Accountability for end-stage organ care: Implications of geographic variation in access to kidney transplantation David A. Axelrod, MD, MBA,a,b Krista L. Lentine, MD, PhD,c Huiling Xiao, MA,c Thomas Bubolz, PhD,b David Goodman, MD,b Richard Freeman, MD,a Janet E. Tuttle-Newhall, MD,c and Mark A. Schnitzler, PhD,c Lebanon, NH, and St. Louis, MO

Background. The provision of effective surgical care for end-stage renal disease (ESRD) requires efficient evaluation and transplantation. Prior assessments of transplant access have focused primarily on waitlisted patients rather than the overall populations served by ‘‘accountable’’ providers of transplant services. Methods. Novel transplant referral regions (TRRs) were defined using United Network for Organ Sharing registry data for 301,092 kidney transplant listings to assign zip codes to ‘‘accountable’’ transplant programs. Subsequently, risk-adjusted observed to expected (O:E) rates of listing and transplant procedures were calculated for each TRR. Finally, the impact of variation in TRR listing and transplant rates on mortality was assessed for ESRD patients 1 transplant center. The likelihood of being evaluated and listed for transplant varied significantly between TRRs (risk-adjusted O:E, 0.58– 1.95). Variation was greater for the overall transplant rate (0.62–2.19), living donor transplantation (0.36–3.08), and donation after cardiac death transplant (0–15.4) than for standard criteria donors (0.64-2.86). Mortality was decreased for ESRD patients living in TRRs in the highest tertile of listings (hazard ratio, 0.89; P < .0001) and transplantation (0.90; P < .0001). Conclusion. Residence in a TRR with care delivery systems that increase access to transplant services is associated with significant, risk-adjusted decreases in ESRD-related mortality. Transplant centers should continue to focus on improving access to care within the communities they serve. (Surgery 2014;155:734-42.) From the Department of Surgery, a Dartmouth Hitchcock Medical Center, and The Dartmouth Institute for Health Policy and Clinical Practice, b Lebanon, NH; and the St. Louis University Center for Outcomes Research, c St. Louis University, St. Louis, MO

KIDNEY TRANSPLANTATION has been established as the optimal form of renal replacement therapy for appropriate candidates with end-stage renal disease (ESRD).1-3 Compared with chronic dialysis, Dr Axelrod was supported by a grant from the Hitchcock Foundation. Drs Lentine, Axelrod, and Schnitzler are partners in XynManagement, LLC., which provides economics consulting and analytic software to transplant centers. Drs Freeman, Goodman, Tuttle-Newhall, Bubholz, and Ms. Xiao report no financial conflicts of interest in regard to this submission. Accepted for publication December 6, 2013. Reprint requests: David A. Axelrod, MD, MBA, Associate Professor of Surgery, Section Chief, Solid Organ Transplant Surgery, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756. E-mail: [email protected]. 0039-6060/$ - see front matter Ó 2014 Mosby, Inc. All rights reserved. http://dx.doi.org/10.1016/j.surg.2013.12.010

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kidney transplantation offers better survival and improved quality of life at less cost to society. Previous analyses have demonstrated relevant disparities in access to transplant persist despite federal regulations designed to ensure that all patients who require chronic dialysis receive transplant education and, when appropriate, referral for transplant evaluation.4-7 Patients of non-white race, low socioeconomic status (SES), and remote residence have reduced rates of wait listing and transplantation.5,7-14 This disparity may reflect the complexity of the transplant evaluation process, which can require multiple health care encounters, substantial resources for dental care and other uncovered services, and appropriate family support to provide transportation.7,15 Once listed, patients face disparate waiting times resulting from a diversity of factors, including the local

