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Original Contribution |

Comorbidity and Survival Disparities Among Black and White Patients With Breast Cancer FREE

C. Martin Tammemagi, PhD; David Nerenz, PhD; Christine Neslund-Dudas, MA; Carolyn Feldkamp, PhD; David Nathanson, MD
[+] Author Affiliations

Author Affiliations: Department of Community Health Sciences, Brock University, St Catharines, Ontario (Dr Tammemagi); and Center for Health Services Research (Dr Nerenz), Josephine Ford Cancer Center (Ms Neslund-Dudas), Department of Pathology and Laboratory Medicine (Dr Feldkamp), and Department of General Surgery (Dr Nathanson), Henry Ford Health System, Detroit, Mich.

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JAMA. 2005;294(14):1765-1772. doi:10.1001/jama.294.14.1765.
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Published online

Context Reasons for the shorter survival of black breast cancer patients compared with their white counterparts are not completely understood.

Objective To evaluate the role of comorbidity in this racial disparity among breast cancer patients.

Design, Setting, and Patients Historical cohort from the Henry Ford Health System (a large comprehensive health system in Detroit, Mich) followed up for a median of 10 years. Patients (n = 906) included 264 black (29.1%) and 642 white (70.9%) women diagnosed as having breast cancer between 1985 and 1990. Detailed comorbidity data (268 comorbidities) and study data were abstracted from medical records and institutional, Surveillance, Epidemiology, and End Results, and Michigan State registries. Associations were analyzed with logistic and Cox regression.

Main Outcome Measures Breast cancer recurrence/progression and survival to death from all, breast cancer, and competing (non–breast cancer) causes.

Results Of blacks, 64 (24.9%) died of breast cancer and 95 (37.0%) died of competing causes. Comparable data for whites were 115 (18.3%) and 202 (32.1%). Blacks had worse all-cause survival (hazard ratio [HR], 1.34; 95% confidence interval [CI], 1.11-1.62), breast cancer–specific survival (HR, 1.47; 95% CI, 1.08-2.00), and competing-causes survival (HR, 1.27; 95% CI, 1.00-1.63). A total of 77 adverse comorbidities were associated with reduced survival. Adverse comorbidity count was associated with all-cause (adjusted HR, 1.29; 95% CI, 1.19-1.40) and competing-causes survival but was not associated with recurrence/progression or breast cancer–specific survival. At least 1 adverse comorbidity was observed in 221 (86.0%) blacks and 407 (65.7%) whites (odds ratio, 3.20; 95% CI, 2.17-4.72). Comparisons of unadjusted and comorbidity-adjusted HRs indicated that adverse comorbidity explained 49.1% of all-cause and 76.7% of competing-causes survival disparity. Diabetes and hypertension were particularly important in explaining disparity.

Conclusions More black breast cancer patients die of competing causes than of breast cancer. Effective control of comorbidity in black breast cancer patients should help improve life expectancy and lead to a reduction in survival disparities.

Figures in this Article

Although breast cancer survival has improved over the last 30 years, disparities in breast cancer survival between blacks and whites have not declined and remain sizeable.1 The 5-year US survival rates in 1995-2000 for black and white breast cancer patients were 75% and 89%, respectively.2 Although several causes have been identified, such as advanced cancer stage, lack of access to medical care, inferior treatment, and lower socioeconomic status (SES), not all reasons for this disparity are understood.317

Numerous studies have found that comorbidity is an independent predictor of survival in breast cancer patients.1824 The extent to which racial/ethnic differences in comorbidity explain disparities in breast cancer survival has not been well studied. Thus, in a group of breast cancer patients we evaluated the associations between adverse comorbidities and the following outcomes: survival to death due to all causes, breast cancer recurrence/progression and survival to breast cancer–specific death, and survival to competing (non–breast cancer) causes of death.

Study Design

A cohort of incident cases of breast cancer (N = 924, 1985-1990 inclusive) was identified from the Henry Ford Health System Tumor Registry, an American College of Surgeons, Commission on Cancer–certified registry. This health system is a large, comprehensive, nonprofit system that annually provides medical care for more than 500 000 people, approximately 30% of whom are black. In 1997, the Henry Ford Health System patient population distribution in 10 age, 2 race, and 2 sex categories (40 strata) differed from the metropolitan Detroit (Wayne, Oakland, and Macomb counties, 1990 census) distribution by 5.3% or less in all strata. These observations suggest that the Henry Ford Health System’s patient population is representative of the community it serves. The study was approved by the Henry Ford Health System institutional review board. Because the study examined patient medical records only, the review board waived the need to obtain patient consent.

Sociodemographic, exposure, and clinicopathologic data, including comorbidity data, were abstracted directly from medical records. Treatment data included surgery, chemotherapy, radiation, and hormone therapy as dichotomous variables (received or not). Additionally, information on type of surgery was collected. For chemotherapy and radiation therapy, data were not available on treatment completion or dose reduction. Survival and cause-of-death data were obtained from the Henry Ford Health System and Metropolitan Detroit Surveillance, Epidemiology, and End Results (SEER) tumor registries and from Michigan Department of Vital Statistics death certificate data. The last date of follow-up was May 1, 2002.

Data on failure to control cancer, either as local recurrence following resection or progression reflected in local expansion or regional or distant spread of cancer, were abstracted from medical records. Socioeconomic status was estimated by area-based socioeconomic measures taken from patients’ addresses and 1990 census data at the block group level, and included median household income, proportion living below poverty level, and proportion not completing high school. Race was classified according to self-report on registration forms. Analysis was limited to blacks and whites; Asians, Pacific Islanders, Native Americans, and others were excluded because the numbers were too small to permit meaningful analysis. There were only 4 patients who were identified as Hispanic and they were included in the analysis according to the race category they chose (3 white, 1 black).

