The Racial Disparity in Breast Cancer Mortality
Steven Whitman•David Ansell•Jennifer Orsi•
? Springer Science+Business Media, LLC 2010
that this racial disparity might be even greater in Chicago
disparity are presented they are sometimes attributed in part
to racial differences in tumor biology. Vital records data
were employed to calculate age-adjusted breast cancer
mortality rates for women in Chicago, New York City and
the United States from 1980–2005. Race-specific rate ratios
Breast cancer mortality rates by race are the main outcome.
In all three geographies the rate ratios were approximately
the white rates started to decline while the black rates
remained rather constant. By 2005 the black:white rate ratio
number of ways these data are inconsistent with the notion
is a function of differential biology. Three societal hypoth-
eses are posited that may explain this disparity. All three are
actionable, beginning today.
Black women die of breast cancer at a much
Mammography access ? Mammography quality ?
Mortality rates ? Racial disparities ? Treatment quality
Breast cancer ? Joinpoint regression ?
It is frequently noted that black women die from breast
cancer at a higher rate than white women [1–3]. In addition,
some analyses suggest that there are racial differences in
biological characteristics of breast tumors [4, 5]. These two
matters are often then conflated to suggest that differential
biology may be a risk factor for the racial disparity in breast
cancer mortality [6–8]. We believe that such a syllogism is
faulty and is proven wrong by an examination of the data
underlying this disparity. The purpose of this article is to
present data relevant to this issue from the United States,
New York and Chicago regarding the racial disparity in
breast cancer mortality and to suggest systems-based
hypotheses that might be examined in order to delineate the
factors responsible for this disparity.
Deaths where the cause was malignant neoplasm of the
breast (ICD-9 = 174, ICD-10 = C50) were included in
this analysis. There was an ICD version change in 1999,
however, we did not apply a comparability ratio to 1998
breast cancer deaths because there was no statistically
significant difference in the number of cases captured
between versions 9 and 10 based on coding changes (the
comparability ratio for malignant neoplasm of breast =
1.01, 95%CI: 1.00–1.01) .
S. Whitman (&)
Sinai Urban Health Institute, Room K437, 1500 S. California
Avenue, Chicago, IL 60608, USA
Rush University Medical Center, Chicago, IL, USA
Women’s Interagency HIV Study, 1900 Polk St, Room 1254,
Chicago, IL 60612, USA
Georgia Department of Community Health, 2 Peachtree Street,
Atlanta, GA 30303, USA
J Community Health
United States Data
All US numerators were abstracted from death files
maintained by the National Center for Health Statistics.
Population-based denominators for 1980, 1990, and 2000
were derived from Census data. Population-based denom-
inators for non-Hispanic White in 2005 were gathered from
the American Community
denominators for the non-Hispanic Black population in
2005 were not readily available so we estimated this pop-
ulation using the same methodology employed to estimate
the 2005 non-Hispanic Black population in Chicago. Pop-
ulation-based denominators for years other than 1980,
1990, 2000, and 2005 were estimated using exponential
interpolation. For 1980 denominator data and 1980–1989
we utilized data on Black and White persons (which
included Hispanics) because Hispanic origin data were not
available for the US overall during that time period.
New York City Data
New York City numerator data for years 1980–1989 and
2005 were obtained through a special request to the New
York Department of Mental Health and Hygiene. Numer-
ator data for years 1990–2004 were abstracted from death
files maintained by the National Center for Health Statis-
tics. Population-based denominators for 1980, 1990, 2000,
and 2005 were obtained via the same avenues as for Chi-
cago (below). Population-based denominators for the non-
Hispanic Black population in 2005 were not readily
available and thus we used the same estimating techniques
as for the non-Hispanic Black population in Chicago to
estimate this population. Population-based denominators
for years other than 1980, 1990, 2000, and 2005 were
estimated using exponential interpolation.
All Chicago numerators were abstracted from the vital
records (birth and death) files maintained by the Illinois
Department of Public Health and provided to us by the
Chicago Department of Public Health. Denominators for
population-based rates in Chicago in 1980, 1990, and 2000
were gathered from the Census. Denominators for non-
Hispanic White (NHW) in 2005 were gathered from the
American Community Survey . Denominators for the
non-Hispanic Black (NHB) population in 2005 were not
readily available so we estimated the population using an
age-specific ratio calculated by dividing the number of
non-Hispanic Blacks by total Blacks in the 2000 Census
and multiplying the proportion by the number of all blacks
in 2005 from the American Community Survey for each
age group. Denominators for years other than 1980, 1990,
2000, and 2005 were estimated using exponential inter-
Analysis of Trends
To measure disparity we calculated the rate ratio between
the NHB and NHW rates. The rate ratio is greater than 1.00
if the NHB rate is higher than the NHW rate and less than
1.00 if the NHW rate is higher than the NHB rate.
