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of Unrelated Donor Hematopoietic Cell

Transplantation

K. Scott Baker,

1

Stella M. Davies,

2

Navneet S. Majhail,

3,4

Anna Hassebroek,

4

John P. Klein,

5

Karen K. Ballen,

6

Carolyn L. Bigelow,

7

Haydar A. Frangoul,

8

Cheryl L. Hardy,

7

Christopher Bredeson,

9

Jason Dehn,

10

Debra Friedman,

8

Theresa Hahn,

11

Gregory Hale,

12

Hillard M. Lazarus,

13

C. F. LeMaistre,

14

Fausto Loberiza,

15

Dipnarine Maharaj,

16

Philip McCarthy,

11

Michelle Setterholm,

10

Stephen Spellman,

10

Michael Trigg,

17

Richard T. Maziarz,

18

Galen Switzer,

19

Stephanie J. Lee,

1

J. Douglas Rizzo

5

Success of hematopoietic cell transplantation (HCT) can vary by race, but the impact of socioeconomic status (SES) is not known. To evaluate the role of race and SES, we studied 6207 unrelated-donor myeloa-blative (MA) HCT recipients transplanted between 1995 and 2004 for acute or chronic leukemia or myelo-dysplastic syndrome (MDS). Patients were reported by transplant center to be White (n55253), African American (n5368), Asian/Pacific-Islander (n5141), or Hispanic (n5445). Patient income was estimated from residential zip code at time of HCT. Cox regression analysis adjusting for other significant factors showed that African American (but not Asian or Hispanic) recipients had worse overall survival (OS) (rel-ative-risk [RR] 1.47; 95% confidence interval [CI] 1.29-1.68, P\.001) compared to Whites. Treatment-related mortality (TRM) was higher in African Americans (RR 1.56; 95% CI 1.34-1.83, P\.001) and in Hispanics (RR 1.30; 95% CI 1.11-1.51,P5.001). Across all racial groups, patients with median incomes in the lowest quartile (\$34,700) had worse OS (RR 1.15; 95% CI 1.04-1.26,P5.005) and higher risks of TRM (RR 1.21; 1.07-1.36,P5.002). Inferior outcomes among African Americans are not fully explained by transplant-related factors or SES. Potential other mechanisms such as genetic polymorphisms that have an impact on drug metabolism or unmeasured comorbidities, socioeconomic factors, and health behaviors may be important. Low SES, regardless of race, has a negative impact on unrelated donor HCT outcomes.

Biol Blood Marrow Transplant 15: 1543-1554 (2009)Ó2009 American Society for Blood and Marrow Transplantation

KEY WORDS: Allogeneic hematopoietic cell transplantation, Unrelated donor, Race, Socioeconomic status, Survival

INTRODUCTION

The use of hematopoietic cell transplantation (HCT) is increasing worldwide. The prognostic impact of patient and donor specific demographic

fac-tors has been well described, but there are limited data regarding the impact of race and socioeconomic status (SES) on outcome of HCT. Many studies have described racial differences in tumor presentation, his-tology, stage at diagnosis, and response to therapy in

From the 1Fred Hutchinson Cancer Research Center, Seattle, Washington;2Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio;3University of Minnesota, Minneapolis, Min-nesota;4Center for International Blood and Marrow Transplant Research, Minneapolis, Minnesota;5Center for International Blood and Marrow Transplant Research, Milwaukee Wisconsin;6Massachusetts General Hospital, Boston, Massa-chusetts;7University of Mississippi Medical Center, Jackson, Mississippi;8Vanderbilt University Medical Center, Nashville, Tennessee; 9Medical College of Wisconsin, Milwaukee, Wisconsin;10National Marrow Donor Program, Minneapolis, Minnesota; 11Roswell Park Cancer Institute, Buffalo, New York;12All Children’s Hospital, Saint Petersburg, FL;13 Uni-versity Hospitals Case Medical Center, Cleveland, Ohio; 14Texas Transplant Institute, San Antonio, Texas;15University

of Nebraska Medical Center, Omaha, Nebraska;16South Flor-ida Bone Marrow/Stem Cell Transplant Institute, Boynton Beach, Florida;17Merck & Co. Inc., Wilmington, Delaware; 18Oregon Health & Science University, Portland, Oregon; and19University of Pittsburgh, Pittsburgh, Pennsylvania. Financial disclosure:See Acknowledgments on page 1552.

Correspondence and reprint requests: K. Scott Baker, MD, MS, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Mailstop D5-280, Seattle, WA 98109 (e-mail: ksbaker@fhcrc.org).

Received May 20, 2009; accepted July 27, 2009

Ó2009 American Society for Blood and Marrow Transplantation 1083-8791/09/1512-0007$36.00/0

doi:10.1016/j.bbmt.2009.07.023

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cancer patients, including prostate cancer[1-3], carci-noma of the breast [4,5], colon [4], oral cancers [5], acute myelogenous leukemia (AML)[6], and Hodgkin lymphoma (HL) [7]. Possible explanations for these differences might include cultural attitudes in seeking medical care, treatment variability, as well as potential lack of access to primary care (and subsequently de-layed diagnosis). SES has also been considered as a con-tributing factor to racial differences in outcome for cancer patients. However, even controlling for stage of disease, most studies suggest that SES alone cannot explain a racial difference in outcome[8-11].

Until recently, differences in the outcome of ethnic minorities undergoing HCT have not been described in detail. A previous study from the Center for Interna-tional Blood and Marrow Transplant Research (CIBMTR) compared trends in survival rates in ethnic minorities after HCT from human leukocyte antigen (HLA)-identical sibling donors[12]. The study found that Hispanics had lower 1-year and 3-year survival rates compared with Whites, whereas no differences were identified between Whites and African Americans or Asians [12]. A follow-up study found that the de-crease in overall survival (OS) among Hispanics was pri-marily related to higher risks of treatment failure (death or relapse) and higher risk of overall mortality [13]. Mielcarek et al.[14], in a cohort of sibling and unrelated donor HCT recipients, have also reported a signifi-cantly higher risk of mortality among African American HCT recipients compared with White recipients.