Comorbidity data were abstracted directly from medical records from 3 years prior to breast cancer diagnosis up until first breast cancer treatment or 6 months following diagnosis if no treatment was administered. In this study, all comorbidity data were collected and coded into 259 essentially mutually exclusive diagnostic categories of the Clinical Classification Software,25 developed by the US Agency for Healthcare Research and Quality to facilitate health research by producing a manageable number of clinically meaningful disease categories from the more than 12 000 codes in the International Classification of Diseases, 9th Revision, Clinical Modifications text.26 This comorbidity list was supplemented by comorbidities suggested to be important in our previous studies of comorbidity and lung cancer outcomes27,28 to yield 268 comorbidities in 16 comorbidity categories.

This study focuses on the impact of adverse comorbidities on outcomes, with adverse comorbidities being defined as those that had significantly elevated Cox regression hazard ratios (all-cause survival) regardless of effect magnitudes, that had hazard ratios (HRs) greater than 1.20 regardless of statistical significance, or that were deemed to be adverse a priori based on clinical knowledge and/or past research (eg, HIV/AIDS). Seventy-seven comorbidities were classified as adverse (Table 1) and in this report mention of comorbidity refers specifically to these 77 comorbidities. The abstraction form and table of HRs and racial distributions for comorbidities are available online at http://www.brocku.ca/communhealthsci/jama.html. To facilitate interpretation of effect estimates and to evaluate dose-response relationships, comorbidity counts were divided into approximate quintiles of comparable size.

Table Graphic Jump LocationTable 1. Distribution of Comorbidities by ICD-9 Categories and Subcategories

For comparative purposes the Charlson Comorbidity Index29 was evaluated. The index was developed using hospital emergency department admissions with 1-year mortality as the outcome and was validated in breast cancer patients. It is computed by weighting and summing 19 comorbidities. Of all comorbidity indices to date, the Charlson has been most extensively studied and has been deemed to be a valid and reliable method of measuring comorbidity for clinical research.30

Cancer stage, based on pathological stage and in its absence on clinical stage, was analyzed according to the American Joint Committee on Cancer TNM staging system.31 Histotype was based on World Health Organization categories.32

Statistical Analysis

Contingency table analyses were carried out and null hypotheses were evaluated using the Fisher exact test and nonparametric tests for trend33 when applied to ordinal data. Logistic regression odds ratios (ORs) and 95% confidence intervals (CIs) were used to evaluate associations between predictors and dichotomous outcomes.

Univariate and multivariate survival analyses were carried out using Kaplan-Meier, life table, and Cox proportional hazards regression analyses.34,35 Modeling proceeded from univariate to multivariate and preparation of parsimonious multivariable models was guided by a priori considerations36 and was aided by backward stepwise elimination. Death was considered to be breast cancer–specific if either the SEER Registry or Michigan death certificate data indicated the cause of death was breast cancer. Classification of survival status into the categories alive, breast cancer death, or competing causes of death in the 2 registries had 91.9% agreement. Where cause of death data were present in both registries agreement was high: the κ statistic37 was 0.98 (lower confidence limit, 0.80) for blacks and 0.96 (lower confidence limit, 0.83) for whites.

When death due to competing causes was analyzed, breast cancer–specific deaths were censored. The reverse-censoring Kaplan-Meier method, which eliminates bias introduced by differential death rates, was used to compare follow-up between groups.38 The c statistic was used to measure the predictive ability of Cox models.39 The c statistic is analogous to the area under the receiver operating characteristic curve and can be thought of as follows: considering all possible combinations of paired individuals under study with differing survival times, the c statistic represents the proportion for which the regression model correctly predicts the survival order.39,40

The amount of racial survival disparity explained by comorbidity was estimated by the proportion decline in HR, black vs white, comparing the comorbidity-adjusted with the unadjusted model.

In survival regression analysis, proportional hazard assumptions were tested graphically and statistically and were met for all presented models. In logistic regression analysis, modeling of collinear variables was avoided, regression diagnostics were carried out, and conventional diagnostic standards were met for all models presented. In multivariate models interaction terms were considered. The α error was set at .05 and all reported P values, except for the κ statistic, are 2-sided. Stata version 7.0 software (Stata Corporation, College Station, Tex) was used to prepare statistics.

Through the tumor registry, 924 individuals were identified as having breast cancer. Analyses were restricted to the 906 individuals who were black (n = 264, 29.1%) or white (n = 642, 70.9%). Follow-up data were missing for 7 blacks (2.7%) and 13 whites (2.0%) (P = .62). Of 886 individuals for whom survival data were available, loss to follow-up occurred prior to 10 years in 56 individuals (6.3%). Cox regression analysis demonstrated that time to loss to follow-up was associated with younger age (HR per 10 years, 0.62; 95% CI, 0.52-0.74) but not with race or comorbidity; adjusted for age, the HR for loss to follow-up for race was 0.63 (95% CI, 0.29-1.37) and the HR for comorbidity was 1.07 (95% CI, 0.56-2.07). Comorbidity data were missing for 7 blacks (2.7%) and 23 whites (3.6%) (P = .55). The most complex multivariate model included race, age, comorbidity, estrogen receptor status, tumor stage, surgery, chemotherapy, and radiation therapy as predictors and was based on 227 of 264 blacks (86.0%) and 555 of 642 whites (86.4%) (P = .83).

