to account for clustering of patients within hospitals. These models adjusted for age, sex, hospital arthroplasty volume and comorbidity; changes in disparities were again assessed using interaction terms for each model. Details of the models are available on request.

    To insure the robustness of our findings, we conducted an array of sensitivity analyses. First, we added back excluded populations to our primary TKA and THA groups (non-elective, as detailed above) and repeated our analyses. Second, we conducted adjusted analyses for each of the principle outcomes described above. Third, we examined each of the outcomes included in our composite measure individually for each of our four cohorts. All analyses were performed using SAS V.9.2 (Cary, North Carolina, USA). The University of Iowa Institutional Review Board approved this study.

    Role of the funding source

    None of the funding agencies had any influence on study design, data collection, data analysis, manuscript preparation or the decision to submit the manuscript. All statistical analyses were done by the team's biostatistician (XL). Dr Cram had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The corresponding author had the final responsibility for the decision to submit the report for publication.

    Results

    Baseline sample characteristics

    Our analytic sample included 2 684 575 primary TKAs and 267 644 revision TKAs and 1 328 902 primary THAs and 317 408 revision THAs performed between 1991 and 2008 (see online supplementary figures S1–S4). Demographic and clinical characteristics of the primary and revision TKA and THA populations are shown in tables 1 and 2. Compared with Caucasian patients, African–American patients undergoing both primary and revision TKA were younger, more likely to be women, and had significantly more comorbidities (table 1). Similar differences were observed for primary and revision THA (table 2).

    View this table:
    Table 1

    Patient characteristics for black and white patients undergoing primary and revision TKA 1991–2008

    View this table:
    Table 2

    Patient characteristics for black and white patients undergoing primary and revision THA 1991–2008

    Usage rates and differences for primary and revision TKA and THA

    Age-standardised and sex-standardised usage rates for both primary and revision TKA were significantly lower for African–Americans when compared with Caucasians throughout the study period (figure 1); results were similar for primary and revision THA (figure 1). For example, in 1991, use of primary TKA was 35.8% lower for African–Americans compared with Caucasians (20.6 per 10 000 for African–Americans; 32.1 per 10 000 for Caucasians; p<0.0001); in 2008, use of primary TKA for African–Americans was 39.7% lower for African–Americans (41.5 per 10 000 for African–Americans; 68.8 per 10 000 for Caucasians; p<0.0001). Over the 18-year study period, the magnitude of the black–white disparity increased for primary TKA usage by 15.8% (p<0.001), was unchanged for primary THA (p=0.25) and revision THA (p=0.94), and decreased modestly (by 12.8%) for revision TKA (p<0.01).

    Figure 1
    Figure 1

    Standardised utilisation rates per 10 000 enrolees for primary and revision TKA and THA for black and white patients.

    TKA and THA outcomes

    In unadjusted analyses for primary and revision TKA, African–Americans experienced worse outcomes than Caucasians according to most measures throughout the study period (table 3). In particular, African–Americans had longer hospital LOS, higher all-cause hospital readmission rates, higher rates of the composite outcome, and were significantly less likely to be discharged to home (table 3). Thirty-day mortality was higher in African–Americans compared with Caucasians for primary TKA, but not revision TKA (table 3).

    View this table:
    Table 3

    Unadjusted outcomes for black and white patients undergoing primary and revision TKA 1991–2008

    In unadjusted analyses, there was substantial variability in the magnitude of change in black–white disparities for primary and revision TKA over the study period depending upon the outcome under consideration (table 3). For example, in 1991, African–Americans were 3.5% less likely than Caucasians to be discharged to home following revision TKA, but 20.7% less likely to be discharged to home in 2008 (p<0.001 for change in disparity). Unadjusted analyses focusing on primary and revision THA yielded similar results (table 4).

    View this table:
    Table 4

    Unadjusted outcomes for black and white patients undergoing primary and revision THA 1991–2008

    In adjusted analyses, we found that the black–white disparity for 30-day mortality for primary and revision TKA remained largely unchanged (figure 2). For example, in 1991 for primary TKA the adjusted 30-day mortality for Caucasians was 0.2% and for African–Americans was 0.3%, while in 2008 the rates were 0.1% and 0.2% for Caucasians and African–Americans, respectively; thus, the magnitude of the black–white disparity was statistically unchanged between 1991 and 2008 (p value for interaction term=0.81). In adjusted analyses focusing on mortality following primary and revision THA, we again found no consistent evidence for reductions in Black–White disparities (figure 2). For example, we found that for primary THA the black–white disparity for 30-day mortality decreased by 0.3% between 1991 and 2008 (p<0.01), while the disparity in 30-day mortality for revision THA increased by a statistically insignificant 0.1% (p=0.76). Results of the sensitivity analyses were similar to our primary results and are available by request.

