Affiliations of authors: Division of Hematology/Oncology, School of Medicine and the Center for Health Promotion and Disease Prevention, (PAG, MM), The University of North Carolina at Chapel Hill Program on Ethnicity, Culture and Health Outcomes (PAG, MAA, VJS), Lineberger Comprehensive Cancer Center (PAG, MAA), Department of Epidemiology (PAG, VJS), and Department of Biostatistics, School of Public Health (MAA, MS), University of North Carolina at Chapel Hill; Medical Review of North Carolina, Inc. (APS, SP); Center for Outcomes Research, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA (JAT).
Correspondence to: Paul Godley, MD, PhD, Division of Hematology/Oncology, University of North Carolina at Chapel Hill, 3009 Old Clinic Bldg., CB# 7305, Chapel Hill, NC 27599-7305 (e-mail: pgodley{at}med.unc.edu)
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Approximately 75% of the estimated 189 100 prostate cancers diagnosed in the United States in 2002 will be clinically localized at the time of diagnosis (5,6), and any disparities in outcomes among such patients are therefore of great interest. Although several studies of localized prostate cancer that were based in health care systems with and without financial barriers to treatment found no differences in survival between black and white patients (7-12), other studies have found poorer survival among black patients (13,14).
Delays in diagnosis and treatment may contribute to poorer outcomes for black American patients, but treatment-specific factors may also play a role. One study found that use of radical prostatectomy is lower in blacks than in whites (15), although the disparity may be declining (16). We extended these studies by investigating whether racial disparities in prostate cancer survival vary by treatment among Medicare patients with clinically localized prostate cancer.
![]() |
PATIENTS AND METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Surveillance, Epidemiology, and End Results (SEER)1 tumor registry data linked with Medicare health care claims were used to identify prostate cancer patients, to determine their initial treatment, and to follow them for outcomes over time. SEERMedicare files are a collaborative effort between the SEER program of the National Cancer Institute (NCI) and the Medicare program, run by the Centers for Medicare & Medicaid Services (17). Briefly, the SEER program uses population-based registries to collect demographic, diagnostic, and initial treatment data on all cancer cases in selected geographic regions. Data from Medicare, which provides health insurance coverage for 97% of Americans aged 65 years and older, include health service claims for care provided by physicians, inpatient hospital stays, hospital outpatient clinics, home health care agencies, skilled nursing facilities, and hospice programs. SEERMedicare linked files create an opportunity to examine care provided to cancer patients throughout the course of their disease. Because claims for noncancer health care are included in Medicare data, SEERMedicare linked files also provide information about other diseases and conditions present in the study population that might influence the relationship between race and survival following a diagnosis of prostate cancer. The research was approved by the University of North Carolina School of Medicine Institutional Review Board.
Study Population
Prostate cancer patients (SEER code 54 and ICD-O-3 code C61.9) (18) diagnosed from January 1, 1986, through December 31, 1996, from five geographic regions were eligible for the study. The five SEER regions used in this study (Atlanta, Connecticut, Detroit, San Francisco, and Seattle) were selected because they provided a substantial number of black patients for analyses and included data collected since 1986. Patients were eligible for the study if they were enrolled in Medicare Part A or Part B for at least 1 month of the study period. We identified 104 537 patients for potential inclusion. Patients were excluded from the study for the following reasons: prior cancers (n = 11 353); second cancer diagnosed in the same month (n = 665); noninvasive tumors (carcinoma in situ) (n = 3386); no month of diagnosis present (n = 720); aged less than 65 years at diagnosis (n = 14 989); neither black nor nonHispanic white (n = 4428); diagnosed at death (n = 814); no Medicare coverage during the study period (n = 40); and aged 85 years or older at diagnosis (n = 2150). A total of 65 992 black and white Medicare-enrolled prostate cancer patients thus remained, of whom 22 003 were staged with locally advanced or metastatic disease or were unstaged. We calculated the survival of the remaining 43 989 black and white Medicare-enrolled prostate cancer patients with American Joint Commission on Cancer (AJCC) stage 1 or 2 (19) or, when AJCC staging was missing, SEER historic stage code 1.