To determine if a disparity widened or narrowed signifi-
cantly between 1980 and 2005 we calculated a two-sided
z-score using a bootstrap technique developed by Keppel
and colleagues  and examined the corresponding
P-value for the z-score. A P-value of\0.05 was consid-
ered significant for all analyses. The significance of trends
was tested using joinpoint analysis . Each joinpoint
represents a significant change in the trend, denoted as a
straight line on a log scale. The overall significance was set
at P = 0.05. No more than three joinpoints were allowed.
Figure 1a presents results for the United States. As Table 1
indicates, the graph for white women contains 4 segments,
the last 3 of which indicate significant declines (the first
corresponds to a significant increase). For black women
there is a significant upward slope for 1980–1993 and a
significant downward slope after that. The black breast
cancer mortality rate was 31.8 in 1980 and the NHB rate
was 35.6 in 2005, a statistically significant increase
(P\0.001). The white rate was 32.6 in 1980 and the
NHW rate was 25.8 in 2005, a statistically significant
decrease (P\0.001). Since 1982 the black/NHB rates
have been higher than the white/NHW rates.
Figure 1b contains NHW and NHB rates for NYC. There
are 3 segments for NHW women, two of which show signif-
the NHW rates between 1980 (39.5) and 2005 (24.5)
(P\0.001). There was only 1 segment for NHB women and
it showed no significant change over the 25 years (Table 1).
The rate changed from 37.1 in 1980 to 33.5 in 2005, but the
difference was not statistically significant (P[0.05).
Figure 1c presents the breast cancer mortality rates for
Chicago from 1980–2005. In 1980 the rates for NHB and
NHW women were essentially equal at about 38. The rates
remained more or less constant until the early 1990s when
the NHW rate began to decline. By 2005 the NHW rate
was 21.8, a decline of 42% (P\0.001) while the NHB rate
had increased (a non-significant amount) to 43.2.
J Community Health
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
NHB NHW White
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Fig. 1 Age adjusted female breast cancer mortality rates, a United
States, By Race*, 1980-2005, b New York City, By Race, 1980-2005,
c Chicago, By Race, 1980-2005. * Open diamonds represent Black
data that includes Hispanics and closed circles represent White data
that includes Hispanics (1980–1989). Closed Diamonds represents
Non-Hispanic Blacks and open circles represents Non-Hispanic
J Community Health
The associated joinpoint analysis (Table 1) locates three
trend lines for NHB mortality, none of them with a sig-
nificant slope, indicating that there has been no change in
the breast cancer mortality rates for Black women in Chi-
cago over the past 25 years. Table 1 also indicates a con-
stant trend for NHW women in Chicago from 1980–1992
and a significant downward trend after that associated with
an annual change of –4.1% (P\0.05).
Table 2 presents selected data points from these three
graphs. All three begin in 1980 with the NHB and NHW
breast cancer mortality rates being approximately equal
and each ends in 2005 with the NHB rate being much
higher than the NHW rate. Over time, the NHB:NHW
relative risk (RR) in the US increased from 0.98 to 1.38; in
NYC from 0.94 to 1.36; and in Chicago from 1.03 to 1.98.
Each of the 2005 RRs within each location is significantly
different than the 1980 RR (P\0.001). For the US as a
whole the NHB:NHW RRs have remained rather constant
in recent years (2000 and 2005) but are significantly dif-
ferent than 1.00 (P\0.001). Changes in New York City
NHB:NHW RRs have varied over time with only the RRs
for 1990 and 2005 being significantly different from 1.00
(P\0.001). In Chicago, the NHB:NHW RRs have
increased steadily over time, with the RRs for 2000 and
2005 being significantly different than 1.00 (P\0.001).