    Figure 2
    Figure 2

    Adjustedi mentality for primary and revision TKA (A,B) and THA (C,D) for black and white patients. iMentality adjusted for age, sex, hospital procedure volume and comorbidity.

    Discussion

    In a longitudinal analysis of administrative data from 1991 to 2008 we found persistent disparities in joint arthroplasty usage and outcomes. We found that African–Americans in the USA had significantly lower usage rates for primary and revision knee and hip arthroplasty compared with Caucasians and that these disparities persisted over our 18-year study period. Considering that African–Americans have a higher prevalence of knee osteoarthritis,31 and the osteoarthritis is more severe and more symptomatic,32–36 this disparity is even more impressive. African–Americans had significantly worse outcomes by most measures (eg, readmission rates, ability to be discharged to home) when compared with Caucasians. In aggregate, these results suggest little progress in reducing black–white disparities for joint arthroplasty over the past 18 years.

    While our analysis identified significant racial disparities in the USA, it is not clear whether disparities in other countries and health settings are as significant. An analysis from the UK found minimal ethnic disparities in access to healthcare outcomes for three common conditions (hypertension, raised cholesterol and diabetes).37 On the other hand, one Australian study found that Greek and Italian immigrants had significantly lower rates of primary joint replacement compared with individuals born in Australia.38 It is also important to recognise that disparities commonly attributed to discrimination may also reflect other factors that differ across racial strata including education and socioeconomic status.39 ,40 Another potential reason for racial/ethnic disparities includes patient preference, which varies by race/ethnicity and influence healthcare usage.41–44 Additionally, patient attitudes towards the joint replacement surgery and their willingness to pay (copayments and other out of pocket costs), differs by race/ethnicity, which may also contribute to these disparities noted in our study.45 ,46 In either case, our study serves as a stark reminder that disparities in the USA persist. We would encourage researchers in other countries to conduct similar research to examine whether disparities are increasing, decreasing, or remain relatively unchanged in their home countries.

    A number of prior studies have demonstrated racial disparities in joint arthroplasty usage, with African–Americans being less likely to receive surgery as compared with Caucasians, but none of these studies examined whether disparities in usage declined over time.13 ,47–49 The notable exception were papers by Jha and Judge et al.14 ,15 They found little evidence of reductions in Black–White disparities in the usage of nine surgical procedures between 1992 and 2001 including TKA and THA in the USA,15 and no evidence of racial disparity in THA usage in 2002–2003 in the UK.15 Our analysis builds on the Jha analysis in a number of important ways.50 First, our analysis extends the prior study by demonstrating persistence of disparities in arthroplasty usage over an 18-year period. Second, our analysis documents not only persistent disparities in arthroplasty usage, but also persistent disparities in arthroplasty outcomes. Lower arthroplasty usage has also been reported previously for patients with lower socioeconomic status in the UK,51 ,52 and in ethnic minorities in Australia.38

    As with prior studies, we observed that African–Americans experienced inferior arthroplasty outcomes when compared with Caucasians.12 ,53–55 Specifically, we found that Caucasians tended to have a shorter hospital LOS, were more likely to be discharged home after hospitalisation, and had lower rates of hospital readmission. Moreover, we found little evidence that disparities, in aggregate, have declined over time. For example, while 30-day mortality decreased over time for African–Americans and Caucasians for two of the four cohorts (primary TKA, primary THA), racial disparities did not decrease in two of the four cohorts (revision TKA, revision THA). The reduction in post-arthroplasty mortality in primary TKA and primary THA cohorts over time is consistent with similar findings from the National Inpatient Sample56 and a US institutional total joint registry.57 The mortality reduction is particularly remarkable, given that patients with higher comorbidity are undergoing these surgeries in more recent years.56 This may be partially attributable to improved perioperative screening and management for cardiac and other chronic medical conditions,58–61 and improvement in postoperative recovery period protocols, as seen in a recent study comparing traditional recovery pathway to enhanced recovery programme that led to early return to function postarthroplasty.62

    The combination of longer hospital LOS for African–Americans accompanied by a reduced probability of being discharged to home is thought-provoking. It is possible that African–Americans experience more inhospital complications and, thus, have longer hospital LOS and are more likely to require admission to rehabilitation facilities after hospital discharge. Alternatively, it is possible that the longer LOS reflects patient preference while lower probability of being discharged home reflects reduced social support for African–Americans when compared with Caucasians,63 ,64 ,65 or lower rates of functional improvement during and after hospitalisation in African–Americans compared with Caucasians.66 Further research is needed to address these issues.