Definitions
SEER staging information is based on clinical information, except for surgery patients, for whom staging information may be updated with pathologic findings from surgery. This situation creates a potential misclassification bias (20) because surgery patients who are initially staged as having clinically localized prostate cancer but reclassified to a more advanced stage based on pathologic findings are categorized in SEER data neither as prostatectomy patients nor as having clinically localized disease, a phenomenon that does not occur among patients treated with radiation and patients treated nonaggressively (i.e., without surgery or radiation therapy). To minimize the impact of this potential misclassification, we considered all prostate cancer patients undergoing surgical procedures as having clinically localized disease, because surgery is typically not indicated for clinically advanced prostate cancer (21,22).
Treatment modalities were identified through examinations of SEER data and Medicare claims covering the first 4 months after diagnosis because this time frame corresponds to a standard interval during which patients undergo treatment after a diagnosis of prostate cancer. Widening the treatment window to 12 months would have increased the number of subjects by less than 3%. For this study, surgery was defined as procedures performed with curative intent or in anticipation of a subsequent curative procedure. The surgery category included radical prostatectomy, procedures performed on regional lymph nodes, and additional radiation therapy, which almost invariably follows incomplete or unsuccessful surgery. Radiation therapy was defined as external beam therapy, brachytherapy, or isotope radiation therapy as listed in SEER, or by outpatient codes indicating radiation therapy. SEER and Medicare data agreement on radiation therapy ascertainment is 93% (23). Patients who underwent both surgery and radiation therapy were classified as surgery patients.
Race was classified by using data from both SEER and Medicare sources. Patients coded as black in either data file (96% agreement) were classified as black. Patients with a race code of white without a classification of black or Hispanic in either file (98% agreement) were classified as white.
Data on the presence of other health conditions were obtained using inpatient claims for the year prior to diagnosis. With the Charlson index (24) as a guide, and following the approach outlined by Deyo et al. (25) and Klabunde et al. (26), we calculated a comorbidity score by using hospital discharge diagnoses contained in the Medicare claims. Because of the comorbidity scores dependence on a hospital claim during the prior year, scores could be calculated only for those Medicare patients covered under a fee-for-service arrangement and those who were hospitalized during the prior year (n = 17 659). To examine the effect of adjusting for patient comorbidities, we compared adjusted and unadjusted analyses on the subset of patients for whom a comorbidity score was available. The validity of the comorbidity score was assessed on the subset of patients who had comorbidity information. In this subset, the hazard ratio for race was essentially the same whether or not the model included the comorbidity score. This finding suggests that the lack of effect seen with comorbidity as part of the adjusted Cox regression model was due not to the relatively small number of subjects with comorbidity information but to the lack of substantial cormorbidity differences between black and white patients with comorbidity information.
Date of death was available in both SEER and Medicare files. Medicare dates of death provided the longest follow-up (through December 31, 1998) and were used for analyses of overall survival. Because only SEER data provide the cause of death, SEER death dates (through December 31, 1996) were used for analyses of prostate cancerspecific survival. Survival was measured in months from the date of diagnosis to the end of the study period or death.
Although no individual measures of socioeconomic status were available, the SEER data included a number of measures linked to the census tract of the patient. We therefore accounted for socioeconomic status by controlling for race- and age-specific median household income in the census tract and by including a binary variable indicating whether the race-specific percentage of those in the tract with less than a high school education was below 25%.
Endpoints
We evaluated three survival endpoints: overall survival, nonprostate cancerspecific survival, and prostate cancerspecific survival. Whereas the latter is the specific objective of prostate cancer treatment, issues in classification of cause of death may distort comparisons of prostate cancerspecific survival (27,28). Moreover, overall survival is relevant because selection of patients for prostatectomy involves anticipated life expectancy. Therefore, we emphasize overall survival in this article.