The image portrayed by the three graphs could not be
explained by biological differences. In all three locations
the black and white rates were similar in the 1980s and then
started to diverge, just as the benefits from early detection
via mammography  and treatment  were mani-
festing themselves. In all cases this divergence took place
because the white rate started improving and the black rate
did not. For the US and Chicago the black rate is higher
than it was 25 years ago. In NYC it is lower but only by a
small, non-significant amount. Although there may be
differences between the races in tumor biology, these
explanations would be inadequate to explain why the
mortality disparity has been growing rapidly in Chicago
but remaining rather constant in NYC and the US. Biology
also cannot explain the variability in the disparities in the
Another recent article has found that for many cancers,
including breast cancer, disparities in survival actually
increase as ‘‘amenability to medical interventions’’ increase
. That is, as we become more able to improve cancer
outcomes, racial disparities widen because more privi-
leged groups are able to gain access to these interventions.
Table 1 Joinpoint analysis of trends in breast cancer mortality rates for the United States, New York and Chicago, 1980–2005
Segment 1 Segment 2Segment 3 Segment 4
Race/placeYearsAPC 95%CI YearsAPC 95%CI YearsAPC 95%CI Years APC 95%CI
US White 1980–88 0.4 (0.0, 0.7)* 1988–95 -1.5 (-2.0, -1.0)* 1995–99 -2.9 (-4.3, -1.4)* 1999–05 -0.8 (-1.3, -0.3)*
US Black1980–93 1.3 (0.9, 1.7)*1993–05 -0.8 (-1.2, -0.5)*
NYC White 1980–92 -2.2 (-2.9, -1.4)* 1992–956.6 (-8.3, 24.0)1995–03 -3.3 (-4.5, -2.1)*
NYC Black 1980–05 -0.3 (-0.7, 0.01)
1980–920.7 (-0.2, 1.7)1992–05 -4.1 (-5.1, -3.1)*
1980–82 -9.2 (-24.4, 9.0)1982–859.4 (-8.4, 30.6)1985–05 -0.1 (-0.05, 0.3)
* Statistically significant annual percent change, P\0.05
Table 2 Breast cancer mortality rates for the United States, New
York City and Chicago, non-hispanic black and non-hispanic white
women, selected years
YearLocationNHB NHW Rate ratio
1980*US31.8 32.6 0.98 (0.94–1.01)–
1990 US35.8 32.2 1.11 (1.08–1.15)
2000 US35.727.0 1.32 (1.28–1.36)
2005US35.6 25.8 1.38 (1.34–1.42)(\0.001)
1980NYC37.139.5 0.94 (0.82–1.08)–
1990NYC 38.530.61.26 (1.10–1.44)
2000NYC 31.629.8 1.06 (0.93–1.21)
2005NYC 33.524.5 1.36 (1.19–1.56)(\0.001)
1980 Chicago 39.0 37.91.03 (0.85–1.25)–
* Data for 1980 for the US are for Black and White women regardless
of Hispanic ethnicity
** Values in parentheses indicate whether RR in 2005 is significantly
different from the one in 1980
J Community Health
This is precisely what has happened with breast cancer
mortality in the three geographies analyzed above.
Since differential biology can’t explain these racial
disparities, what might? We have been able to identify
three hypotheses .
Differential Access to Mammography
Most surveys of self-reported mammography utilization
have shown that Black and White women have equal
screening rates at about the national goals [17, 18]. How-
ever, several studies of medical records and chart reviews
demonstrate that self-report of mammography utilization is
substantially inaccurate because many women over-report
utilization [19, 20]. A recent comprehensive meta-analysis
indicates that poor women, and thus black women, over-
report more than other (white) women, rendering the
equality of self-reported mammography use a misleading
measure and leaving a substantial racial gap . If
mammography is an effective screening tool then differ-
ential access favoring white women would contribute to the
disparity in breast cancer mortality. For example, we know
that breast cancers detected by screening are smaller, less
likely to be estrogen receptor negative, and less likely to be
undifferentiated than unscreened cancers .
Differential Quality of Mammography
There are a number of different measures of mammogra-
phy quality [23, 24], but we focus on just one here as an
example of how such thinking might proceed. The litera-
ture suggests that for every 1,000 screening mammograms
we should expect to find about 6 breast cancers. This rate
of 0.006 is an average that is based on millions of mam-
mogram exams worldwide [25–27]. The detection rate will
be lower for women who are screened regularly (as low as
2 breast cancers per 1,000) and higher for women who are
rarely screened (10 per 1,000) . For example, the
National Breast and Cervical Cancer Early Detection Pro-
gram, which provides mammograms to poor women who
tend not to receive regular screening, found a breast cancer
detection rate of 0.0094 based upon the experiences of
about 1.2 million women between 1991 and 2002 .
Breast cancer screening programs that find cancer detection
rates well below 0.006 may suffer from quality problems.