Increased genetic disparity at both the HLA locus and at minor transplantation antigens may have an im-portant influence on the outcome of HCT. In addition, polymorphism in cytokine genes can also influence HCT outcomes [15,16]. These genetic factors might be expected to vary between ethnicities, and may con-tribute to disparate outcomes. A previous CIBMTR study among recipients of sibling donor HCT, per-formed in collaboration with transplant registries in Japan, Scandinavia, and Ireland, showed reduced risk of graft-versus-host disease (GVHD) in the less geneti-cally diverse Japanese and Scandinavian populations compared with White-American and African-American populations [17]. However, the risks of GVHD were similar between the Irish and the White-American and African-American populations. The study concluded that the etiology of ethnic disparities in GVHD are complex, and may include differences in HLA and minor antigen diversity, frequencies of cytokine poly-morphisms, and nongenetic variables such as diet, envi-ronment, and differences in GVHD diagnosis and management.

Available studies investigating race in HCT are largely limited to recipients of sibling donor HCT and suggest important and as yet unexplained racial dif-ferences. Also, the impact of sociocultural factors on outcomes of HCT has not been well described [14].

In the current study, we explore the association of race and SES with outcomes for unrelated HCT recipients.

METHODS Data Source

The CIBMTR is a research affiliation of the Inter-national Bone Marrow Transplant Registry (IBMTR), Autologous Blood and Marrow Transplant Registry (ABMTR), and the National Marrow Donor Program (NMDP) that comprises a voluntary working group of more than 450 transplantation centers worldwide that contribute detailed data on consecutive allogeneic and autologous hematopoietic SCT to a Statistical Center at the Medical College of Wisconsin in Milwaukee and the NMDP Coordinating Center in Minneapolis. Par-ticipating centers are required to report all transplants consecutively; compliance is monitored by on-site au-dits. Patients are followed longitudinally, with yearly follow-up. The overall follow-up of the cohort was 100% at 1 year and 95% overall, and did not differ sig-nificantly among the various racial categories. Com-puterized checks for errors, physicians’ review of submitted data, and on-site audits of participating centers ensure data quality. Observational studies con-ducted by the CIBMTR are done so with a waiver of informed consent and in compliance with HIPAA regulations as determined by the institutional review board and the Privacy Officer of the Medical College of Wisconsin.

Participants

The study included patients who received an unre-lated donor allogeneic HCT with a myeloablative (MA) preparative regimen using either a bone marrow (BM) or peripheral blood stem cell (PBSC) source for AML, acute lymphoblastic leukemia (ALL), chronic myeloge-nous leukemia (CML), or myelodysplastic syndrome (MDS) between 1995 and 2004. Only patients trans-planted in a center in the United States and with avail-able residential postal zip codes were eligible for this analysis; 8 patients with missing zip code information were excluded. Patients who received unrelated umbil-ical cord blood (UCB) as donor source (n5163) or had previously undergone HCT (n 5 516) were also ex-cluded from the study. Information about patient race was reported by transplant centers and was categorized according to the U.S. Office of Management and Bud-get classification as White, African American, Hispanic, or Asian/Pacific-Islander. Patient income was estimated by the mean household income of their zip code from the 2004 U.S. Census. Distances between the center of a patient’s residence and the transplant center were approximated using the Haversine approximation on the latitude and longitude of the zip code [18]. The

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package ‘‘zip Code Deluxe’’ [19] was used to obtain income and location data from the zip code.

All surviving recipients included in this analysis were retrospectively contacted and they provided in-formed consent for participation in the NMDP re-search program. Informed consent was waived by the NMDP institutional review board for all deceased recipients. Approximately 10% of surviving patients would not provide consent for use of research data. To adjust for the potential bias introduced by exclu-sion of nonconsenting surviving patients, a corrective action plan modeling process randomly excluded ap-propriately the same percentage of deceased patients (n5532) using a biased coin randomization with ex-clusion probabilities based on characteristics associ-ated with not providing consent for use of the data in survivors [20]. The final study cohort consisted of 6207 patients (Table 1). The followup completeness index from time of HCT, which is the ratio of total observed person-time and the potential person-time of followup in a study [21], was 98% at 1 year and 90% at 5 years post-HCT.

Outcomes and Study Definitions

The primary objective of this study was to deter-mine the impact of race and household income on OS, disease-free survival (DFS), relapse, and treat-ment-related mortality (TRM). DFS was defined as survival in complete remission after HCT. For OS, death from any cause was considered an event. Relapse was defined as disease recurrence at any site. TRM was defined as death in complete remission. OS, DFS, re-lapse, and TRM were assessed from the date of HCT. All patients were assessed for acute and chronic GVHD (aGVHD, cGVHD) by standard criteria

[22,23].

Based on previous CIBMTR publications of dis-ease specific outcomes and differential outcomes in ethnic minorities with related donor transplants

[12,13,24-27], disease status was classified as early, intermediate, or advanced. Early disease included AML and ALL in first complete remission, CML in first chronic phase, and MDS with refractory anemia (RA) or refractory anemia with ringed sideroblasts (RARS). AML and ALL in second or greater remission or CML in accelerated phase or second or greater chronic phase was categorized as intermediate disease. Patients with advanced disease had AML and ALL in relapse or primary induction failure, CML in blast phase, or MDS with refractory anemia with excess blasts (RAEB) or excess blasts in transformation.

The NMDP classification of HLA matching status that allows adequate adjustment for donor-recipient HLA compatibility while accounting for best available resolution of typing was used to categorize HLA matching status as well-matched, partially matched,

or mismatched [25]. Briefly, well-matched patients had no identified mismatches at HLA-A, -B, -C, and -DRB1 with low/intermediate- or high-resolution data available at HLA-A,-B and high-resolution -DRB1. Partially matched patients had a single locus mismatch at any of the 4 loci and/or missing HLA-C data. Mismatched patients had 2 or more allele or antigen mismatches.