Statistical Methods
Treatment outcomes by race for patients with clinically localized prostate cancer cases in the five SEER sites were assessed using KaplanMeier survival curves (29) and Cox regression models. The mean covariate method was used to obtain covariate-adjusted survival curves for the entire cohort and for the sub-analysis of the surgery patients. This method uses means of covariates for each stratum (black or white) to estimate survival estimates using Cox regression models (30). Survival curves were used to describe the overall racial comparisons within treatment groups and prostate-specific antigen (PSA) testing eras (i.e., 19861988, the pre-PSA period; 19891991, the early PSA period; 19921996, the recent PSA period). Survival curves were examined with all-cause, nonprostate cancerspecific, and prostate cancerspecific survival as endpoints. Log-rank tests were used to analyze the differences between the survival curves. Cox regression models were used to assess the effect on survival of various covariates, including SEER site, census tract education and income level, age at diagnosis, PSA testing era, tumor grade, race (black/white), treatment (surgery, with and without radiation; radiation alone; and nonaggressive therapy), and comorbidity score. Models fit for surgery patients included pathologic stage as well (and did not include a treatment variable). All statistical tests were two-sided, with = .05. Early analyses indicated interactions between age, race, and treatment but only when men aged 85 years or older were included. To better quantify the possible racial differences in the presence of these interactions, the analyses in this article were restricted to men aged 6584 years, avoiding the inconsistencies seen in the older populations. We also conducted analyses stratified by treatment to further account for any residual interactions between treatment, race, and age. Among patients younger than 85 years, the interaction term race*age was not statistically significant for patients within each treatment category. The proportional hazards assumption for our Cox regression models was visually evaluated by inspecting log(log[survival])*log (time) plots. In addition, we evaluated time-varying covariates (i.e., introducing a covariate*time interaction term into the model) for all covariates in the model. We stratified by treatment in an attempt to satisfy the proportional hazards assumptions for radiation and surgery and examined the effect of including the remaining time-varying covariates in the model on the outcome of interest. We considered the results in the context of the robustness of the Cox regression model before proceeding with the analyses.
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Our study population was the 43 989 Medicare beneficiaries aged 6584 years who were diagnosed with clinically localized prostate cancer from 1986 through 1996 in five SEER areas (Table 1). The 38 242 white patients (87%) differed substantially from the 5747 (13%) black patients across most sociodemographic characteristics. Black patients were more likely to be aged 6569 years (37% versus 34%) and less likely to be married (60% versus 77%). Black patients were far more likely to live in a census tract with more than 25% of residents having less than a high school education (70% versus 16%) or one with a household income below $23 000 (54% versus 20%). Black patients were less likely to have undergone surgery as their primary prostate cancer treatment (24% versus 33%) and were more likely to have had nonaggressive treatment (38% versus 27%). Among patients undergoing surgery, rates of pathologically localized prostate cancer did not differ between black patients (50%) and white patients (50%), although cancers of black patients were unstaged more frequently than those of whites (9% versus 6%).
|
Survival after a prostate cancer diagnosis, as represented by survival curves for overall survival, prostate cancerspecific survival, and nonprostate cancerspecific survival, was statistically significantly longer for whites than for blacks (Fig. 1). When patients were stratified by treatment, the racial gap in survival was largest among patients receiving surgery and smallest among patients receiving radiation (Fig. 2). The hazard ratio (HR) of death from all causes for black patients relative to that for white patients was 1.43 (95% confidence interval [CI] = 1.29 to 1.58) among patients receiving surgery, 1.12 (95% CI = 1.03 to 1.22) among patients receiving radiation therapy, and 1.19 (95% CI = 1.12 to 1.26) among patients treated nonaggressively. The pattern of racial survival disparity was also evident from absolute differences in median survival. The median survival for black patients was 1.7 years (95% CI = 1.6 to 1.9 years) less than that of white patients (Table 2). Among surgery patients, black patients had a 10.8-year median survival compared with a median survival of 12.6 years among white patients, a difference of 1.8 years (95% CI = 1.5 to 2.0 years). By contrast, differences in median survival between black and white patients were smaller in patients treated with radiation (0.7 years, 95% CI = 0.5 to 1.0 years) and in patients treated nonaggressively (1.0 years, 95% CI = 0.7 to 1.1 years). The survival disadvantage for black patients persisted across strata of age, marital status, SEER site, and era of diagnosis, although the disadvantage was less among the more recently diagnosed patients (Table 3).