The distribution of baseline characteristics for selected variables by race are presented in Table 2. Blacks were generally older than whites. Significantly fewer blacks were married. Blacks had significantly lower SES as measured by median household income, poverty level, or education (all 3, rank sum test P<.001). Blacks were diagnosed at a higher tumor stage: 52 blacks (21.5%) and 88 whites (14.8%) had stage III or IV disease (OR for stage III-IV vs lower stages, 1.57; 95% CI, 1.07-2.30). In univariate analysis, blacks tended to have fewer estrogen receptor–positive tumors (Table 2). Adjusted for stage and age, this association was significant (OR for black vs white, 0.62; 95% CI, 0.40-0.97).

Table Graphic Jump LocationTable 2. Characteristics of the Study Population, by Race*
Survival Data

Overall, the median follow-up was 10.0 years (range, 0.04-17.8 years). Of those who survived, median follow-up did not differ significantly between blacks (12.8 years) and whites (12.7 years), indicating comparable follow-up data quality. A total of 159 blacks (61.9%) and 317 whites (50.4%) died (P = .002) (Table 2). Overall, 62.4% of deaths were attributed to competing causes. Proportionately more blacks than whites died of breast cancer (64 [24.9%] vs 115 [18.3%], P = .03) and of competing causes (95 [37.0%] vs 202 [32.1%], P = .18).

Comorbidity and Survival

For those with comorbidity data, 28.3% had no comorbidities. Patients had a mean of 2.02 (median, 1; range, 0-13) comorbidities. The univariate HR for comorbidity count as a single 5-level variable (0, 1, 2, 3, and 4-13 comorbidities) was 1.40 (95% CI, 1.31-1.49). Following adjustment for age, tumor stage, estrogen receptor positivity, surgery, chemotherapy, and radiation therapy, the HR for comorbidity count (5-level) was 1.29 (95% CI, 1.19-1.40). A dose-response relationship was indicated by a monotonic increase in HRs with increasing numbers of comorbidities (Table 3 and Figure 1). The effect of comorbidity on survival did not differ by age; the comorbidity × age interaction P value was .46.

Table Graphic Jump LocationTable 3. Univariate and Multivariate Cox Regression Hazard Ratios for All-Cause Mortality for Study Comorbidities and Charlson Comorbidity Index
Figure 1. Kaplan-Meier All-Cause Survival of Black and White Breast Cancer Patients Stratified by 5 Levels of Comorbidities
Graphic Jump Location

The study comorbidities are detailed in Table 1.

Comorbidity and Disparity in All-Cause Survival

Compared with whites, blacks had shorter overall survival (HR, 1.34; 95% CI, 1.11-1.62; Figure 2). One or more comorbidities were reported in 221 blacks (86%) and 407 whites (65.7%) (OR, 3.20; 95% CI, 2.17-4.72). The distributions of comorbidities by race are presented in Table 2 and Figure 3. Comorbidity count evaluated as a single 5-level variable explained 49.1% of the racial disparity in overall survival (comorbidity-adjusted HR, 1.17; 95% CI, 0.96-1.43 (Table 4). Adjusted for age, tumor stage, estrogen receptor status, surgery, chemotherapy, and radiation therapy, the HR for black vs white was 1.14 (95% CI, 0.92-1.40), and when additionally adjusted for comorbidity count (5 levels) the HR was 1.02 (95% CI, 0.83-1.27). These data indicate that comorbidity explains disparity in all-cause survival in addition to other prognostic factors. The effect of comorbidity on survival did not differ by race (P value for interaction = .99).

Figure 2. Kaplan-Meier All-Cause Survival of Black and White Breast Cancer Patients
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Figure 3. Distribution of Comorbidity Count Stratified by Race
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Table Graphic Jump LocationTable 4. Prediction of Survival and Explanation of Survival Disparity by Study Comorbidities vs Charlson Comorbidity Index*

A total of 280 patients (30.9%) were older than 70 years. Racial disparity in survival was evident in patients younger than 70 years (HR, 1.23; 95% CI, 0.94-1.61) and in those 70 years or older (HR, 1.25; 95% CI, 0.95-1.65). The effect of race on all-cause survival did not differ by age (P for interaction = .89). In the group aged younger than 70 years, at least 1 comorbidity was present in 78.7% of blacks and 56.9% of whites (P<.001); in the group aged 70 years or older, at least 1 comorbidity was present in 97.1% of blacks and 89.3% of whites (P = .02). Adjusted for comorbidities, the HR for black vs white was 1.13 (95% CI, 0.85-1.50) in the younger group and 1.18 (95% CI, 0.89-1.57) in the older group, declines of 43.4% and 27.0%, respectively.

We also evaluated the impacts of all specific comorbidities with elevated frequencies in blacks compared with whites. Two of the most important comorbidities explaining survival disparity were diabetes and hypertension. The HR associated with diabetes was 1.85 (95% CI, 1.47-2.32) and the prevalence of diabetes in blacks and whites was 68 (26.4%) and 59 (9.5%), respectively (OR for black vs white, 3.41; 95% CI, 2.32-5.02). The HR associated with hypertension was 1.65 (95% CI, 1.37-1.99) and the prevalence of hypertension in blacks and whites was 163 (63.4%) and 220 (35.5%), respectively (OR, 3.14; 95% CI, 2.32-4.26). Adjusted for these 2 comorbidities, the HR for black vs white was 1.09 (95% CI, 0.89-1.35), a decline of 72.4% from the unadjusted estimate. Most of the complications of diabetes and hypertension that led to patient deaths developed well after the breast cancer diagnosis and outside the record abstraction period, and thus these data were unavailable for analysis. At the time of diagnosis, 8 black (3.1%) and 7 white (1.1%) patients (OR, 2.81, 95% CI, 1.01-7.83) had diabetic complications, which included circulatory problems, retinopathy, neuropathy, and ketoacidosis in both groups. Coronary disease occurred in 31 blacks (12.1%) and 51 whites (8.2%) (OR, 1.53; 95% CI, 0.95-2.45) and was associated with increased hazard (HR, 1.78; 95% CI, 1.35-2.35).