Statistical Analysis

Patient, disease, and HCT-related characteristics were compared by chi-square statistic for categoric variables and the Kruskal-Wallis test for continuous variables. Probabilities of OS and DFS were calculated using the Kaplan-Meier method. Probabilities of TRM, relapse, neutrophil engraftment, and aGVHD and cGVHD were calculated by the cumulative-inci-dence function method.

To adjust for differences in baseline characteris-tics, multivariate Cox proportional-hazards regression models were used. Household income was correlated with outcome using a series of threshold models in the Cox model framework. These models were then constructed to find cut off points that best described the impact of income on outcome by picking the model with the largest partial likelihood [28]. Associations between each outcome and potential prognostic vari-ables (Table 2) were evaluated using a stepwise approach. Variables significantly associated with each outcome event (P\.05) were included as covariate factors in subsequent comparisons. The assumption of proportional hazards was tested in a time-dependent covariate fashion. Results were expressed as relative risks (RR) of each outcome. The models for OS, DFS, relapse, and TRM were stratified on a Karnofsky Score. The models for aGVHD and cGVHD were stratified on patient age. For engraftment, logistic re-gression was used to model the chance of neutrophil recovery at day 28. A similar analysis to the Cox anal-ysis was performed; the results are reported as the odds in favor of engraftment. For each outcome the main effects of race and household income were tested for an interaction with each of the other covariates (including sex) that entered the models. This was done for each outcome. None of these were found to be significant at a 5% significance level.

The analysis found a significant transplant center effect using a random effects test on the survival times

[29]. To adjust for center, a stepwise regression model was used that included at each step the main effects as well as all covariates adjusted for in the model for the given event. The centers found to enter and stay in the model at a 5% significance level were included in the final model.

To examine the robustness of our results in patients with high-resolution HLA typing, a subset

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Table 1. Characteristics of Patients Transplanted at U.S. Centers Who Received Unrelated Donor Myeloablative Hematopoietic Cell Transplants for AML, ALL, CML, and MDS from 1995-2004 by Race

Variable White N (%) African-American N (%) Asian/Pacific Islander N (%) Hispanic N (%) PValue

Number of patients 5253 368 141 445 Number of centers 119 87 40 80

Age, median (range), years 35 (<1-70) 25 (<1-58) 30 (<1-56) 22 (<1-62) <.001 Age at transplant, years <.001

<10 535 (10) 64 (17) 22 (16) 99 (22) 10-19 683 (13) 83 (23) 24 (17) 92 (21) 20-29 846 (16) 70 (19) 23 (16) 97 (22) 30-39 1091 (21) 65 (18) 35 (25) 81 (18) 40-49 1291 (25) 69 (19) 28 (20) 54 (12) $50 807 (15) 17 (5) 9 (6) 22 (5) Male sex 2979 (57) 214 (58) 83 (59) 270 (61) .39 Median income, 2000* $44,776 $36,275 $50,991 $40,111 <.001 Missing 524 39 12 60

Distance to transplant center <.001 <17 miles 1109 (21) 159 (43) 64 (46) 181 (41)

17-55 miles 1321 (25) 69 (19) 34 (24) 113 (25) 55-150 miles 1398 (27) 76 (21) 17 (12) 74 (17) >150 miles 1364 (26) 62 (17) 23 (16) 74 (17) Missing ZIP code 61 (1) 2 (<1) 3 (2) 3 (<1)

Karnofsky status .06 $90 3574 (68) 268 (73) 92 (65) 323 (73) <90 1352 (26) 75 (20) 43 (30) 94 (21) Missing 327 (6) 25 (7) 6 (4) 28 (6) Comorbid conditions .01 0-1 conditions 4792 (91) 342 (93) 136 (96) 420 (94) $2 conditions 461 (9) 26 (7) 5 (4) 25 (6)

Body mass index, kg/m2 25 (11-44) 24 (11-42) 21 (13-41) 23 (11-44) <.001

Disease <.001

AML 1825 (35) 108 (29) 39 (28) 98 (22) ALL 1261 (24) 90 (24) 48 (34) 193 (43) CML 1531 (29) 153 (42) 39 (28) 121 (27) MDS 636 (12) 17 (5) 15 (11) 33 (7)

Disease status at transplant <.001 Early 2067 (39) 120 (33) 47 (33) 158 (36)

Intermediate 1528 (29) 163 (44) 49 (35) 182 (41) Advanced 1490 (28) 80 (22) 38 (27) 99 (22) Unknown 168 (3) 5 (1) 7 (5) 6 (1)

HLA match status <.001

Well matched 2421 (46) 81 (22) 41 (29) 122 (27) Partially matched 1939 (37) 135 (37) 48 (34) 160 (36) Mismatched 893 (17) 152 (41) 52 (37) 163 (37)

Race match (donor/recipient) <.001 Match 4138 (78) 251 (68) 116 (82) 211 (47)

Mismatch 431 (9) 110 (30) 21 (15) 205 (46) Unknown 684 (13) 7 (2) 4 (3) 29 (7)

Donor age, median (range), years 35 (18-61) 36 (19-60) 34 (18-59) 34 (19-59) .09

Age at transplant, years .11

18-19 38 (1) 3 (1) 2 (1) 5 (1) 20-29 1475 (28) 102 (28) 46 (33) 125 (28) 30-39 2029 (39) 115 (31) 51 (36) 184 (41) 40-49 1354 (26) 120 (33) 32 (23) 100 (22) $50 357 (7) 28 (8) 10 (7) 31 (7)