    The lack of a reduction in disparities in either usage or outcomes over an 18-year period is sobering. It is possible that the lack of reduction we observed is unique to joint arthroplasty, and that disparities in usage and outcomes have declined more substantially in other areas of healthcare.67 ,68 Alternatively, we believe it to be more likely that the lack of a reduction in disparities that we observed extends to other areas of healthcare.69 ,70 It is increasingly apparent that while broad-based policy briefs and consensus statements disavowing disparities are symbolically important,16 ,18 ,19 ,71 they are insufficient to meaningfully reduce disparities on their own.72 Conversely, there is growing evidence that disparities can be reduced at the local level73 when carefully targeted at a single disease, and when interventions are tailored to address the particular barriers in a community.74–76 It is also important to recognise that our finding of a lack of meaningful reductions in arthroplasty disparities over the past 18 years at a national level should not be interpreted as evidence of a lack of reductions in disparities in all communities. In fact, it is nearly certain that disparities declined in certain communities, but increased in others. This too is an important area for further research. Future research should also investigate the potential predictors of lack of disparity reduction in arthroplasty usage and outcomes including healthcare access, patient attitudes and preferences, education level, patient health literacy and numeracy, patient–physician communication and sociodemographic factors.

    Our study has a number of limitations that warrant brief mention. First, our study was limited to Caucasian and African–American fee-for-service Medicare beneficiaries; extrapolation to other populations should be done with caution. Second, our study relied upon administrative data, and thus, we were unable to evaluate key arthroplasty outcomes including quality of life and functional status or important predictors, such as socioeconomic status and severity of arthritis or comorbidities. Third, in using administrative data, our results could be biased by unmeasured differences in patient complexity. Fourth, no universal appropriateness criteria for arthroplasty are available (none have been implemented to our knowledge in any setting), therefore, we are unable to measure the ‘correct’ arthroplasty rate for our population, or the mismatch between the need and current arthroplasty rates. Lastly, since healthcare systems and racial/ethnic compositions differ by country, it remains to be seen whether racial/ethnic disparities in arthroplasty usage and outcomes are smaller or larger in other countries, compared to our findings. However, as was evident in a recent Australian study, racial/ethnic minority had lower usage rates, indicating that racial health disparities are a problem, not only in the USA.38

    In summary, we found lower rates of arthroplasty usage in African–Americans compared with Caucasians. We also found little evidence of reductions in racial disparities for knee and hip arthroplasty between 1991 and 2008. Our results highlight the need for better understanding of racial disparities and call into question the success of policies designed to reduce disparities over the past two decades. Although some progress is being made in reducing disparities, our study shows that much still needs to be done.