Comorbidity and Disparity in Progression/Recurrence and Breast Cancer–Specific Survival

Blacks had significantly more breast cancer recurrence/progression than did whites (35.8% vs 27.6%; OR, 1.47; 95% CI, 1.06-2.03). Breast cancer recurrence/progression was strongly predictive of reduced breast cancer–specific survival (HR, 24.40; 95% CI, 15.41-38.62) and compared with whites, blacks experienced shorter breast cancer–specific survival (HR, 1.47; 95% CI, 1.08-2.00). Comorbidity (5 levels) was not associated with breast cancer recurrence/progression (OR,  0.98; 95% CI, 0.88-1.08) or with breast cancer–specific survival (HR,  1.01; 95% CI, 0.91-1.12) and did not explain racial disparity in recurrence/progression or breast cancer–specific survival. Adjusted for comorbidities (5 levels), the recurrence/progression OR for black vs white was 1.47 (95% CI, 1.06-2.03) and breast cancer–specific HR was 1.50 (95% CI, 1.09-2.05).

Comorbidity may lead to breast cancer recurrence/progression and reduced breast cancer−specific survival by causing less aggressive or no treatment. The single most protective cancer treatment was surgery (HR, 0.29; 95% CI, 0.17-0.50), and comorbidity (5 levels) predicted nonreceipt of surgery (OR, 0.72, 95% CI, 0.58-0.88). Adjusted for marital status, SES (block group poverty level), and tumor stage, comorbidity remained predictive of nonreceipt of surgery (OR, 0.71; 95% CI, 0.54-0.91). In univariate analysis age was associated with nonreceipt of surgery (OR per 10 years, 0.70; 95% CI, 0.55-0.89). Age and comorbidity were collinear (OR for ≥1 vs 0 comorbidity per 10 years, 2.37; 95% CI, 2.06-2.71) and in multivariate analysis comorbidity was more predictive of surgery than age was. However, in this study population, regardless of the higher frequency of comorbidities in blacks, similarly high proportions underwent surgery: 239 blacks (94.1%) and 584 whites (95.6%) (P = .39). This latter observation in part explains why comorbidity did not account for the racial disparity in recurrence/progression and breast cancer−specific survival.

Comorbidity and Disparity in Competing-Causes Survival

For competing-causes survival, the HR for black vs white was 1.27 (95% CI, 1.00-1.63) and for comorbidity (5 levels) the HR was 1.69 (95% CI, 1.55-1.84). Adjusted for comorbidity, the HR for black vs white was 1.06 (95% CI, 0.83-1.36), a decline of 76.7% from the unadjusted analysis (Table 4).

Body mass index (calculated as weight in kilograms divided by height in meters squared) of 25 or higher (ie, overweight) was observed in 72% of blacks and 49.7% of whites (P<.001) and had the greatest impact on competing-causes survival. Compared with a BMI of 18.5 to less than 25, the univariate HRs (competing-causes survival) for BMI were as follows: for BMI of 25 to less than 30: 1.12 (95% CI, 0.84-1.50); for BMI of 30 to less than 35: 1.16 (95% CI, 0.81-1.65); and for BMI of 35 or higher: 1.35 (95% CI, 0.91-2.00). Adjusted for diabetes, hypertension, or comorbidity, all of these HRs approached the null, suggesting that adverse effects of obesity are mediated through comorbidity. Low BMI carried significant risk (HR for ≤18.5 vs >18.5 to <25, 2.34; 95% CI, 1.22-4.50). However, low BMI occurred less frequently in blacks than in whites (0.8% vs 4.0%, P = .02) and did not explain disparity in competing-causes or breast cancer–specific survival (low BMI–adjusted HR for black vs white, 1.26; 95% CI, 0.98-1.63 and HR, 1.49; 95% CI, 1.07-2.07, respectively).

Comorbidity Measurement

In the current study, comorbidity was an important predictor of survival and explained important amounts of survival disparity. Comorbidity is complex and optimal measurement methods that are robust across different outcomes and populations have not been established. Our study comorbidities demonstrated criterion (concurrent) validity41 because they correlated well with the established Charlson Comorbidity Index (r2 = 0.47, P<.001). Charlson index score greater than 0 occurred in 51.7% of blacks and 40.4% of whites (P for trend = .002) (Table 2). The Charlson index demonstrated a dose response with all-cause survival (Table 3). Table 4 presents a comparison of the ability of the study comorbidities and Charlson index to predict survival to all-cause and competing-causes death and explain disparity in these outcomes. The predictive ability as measured by the c statistic for both outcomes was consistently greater for study comorbidities than for the Charlson index, and this held true whether comorbidity was modeled as a single 5-level variable or multiple categorical variables. Similarly, the study comorbidities explained more survival disparity for all-cause and competing-causes survival (Table 4). These statistics suggest that the Charlson index failed to capture some relevant information present in the study comorbidities, which were based on a more extensive comorbidity inventory.

The current study found that black breast cancer patients have more cancer recurrence/progression and worse all-cause, breast cancer–specific, and competing-causes survival. Comorbidity explained approximately half of the overall survival disparity and the majority of competing-causes survival disparity, which accounted for the preponderance of deaths in black patients. Comorbidity was not associated with recurrence/progression or breast cancer–specific survival.