Sex match (donor/recipient) <.001 Male/male 2050 (39) 103 (28) 42 (30) 146 (33) Male/female 1262 (24) 72 (20) 34 (24) 81 (18) Female/male 929 (18) 111 (30) 41 (29) 124 (28) Female/female 1012 (19) 82 (22) 24 (17) 94 (21) CMV match (donor/recipient) <.001 Negative/negative 1945 (37) 63 (17) 8 (6) 59 (13) Negative/positive 1514 (29) 94 (26) 36 (26) 131 (29) Positive/negative 820 (16) 69 (19) 20 (14) 72 (16) Positive/positive 887 (17) 133 (36) 75 (53) 176 (40) Unknown 87 (2) 9 (2) 2 (1) 7 (2) Year of transplant <.001 1995-1999 2716 (52) 181 (49) 71 (50) 177 (40) 2000-2004 2537 (48) 187 (51) 70 (50) 268 (60) Conditioning regimen <.001 Bu + Cy ± other 1066 (20) 50 (14) 16 (11) 52 (12) Cy + TBI ± other 3720 (71) 279 (76) 120 (85) 336 (76) TBI ± other 183 (3) 10 (3) 5 (4) 26 (6) (Continued)

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analysis limited to patients with allele-level typing at the HLA-A, -B, -C, and -DRB1 loci (n 53864) was performed for the endpoints of OS, DFS, relapse, and TRM.

AllP-values are 2 sided and, to account for multi-ple comparisons, a value ofP\.01 was considered to be significant. Analyses were performed using SAS software, version 9.1 (SAS Institute, Cary, NC).

RESULTS

Patient Characteristics

Table 1describes patient, disease, and treatment characteristics by race. Asian/Pacific Islanders had the highest median household income ($50,991) whereas African Americans had the lowest ($36,275).

Table 3 describes our cohort’s characteristics by socioeconomic status. A greater proportion of African Americans (45%) belonged to the lowest income quartile (median income \$34,700), com-pared to Hispanics (35%), Whites (21%), or Asian/ Pacific-Islanders (9%).

Race and Outcomes

In univariate analyses, African-Americans had the lowest probability of OS and DFS and the highest rates

of TRM (Figures 1 and 2). Cumulative incidence of relapse was similar among all 4 racial groups.

In a multivariate analysis adjusting for other prog-nostic variables (including annual income), African American race was associated with significantly worse OS and DFS and higher TRM than Whites (Table 4). Risk of TRM was also increased in Hispanics, but OS and DFS was comparable. The risk for relapse was sim-ilar among the 4 racial groups. Race had no impact on neutrophil engraftment or risks of grades 2-4 aGVHD. The RR of grade III-IV aGVHD was slightly higher in African-Americans (1.26 [1.03-1.54],P5NS), whereas that for cGVHD was higher in African-Americans (1.34 [1.10-1.63],P5 .003) and Hispanics (1.25 [1.06-1.48],

P5 .008). A subset analysis limited to 3864 recipients with allele-level typing at the HLA-A, -B, -C, and -DRB1 loci showed risks for OS, DFS, relapse, and TRM similar to those observed in analyses that included the whole cohort (data not shown).

Income and Outcomes

Patients with a median annual income in the lowest quartile (\$37,400) had lower probability of OS and DFS and higher rates of TRM (Figures 3 and 4). Relapse was similar in all income categories.

In a multivariate analysis adjusting for other prog-nostic variables (including race), patients with incomes in the lowest quartile (\$37,400) had significantly

Table 1.(Continued)

Variable White N (%) African-American N (%) Asian/Pacific Islander N (%) Hispanic N (%) PValue

Other 284 (6) 29 (8) 0 31 (7) GVHD prophylaxis†

CsA + MTX ± other 2681 (51) 167 (45) 86 (61) 187 (42) Tacrolimus + MTX ± other 1200 (23) 85 (23) 25 (18) 144 (32) T cell depletion ± other 941 (18) 88 (24) 16 (11) 73 (16) CsA or tacrolimus ± other 371 (7) 27 (7) 13 (9) 37 (8) Other 60 (1) 1 (<1) 1 (<1) 4 (<1)

Graft type .02

Bone marrow 4298 (82) 321 (87) 109 (77) 358 (80) Peripheral blood 955 (18) 47 (13) 32 (23) 87 (20) Infused cell dose†

BM >2108 2659 (62) 173 (54) 61 (56) 219 (61) BM#2108 1601 (37) 146 (45) 43 (39) 139 (39) BM missing 38 (<1) 2 (<1) 5 (5) 0 PB >5108 592 (62) 28 (60) 18 (56) 62 (71) PB#5108 292 (31) 19 (40) 12 (38) 22 (25) PB missing 71 (7) 0 2 (6) 3 (3) Time from diagnosis to transplant, median

(range), months

10 (1-309) 15 (2-273) 13 (2-179) 16 (2-170) <.001

Donor search time, median (range), months‡

Diagnosis to preliminary search 3 (<1-303) 5 (<1-268) 5 (<1-134) 5 (<1-164) <.001 Preliminary search to formal search <1 (<1-116) <1 (<1-51) <1 (<1-85) <1 (<1-41) .03 Formal search to transplant 3 (<1-125) 4 (1-91) 3 (<1-89) 4 (1-58) <.001 Follow-up of survivors, median (range), months 66 (3-138) 48 (3-130) 64 (11-127) 48 (11-132) <.001

AML indicates acute myelogenous leukemia; ALL, acute lymphoblastic leukemia; MDS, myelodysplastic syndrome; CML, chronic myelogenous leukemia; HLA, human leukocyte antigen; CMV, cytomegalovirus; Cy, cyclophosphamide; Bu, busulfan; TBI, total body radiation; CsA, cyclosporine; MTX, meth-otrexate; GVHD, graft-versus-host disease; BM, bone marrow; PB, peripheral blood.

*Based on 2004 Census tract data linking income to residential ZIP code. †Univariate comparison not done because of small cell counts.

‡Preliminary search provides a list of potential donors at a given time but does not initiate contact with no further testing of the donors. If the transplant center decides to proceed with unrelated donor HCT, formal search is initiated on behalf of the patient. This includes confirmatory HLA typing of the patient and donor and confirmation of the availability of the donor for obtaining hematopoietic stem cells.