|
|
|
|
|
|
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Multiple social, environmental, and biologic factors influencing both patient selection and the effectiveness of prostate cancer treatment may have produced these results. Among patients selected for prostatectomy, black patients may have had worse overall health than white patients, as might be inferred from black patients higher mortality from causes other than prostate cancer. In general, blacks, along with poorer, less well educated, and older patients, are less likely to be selected for aggressive procedures for the treatment of prostate cancer (3,34). Moreover, black patients tend to present with more advanced, less curable prostate cancers (3,35), potentially justifying a more aggressive preoperative search for evidence of advanced cancer, which contraindicates surgery. However, the worse outcomes for black surgery patients in this study provide little evidence of disproportionately stringent staging. Although comorbidity differences (in the subset with available data) were small, the greater risk of and worse care for conditions that might develop after detection of prostate cancer could contribute to racial differences in overall mortality.
Our observation of blacks worse prostate cancer survival may also indicate that their surgery was less technically adequate, perhaps because of a greater likelihood of surgery in facilities with low radical prostatectomy volume, which have been shown to be associated with increased intra-operative and 30-day mortality (36-38). Similarly, low colorectal surgery volumes have been shown to be associated with high overall mortality from colorectal cancer (39). However, an interim analysis of a Scandinavian randomized trial (40) found that radical prostatectomy did not improve overall survival compared with observation, suggesting that variation in surgical technique may explain little of the difference in racial outcomes in our study.
Alternatively, if black patients had reduced access to specialized radiation therapy, which is preferred over surgery in patients in whom locally advanced cancer is suspected, black patients with poorer prognoses could have undergone radical prostatectomy as the default option. The result would have been the enrichment of the black surgery group with poorer-prognosis radiation therapy candidates, which would improve outcomes among black radiation therapy patients (thereby decreasing racial differences in outcomes among radiation therapy patients), and worsen outcomes among black surgery patients (thereby increasing racial differences in outcomes among surgery patients).
Another possible explanation for our results is genetic differences between races in response to prostate cancer treatment. Racial differences in the frequency of allelic polymorphisms in drug-metabolizing enzymes have been proposed as explanations for racially disparate outcomes in the treatment of congestive heart failure (41), but no genetic explanations of racial differences in a surgical treatment such as radical prostatectomy have been proposed. Individuals heterozygous for a mutation in the ataxia telangiectasiamutated (ATM) gene have enhanced responses to radiation therapy for breast cancer and prostate cancer, which could potentially improve prostate cancer control after radiation (and increase radiation-related toxicity), but we are unaware of an increased prevalence of mutations at this gene in black patients (42-44).
The racial disparity we observed in overall survival among surgery patients decreased as PSA screening came into wider use. The disparity for patients diagnosed in the recent PSA period is smaller than that for patients diagnosed in the early PSA period, which suggests that the racial gap may be shrinking. However, the power of these subgroup analyses is attenuated by both the shorter follow-up period for more recently diagnosed patients and the longer follow-up required by the substantial lead time that is introduced by PSA screening (45). If this finding is borne out by longer follow-up, PSA screening may have helped to decrease racial prognostic differences by providing a numerical indicator of cancer prognosis and surgical appropriateness that is less susceptible to bias than the more subjective nonmedical factors that have been postulated to limit aggressive medical treatment for minority patients (46-49).
Our study had several limitations. Although we adjusted the analysis for several possible confounders of clinically localized prostate cancer treatment outcomes, information on several potentially important covariates, such as serum PSA value and tumor size, was not available. In addition, we used census tractlevel indicators of socioeconomic status because we did not have access to individual-level measures of socioeconomic status; measures of the latter type might minimize misclassification and allow more precise estimates of income and education levels (50,51). Confining our analysis to the Medicare population (i.e., patients aged 65 years or older) limits generalizability to younger patients who are increasingly diagnosed through widespread PSA screening. Finally, we were unable to evaluate whether differential use of hormonal treatment, particularly with oral estrogens, for patients whose prostate cancer later recurred may have contributed to the racial disparities seen after treatment.