As in the current study, Eley et al5 found that comorbidity was an important independent predictor of all-cause survival but not of breast cancer–-specific survival, was significantly more frequent in blacks, and explained 25% of all-cause survival disparity (vs 49% in this study). Eley and colleagues considered only 6 categories of comorbidity and their comorbidity frequencies were substantially below those observed in our study, suggesting that their comorbidity adjustment may have been incomplete. Additionally, their follow-up was shorter than in the current study, which led to a heavier weighting of breast cancer vs competing-causes deaths, thus underemphasizing the impact of comorbidity on overall survival.

Analysis of all-cause survival may seem superfluous following analysis of breast cancer–specific and competing-causes survival. However, the misclassification that can occur between the latter 2 categories does not occur with all-cause death. Also, all-cause survival to some extent captures combined effects, because it is likely that in some cases comorbidity and breast cancer are not mutually exclusive but in combination contribute to shortened survival. Thus, survival to all-cause death serves as a useful outcome for summarizing the overall impact of comorbidity on the study cohort.

Optimal methods for comorbidity measurement are under development. The Charlson Comorbidity Index was validated, that is, found to significantly predict survival in breast cancer patients, and has been shown to be comparably predictive in black and white breast cancer patients.42 However, demonstration of significant association with survival does not alone indicate that a measure has high content validity and that it is optimized for studying disparities. Our previous study of comorbidity and lung cancer survival found that infrequent comorbidities in aggregate had an important impact on survival, that the Charlson index omitted several important predictive comorbidities, that the Charlson index’s weighting scheme did not correspond to HRs for several comorbidities, and that the Charlson index’s explanatory ability was at par with count of any comorbidity.27 Similarly, this study’s findings suggest that the Charlson index might not be an optimal scale for studies of breast cancer survival and disparity. The current data set served as a test/validation set for the Charlson index, whereas it was the training set for the current study’s comorbidity inventory. However, study comorbidities appeared to be consistently better than the Charlson index in predicting survival and explaining disparity, even though no weighting of comorbidity effects was applied.

The current study had design features that overcame limitations of some earlier studies. It included a relatively large, single-institution cohort of breast cancer patients with a relatively lengthy follow-up. Comorbidity data were collected systematically in detail from medical records, which are generally considered superior to data collected from administrative databases.4346 Although cancer treatment was controlled for in the analysis, data indicate that treatment differences were not important in leading to survival disparity within this health care system. This report presents surgery as a dichotomous variable. Specific types of surgery (eg, lumpectomy, mastectomy) were also analyzed but did not explain survival or disparity beyond the binary surgery variable (data not shown). The generalizability of our study findings to other populations needs to be established through further investigation. Important factors that impede access to quality health care were not evaluated in this analysis but must be considered in overall understanding of disparity.

Our findings indicate that control of comorbidity may be an important way of improving the survival of black breast cancer patients and reducing racial disparity. That comorbidity explained more than 40% of the survival disparity in patients younger than 70 years indicates that effective management of comorbidity has the potential to lead to a substantial increase in person-years of life gained. Control of just 2 comorbidities, diabetes and hypertension, could have a major beneficial impact.

Corresponding Author: C. Martin Tammemagi, PhD, Department of Community Health Sciences, Brock University, 500 Glenridge Ave, St Catharines, Ontario, Canada L2S 3A1 (martin.tammemagi@brocku.ca).

Author Contributions: Dr Tammemagi had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Tammemagi.

Acquisition of data: Tammemagi, Neslund-Dudas, Feldkamp, Nathanson.

Analysis and interpretation of data: Tammemagi, Nerenz, Nathanson.

Drafting of the manuscript: Tammemagi, Nathanson.

Critical revision of the manuscript for important intellectual content: Tammemagi, Nerenz, Neslund-Dudas, Feldkamp, Nathanson.

Statistical analysis: Tammemagi.

Obtained funding: Tammemagi.

Administrative, technical, or material support: Tammemagi, Neslund-Dudas, Feldkamp.

Study supervision: Tammemagi.

Financial Disclosures: None reported.

Funding/Support: This study was funded by US Department of Defense (US Army Medical Research and Material Command) grant DAMD17-00-1-0287.