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worse OS (RR 1.15 [1.04-1.26],P5 .005) and TRM (RR 1.21 [1.07-1.36], P 5 .002) than those with incomes in the highest quartile (.$56,000) (Table 4). Household income had no impact on risks of DFS, relapse, aGVHD or cGVHD. A subset analysis limited to patients with allele-level typing again showed risks similar to those observed for the whole cohort (data not shown).

Causes of Death

For the whole cohort, the principal causes of death included recurrence of primary malignancy (25%), infection (20%), organ failure (18%), aGVHD or cGVHD (14%), and interstitial pneumonitis (12%), and these did not differ when compared by race or SES. Table 5 describes the causes of death within and after 100 days following transplantation by race and SES.

DISCUSSION

Our study shows notably decreased survival in African-American unrelated donor HCT recipients

compared with White, Asian, and Hispanic recipients, despite adjustment in multivariate analysis for HLA matching, disease status, family income, and other variables likely to influence outcome. Reduced survival was because of increased TRM in African-American recipients with no increased risk of aGVHD or relapse detected. There are many possible reasons for reduced survival in African-American HCT recipients, includ-ing biology (e.g., polymorphism at non-HLA loci), ac-cess to care including post-HCT follow-up, disparities in treatment or follow-up practices, environmental factors (that may also be influenced by SES), and health behaviors. Additionally, there were several dif-ferences in baseline patient characteristics between the groups. However, the analysis was adjusted for the potential patient, disease, and center characteris-tics that differ between the groups. If the survival dif-ferences found were caused by these baseline differences, this adjustment would actually tend to lower, not increase, the reported effect of race. Addi-tionally, there was no evidence of an interaction be-tween these factors in the model with race or income. Biologic factors, such as increased genetic polymor-phism in African-American recipients, could impact outcomes. It is clear that improved HLA matching im-proves HCT outcome, and it is known that there is greater diversity at HLA loci in the African-American population compared with other races and ethnicities

[17]. In our study, however, outcomes were inferior in African-American recipients even after adjustment for HLA matching with the donor. This raises the possibil-ity that genetic variation at other loci may be modifying outcomes. At a population level, African Americans show significantly higher levels of nucleotide heterozy-gosity compared with Americans of European origin

[30]. Polymorphisms that modify expression of cyto-kine genes have been shown to modify a number of HCT endpoints, and it is possible that less favorable al-leles may occur more frequently in African-American HCT recipients[12,15,16,31]. In agreement with our study, reduced graft survival and OS have been reported in African-American recipients of renal transplants

[32], with a similar finding reported after liver trans-plantation in African- American recipients secondary to chronic rejection[33]. In cancer patients, pharmaco-genetic variation in drug metabolism has been reported for cyclophosphamide (Cy), methotrexate (MTX), and busulfan (Bu), all of which had an impact on HCT out-comes[34-38]. Frequencies of many pharmacogenetic variants influencing metabolism of these drugs vary by race, and future pharmacogenetic studies, which would likely need to be multicenter to achieve adequate sample size, might determine whether this is an impor-tant variable, as personalized drug dosing might im-prove outcomes[39-41].

Racial disparities exist in health care access and outcomes and are related to SES. It has been reported

Table 2. Variables Tested in Multivariate Analysis Main effect variable

Race/ethnicity: White*versus African-American versus Asian/Pacific-Islander versus Hispanic

Patient-related variables

Age:#10*years versus 11-20 years versus 21-30 years versus 31-40 years versus 41-50 years versus >50 years

Sex: Male*versus female

Karnofsky performance status at transplant: <90% versus$90%*

versus missing

Median income: above*versus below median versus missing Comorbid medical conditions: 0-1*versus$2 comorbidities

Distance to transplant center: above*versus below median versus missing Disease-related variables

Disease: AML*versus ALL versus CML versus MDS

Disease status: early*versus intermediate versus advanced disease Time from diagnosis to transplant: continuous

Transplant-related variables

Source of stem cells: bone marrow*versus peripheral blood

HLA match: well-matched*versus partially matched versus mismatched Donor age: 18-20*years versus 21-30 years versus 31-40 years versus

41-50 years versus >50 years

Donor-recipient sex match: F-M versus M-F versus M-M*versus F-F Donor-recipient CMV status:2/2*versus2/+ versus +/2versus +/+

versus unknown

Year of transplant: 1995-1999 versus 2000-2004*

Conditioning regimen: Bu + Cy ± others versus Cy + TBI ± others*versus TBI ± others versus other

Donor-recipient race match: same ethnicity*versus disparate ethnicity versus unknown

Infused cell dose:#2108versus >2108*nucleated cells/kg for bone marrow and#5 x 108versus >5108*nucleated cells/kg for

peripheral blood

Donor search time (months) (diagnosis to preliminary search, preliminary search to formal search and formal search to transplant): above*

versus below median

AML indicates acute myelogenous leukemia; ALL, acute lymphoblastic leuke-mia; MDS, myelodysplastic syndrome; CML, chronic myelogenous leukeleuke-mia; HLA, human leukocyte antigen; F, female; M, male; CMV, cytomegalovirus; Cy, cyclophosphamide; Bu, busulfan; TBI, total body radiation.