A well-publicized example may be suggestive of our
hypothesis. In October of 2002 the New York Times ran a
very long front page story about a woman who obtained a
mammogram at a city clinic, was told she was fine and
8 months later was diagnosed with breast cancer. The
clinic that missed the cancer was investigated and found to
be detecting breast cancers at a rate of only 1 per 1,000
screening mammograms. As a result, the State Department
of Health offered free mammograms to women who had
been seen recently by the clinic. Over 4,500 women
returned and were re-screened and 25 cancers that had been
missed were detected [29, 30].
Substantial information suggests that there is variation
in the quality of the mammography process . If this
quality tended to be inferior at institutions that serve poorer
women then this would contribute to the racial disparity in
breast cancer mortality. Despite the logic to this argument
we have been able to locate only one paper that investi-
gated this hypothesis and it found negative results . We
thus discuss this topic in somewhat greater detail.
Some reports have found that radiologists who spend
more time (variously defined) reading mammograms tend
to find more tumors and to find them smaller . How-
ever, this relationship between volume and quality is not
uniformly agreed upon [34, 35]. It has also been found that
breast imaging specialists find more cancers than general
radiologists. For example, Sickles and his colleagues
reported that specialists found breast cancers at a rate
almost twice as high as general radiologists when reading
screening mammograms (6.0/1,000 compared with 3.4—
our calculations) . Breast imagers tend to do better
when seeking to resolve diagnostic mammograms as well
[27, 36]. Again, if such imagers tended to work at insti-
tutions that served wealthier women, then this too would
serve to increase disparities in mammography interpreta-
tion, cancer detection and ultimately mortality.
But the reading is just one part of the mammography
process. Another is recalling women who have had
abnormal mammograms that require follow-up. In our
experience it is not uncommon for the lost to follow-up rate
at community institutions for this group to be as high as
33% . Reports in the literature have found similar
results with varying definitions, resulting in 28% without
diagnostic resolution within 6 months  and 16% for
which a final diagnosis was not recorded . Such loss to
follow-up and/or an incomplete diagnostic process would
also decrease the cancer detection rates found by screening
Furthermore, it has been documented that black women
experience longer delays between an initial abnormal
finding on a mammogram and obtaining a diagnosis .
There are other issues as well. For example, a recent study
found that black women were twice as likely not to be
notified about an abnormal result or to not correctly be able
to interpret the information they received .
Differential Access to Quality Treatment
Multiple studies have demonstrated that Black people
receive inferior medical care for almost every medical
condition [41–43]. Breast cancer is no exception [39, 44,
J Community Health
45]. The disparities in breast cancer treatment occur for
various reasons such as delays in treatment, inadequate
access to adjuvant therapy, non-receipt of designated care,
co-morbidities, financial barriers, etc. [46–49]. Certainly
such disparate treatment would contribute to the racial
disparity in breast cancer mortality.
The Biological Explanation
Taken together, these three hypotheses might be enough to
explain the racial disparity in breast cancer mortality
without invoking genetic etiologies. This is certainly the
view of the Metropolitan Chicago Breast Cancer Task
Force which has been organizing from this point of view
. It is nonetheless instructive to review the biological
explanation. The fabric of this argument has two main
threads that are sometimes presented simultaneously.
Racial Differences in Tumor Biology
Studies have revealed racial differences in diagnosed tumor
size, stage, lymph node involvement, grade, estrogen
receptor status, etc. [7, 51, 52]. However, in some cases
these differences might be generated by the later detection
of tumors in black women and thus be a function of the
detection process and not the race of the women. A recent
prominent study lends support to this view.
Smith-Bindman and her colleagues examined data on
over 1,000,000 women. Unadjusted data revealed statically
significant black:white differences in tumor size, stage,
grade, and lymph node involvement. However, these
observed differences ‘‘were attenuated or eliminated after
the cohort was stratified by screening history.’’ (p 541)
Specifically, after mammography history was taken into
account racial differences in tumor size, stage and lymph
node involvement disappeared. Only tumor grade remained
significant, though the differences varied in inexplicable
ways (some significant, some not significant) across
mammography history .
The Independent Predictor Explanation
Even if there were innate racial differences in breast cancer
biology it would still be necessary to tie these to disparities
in breast cancer mortality to complete the syllogism. This
is the task that generally falls to various versions of
regression analysis. In such analyses researchers gather
data for breast cancer mortality, race and other variables
(confounders), notably some measure of socioeconomic
status. Initially racial differences in breast cancer mortality
are observed. If, after ‘‘adjustment’’ or ‘‘correction’’ for
these other confounders race remains statistically signifi-
cant, then these researchers conclude that race is an
independent (i.e., innate) risk factor for breast cancer
mortality. Consider a recent example, simply one among
Woodward and her colleagues compared two indepen-
dent cohorts (consisting of NHB and NHW women, among
others) attending the same university hospital. The two
cohorts were defined based upon their treatment needs.