    References

    1. ↵
      1. Carr AJ,
      2. Robertsson O,
      3. Graves S,
      4. et al
      . Knee replacement. Lancet 2012;379:1331–40.
      OpenUrlCrossRefPubMedWeb of Science
    2. ↵
      1. Learmonth ID,
      2. Young C,
      3. Rorabeck C
      . The operation of the century: total hip replacement. Lancet 2007;370:1508–19.
      OpenUrlCrossRefPubMedWeb of Science
    3. ↵
      1. Culliford DJ,
      2. Maskell J,
      3. Beard DJ,
      4. et al
      . Temporal trends in hip and knee replacement in the United Kingdom: 1991 to 2006. J Bone Joint Surg Br 2010;92:130–5.
      OpenUrlCrossRefPubMed
    4. ↵
      1. Robertsson O,
      2. Bizjajeva S,
      3. Fenstad AM,
      4. et al
      . Knee arthroplasty in Denmark, Norway and Sweden. Acta Orthop 2010;81:82–9.
      OpenUrlCrossRefPubMedWeb of Science
    5. ↵
      HCUP. HCUP Facts and Figures 2009- Section 3: Inpatient Hospital Stays by Procedure. Exhibit 3.1 Most Frequent All-listed Procedures. http://hcup-us.ahrq.gov/reports/factsandfigures/2009/pdfs/FF_2009_section3.pdf
    6. ↵
      1. Cram P,
      2. Lu X,
      3. Kaboli PJ,
      4. et al
      . Clinical characteristics and outcomes of Medicare patients undergoing total hip arthroplasty, 1991–2008. JAMA 2011;305:1560–7.
      OpenUrlCrossRefPubMedWeb of Science
    7. ↵
      1. Kurtz S,
      2. Ong K,
      3. Lau E,
      4. et al
      . Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to 2030. J Bone Joint Surg Am 2007;89:780–5.
      OpenUrlAbstract/FREE Full Text
    8. ↵
      1. Cram P,
      2. Lu X,
      3. Callaghan JJ,
      4. et al
      . Long-term trends in hip arthroplasty use and volume. J Arthroplasty 2012;27:278–85 e272.
      OpenUrlCrossRefPubMedWeb of Science
    9. ↵
      1. Freid VM,
      2. Bernstein AB
      . Health care utilization among adults aged 55–64 years: how has it changed over the past 10 years? NCHS Data Brief 2010;(32):1–8.
    10. ↵
      Centers for Disease Control and Prevention. National Center for Health Statistics. Health Data Interactive. 2012. Cited 26 March 2012. http://www.cdc.gov/nchs/hdi.htm
    11. ↵
      1. Ibrahim SA,
      2. Siminoff LA,
      3. Burant CJ,
      4. et al
      . Understanding ethnic differences in the utilization of joint replacement for osteoarthritis: the role of patient-level factors. Med Care 2002;40(1 Suppl):I44–51.
      OpenUrlPubMed
    12. ↵
      1. Ibrahim SA,
      2. Stone RA,
      3. Han X,
      4. et al
      . Racial/ethnic differences in surgical outcomes in veterans following knee or hip arthroplasty. Arthritis Rheum 2005;52:3143–51.
      OpenUrlCrossRefPubMedWeb of Science
    13. ↵
      1. Skinner J,
      2. Weinstein JN,
      3. Sporer SM,
      4. et al
      . Racial, ethnic, and geographic disparities in rates of knee arthroplasty among Medicare patients. N Engl J Med 2003;349:1350–9.
      OpenUrlCrossRefPubMedWeb of Science
    14. ↵
      1. Judge A,
      2. Welton NJ,
      3. Sandhu J,
      4. et al
      . Equity in access to total joint replacement of the hip and knee in England: cross sectional study. BMJ 2010;341:c4092.
      OpenUrlAbstract/FREE Full Text
    15. ↵
      1. Jha AK,
      2. Fisher ES,
      3. Li Z,
      4. et al
      . Racial trends in the use of major procedures among the elderly. N Engl J Med 2005;353:683–91.
      OpenUrlCrossRefPubMedWeb of Science
    16. ↵
      Institute of Medicine. Unequal treatment: confronting racial and ethnic disparities in health care. Washington, DC: National Academy Press, National Academy of Sciences, 2002. http://www.iom.edu/Reports/2002/Unequal-Treatment-Confronting-Racial-and-Ethnic-Disparities-in-Health-Care.aspx
    17. ↵
      1. Koh HK,
      2. Graham G,
      3. Glied SA
      . Reducing racial and ethnic disparities: the action plan from the department of health and human services. Health Aff (Millwood) 2011;30:1822–9.
      OpenUrlAbstract/FREE Full Text
    18. ↵
      NIAMS National Institute of Arthritis Musculoskeletal and Skin Diseases. Department of Health and Human Services. Strategic Pan for Reducing Racial Disparities. 2006. Cited 26 March 2012. http://www.niams.nih.gov/About_Us/Mission_and_Purpose/strat_plan_hd.asp
    19. ↵
      1. Rogers C
      . American Academy of Orthopaedic Surgeons. February 2004 Bulletin. AAOS look at racial, ethnic and gender disparities in orthopaedics. 2004. Cited 26 March 2012. http://www2.aaos.org/aaos/archives/bulletin/feb04/fline2.htm
    20. ↵
      1. Mitchell JB,
      2. Bubolz T,
      3. Paul JE,
      4. et al
      . Using Medicare claims for outcomes research. Medical Care 1994;32(7 Suppl):JS38–51.
      OpenUrlCrossRefPubMedWeb of Science
    21. ↵
      1. Katz JN,
      2. Barrett J,
      3. Mahomed NN,
      4. et al
      . Association between hospital and surgeon procedure volume and the outcomes of total knee replacement. J Bone Joint Surg Am 2004;86-A:1909–16.