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Table 3. Characteristics of Patients Transplanted at U.S. Centers Who Received Unrelated Donor Myeloablative Hematopoietic Cell Transplants for AML, ALL, CML, and MDS from 1995-2004 by Socioeconomic Status

Median Income*

Variable <$34,700 N (%) $34,700-43,600 N (%) $43,600-56,300 N (%) >$56,300 N (%) PValue

Number of patients 1414 1541 1517 1559 Number of centers 107 116 111 107

Age, median (range), years 31 (<1-65) 33 (<1-67) 34 (<1-70) 35 (<1-67) <.001 Age at transplant, years <.001

<10 172 (12) 189 (12) 167 (11) 177 (11) 10-19 234 (17) 215 (14) 216 (14) 191 (12) 20-29 264 (19) 264 (17) 245 (16) 233 (15) 30-39 298 (21) 316 (21) 303 (20) 316 (20) 40-49 293 (21) 351 (23) 370 (24) 388 (25) $50 153 (11) 206 (13) 216 (14) 254 (16) Male sex 814 (58) 889 (58) 849 (56) 885 (57) .76 Race <.001 White 1091 (77) 1308 (85) 1321 (87) 1383 (89) African-American 160 (11) 89 (6) 53 (3) 54 (3) Asian/Pacific-Islander 12 (1) 37 (2) 28 (2) 57 (4) Hispanic 151 (11) 107 (7) 115 (8) 65 (4) Distance to transplant center <.001 <20 miles 274 (19) 312 (20) 414 (27) 500 (32) 20-70 miles 157 (11) 331 (21) 464 (31) 558 (36) 70-175 miles 509 (36) 505 (33) 353 (23) 169 (11) >175 miles 474 (34) 393 (26) 286 (19) 332 (21) Karnofsky status .29 $90 959 (68) 1070 (69) 1047 (69) 1063 (68) <90 378 (27) 386 (25) 371 (24) 384 (25) Missing 77 (5) 85 (6) 99 (7) 112 (7) Comorbid conditions .21 0-1 conditions 1292 (91) 1414 (92) 1378 (91) 1448 (93) $2 conditions 122 (9) 127 (8) 139 (9) 111 (7)

Body mass index, kg/m2 24 (11-44) 25 (12-44) 25 (13-44) 24 (13-43) .008

Disease .91

AML 471 (33) 508 (33) 511 (34) 520 (33) ALL 382 (27) 390 (25) 384 (25) 391 (25) CML 417 (29) 461 (30) 443 (29) 467 (30) MDS 144 (10) 182 (12) 179 (12) 181 (12)

Disease status at transplant .37 Early 515 (36) 588 (38) 602 (40) 608 (39)

Intermediate 464 (33) 482 (31) 445 (29) 477 (31) Advanced 403 (29) 424 (28) 417 (27) 424 (27) Unknown 32 (2) 47 (3) 53 (3) 50 (3)

HLA match status .009

Well matched 582 (41) 630 (41) 675 (44) 705 (45) Partially matched 523 (37) 584 (38) 531 (35) 586 (38) Mismatched 309 (22) 327 (21) 311 (21) 268 (17) Race match (donor/recipient) .54 Match 187 (13) 180 (12) 188 (12) 190 (12) Mismatch 1069 (76) 1184 (77) 1163 (77) 1168 (75) Unknown 158 (11) 177 (11) 166 (11) 201 (13) Donor age, median

(range), years

35 (18-60) 35 (18-60) 36 (18-60) 35 (18-61) .011

Sex match (donor/ recipient) .89 Male/male 535 (38) 573 (37) 573 (38) 592 (38) Male/female 321 (23) 354 (23) 364 (24) 379 (24) Female/male 279 (20) 316 (21) 276 (18) 293 (19) Female/female 279 (20) 298 (19) 304 (20) 295 (19) CMV match (donor/ recipient) .002 Negative/negative 417 (29) 511 (33) 543 (36) 546 (35) Negative/positive 456 (32) 412 (27) 416 (27) 434 (28) Positive/negative 207 (15) 250 (16) 228 (15) 267 (17) Positive/positive 308 (22) 342 (22) 304 (20) 288 (18) Unknown 26 (2) 26 (2) 26 (2) 24 (2) Year of transplant .19 1995-1999 741 (52) 761 (49) 783 (52) 766 (49) 2000-2004 673 (48) 780 (51) 734 (48) 793 (51) (Continued)

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that African-American patients with a myocardial in-farction and heart failure receive less intensive and poorer quality care Whites [42,43-45]. In a study of Medicare beneficiaries, who are presumed to have no financial obstacle to care, treatment and outcomes of heart failure were similar among White and African-American patients[46]. All of the patients in our study

received HCT, so, therefore, they had access to high-cost, technologically demanding care, although it is unknown whether their overall insurance coverage was similar. Despite this, survival was lower in those with the lowest income, even after adjustment for race and measured comorbidities, and the excess mor-tality was treatment related. It is still possible though that reduced access to or utilization of post-HCT fol-low-up care might contribute to the inferior outcomes seen in our study.

The mechanism for the excess mortality seen in African-American recipients seems likely to be com-plex. Additional contributors may include unmeasured comorbidities such as poor nutrition, inability to com-ply with medication regimens, and poor access to follow-up care. In addition, the National Health and Nutrition Examination Survey reports increased fre-quency of high blood pressure, high body mass index, physical inactivity, and diabetes in African American women, all of which, as unmeasured comorbidities, might contribute to increased late mortality after

Table 3.(Continued) Median Income* Variable <$34,700 N (%) $34,700-43,600 N (%) $43,600-56,300 N (%) >$56,300 N (%) PValue Conditioning regimen .003 Bu + Cy ± other 234 (17) 304 (20) 337 (22) 288 (18) Cy + TBI ± other 1022 (72) 1118 (73) 1045 (69) 1129 (72) TBI ± other 72 (5) 47 (3) 51 (3) 50 (3) Other 86 (6) 72 (5) 84 (6) 92 (6) GVHD prophylaxis .10 CsA + MTX ± other 713 (50) 777 (50) 783 (52) 770 (49) Tacrolimus + MTX ± other 300 (21) 356 (23) 342 (23) 404 (26)

T cell depletion ± other 279 (20) 289 (19) 263 (17) 259 (17) CsA or tacrolimus ± other 112 (8) 101 (6) 114 (7) 107 (7) Other 10 (1) 18 (1) 14 (1) 19 (2) Graft type .007 Bone marrow 1201 (85) 1262 (82) 1232 (81) 1251 (80) Peripheral blood 213 (15) 279 (18) 285 (19) 308 (20) Infused cell dose