The assignment of a racial classification encompasses a multitude of social, environmental, dietary, and lifestyle factors that may affect response to specific treatments, none of which can be completely controlled in a statistical model. Our research findings highlight areas in which treatment outcomes differ by race. Whether recent reports of poorer cancer outcomes for black patients are due to a health care system that is less responsive to patients of a minority race per se (46), to adverse socioeconomic factors that are more prevalent among blacks (52,53), or to some as-yet-undefined race-related biologic factors (54) is highly controversial, although these factors need not be mutually exclusive. Researchers should continue to investigate racial disparities in treatment outcomes as well as the specific social, biologic, or environmental conditions that may be responsible for these disparities.
![]() |
NOTES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Although this study used the linked SEERMedicare database, the interpretation and reporting of these data are the sole responsibility of the authors.
Supported by U48/CCU409660 from the Centers for Disease Control and Prevention and the Association of Teachers of Preventive Medicine, and 1 P60 MD00244-01 from the National Center on Minority Health and Health Disparities (to P. Godley).
We acknowledge the efforts of the Applied Research Program of the National Cancer Institute (NCI); the Office of Information Services and the Office of Strategic Planning, Center for Medicare & Medicaid Studies; Information Management Services, Inc.; and the NCI Surveillance, Epidemiology, and End Results (SEER) Program tumor registries in the creation of the SEERMedicare database.
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
1 Patterns of cancer in five continents. IARC Sci Publ 1990;(120):1129.
2 Wingo PA, Bolden S, Tong T, Parker SP, Martin LM, Heath CW. Cancer statistics for African Americans, 1996. CA Cancer J Clin 1996;46:11325.
3 Hoffman RM, Gilliland FD, Eley JW, Harlan LC, Stephenson RA, Stanford JL, et al. Racial and ethnic differences in advanced-stage prostate cancer: the prostate cancer outcomes study. J Natl Cancer Inst 2001;93:38895.
4 Thompson I, Tangen C, Tolcher A, Crawford E, Eisenberger M, Moinpour C. Association of African-American ethnic background with survival in men with metastatic prostate cancer. J Natl Cancer Inst 2001;93:21925.
5 Jemal A, Thomas A, Murray T, Thun M. Cancer statistics, 2002. CA Cancer J Clin 2002;52:2347.
6 Mettlin CJ, Murphy GP, Rosenthal DS, Menck HR. The National Cancer Data Base report on prostate carcinoma after the peak in incidence rates in the U.S. The American College of Surgeons Commission on Cancer and the American Cancer Society. Cancer 1998;83:167984.[CrossRef][ISI][Medline]
7 Fowler JE Jr, Terrell F. Survival in blacks and whites after treatment for localized prostate cancer. J Urol 1996;156:1336.[ISI][Medline]
8 Fowler JE Jr, Braswell NT, Pandey P, Seaver L. Experience with radical prostatectomy and radiation therapy for localized prostate cancer at a Veterans Affairs Medical Center. J Urol 1995;153:102631.[ISI][Medline]
9 Roach M 3rd, Krall J, Keller JW, Perez CA, Sause WT, Doggett RL, et al. The prognostic significance of race and survival from prostate cancer based on patients irradiated on Radiation Therapy Oncology Group protocols (19761985). Int J Radiat Oncol Biol Phys 1992;24:4419.[ISI][Medline]
10 Brawn PN, Johnson EH, Kuhl DL, Riggs MW, Speights VO, Johnson CF 3rd, et al. Stage at presentation and survival of white and black patients with prostate carcinoma. Cancer 1993;71:256973.[ISI][Medline]
11 Powell IJ, Schwartz K, Hussain M. Removal of the financial barrier to health care: does it impact on prostate cancer at presentation and survival? A comparative study between black and white men in a Veterans Affairs system. Urology 1995;46:82530.[CrossRef][ISI][Medline]
12 Optenberg SA, Thompson IM, Friedrichs P, Wojcik B, Stein CR, Kramer B. Race, treatment, and long-term survival from prostate cancer in an equal-access medical care delivery system. JAMA 1995;274:1599605.[Abstract]
13 Pienta KJ, Demers R, Hoff M, Kau TY, Montie JE, Severson RK. Effect of age and race on the survival of men with prostate cancer in the Metropolitan Detroit tricounty area, 1973 to 1987. Urology 1995;45:93101.[CrossRef][ISI][Medline]
14 Robbins AS, Whittemore AS, Van Den Eeden SK. Race, prostate cancer survival, and membership in a large health maintenance organization. J Natl Cancer Inst 1998;90:98690.