      OpenUrlAbstract/FREE Full Text
    22. ↵
      1. Losina E,
      2. Barrett J,
      3. Mahomed NN,
      4. et al
      . Early failures of total hip replacement: effect of surgeon volume. Arthritis Rheum 2004;50:1338–43.
      OpenUrlCrossRefPubMedWeb of Science
    23. ↵
      1. Katz JN,
      2. Losina E,
      3. Barrett J,
      4. et al
      . Association between hospital and surgeon procedure volume and outcomes of total hip replacement in the United States medicare population.[see comment]. J Bone Joint Surg Am 2001;83-A:1622–9.
      OpenUrlAbstract/FREE Full Text
    24. ↵
      1. Escarce JJ,
      2. McGuire TG
      . Methods for using Medicare data to compare procedure rates among Asians, blacks, Hispanics, Native Americans, and whites. Health Serv Res 2003;38:1303–17.
      OpenUrlCrossRefPubMedWeb of Science
    25. ↵
      1. Waldo DR
      . Accuracy and bias of race/ethnicity codes in the Medicare Enrollment Database. . Health Care Financ Rev 2004–2005;26:61–72.
      OpenUrlWeb of Science
    26. ↵
      1. Katz JN,
      2. Losina E,
      3. Barrett J,
      4. et al
      . Association between hospital and surgeon procedure volume and outcomes of total hip replacement in the United States medicare population. J Bone Joint Surg Am 2001;83-A:1622–9.
      OpenUrlAbstract/FREE Full Text
    27. ↵
      1. Cram P,
      2. Vaughan-Sarrazin MS,
      3. Wolf B,
      4. et al
      . A comparison of total hip and knee replacement in specialty and general hospitals. J Bone Joint Surg Am 2007;89:1675–84.
      OpenUrlAbstract/FREE Full Text
    28. ↵
      1. Hagen TP,
      2. Vaughan-Sarrazin MS,
      3. Cram P
      . Relation between hospital orthopaedic specialisation and outcomes in patients aged 65 and older: retrospective analysis of US Medicare data. BMJ 2010;340:c165.
      OpenUrlAbstract/FREE Full Text
    29. ↵
      1. Elixhauser A,
      2. Steiner C,
      3. Harris DR,
      4. et al
      . Comorbidity measures for use with administrative data. Med Care 1998;36:8–27.
      OpenUrlCrossRefPubMedWeb of Science
    30. ↵
      1. Quan H,
      2. Sundararajan V,
      3. Halfon P,
      4. et al
      . Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005;43:1130–9.
      OpenUrlCrossRefPubMedWeb of Science
    31. ↵
      1. Nelson AE,
      2. Renner JB,
      3. Schwartz TA,
      4. et al
      . Differences in multijoint radiographic osteoarthritis phenotypes among African Americans and Caucasians: the Johnston County Osteoarthritis project. Arthritis Rheum 2011;63:3843–52.
      OpenUrlCrossRefPubMedWeb of Science
    32. ↵
      1. Braga L,
      2. Renner JB,
      3. Schwartz TA,
      4. et al
      . Differences in radiographic features of knee osteoarthritis in African-Americans and Caucasians: the Johnston county osteoarthritis project. Osteoarthritis Cartilage 2009;17:1554–61.
      OpenUrlCrossRefPubMedWeb of Science
    33. ↵
      1. Allen KD,
      2. Chen JC,
      3. Callahan LF,
      4. et al
      . Racial differences in knee osteoarthritis pain: potential contribution of occupational and household tasks. J Rheumatol 2012;39:337–44.
      OpenUrlAbstract/FREE Full Text
    34. ↵
      1. Allen KD
      . Racial and ethnic disparities in osteoarthritis phenotypes. Curr Opin Rheumatol 2010;22:528–32.
      OpenUrlCrossRefPubMed
    35. ↵
      1. Allen KD,
      2. Oddone EZ,
      3. Coffman CJ,
      4. et al
      . Racial differences in osteoarthritis pain and function: potential explanatory factors. Osteoarthritis Cartilage 2010;18:160–7.
      OpenUrlCrossRefPubMedWeb of Science
    36. ↵
      1. Burns R,
      2. Graney MJ,
      3. Lummus AC,
      4. et al
      . Differences of self-reported osteoarthritis disability and race. J Natl Med Assoc 2007;99:1046–51.
      OpenUrlPubMed
    37. ↵
      1. Nazroo JY,
      2. Falaschetti E,
      3. Pierce M,
      4. et al
      . Ethnic inequalities in access to and outcomes of healthcare: analysis of the Health Survey for England. J Epidemiol Community Health 2009;63:1022–7.
      OpenUrlAbstract/FREE Full Text
    38. ↵
      1. Wang Y,
      2. Simpson JA,
      3. Wluka AE,
      4. et al
      . Reduced rates of primary joint replacement for osteoarthritis in Italian and Greek migrants to Australia: the Melbourne Collaborative Cohort Study. Arthritis Res Ther 2009;11:R86.
      OpenUrlCrossRefPubMed
    39. ↵
      1. Karlsen S
      . ‘Black like Beckham’? Moving beyond definitions of ethnicity based on skin colour and ancestry. Ethn Health 2004;9:107–37.
      OpenUrlCrossRefPubMedWeb of Science
    40. ↵
      1. Kelaher M,
      2. Paul S,
      3. Lambert H,
      4. et al
      . Discrimination and health in an English study. Soc Sci Med 2008;66:1627–36.
      OpenUrlCrossRefPubMedWeb of Science
    41. ↵
      1. Ayanian JZ,
      2. Cleary PD,
      3. Weissman JS,
      4. et al