BM >2108 744 (62) 748 (59) 756 (61) 786 (63) .63 BM#2108 445 (37) 501 (40) 466 (38) 456 (36) BM missing 12 (1) 13 (1) 10 (1) 9 (1) PB >5108 132 (62) 181 (65) 178 (62) 190 (62) .36 PB#5108 68 (32) 86 (31) 85 (30) 89 (29) PB missing 13 (6) 12 (4) 22 (8) 29 (9) Time from diagnosis to

transplant, median (range), months

12 (2-309) 11 (1-309) 11 (2-242) 10 (<1-232) .04

Donor search time, median (range), months Diagnosis to preliminary search 4 (<1-300) 3 (<1-303) 3 (<1-231) 3 (<1-192) .06 Preliminary search to formal search <1 (<1-69) <1 (<1-77) <1 (<1-116) <1 (<1-106) .33 Formal search to transplant 3 (<1-81) 3 (<1-94) 3 (<1-121) 3 (<1-125) .81 Follow-up of survivors, median (range), months

60 (5-133) 60 (5-135) 69 (3-138) 64 (3-137) <.001

AML indciates acute myelogenous leukemia; ALL, acute lymphoblastic leukemia; MDS, myelodysplastic syndrome; CML, chronic myelogenous leukemia; HLA, human leukocyte antigen; CMV, cytomegalovirus; Cy, cyclophosphamide; Bu, busulfan; TBI, total body radiation; CsA, cyclosporine; MTX, meth-otrexate; GVHD, graft-versus-host disease; BM, bone marrow; PB, peripheral blood.

*Based on 2004 Census tract data linking income to residential ZIP code.

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HCT [47]. Addressing and improving such issues is challenging, as some are societal rather than medical issues. However, awareness of the problem is an impor-tant first step, so that care providers can consider the is-sues and perhaps provide specific resources for this high-risk population after leaving the transplant center. Similar to the findings in our study, low SES, as-sessed independently of race, has been reported to have an adverse impact on outcomes of many diseases, including cancer, chronic renal disease, and solid or-gan transplantation [38,48,49]. Mackillop et al. [50]

studied the impact of SES on outcome of treatment of cancer in Ontario, Canada, where the heath system is designed to provide equitable access to health care for all. Their study demonstrated higher mortality rates from cancer among poorer communities com-pared with wealthier areas. Recipients of renal and liver transplants with private insurance have been re-ported to have better survival than Medicare recipi-ents, but SES measured by census tract was not associated with outcome[51]. The difference in these observations may reflect the dominant influence of the quality of the surgical procedure and inpatient

hos-pital care on long-term outcomes of solid organ trans-plantation, in contrast to HCT where the period of immune reconstitution is long (lasting months to years) and the incidence of late mortality after the pa-tient has left the transplant center is significant [51]. Clearly, low SES has a significant negative impact on the unrelated donor HCT outcomes and transplant centers need to carefully examine and optimize the resources available to these individuals during the peri-HCT time period as well as during ongoing long-term follow-up.

Despite the large size of this study and the mean-ingful cohorts of racial minority groups, there are lim-itations that should be considered. Our study only included patients who actually received HCT, and thus we could not address the impact of race or SES on access to transplantation. A challenge for our study, in common with all studies of race, is a lack of precision in the definition of race. In our study, race was re-ported by the transplant center, and may not reflect accurately persons of mixed heritage. Although the relative proportions of minority racial groups in this study do not match that of the U.S. population, we

Figure 2. Cumulative incidence of TRM by race.

Table 4. Multivariate Analysis for Overall Survival, Disease-Free Survival, Relapse, and Treatment-Related Mortality Relative risk

(95% Confidence Intervals)*

Variable Overall Survival Disease-Free Survival Relapse Transplant-Related Mortality

Race White† 1.00 1.00 1.00 1.00 African American 1.47 (1.29-1.68)§ 1.48 (1.30-1.69)§ 1.32 (1.03-1.68) 1.56 (1.34-1.83)§ Asian/Pacific-Islander 0.96 (0.76-1.20) 1.04 (0.83-1.31) 1.13 (0.80-1.61) 0.99 (0.75-1.32) Hispanic 1.15 (1.01-1.30) 1.14 (1.00-1.29) 0.87 (0.70-1.09) 1.30 (1.11-1.51)§ Income‡ >$56,300† 1.00 1.00 1.00 1.00 $43,600-56,300 1.06 (0.97-1.16) 1.03 (0.94-1.13) 0.94 (0.81-1.10) 1.11 (0.99-1.24) $34,700-43,600 1.06 (0.97-1.16) 1.02 (0.93-1.12) 0.97 (0.83-1.13) 1.11 (0.99-1.24) <$34,700 1.15 (1.04-1.26)§ 1.12 (1.01-1.23) 1.07 (0.92-1.26) 1.21 (1.07-1.36)§ Missing 1.10 (0.86-1.39) 1.08 (0.85-1.38) 0.97 (0.63-1.48) 1.16 (0.87-1.55)

*Models were stratified on Karnofsky performance status prior to hematopoietic cell transplant. †Reference group;Table 2lists variables tested in multivariate analysis.

‡Based on 2004 Census data linking income to residential ZIP code. §P< .01.

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feel this is less of an issue related to access to care, or to misclassification of race, but rather because of the fact that minorities are underrepresented in the unrelated donor pool making it more difficult to obtain a donor for minority patients. Additionally, cancer incidence rates for acute leukemia have been shown to vary by race with a higher incidence being found in Whites compared to Blacks [52]. Also, we do not have data to reflect the insurance status of patients repre-sented in the study, and our study could not address other important factors that may have an impact on access to transplant and access to and quality of post-transplant care. Some of these factors, for instance, in-surance coverage, lack of adequate support services, and cultural biases, are potentially modifiable. In addi-tion, socioeconomic status was estimated from zip code of residence and was not self-reported by pa-tients. Despite these limitations, we believe our data

indicate importantly inferior outcomes in African-American unrelated donor HCT recipients that are not explicable by reduced family income, and should lead to future biologic, sociologic and epidemiologic studies to address and improve this disparity. In addi-tion, we show that reduced family income reduced survival in recipients of all races, indicating the need for careful support and follow-up of such patients.