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organ supply, center organ acceptance practices, and the ability to identify an appropriate living donor (LD).12,14,16-18 Responsibility for improving processes to provide care for patients with ESRD is shared among a variety of health care providers. Patients are managed generally by a primary care physician, a private nephrologist, and one of the nearly 200 transplant centers in the United States. Dialysis units and their consulting physicians are required by law to maintain relationships with local transplant centers to ensure that patients are evaluated appropriately.19 These care providers also document the referral or denial in reports submitted to the US Renal Data Service (USRDS). Transplant centers then have the obligation to evaluate patients referred by area dialysis centers, assess their candidacy, and facilitate listing and transplantation. Designing efficient, effective processes that facilitate successful transplantation requires substantial resources and coordination of pretransplant care. Unfortunately, prior analyses of these processes have been limited to single-center studies or examinations of larger regions for which it is difficult to identify an accountable provider.7,15,20 We hypothesized that, like most health care services, renal transplant referral patterns tend to follow predictable referral patterns and utilize local transplant centers. Therefore, it should be possible to assign accountability for a geographically defined population of ESRD to one or a small group of transplant centers based on prior referral and transplant patterns and to compare utilization across regions.21,22 These geographically defined transplant referral regions (TRRs) provide a novel unit of analysis to assess the effectiveness of transplant care for the ESRD populations. As with other efforts to assess small area variation in the delivery of care, the assignment of a zip code to a specific TRR does not require every patient to receive his or her care at the assigned transplant center; however, because care for the plurality of patients in the region is provided generally by the responsible center(s), the resulting differences in cost and outcome of transplant services between TRRs provide estimates of the impact of variation in care provided by the accountable centers. The purposes of this analysis were to utilize national transplant and registry data to define kidney TRRs for the nation, assess differences in the demographically adjusted rate of listing and transplantation between these regions, and assess the impact of variation in access to transplant care on overall ESRD-related mortality.

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METHODS Development of TRRs. Baseline and follow-up clinical and demographic data describing all patients listed for and receiving kidney transplants in the United States from 1987 to 2010 (N = 301,092) was obtained from the Organ Procurement and Transplantation Network (OPTN). The OPTN maintains records for all patients who have been waitlisted or received a solid organ transplant in the United States. In addition, the network tracks the characteristics of deceased and LD organs to facilitate organ allocations and compliance with performance guidelines. These data include zip code of residence and the identity of the transplant center at which patients were listed or transplanted. Transplant candidates’ zip code of residence was used to assign each patient to a hospital service area (HSA) that includes several contiguous zip codes.23 HSAs represent areas of shared referral and practice patterns based on analysis of inpatient hospitalizations for common conditions. HSAs were defined previously as part of the Dartmouth Atlas of Health care project using a 3-step process. First, all acute care hospitals in the 50 states and the District of Columbia were identified from the American Hospital Association and Medicare provider files. Next, zip codes were assigned to specific HSAs based on analysis of Medicare beneficiary hospitalization patterns. Zip codes were assigned to the hospital at which the plurality of inpatient care was provided to Medicare beneficiaries. Finally, maps were reviewed manually to create contiguous regions of care, resulting in 3,067 hospital services areas. Next, the HSAs were assigned to a TRR by examining the referral pattern of all listed patients that resided within the HSA. The zip codes included within the HSA were assigned to the transplant center at which the greatest number of patients listed. HSAs with 18 years of age) who were 1 transplant center. The average localization index for the TRR was 0.72 (0.26–0.96), which means that >70% of ESRD patients listed for transplant who resided with a TRR actually received their care at the assigned center(s). Characteristics of the ESRD population residing differed significantly across TRRs (Table I) reflecting the well-recognized differences in the regional ESRD populations nationally. Among the dramatic demographic differences in the ESRD populations between TRRs was the proportion of African American (0–86.8%), Hispanics (0–87%), and patients with diabetic kidney disease (34.5–66.4%). Variation in observed rates. Examination of unadjusted listing rates among ESRD patients under age 60 reveled a nearly 3-fold variation in the rate of listing for transplant per 1,000 patientyears (mean, 2.83; range, 1.65–4.42; Table II). The unadjusted rate of transplantation per 1,000 patient-years varied by >5-fold across TRRs (mean, 2.1; range, 0.83–4.54). Rates varied even more for nonstandard criteria donor transplants. DCD transplant rates, for instance, were >40-fold greater in the highest utilization regions compared with the lowest. Determination of demographically adjusted performance. To account for the significant differences in the population demographic characteristics, statistical models were developed to estimate the likelihood of listing or transplantation. Patient characteristics associated with a lesser hazard ratio (HR) of being placed on the waiting list rate included older age, African-American race, Hispanic ethnicity, and ESRD related to diabetes (Table III). Factors associated with a decreased likelihood of receiving a transplant included older age at onset of ESRD, female sex, African-American race, Hispanic ethnicity, and