      . The effect of patients’ preferences on racial differences in access to renal transplantation. N Engl J Med 1999;341:1661–9.
      OpenUrlCrossRefPubMedWeb of Science
    42. ↵
      1. Hannan EL,
      2. van Ryn M,
      3. Burke J,
      4. et al
      . Access to coronary artery bypass surgery by race/ethnicity and gender among patients who are appropriate for surgery. Med Care 1999;37:68–77.
      OpenUrlCrossRefPubMedWeb of Science
    43. ↵
      1. Ibrahim SA,
      2. Siminoff LA,
      3. Burant CJ,
      4. et al
      . Variation in perceptions of treatment and self-care practices in elderly with osteoarthritis: a comparison between African American and white patients. Arthritis Rheum 2001;45:340–5.
      OpenUrlCrossRefPubMedWeb of Science
    44. ↵
      1. Ibrahim SA,
      2. Siminoff LA,
      3. Burant CJ,
      4. et al
      . Differences in expectations of outcome mediate African American/white patient differences in “willingness” to consider joint replacement. Arthritis Rheum 2002;46:2429–35.
      OpenUrlCrossRefPubMedWeb of Science
    45. ↵
      1. Figaro MK,
      2. Russo PW,
      3. Allegrante JP
      . Preferences for arthritis care among urban African Americans: “I don't want to be cut”. Health Psychol 2004;23:324–9.
      OpenUrlCrossRefPubMedWeb of Science
    46. ↵
      1. Byrne MM,
      2. O'Malley KJ,
      3. Suarez-Almazor ME
      . Ethnic differences in health preferences: analysis using willingness-to-pay. J Rheumatol 2004;31:1811–18.
      OpenUrlAbstract/FREE Full Text
    47. ↵
      1. Dunlop DD,
      2. Manheim LM,
      3. Song J,
      4. et al
      . Age and racial/ethnic disparities in arthritis-related hip and knee surgeries. Med Care 2008;46:200–8.
      OpenUrlCrossRefPubMedWeb of Science
    48. ↵
      1. Jones A,
      2. Kwoh CK,
      3. Kelley ME,
      4. et al
      . Racial disparity in knee arthroplasty utilization in the veterans health administration. Arthritis Rheum 2005;53:979–81.
      OpenUrlCrossRefPubMedWeb of Science
    49. ↵
      1. Steel N,
      2. Clark A,
      3. Lang IA,
      4. et al
      . Racial disparities in receipt of hip and knee joint replacements are not explained by need: the Health and Retirement Study 1998–2004. J Gerontol A Biol Sci Med Sci 2008;63:629–34.
      OpenUrlAbstract/FREE Full Text
    50. ↵
      1. Ackerman IN,
      2. Graves SE,
      3. Wicks IP,
      4. et al
      . Severely compromised quality of life in women and those of lower socioeconomic status waiting for joint replacement surgery. Arthritis Rheum. 2005;53:653–8.
      OpenUrlCrossRefPubMedWeb of Science
    51. ↵
      1. Brennan SL,
      2. Stanford T,
      3. Wluka AE,
      4. et al
      . Cross-sectional analysis of association between socioeconomic status and utilization of primary total hip joint replacements 2006–7: Australian Orthopaedic Association National Joint Replacement Registry. BMC Musculoskelet Disord 2012;13:63.
      OpenUrlCrossRefPubMed
    52. ↵
      1. Steel N,
      2. Melzer D,
      3. Gardener E,
      4. et al
      . Need for and receipt of hip and knee replacement—a national population survey. Rheumatology (Oxford) 2006;45:1437–41.
      OpenUrlAbstract/FREE Full Text
    53. ↵
      1. Bang H,
      2. Chiu YL,
      3. Memtsoudis SG,
      4. et al
      . Total hip and total knee arthroplasties: trends and disparities revisited. Am J Orthop (Belle Mead NJ) 2010;39:E95–102.
      OpenUrl
    54. ↵
      1. Lavernia CJ,
      2. Alcerro JC,
      3. Rossi MD
      . Fear in arthroplasty surgery: the role of race. Clin Orthop Relat Res 2010;468:547–54.
      OpenUrlCrossRefPubMed
    55. ↵
      1. Berges IM,
      2. Kuo YF,
      3. Ostir GV,
      4. et al
      . Gender and ethnic differences in rehabilitation outcomes after hip-replacement surgery. Am J Phys Med Rehabil 2008;87:567–72.
      OpenUrlCrossRefPubMed
    56. ↵
      1. Kirksey M,
      2. Chiu YL,
      3. Ma Y,
      4. et al
      . Trends in in-hospital major morbidity and mortality after total joint arthroplasty: United States 1998–2008. Anesth Analg 2012;115:321–7.
      OpenUrlCrossRefPubMedWeb of Science
    57. ↵
      1. Singh JA,
      2. Lewallen DG
      . Ninety-day mortality in patients undergoing elective total hip or total knee arthroplasty. J Arthroplasty 2012;27:1417–22 e1411.
      OpenUrlCrossRefPubMedWeb of Science
    58. ↵
      1. Karnath BM
      . Preoperative cardiac risk assessment. Am Fam Physician 2002;66:1889–96.
      OpenUrlPubMedWeb of Science
    59. ↵
      1. Almanaseer Y,
      2. Mukherjee D,
      3. Kline-Rogers EM,
      4. et al
      . Implementation of the ACC/AHA guidelines for preoperative cardiac risk assessment in a general medicine preoperative clinic: improving efficiency and preserving outcomes. Cardiology 2005;103:24–9.
      OpenUrlCrossRefPubMedWeb of Science
    60. ↵
      1. Hoeks SE,
      2. Poldermans D