ACKNOWLEDGMENTS

Financial disclosure: This article was presented in

part at the Annual Meeting of the American Society of Hematology, 2007. The CIBMTR is supported by Public Health Service Grant/Cooperative Agreement U24-CA76518 from the National Cancer Institute (NCI), the National Heart, Lung and Blood Institute (NHLBI), and the National Institute of Allergy and Infectious Diseases (NIAID); a Grant/Cooperative Agreement 5U01HL069294 from NHLBI and NCI; a contract HHSH234200637015C with Health Re-sources and Services Administration (HRSA/ DHHS); 2 Grants 06-1-0704 and N00014-08-1-0058 from the Office of Naval Research; and grants from AABB; Aetna; American Society for Blood and Marrow Transplantation; Amgen, Inc.; Anony-mous donation to the Medical College of Wisconsin; Association of Medical Microbiology and Infectious Disease Canada; Astellas Pharma US, Inc.; Baxter In-ternational, Inc.; Bayer HealthCare Pharmaceuticals; BloodCenter of Wisconsin; Blue Cross and Blue Shield Association; Bone Marrow Foundation;

Figure 4. Cumulative incidence of TRM by income.

Table 5. Important Causes of Death by Race and Socioeconomic Status

Cause of Death

Variable Total Deaths Relapse Infection GVHD Organ toxicity* Causes of death <100 days

Race White 1531 167 (11%) 339 (22%) 236 (15%) 593 (39%) African American 147 10 (7%) 24 (16%) 28 (19%) 57 (39%) Asian/Pacific-Islander 51 2 (4%) 10 (20%) 8 (16%) 25 (49%) Hispanic 139 8 (6%) 32 (23%) 31 (22%) 51 (37%) Income >$56,300 417 47 (11%) 95 (23%) 69 (17%) 151 (36%) $43,600-56,300 438 43 (10%) 84 (19%) 75 (17%) 180 (41%) $34,700-43,600 458 44 (10%) 100 (22%) 66 (14%) 188 (41%) <$34,700 490 50 (10%) 108 (22%) 80 (16%) 182 (37%) Causes of death$100 days

Race White 1969 735 (37%) 357 (18%) 266 (14%) 457 (23%) African American 132 40 (30%) 30 (23%) 22 (17%) 27 (21%) Asian/Pacific-Islander 46 21 (46%) 8 (17%) 6 (13%) 10 (22%) Hispanic 174 60 (35%) 31 (18%) 25 (14%) 40 (23%) Income >$56,300 550 213 (39%) 83 (15%) 77 (14%) 142 (26%) $43,600-56,300 580 207 (36%) 113 (20%) 77 (13%) 131 (23%) $34,700-43,600 598 221 (37%) 116 (19%) 83 (14%) 125 (21%) < $34,700 528 193 (37%) 103 (20%) 67 (13%) 124 (24%)

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Canadian Blood and Marrow Transplant Group; Cel-gene Corporation; CellGenix, GmbH; Centers for Disease Control and Prevention; ClinImmune Labs; CTI Clinical Trial and Consulting Services; Cubist Pharmaceuticals; Cylex Inc.; CytoTherm; DOR Bio-Pharma, Inc.; Dynal Biotech, an Invitrogen Company; Enzon Pharmaceuticals, Inc.; European Group for Blood and Marrow Transplantation; Gambro BCT, Inc.; Gamida Cell, Ltd.; Genzyme Corporation; His-togenetics, Inc.; HKS Medical Information Systems; Hospira, Inc.; Infectious Diseases Society of America; Kiadis Pharma; Kirin Brewery Co., Ltd.; Merck & Company; The Medical College of Wisconsin; MGI Pharma, Inc.; Michigan Community Blood Centers; Millennium Pharmaceuticals, Inc.; Miller Pharmacal Group; Milliman USA, Inc.; Miltenyi Biotec, Inc.; Na-tional Marrow Donor Program; Nature Publishing Group; New York Blood Center; Novartis Oncology; Oncology Nursing Society; Osiris Therapeutics, Inc.; Otsuka Pharmaceutical Development & Commercial-ization, Inc.; Pall Life Sciences; PDL BioPharma, Inc; Pfizer Inc; Pharmion Corporation; Saladax Biomedi-cal, Inc.; Schering Plough Corporation; Society for Healthcare Epidemiology of America; StemCyte, Inc.; StemSoft Software, Inc.; Sysmex; Teva Phar-maceutical Industries; The Marrow Foundation; THERAKOS, Inc.; Vidacare Corporation; Vion Phar-maceuticals, Inc.; ViraCor Laboratories; ViroPharma, Inc.; and Wellpoint, Inc.

The views expressed in this article do not reflect the official policy or position of the National Institutes of Health, the Department of the Navy, the Depart-ment of Defense, or any other agency of the U.S. Government.

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Figure

Table 1. Characteristics of Patients Transplanted at U.S. Centers Who Received Unrelated Donor Myeloablative Hematopoietic Cell Transplants for AML, ALL, CML, and MDS from 1995-2004 by Race
Table 1 describes patient, disease, and treatment characteristics by race. Asian/Pacific Islanders had the highest median household income ($50,991) whereas African Americans had the lowest ($36,275).
Table 3. Characteristics of Patients Transplanted at U.S. Centers Who Received Unrelated Donor Myeloablative Hematopoietic Cell Transplants for AML, ALL, CML, and MDS from 1995-2004 by Socioeconomic Status
Figure 1. Probability of OS by race.
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