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Table II. Variation in the unadjusted rate rates of listing and kidney transplantation by transplant referral regions

Unadjusted rate Listings Overall transplant Transplant types Living donor Standard criteria donor Extended criteria donor Donation after cardiac death

Mean no. of transplant events per 1,000 ESRD patient-years (range) 2.83 (1.65–4.42) 2.12 (0.83–4.54) 1.05 0.77 0.22 0.09

(0.23–3.09) (0.38–1.92) (0.03–0.59) (0.01–0.45)

ESRD, End-stage renal disease.

Fig 1. Map of kidney transplant referral region (TRR) illustrating the 113 TRRs developed from listing records for 301,092 kidney transplant candidate registrations. TRR were defined by examining linking zip code at listings to hospital service areas (HSAs). HSAs were assigned to TRRs in which a plurality of the patients sought care. TRRs included $1 kidney transplant centers (if the centers were 100%. Variation in O:E was greatest for DCD transplantation (0–15) and least for deceased donor transplant with a standard criteria donor procedures. Relationship between transplant rates and outcome. The O:E ratio within a TRR was strongly associated with a decrease in overall ESRD-related mortality (Fig 2). Residence in a TRR that was categorized in the highest tercile based on the adjusted rate of listings was associated with an 11% decreased in the HR of ESRD mortality when compared with residence in the lowest performing TRRs (HR, 0.89; P < .0001; Table V) Similarly, residence in TRRs with greater transplant rates decreased the risk of death from ESRD significantly (HR, 0.90; P < .0001). These decreases accrue to the entire ESRD population 30% of ESRD patients were not informed about the possibility of transplant at the time of initiation of dialysis. Patients who were older, obese, uninsured, Medicaid insured, and cared for at for-profit dialysis centers were all

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Fig 2. Survival after the onset of end-stage renal disease (ESRD; defined as initiation of ESRD or receipt of a renal transplant) categorized by the demographically adjusted observed-to-expected rate of listing for transplant in the transplant referral region (TRR) of residence. TRRs (n = 113) were categorized into 3 equal terciles based upon the rate of listings per ESRD patient-year at risk (P < .0001).

more likely to be uninformed about transplantation. The lack of early education resulted in a substantial decrease in the proportion of patients who were transplanted successfully (3.3% vs 14.1%). Even among patients who were informed and referred for evaluation, access to care differs markedly by socioeconomic, racial, and educational achievement.5,7-11,13-15,26 Patzer et al7 reviewed the outcome of 2,291 referrals to a large southeastern medical center; within this population, only 54.7% of referred patients actually attended the initial evaluation appointment. African Americans, patients with less educational achievement, and those living in neighborhoods with low SES, and patients living farther away from the center were all less likely to initiate their evaluation. Among patients who initiated evaluation, >90% completed the evaluation successfully, although the rate of noncompletion was greater among blacks than whites. The rate of evaluation completion was associated with neighborhood SES status (P < .0001). Other investigators have confirmed the strong association between educational achievement, insurance status, and zip code SES indices on listing rates and access to transplant care. Among patients who are waitlisted successfully, significant differences persist in access to transplantation-related services and differences in SES and demographic factors. Racial and ethnic minorities face a variety of transplant barriers, including immunologic differences, greater rates of medical comorbidities, decreased access to