      . European Society of Cardiology 2009 guidelines for preoperative cardiac risk assessment and perioperative cardiac management in noncardiac surgery: key messages for clinical practice. Polskie Archiwum Medycyny Wewnetrznej 2010;120:294–9.
      OpenUrlPubMed
    61. ↵
      1. Poldermans D,
      2. Bax JJ,
      3. Boersma E,
      4. et al
      . Guidelines for pre-operative cardiac risk assessment and perioperative cardiac management in non-cardiac surgery: the Task Force for Preoperative Cardiac Risk Assessment and Perioperative Cardiac Management in Non-cardiac Surgery of the European Society of Cardiology (ESC) and endorsed by the European Society of Anaesthesiology (ESA). Eur J Anaesthesiol 2010;27:92–137.
      OpenUrlCrossRefPubMedWeb of Science
    62. ↵
      1. Savaridas T,
      2. Serrano-Pedraza I,
      3. Khan SK,
      4. et al
      . Reduced medium-term mortality following primary total hip and knee arthroplasty with an enhanced recovery program. A study of 4,500 consecutive procedures. Acta Orthop 2013;84:40–3.
      OpenUrlCrossRefPubMedWeb of Science
    63. ↵
      1. Ottenbacher KJ,
      2. Smith PM,
      3. Illig SB,
      4. et al
      . Disparity in health services and outcomes for persons with hip fracture and lower extremity joint replacement. Med Care 2003;41:232–41.
      OpenUrlCrossRefPubMedWeb of Science
    64. ↵
      1. Peng TR,
      2. Navaie-Waliser M,
      3. Feldman PH
      . Social support, home health service use, and outcomes among four racial-ethnic groups. Gerontologist 2003;43:503–13.
      OpenUrlAbstract/FREE Full Text
    65. ↵
      1. Morrow-Howell N,
      2. Proctor E
      . Discharge destinations of Medicare patients receiving discharge planning: who goes where? Med Care 1994;32:486–97.
      OpenUrlCrossRefPubMedWeb of Science
    66. ↵
      1. Sands LP,
      2. Landefeld CS,
      3. Ayers SM,
      4. et al
      . Disparities between black and white patients in functional improvement after hospitalization for an acute illness. J Am Geriatr Soc 2005;53:1811–16.
      OpenUrlCrossRefPubMedWeb of Science
    67. ↵
      1. Al-Khatib SM,
      2. Hellkamp AS,
      3. Hernandez AF,
      4. et al
      . Trends in use of implantable cardioverter defibrillator therapy among patients hospitalized for heart failure: have the previously observed sex and racial disparities changed over time? Circulation 2012;125:1094–101.
      OpenUrlAbstract/FREE Full Text
    68. ↵
      1. Richards CA,
      2. Kerker BD,
      3. Thorpe L,
      4. et al
      . Increased screening colonoscopy rates and reduced racial disparities in the New York Citywide campaign: an urban model. Am J Gastroenterol 2011;106:1880–6.
      OpenUrlCrossRefPubMed
    69. ↵
      1. Robbins AS,
      2. Siegel RL,
      3. Jemal A
      . Racial disparities in stage-specific colorectal cancer mortality rates from 1985 to 2008. J Clin Oncol 2012;30:401–5.
      OpenUrlAbstract/FREE Full Text
    70. ↵
      1. McWilliams JM,
      2. Meara E,
      3. Zaslavsky AM,
      4. et al
      . Differences in control of cardiovascular disease and diabetes by race, ethnicity, and education: U.S. trends from 1999 to 2006 and effects of medicare coverage. Ann Intern Med 2009;150:505–15.
      OpenUrlCrossRefPubMedWeb of Science
    71. ↵
      U.S. Department of Health and Human Services. National Partnership for Action to End Health Disparities. HHS Action Plan to Reduce Racial and Ethnic Health Disparities. 2011. Cited 29 March 2012. http://minorityhealth.hhs.gov/npa/templates/content.aspx?lvl=1&lvlid=33&ID=285 Last Modified: 09/19/2011.
    72. ↵
      1. Chin MH
      . Quality improvement implementation and disparities: the case of the health disparities collaboratives. Med Care 2010;48:668–75.
      OpenUrlCrossRefPubMedWeb of Science
    73. ↵
      1. Chin MH,
      2. Walters AE,
      3. Cook SC,
      4. et al
      . Interventions to reduce racial and ethnic disparities in health care. Med Care Res Rev 2007;64(5 Suppl):7S–28S.
      OpenUrlAbstract/FREE Full Text
    74. ↵
      1. Long JA,
      2. Jahnle EC,
      3. Richardson DM,
      4. et al
      . Peer mentoring and financial incentives to improve glucose control in african american veterans: a randomized trial. Ann Intern Med 2012;156:416–24.
      OpenUrlCrossRefPubMedWeb of Science
    75. ↵
      1. Ogedegbe GO,
      2. Boutin-Foster C,
      3. Wells MT,
      4. et al
      . A randomized controlled trial of positive-affect intervention and medication adherence in hypertensive african americans. Arch Intern Med 2012;172:322–6.
      OpenUrlCrossRefPubMedWeb of Science
    76. ↵
      1. Schlotthauer AE,
      2. Badler A,
      3. Cook SC,
      4. et al
      . Evaluating interventions to reduce health care disparities: an RWJF program. Health Aff (Millwood) 2008;27:568–73.
      OpenUrlAbstract/FREE Full Text
    View Abstract