Role of the Sponsor: The funding agency did not have any control or influence over the design and conduct of the study in any fashion, including collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Ghafoor A, Jemal A, Ward E, Cokkinides V, Smith R, Thun M. Trends in breast cancer by race and ethnicity.  CA Cancer J Clin. 2003;53:342-355
PubMed   |  Link to Article
Jemal A, Murray T, Ward E.  et al.  Cancer statistics, 2005.  CA Cancer J Clin. 2005;55:10-30
PubMed   |  Link to Article
Dayal HH, Power RN, Chiu C. Race and socio-economic status in survival from breast cancer.  J Chronic Dis. 1982;35:675-683
PubMed   |  Link to Article
Bassett MT, Krieger N. Social class and black-white differences in breast cancer survival.  Am J Public Health. 1986;76:1400-1403
PubMed   |  Link to Article
Eley JW, Hill HA, Chen VW.  et al.  Racial differences in survival from breast cancer: results of the National Cancer Institute Black/White Cancer Survival Study.  JAMA. 1994;272:947-954
PubMed   |  Link to Article
Weiss SE, Tartter PI, Ahmed S.  et al.  Ethnic differences in risk and prognostic factors for breast cancer.  Cancer. 1995;76:268-274
PubMed   |  Link to Article
Krieger N, van den Eeden SK, Zava D, Okamoto A. Race/ethnicity, social class, and prevalence of breast cancer prognostic biomarkers: a study of white, black, and Asian women in the San Francisco Bay area.  Ethn Dis. 1997;7:137-149
PubMed
Lyman GH, Kuderer NM, Lyman SL, Cox CE, Reintgen D, Baekey P. Importance of race on breast cancer survival.  Ann Surg Oncol. 1997;4:80-87
PubMed   |  Link to Article
Simon MS, Severson RK. Racial differences in breast cancer survival: the interaction of socioeconomic status and tumor biology.  Am J Obstet Gynecol. 1997;176:S233-S239
PubMed   |  Link to Article
Velanovich V, Yood MU, Bawle U.  et al.  Racial differences in the presentation and surgical management of breast cancer.  Surgery. 1999;125:375-379
PubMed   |  Link to Article
Yood MU, Johnson CC, Blount A.  et al.  Race and differences in breast cancer survival in a managed care population.  J Natl Cancer Inst. 1999;91:1487-1491
PubMed   |  Link to Article
Roetzheim RG, Gonzalez EC, Ferrante JM, Pal N, Van Durme DJ, Krischer JP. Effects of health insurance and race on breast carcinoma treatments and outcomes.  Cancer. 2000;89:2202-2213
PubMed   |  Link to Article
Joslyn SA, West MM. Racial differences in breast carcinoma survival.  Cancer. 2000;88:114-123
PubMed   |  Link to Article
Newman LA, Carolin K, Simon M.  et al.  Impact of breast carcinoma on African-American women: the Detroit experience.  Cancer. 2001;91:1834-1843
PubMed   |  Link to Article
Bradley CJ, Given CW, Roberts C. Race, socioeconomic status, and breast cancer treatment and survival.  J Natl Cancer Inst. 2002;94:490-496
PubMed   |  Link to Article
Chu KC, Lamar CA, Freeman HP. Racial disparities in breast carcinoma survival rates: seperating factors that affect diagnosis from factors that affect treatment.  Cancer. 2003;97:2853-2860
PubMed   |  Link to Article
Jones BA, Kasl SV, Howe CL.  et al.  African-American/white differences in breast carcinoma: p53 alterations and other tumor characteristics.  Cancer. 2004;101:1293-1301
PubMed   |  Link to Article
Satariano WA. Comorbidity and functional status in older women with breast cancer: implications for screening, treatment, and prognosis.  J Gerontol. 1992;47:24-31
PubMed
Satariano WA. Aging, comorbidity, and breast cancer survival: an epidemiologic view.  Adv Exp Med Biol. 1993;330:1-11
PubMed
Satariano WA, Ragland DR. The effect of comorbidity on 3-year survival of women with primary breast cancer.  Ann Intern Med. 1994;120:104-110
PubMed   |  Link to Article
Newschaffer CJ, Bush TL, Penberthy LT. Comorbidity measurement in elderly female breast cancer patients with administrative and medical records data.  J Clin Epidemiol. 1997;50:725-733
PubMed   |  Link to Article
Yancik R, Wesley MN, Ries LA, Havlik RJ, Edwards BK, Yates JW. Effect of age and comorbidity in postmenopausal breast cancer patients aged 55 years and older.  JAMA. 2001;285:885-892
PubMed   |  Link to Article
Houterman S, Janssen-Heijnen ML, Verheij CD.  et al.  Comorbidity has negligible impact on treatment and complications but influences survival in breast cancer patients.  Br J Cancer. 2004;90:2332-2337
PubMed
Nagel G, Wedding U, Rohrig B, Katenkamp D. The impact of comorbidity on the survival of postmenopausal women with breast cancer.  J Cancer Res Clin Oncol. 2004;130:664-670
PubMed   |  Link to Article
Elixhauser A, Steiner C, Palmer L. Clinical Classification Software (CCS),2004. US Agency for Healthcare Research and Quality. Available at: http://www.ahrq.gov/data/hcup/ccs.htm#download. Accessed February 28, 2005
 International Classification of Diseases, 9th Revision, Clinical Modification.  5th ed. Washington, DC: Public Health Service and Health Care Financing Administration; 1994. DHHS publication (PHS) 94-1260
Tammemagi CM, Neslund-Dudas C, Simoff M, Kvale P. Impact of comorbidity on lung cancer survival.  Int J Cancer. 2003;103:792-802
PubMed   |  Link to Article
Tammemagi CM, Neslund-Dudas C, Simoff M, Kvale P. In lung cancer patients, age, gender, race-ethnicity and smoking predict adverse comorbidity, which in turn predicts lung cancer treatment and survival.  J Clin Epidemiol. 2004;57:597-609
PubMed   |  Link to Article
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.  J Chronic Dis. 1987;40:373-383
PubMed   |  Link to Article
de Groot V, Beckerman H, Lankhorst GJ, Bouter LM. How to measure comorbidity: a critical review of available methods.  J Clin Epidemiol. 2003;56:221-229
PubMed   |  Link to Article
Beahrs OH.American Joint Committee on Cancer, American Cancer Society.  Manual for Staging of Cancer, 4th ed. Philadelphia, Pa: Lippincott; 1992
World Health Organization.  The World Health Organization histological typing of lung tumours, 2nd ed.  Am J Clin Pathol. 1982;77:123-136
PubMed
Cuzick J. A Wilcoxon-type test for trend.  Stat Med. 1985;4:87-90
PubMed   |  Link to Article
Kaplan D, Meier P. Nonparametric estimation from incomplete observations.  J Am Stat Assoc. 1958;53:457-481
Link to Article
Cox D. Regression models and life tables.  J R Stat Soc A. 1972;34:187-220
Greenland S. Modeling and variable selection in epidemiologic analysis.  Am J Public Health. 1989;79:340-349
PubMed   |  Link to Article
Armstrong BK, White E, Saracci R. Principles of Exposure Measurement in EpidemiologyNew York, NY: Oxford University Press; 1992
Parmar MKB, Machin D. Survival Analysis: A Practical ApproachChichester, England: Wiley; 1995
Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.  Stat Med. 1996;15:361-387
PubMed   |  Link to Article
Hosmer DW Jr, Lemeshow S. Applied Logistic Regression2nd ed. New York, NY: John Wiley & Sons; 1999
Streiner DL, Norman GR. Health Measurement Scales: A Practical Guide to Their Development and Use3rd ed. New York, NY: Oxford University Press; 2003
West DW, Satariano WA, Ragland DR, Hiatt RA. Comorbidity and breast cancer survival: a comparison between black and white women.  Ann Epidemiol. 1996;6:413-419
PubMed   |  Link to Article
Malenka DJ, McLerran D, Roos N, Fisher ES, Wennberg J. Using administrative data to describe casemix: a comparison with the medical record.  J Clin Epidemiol. 1994;47:1027-1032
PubMed   |  Link to Article
Kieszak SM, Flanders WD, Kosinski AS, Shipp CC, Karp H. A comparison of the Charlson comorbidity index derived from medical record data and administrative billing data.  J Clin Epidemiol. 1999;52:137-142
PubMed   |  Link to Article
Powell H, Lim LL, Heller RF. Accuracy of administrative data to assess comorbidity in patients with heart disease: an Australian perspective.  J Clin Epidemiol. 2001;54:687-693
PubMed   |  Link to Article
Preen DB, Holman CD, Lawrence DM, Baynham NJ, Semmens JB. Hospital chart review provided more accurate comorbidity information than data from a general practitioner survey or an administrative database.  J Clin Epidemiol. 2004;57:1295-1304
PubMed   |  Link to Article