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living donation, and less educational achievement.16,26-31 Although the allocation policy has been altered in an effort to decrease the disparities that result from immunologic matching algorithms, population-based interventions have been less successful, perhaps reflecting the pervasive impact of SES on access.12 In previous reports, we have demonstrated that patients with greater SES are more likely to be transplanted (adjusted HR, 1.2; P < .001) resulting principally from a 76% greater likelihood of receiving a LD transplant. Individuals with greater SES are also more likely to benefit from the opportunity to travel to a different donation service area with decreased waiting times than those in lesser SES strata (10.2% vs 5% of patients). Travelling to a new location doubled the rate of deceased donor transplantation (adjusted HR, 1.94; P < .001) and modestly increased the rate of LD transplantation. Similar findings were reported by Patzer and McClellan,13 who reported that SES-related factors explained nearly 30% of the disparities in transplant access. Transplant centers serving disadvantaged populations can justly point to the marked impact of demographic and socioeconomic factors on the outcomes observed in this study. However, after adjusting for population-based characteristics including race, age, sex, and diagnosis; however, our findings show clearly that significant differences in access and outcome persist across TRRs. Our analyses suggest that TRRs with lower rates of listing and transplantation result in greater rates of overall mortality for all patients. This observation is mediated, in part, by the strong relationship between access to LD kidney transplant and overall mortality rates. This study also further extends this finding by demonstrating the beneficial impact of the aggressive use of donor organs beyond the standard criteria donors. Residence in a TRR served by centers that facilitate listings and use more kidneys from ECDs and DCDs was strongly associated with a decrease in mortality rate from ESRD.17 The substantial and important economic benefits associated with transplantation create the potential to design a program of shared savings to allow transplant centers to invest resources in education, local care delivery, and LD recruitment, and subsequently to share in a portion of the overall savings to the public payer system. Based on a model of accountable health care organization, networks of ESRD care providers can be identified through the TRR process and encouraged to facilitate early referral and transplantation, and thereby save lives and money.32-34 This requires a

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Table V. Impact of TRR observed to expected rates of listing and transplantation on the risk ESRD related mortality* Characteristic Age (y) 0–18 19–30 31–44 45–60 Sex Female Male Race White (Non-Hispanic) Black (Non-Hispanic) Hispanic Other race Cause of disease-ESRD Hypertension Polycystic Glomerular Diabetes Other Tertile Lowest Middle Highest

Listings

All Txp

LD Txp

SCD

ECD

DCD

0.52 Ref 1.66 2.69

0.52 Ref 1.66 2.69

0.52 Ref 1.66 2.69

0.52 Ref 1.66 2.69

0.52 Ref 1.66 2.69

0.52 Ref 1.66 2.69

Ref 0.96

Ref 0.96

Ref 0.96

Ref 0.96

Ref 0.96

Ref 0.96

Ref 0.89 0.69 0.62

Ref 0.89 0.69 0.63

Ref 0.89 0.69 0.63

Ref 0.89 0.69 0.63

Ref 0.89 0.69 0.63

Ref 0.89 0.69 0.63

Ref 0.32 0.63 1.46 1.45

Ref 0.32 0.63 1.46 1.44

Ref 0.32 0.63 1.46 1.45

Ref 0.32 0.63 1.46 1.45

Ref 0.32 0.63 1.46 1.45

Ref 0.32 0.63 1.46 1.45

Ref 0.96 0.89

Ref 0.94 0.90

Ref 0.92 0.92

Ref 1.01 0.96

Ref 1.01 0.94

Ref 0.95 0.93

*Multivariate, patient-level Cox proportionate hazard model assessing the hazard for death from time to listing for transplant. Separate models were developed to consider the independent impact of residing on a transplant referral regions (TRR) categorized as low, middle, or higher tercile based on the adjusted observed-to-expected (O:E) ratio for each of the following: Listing rate (Listings), any transplant (All Txp), living donor transplant (LD Txp), standard criteria donor transplant (SCD Txp), extended criteria donor transplant (ECD Txp), and donation after cardiac death transplant (DCD Txp). All values are significant with P < .01 unless displayed in italics. Ref, Reference group.

revision in payment models, because chronic dialysis generates more physician and technical revenue for referring nephrologists. Furthermore, transplantation of higher risk candidates using DCD and ECD transplant is more expensive and generates less profit margins for transplant centers.35,36 In contrast, if networks of physicians caring for patients with ESRD were compensated for expenses and outcomes based on the cost and outcome of ESRD from diagnosis to death, aggressively pursuing transplantation, even with marginal organs, would provide meaningful cost savings as well as improved quality and quantity of life for patients with ESRD. The conclusion of our study should be evaluated in light of several important caveats. First, the determination of TRRs utilized a long study period for data accumulation, and care patterns may have changed over time. The TRRs should be reevaluated periodically to ensure that they reflect actual referral patterns as contemporaneously as possible. TRR designation does not capture all patients seeking transplant in the region, because some patients seek care at alternative programs based on their insurance