    Supplementary materials

    • Supplementary Data

      This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

      Files in this Data Supplement:

    Footnotes

    • Handling editor Tore K Kvien

    • Contributors JAS, PC and XL designed the study, collected and interpreted the data and wrote the report. SI and GER made revisions and edits to the report. All authors approved the final version of the report.

    • Funding National Institute of Aging, National Cancer Institute, Agency for Health Quality and Research Center for Education and Research on Therapeutics (CERTs), Department of Veterans Affairs, National Institute of Arthritis and Musculoskeletal and Skin Diseases and National Heart, Lung and Blood Institute.

    • Competing interests JAS is supported by research grants from National Institute of Aging, National Cancer Institute, Agency for Health Quality and Research Center for Education and Research on Therapeutics (CERT), and the resources and the use of facilities at the Birmingham VA Medical Center, Alabama, USA. PC is supported by a K24 award from NIAMS (AR062133) and by the Department of Veterans Affairs. This work is also funded in part by R01 HL085347 from NHLBI and R01 AG033035 from NIA at the NIH. Dr Ibrahim is supported by Grant Number K24AR055259 from the National Institute of Arthritis and Musculoskeletal and Skin Diseases. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. JAS has received investigator-initiated research grants from Takeda and Savient; consultant fees from Ardea, Savient, Allergan and Novartis; and is a member of the executive of OMERACT, an organisation that develops outcome measures in rheumatology and receives arms-length funding from 36 companies. Dr Singh is also a member of the American College of Rheumatology's Guidelines Subcommittee of the Quality of Care Committee and Veterans Affairs Rheumatology Field Advisory Committee. Dr Cram, Dr Ibrahim and Xin Lu have no conflicts to disclose.

    • Ethics approval University of Iowa Institutional Review Board.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data sharing statement We will share data with any investigator interested in replicating these findings or interested in future collaborations, pursuant to institutional and IRB regulations, in accordance with patient privacy, confidentiality and HIPAA laws/regulations.