Figures

Figure 1. Kaplan-Meier All-Cause Survival of Black and White Breast Cancer Patients Stratified by 5 Levels of Comorbidities
Graphic Jump Location

The study comorbidities are detailed in Table 1.

Figure 2. Kaplan-Meier All-Cause Survival of Black and White Breast Cancer Patients
Graphic Jump Location
Figure 3. Distribution of Comorbidity Count Stratified by Race
Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Distribution of Comorbidities by ICD-9 Categories and Subcategories
Table Graphic Jump LocationTable 2. Characteristics of the Study Population, by Race*
Table Graphic Jump LocationTable 3. Univariate and Multivariate Cox Regression Hazard Ratios for All-Cause Mortality for Study Comorbidities and Charlson Comorbidity Index
Table Graphic Jump LocationTable 4. Prediction of Survival and Explanation of Survival Disparity by Study Comorbidities vs Charlson Comorbidity Index*

References

Ghafoor A, Jemal A, Ward E, Cokkinides V, Smith R, Thun M. Trends in breast cancer by race and ethnicity.  CA Cancer J Clin. 2003;53:342-355
PubMed   |  Link to Article
Jemal A, Murray T, Ward E.  et al.  Cancer statistics, 2005.  CA Cancer J Clin. 2005;55:10-30
PubMed   |  Link to Article
Dayal HH, Power RN, Chiu C. Race and socio-economic status in survival from breast cancer.  J Chronic Dis. 1982;35:675-683
PubMed   |  Link to Article
Bassett MT, Krieger N. Social class and black-white differences in breast cancer survival.  Am J Public Health. 1986;76:1400-1403
PubMed   |  Link to Article
Eley JW, Hill HA, Chen VW.  et al.  Racial differences in survival from breast cancer: results of the National Cancer Institute Black/White Cancer Survival Study.  JAMA. 1994;272:947-954
PubMed   |  Link to Article
Weiss SE, Tartter PI, Ahmed S.  et al.  Ethnic differences in risk and prognostic factors for breast cancer.  Cancer. 1995;76:268-274
PubMed   |  Link to Article
Krieger N, van den Eeden SK, Zava D, Okamoto A. Race/ethnicity, social class, and prevalence of breast cancer prognostic biomarkers: a study of white, black, and Asian women in the San Francisco Bay area.  Ethn Dis. 1997;7:137-149
PubMed
Lyman GH, Kuderer NM, Lyman SL, Cox CE, Reintgen D, Baekey P. Importance of race on breast cancer survival.  Ann Surg Oncol. 1997;4:80-87
PubMed   |  Link to Article
Simon MS, Severson RK. Racial differences in breast cancer survival: the interaction of socioeconomic status and tumor biology.  Am J Obstet Gynecol. 1997;176:S233-S239
PubMed   |  Link to Article
Velanovich V, Yood MU, Bawle U.  et al.  Racial differences in the presentation and surgical management of breast cancer.  Surgery. 1999;125:375-379
PubMed   |  Link to Article
Yood MU, Johnson CC, Blount A.  et al.  Race and differences in breast cancer survival in a managed care population.  J Natl Cancer Inst. 1999;91:1487-1491
PubMed   |  Link to Article
Roetzheim RG, Gonzalez EC, Ferrante JM, Pal N, Van Durme DJ, Krischer JP. Effects of health insurance and race on breast carcinoma treatments and outcomes.  Cancer. 2000;89:2202-2213
PubMed   |  Link to Article
Joslyn SA, West MM. Racial differences in breast carcinoma survival.  Cancer. 2000;88:114-123
PubMed   |  Link to Article
Newman LA, Carolin K, Simon M.  et al.  Impact of breast carcinoma on African-American women: the Detroit experience.  Cancer. 2001;91:1834-1843
PubMed   |  Link to Article
Bradley CJ, Given CW, Roberts C. Race, socioeconomic status, and breast cancer treatment and survival.  J Natl Cancer Inst. 2002;94:490-496
PubMed   |  Link to Article
Chu KC, Lamar CA, Freeman HP. Racial disparities in breast carcinoma survival rates: seperating factors that affect diagnosis from factors that affect treatment.  Cancer. 2003;97:2853-2860
PubMed   |  Link to Article
Jones BA, Kasl SV, Howe CL.  et al.  African-American/white differences in breast carcinoma: p53 alterations and other tumor characteristics.  Cancer. 2004;101:1293-1301
PubMed   |  Link to Article
Satariano WA. Comorbidity and functional status in older women with breast cancer: implications for screening, treatment, and prognosis.  J Gerontol. 1992;47:24-31
PubMed
Satariano WA. Aging, comorbidity, and breast cancer survival: an epidemiologic view.  Adv Exp Med Biol. 1993;330:1-11
PubMed
Satariano WA, Ragland DR. The effect of comorbidity on 3-year survival of women with primary breast cancer.  Ann Intern Med. 1994;120:104-110
PubMed   |  Link to Article