15 Lu-Yao GL, Friedman M, Yao SL. Use of radical prostatectomy among Medicare beneficiaries before and after the introduction of prostate specific antigen testing. J Urol 1997;157:221922.[ISI][Medline]
16 Klabunde CN, Potosky AL, Harlan LC, Kramer BS. Trends and black/white differences in treatment for nonmetastatic prostate cancer. Med Care 1998;36:133748.[CrossRef][ISI][Medline]
17 Potosky AL, Riley GF, Lubitz JD, Mentnech RM, Kessler LG. Potential for cancer related health services research using a linked Medicare-tumor registry database. Med Care 1993;31:73248.[ISI][Medline]
18 Fritz A, Percy C, Jack A, Shanmugaratnam K, Sobin L, Parkin DM, et al., editors. International Classification of Diseases for Oncology. 3rd ed. Geneva (Switzerland): World Health Organization; 2000.
19 American Joint Committee on Cancer (AJCC). Prostate. In: Greene FL, Page DL, Fleming ID, Fritz AG, Balch CM, Haller DG, et al., editors. AJCC cancer staging manual. 6th ed. New York (NY): Springer; 2002. p. 30916.
20 Lu-Yao GL, Yao SL. Population-based study of long-term survival in patients with clinically localised prostate cancer. Lancet 1997;349:90610.[CrossRef][ISI][Medline]
21 National Institutes of Health Consensus Development Conference on the Management of Clinically Localized Prostate Cancer. Bethesda, Maryland, June 1517, 1987: NCI Monogr 1988;(7):36.
22 Garnick MB. Prostate cancer: screening, diagnosis, and management. Ann Intern Med 1993;118:80418.
23 Virnig BA, Warren JL, Cooper GS, Klabunde CN, Schussler N, Freeman J. Studying radiation therapy using SEER-Medicare-linked data. Med Care 2002;40(8 Suppl):IV-4954.[Medline]
24 Charlson M, Pompei P, Ales K, MacKenzie C. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis 1987;40:373.[ISI][Medline]
25 Deyo R, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992;45:613.[ISI][Medline]
26 Klabunde CN, Potosky AL, Legler JM, Warren JL. Development of a comorbidity index using physician claims data. J Clin Epidemiol 2000;53:125867.[CrossRef][ISI][Medline]
27 Feuer EJ, Merrill RM, Hankey BF. Cancer surveillance series: interpreting trends in prostate cancerpart II: cause of death misclassification and the recent rise and fall in prostate cancer mortality. J Natl Cancer Inst 1999;91:102532.
28 Welch HG, Black WC. Are deaths within 1 month of cancer-directed surgery attributed to cancer? J Natl Cancer Inst 2002;94:106670.
29 Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc 1958:45:7481.
30 Thakkar B, Hur K, Henderson WG, Oprian C. A method to generate Kaplan-Meier and adjusted survival curves using SAS. SAS Users Group International 23rd Proceedings, Nashville, TN, March 2225. Paper 226; 1998.
31 Fritz A, Ries L, editors. The SEER program code manual. 3rd ed. Bethesda (MD): National Cancer Institute; 1998.
32 Burns RB, McCarthy EP, Freund KM, Marwill SL, Shwartz M, Ash A, et al. Black women receive less mammography even with similar use of primary care. Ann Intern Med 1996;125:17382.
33 Bach PB, Cramer LD, Warren JL, Begg CB. Racial differences in the treatment of early-stage lung cancer. N Engl J Med 1999;341:1198205.