or personal preference. The localization indices reported herein, however, are consistent with a variety of studies examining variation in care between health care service areas.21,22 Because the purpose of the TRR analytic framework is to consider the impact of systems of care on populations rather than to judge the performance of specific providers, these regions provide a novel understanding of local patterns of care. Second, outcomes may have been adjusted insufficiently for patient characteristics and organ availability. Although, our study used clinical data from the USRDS and the OPTN to adjust reported O:E ratios, it is likely that a portion of the observed differences are owing to unmeasured factors beyond these captured by these databases, including the availability of donor organs, socioeconomic factors, and regional logistical barriers (eg, travel times). Although these factors certainly impact overall mortality, the rate of standard criteria donor use is less strongly associated with the observed differences than rate of living, DCD, and ECD transplant. Use of these transplant types requires a greater effort by the centers and, in the case of DCD and ECD transplants, is less profitable under current models of

Surgery Volume 155, Number 5 reimbursement.35,36 Furthermore, organ availability should not a priori result in lower listing rates, although centers may develop more conservative listing practices in areas with prohibitively long waiting times. Socioeconomic barriers, although real, may be less of an issue in renal transplantation, because all patients with ESRD are eligible for Medicare and, therefore, could be eligible for listing for transplantation. In conclusion, ESRD care patterns differ significantly across TRRs. Regions with greater adjusted listing and transplant rates are associated with lesser mortality rates for all ESRD patients regardless of whether they actually undergo a transplant. This analysis suggests that interventions that increase the rates of effective, integrated models of care based on results achieved by high-performing regions may benefit patients with ESRD and the payers that support their care. Novel strategies of reimbursement based on care for patients over the course of their illness rather than for specific procedures (dialysis, transplantation) may further the development of effective systems of care. REFERENCES 1. Wolfe RA, Ashby VB, Milford EL, Ojo AO, Ettenger RE, Agodoa LY, et al. Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. N Engl J Med 1999;341:1725-30. 2. Wolfe RA, McCullough KP, Schaubel DE, Kalbfleisch JD, Murray S, Stegall MD, et al. Calculating life years from transplant (LYFT): methods for kidney and kidney-pancreas candidates. Am J Transplant 2008;8:997-1011. 3. Tomasz W, Piotr S. A trial of objective comparison of quality of life between chronic renal failure patients treated with hemodialysis and renal transplantation. Ann Transpl 2003;8: 47-53. 4. Dudley CR, Johnson RJ, Thomas HL, Ravanan R, Ansell D. Factors that influence access to the national renal transplant waiting list. Transplantation 2009;88:96-102. 5. Patzer RE, Amaral S, Wasse H, Volkova N, Kleinbaum D, McClellan WM. Neighborhood poverty and racial disparities in kidney transplant waitlisting. J Am Soc Nephrol 2009;20:1333-40. 6. Patzer RE, Perryman JP, Pastan S, Amaral S, Gazmararian JA, Klein M, et al. Impact of a patient education program on disparities in kidney transplant evaluation. Clin J Am Soc Nephrol 2012;7:648-55. 7. Patzer RE, Perryman JP, Schrager JD, Pastan S, Amaral S, Gazmararian JA, et al. The role of race and poverty on steps to kidney transplantation in the southeastern United States. Am J Transplant 2012;12:358-68. 8. Alexander GC, Sehgal AR. Barriers to cadaveric renal transplantation among blacks, women, and the poor. JAMA 1998;280:1148-52. 9. Epstein AM, Ayanian JZ, Keogh JH, Noonan SJ, Armistead N, Cleary PD, et al. Racial disparities in access to renal transplantation–clinically appropriate or due to underuse or overuse? N Engl J Med 2000;343:1537-44.