Newschaffer CJ, Bush TL, Penberthy LT. Comorbidity measurement in elderly female breast cancer patients with administrative and medical records data.  J Clin Epidemiol. 1997;50:725-733
PubMed   |  Link to Article
Yancik R, Wesley MN, Ries LA, Havlik RJ, Edwards BK, Yates JW. Effect of age and comorbidity in postmenopausal breast cancer patients aged 55 years and older.  JAMA. 2001;285:885-892
PubMed   |  Link to Article
Houterman S, Janssen-Heijnen ML, Verheij CD.  et al.  Comorbidity has negligible impact on treatment and complications but influences survival in breast cancer patients.  Br J Cancer. 2004;90:2332-2337
PubMed
Nagel G, Wedding U, Rohrig B, Katenkamp D. The impact of comorbidity on the survival of postmenopausal women with breast cancer.  J Cancer Res Clin Oncol. 2004;130:664-670
PubMed   |  Link to Article
Elixhauser A, Steiner C, Palmer L. Clinical Classification Software (CCS),2004. US Agency for Healthcare Research and Quality. Available at: http://www.ahrq.gov/data/hcup/ccs.htm#download. Accessed February 28, 2005
 International Classification of Diseases, 9th Revision, Clinical Modification.  5th ed. Washington, DC: Public Health Service and Health Care Financing Administration; 1994. DHHS publication (PHS) 94-1260
Tammemagi CM, Neslund-Dudas C, Simoff M, Kvale P. Impact of comorbidity on lung cancer survival.  Int J Cancer. 2003;103:792-802
PubMed   |  Link to Article
Tammemagi CM, Neslund-Dudas C, Simoff M, Kvale P. In lung cancer patients, age, gender, race-ethnicity and smoking predict adverse comorbidity, which in turn predicts lung cancer treatment and survival.  J Clin Epidemiol. 2004;57:597-609
PubMed   |  Link to Article
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.  J Chronic Dis. 1987;40:373-383
PubMed   |  Link to Article
de Groot V, Beckerman H, Lankhorst GJ, Bouter LM. How to measure comorbidity: a critical review of available methods.  J Clin Epidemiol. 2003;56:221-229
PubMed   |  Link to Article
Beahrs OH.American Joint Committee on Cancer, American Cancer Society.  Manual for Staging of Cancer, 4th ed. Philadelphia, Pa: Lippincott; 1992
World Health Organization.  The World Health Organization histological typing of lung tumours, 2nd ed.  Am J Clin Pathol. 1982;77:123-136
PubMed
Cuzick J. A Wilcoxon-type test for trend.  Stat Med. 1985;4:87-90
PubMed   |  Link to Article
Kaplan D, Meier P. Nonparametric estimation from incomplete observations.  J Am Stat Assoc. 1958;53:457-481
Link to Article
Cox D. Regression models and life tables.  J R Stat Soc A. 1972;34:187-220
Greenland S. Modeling and variable selection in epidemiologic analysis.  Am J Public Health. 1989;79:340-349
PubMed   |  Link to Article
Armstrong BK, White E, Saracci R. Principles of Exposure Measurement in EpidemiologyNew York, NY: Oxford University Press; 1992
Parmar MKB, Machin D. Survival Analysis: A Practical ApproachChichester, England: Wiley; 1995
Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.  Stat Med. 1996;15:361-387
PubMed   |  Link to Article
Hosmer DW Jr, Lemeshow S. Applied Logistic Regression2nd ed. New York, NY: John Wiley & Sons; 1999
Streiner DL, Norman GR. Health Measurement Scales: A Practical Guide to Their Development and Use3rd ed. New York, NY: Oxford University Press; 2003
West DW, Satariano WA, Ragland DR, Hiatt RA. Comorbidity and breast cancer survival: a comparison between black and white women.  Ann Epidemiol. 1996;6:413-419
PubMed   |  Link to Article
Malenka DJ, McLerran D, Roos N, Fisher ES, Wennberg J. Using administrative data to describe casemix: a comparison with the medical record.  J Clin Epidemiol. 1994;47:1027-1032
PubMed   |  Link to Article
Kieszak SM, Flanders WD, Kosinski AS, Shipp CC, Karp H. A comparison of the Charlson comorbidity index derived from medical record data and administrative billing data.  J Clin Epidemiol. 1999;52:137-142
PubMed   |  Link to Article
Powell H, Lim LL, Heller RF. Accuracy of administrative data to assess comorbidity in patients with heart disease: an Australian perspective.  J Clin Epidemiol. 2001;54:687-693
PubMed   |  Link to Article
Preen DB, Holman CD, Lawrence DM, Baynham NJ, Semmens JB. Hospital chart review provided more accurate comorbidity information than data from a general practitioner survey or an administrative database.  J Clin Epidemiol. 2004;57:1295-1304
PubMed   |  Link to Article

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