34 Iselin CE, Box JW, Vollmer RT, Layfield LJ, Robertson JE, Paulson DF. Surgical control of clinically localized prostate carcinoma is equivalent in African-American and white males. Cancer 1998;83:235360.[CrossRef][ISI][Medline]
35 Polednak AP, Flannery JT. Black versus white racial differences in clinical stage at diagnosis and treatment of prostatic cancer in Connecticut. Cancer 1992;70:21528.[ISI][Medline]
36 Ellison LM, Heaney JA, Birkmeyer JD. The effect of hospital volume on mortality and resource use after radical prostatectomy. J Urol 2000;163:8679.[ISI][Medline]
37 Hillner BE, Smith TJ, Desch CE. Hospital and physician volume or specialization and outcomes in cancer treatment: importance in quality of cancer care. J Clin Oncol 2000;18:232740.
38 Yao SL, Lu-Yao G. Population-based study of relationships between hospital volume of prostatectomies, patient outcomes, and length of hospital stay. J Natl Cancer Inst 1999;91:19506.
39 Schrag D, Cramer LD, Bach PB, Cohen AM, Warren JL, Begg CB. Influence of hospital procedure volume on outcomes following surgery for colon cancer. JAMA 2000;284:302835.
40 Holmberg L, Bill-Axelson A, Helgesen F, Salo JO, Folmerz P, Haggman M, et al. A randomized trial comparing radical prostatectomy with watchful waiting in early prostate cancer. N Engl J Med 2002;347:7819.
41 Wood AJ. Racial differences in the response to drugspointers to genetic differences. N Engl J Med 2001;344:13946.[CrossRef][Medline]
42 Hall EJ, Schiff PB, Hanks GE, Brenner DJ, Russo J, Chen J, et al. A preliminary report: frequency of A-T heterozygotes among prostate cancer patients with severe late responses to radiation therapy. Cancer J Sci Am 1998;4:3859.[ISI][Medline]
43 Varghese S, Schmidt-Ullrich RK, Dritschilo A, Jung M. Enhanced radiation late effects and cellular radiation sensitivity in an ATM heterozygous breast cancer patient. Radiat Oncol Investig 1999;7:2317.[CrossRef][ISI][Medline]
44 Abadir R, Hakami N. Ataxia telangiectasia with cancer. An indication for reduced radiotherapy and chemotherapy doses. Br J Radiol 1983;56:3435.
45 Gann PH, Hennekens CH, Sacks FM, Grodstein F, Giovannucci EL, Stampfer MJ. Prospective study of plasma fatty acids and risk of prostate cancer. J Natl Cancer Inst 1994;86:2816.[Abstract]
46 Epstein AM, Ayanian JZ. Racial disparities in medical care. N Engl J Med 2001;344:14713.
47 Ayanian JZ, Weissman JS, Chasan-Taber S, Epstein AM. Quality of care by race and gender for congestive heart failure and pneumonia. Med Care 1999;37:12609.[CrossRef][ISI][Medline]
48 van Ryn M, Burke J. The effect of patient race and socio-economic status on physicians perceptions of patients. Soc Sci Med 2000;50:81328.[CrossRef][ISI][Medline]
49 Ayanian JZ. Race, class, and the quality of medical care. JAMA 1994;271:12078.[CrossRef][ISI][Medline]
50 Kaufman JS, Cooper RS, McGee DL. Socioeconomic status and health in blacks and whites: the problem of residual confounding and the resiliency of race. Epidemiology 1997;8:6218.[ISI][Medline]
51 Geronimus AT, Bound J. Use of census-based aggregate variables to proxy for socioeconomic group: evidence from national samples. Am J Epidemiol 1998;148:47586.[Abstract]
52 Robbins AS, Whittemore AS, Thom DH. Differences in socioeconomic status and survival among white and black men with prostate cancer. Am J Epidemiol 2000;151:40916.[Abstract]
53 Smith JA Jr. Prostate cancer: if we only had more data [editorial]. J Urol 1994;152:135.[ISI][Medline]
54 Wood AJ. Ethnic differences in drug disposition and response. Ther Drug Monit 1998;20:5256.[CrossRef][ISI][Medline]
Manuscript received March 14, 2003; revised August 28, 2003; accepted September 11, 2003.
This article has been cited by other articles in HighWire Press-hosted journals:
Correspondence about this Article
![]() |
||||
|
Oxford University Press Privacy Policy and Legal Statement |