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10. Alexander GC, Sehgal AR. Variation in access to kidney transplantation across dialysis facilities: using process of care measures for quality improvement. Am J Kidney Dis 2002;40:824-31. 11. Ladin K, Hanto DW. Understanding disparities in transplantation: do social networks provide the missing clue? Am J Transplant 2010;10:472-6. 12. Axelrod DA, Dzebisashvili N, Schnitzler MA, Salvalaggio PR, Segev DL, Gentry SE, et al. The interplay of socioeconomic status, distance to center, and interdonor service area travel on kidney transplant access and outcomes. Clin J Am Soc Nephrol 2010;5:2276-88. 13. Patzer RE, McClellan WM. Influence of race, ethnicity and socioeconomic status on kidney disease. Nat Rev Nephrol 2012;8:533-41. 14. Hall EC, James NT, Garonzik Wang JM, Berger JC, Montgomery RA, Dagher NN, et al. Center-level factors and racial disparities in living donor kidney transplantation. Am J Kidney Dis 2012;59:849-57. 15. Weng FL, Joffe MM, Feldman HI, Mange KC. Rates of completion of the medical evaluation for renal transplantation. Am J Kidney Dis 2005;46:734-45. 16. Axelrod DA, McCullough KP, Brewer ED, Becker BN, Segev DL, Rao PS. Kidney and pancreas transplantation in the United States, 1999-2008: the changing face of living donation. Am J Transplant 2010;10:987-1002. 17. Garonzik-Wang JM, James NT, Weatherspoon KC, Deshpande NA, Berger JA, Hall EC, et al. The aggressive phenotype: center-level patterns in the utilization of suboptimal kidneys. Am J Transplant 2012;12:400-8. 18. Reese PP, Shea JA, Bloom RD, Berns JS, Grossman R, Joffe M, et al. Predictors of having a potential live donor: a prospective cohort study of kidney transplant candidates. Am J Transplant 2009;9:2792-9. 19. Kucirka LM, Grams ME, Balhara KS, Jaar BG, Segev DL. Disparities in provision of transplant information affect access to kidney transplantation. Am J Transplant 2012;12:351-7. 20. Alexander GC, Sehgal AR. Why hemodialysis patients fail to complete the transplantation process. Am J Kidney Dis 2001;37:321-8. 21. Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in Medicare spending. Part 2: health outcomes and satisfaction with care. Ann Intern Med 2003;138:288-98. 22. Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in Medicare spending. Part 1: the content, quality, and accessibility of care. Ann Intern Med 2003;138:273-87. 23. Atlas D. The Dartmouth Atlas of Health Care: Research Methods. 2012 [cited 2012 Jun 29]. Available from http:// www.dartmouthatlas.org/downloads/methods/research_ methods.pdf. 24. Grams ME, Kucirka LM, Hanrahan CF, Montgomery RA, Massie AB, Segev DL. Candidacy for kidney transplantation of older adults. J Am Geriatr Soc 2012;60:1-7. 25. Ladin K, Rodrigue JR, Hanto DW. Framing disparities along the continuum of care from chronic kidney disease to transplantation: barriers and interventions. Am J Transplant 2009;9:669-74. 26. Weng FL, Reese PP, Mulgaonkar S, Patel AM. Barriers to living donor kidney transplantation among black or older transplant candidates. Clin J Am Soc Nephrol 2010;5: 2338-47. 27. Roberts JP, Wolfe RA, Bragg-Gresham JL, Rush SH, Wynn JJ, Distant DA, et al. Effect of changing the priority for HLA

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32. Fisher ES, Staiger DO, Bynum JP, Gottlieb DJ. Creating accountable care organizations: the extended hospital medical staff. Health Aff (Millwood) 2007;26:w44-57. 33. Fisher ES, Shortell SM. Accountable care organizations: accountable for what, to whom, and how. JAMA 2010;304: 1715-6. 34. Fisher ES, McClellan MB, Safran DG. Building the path to accountable care. N Engl J Med 2011;365:2445-7. 35. Englesbe MJ, Ads Y, Cohn JA, Sonnenday CJ, Lynch R, Sung RS, et al. The effects of donor and recipient practices on transplant center finances. Am J Transplant 2008;8:586-92. 36. Englesbe MJ, Dimick JB, Fan Z, Baser O, Birkmeyer JD. Case mix, quality and high-cost kidney transplant patients. Am J Transplant 2009;9:1108-14.

Accountability for end-stage organ care: implications of geographic variation in access to kidney transplantation.

The provision of effective surgical care for end-stage renal disease (ESRD) requires efficient evaluation and transplantation. Prior assessments of tr...
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