After adjustment for many biological variables black race
remained an independent predictor of lower overall sur-
vival in both cohorts. The authors note that ‘‘Such differ-
ences in tumor biology, as well as previously described
socioeconomic factors, likely contribute to the lower rate
of survival in the AA breast cancer population.’’ They then
reflect on the following: ‘‘It is clear that, as with any cohort
grouped by self-reported race, those who self-report their
race as AA or black represent a genetically and culturally
diverse group. Therefore, explaining how AA race is
associated with biologically more aggressive breast cancer
will likely be difficult’’  (our italics). Similar analyses are
employed by other authors .
Two major factors suggest that the analytical structure
used in these studies is faulty. First, it is not clear that there
exists a biological black or African-American race/group-
ing. Such a construct is heatedly debated in the medical
literature [53, 54] while the social science literature has
virtually unanimously agreed that this race is a social (not
biological) construct [55, 56]. If such a genetic group does
not exist, then of course genetics could not be responsible
for the mortality disparity.
Second, the mathematical assumptions underlying this
type of ‘‘independence after regression analysis’’ and the
‘‘problem of residual confounding’’ are troublesome to say
the least, involving issues such as difficulties with catego-
rization, measurement, aggregation and non-commensurate
indicators, in addition to contributors to racial differences
not even touched on by measures of socioeconomic status
(e.g., racism) . We have not been able to locate even one
paper that has used this ‘‘independence after regression’’
model that has even noted these issues, let alone tried to
explain how they might affect their findings as they cited
biology as a risk factor for the mortality disparity.
The causes of the black:white disparity in breast cancer
mortality is a complex matter that deserves serious analy-
sis. We would like to make it clear that we are not sug-
gesting that there are no racial biological differences in
tumor characteristics. Biological risk factors other than
tumor histology which could affect the observed mortality
disparity might involve issues like obesity and estrogen
status. Another possible factor involved in the racial dis-
parity in breast cancer mortality is the use of hormone
replacement therapy (HRT). It is established that white
women use HRT more. In fact, since the Women’s Health
Initiative’s report the use of HRT has declined a great deal
J Community Health
and following this so has the incidence of breast cancer
among white (but not black) women . This will
decrease the breast cancer mortality rate among white
women and thus increase the racial disparity.
However, we cannot see how these could generate the
racial disparities in mortality that are the subject of this
report, most of which have appeared recently and which
vary substantially by the three analyzed geographies. Some
authors discuss the possibility of a gene-environment
interaction . This may be a possibility but here too we
cannot envision how such an interaction would produce the
nature of the racial disparities delineated in this paper.
Rather, we hypothesize that these disparities are a function
of racial disparities in screening and treatment. Such
hypotheses are, of course, subject to empirical analysis.
Until empirical verification these hypotheses remain just
Racial disparities in breast cancer mortality cannot be
viewed outside the context of racism in the US. Misdi-
rected focus on biological causes places the burden of this
disparity on the innate genetic characteristics of the woman
and not on external modifiable factors such as access and
quality. As Sankar and her colleagues have written,
‘‘Overemphasis on genetics as a major explanatory factor
in health disparities could lead researchers to miss factors
that contribute to disparities more substantially and may
also reinforce racial stereotypes, which may contribute to
disparities in the first place’’ .
In a more general sense Cooper and his colleagues have
noted that: ‘‘Minority groups, particularly Blacks in the
United States, are assumed to be genetically predisposed to
virtually all common chronic diseases. The correlation
between the use of unsupported genetic inferences and the
social standing of a group is glaring evidence of bias and
demonstrates how race is used both to categorize and to
rank order subpopulations’’.
It is easy for us to look back now and see the folly—and
racism—of the claim that differential racial biology was
responsible for the elevated prevalence of syphilis among
black people in Alabama—so much so that it had to be
studied as a unique entity . We fear that current
research claims about race, biology and breast cancer
mortality similarly perpetuate racial stereotypes about
disease and have the potential to harm black people still
once again. This is why we must get this correct. As
Brawley and Freeman have pointed out: ‘‘Deep ethical and
moral questions [are raised] concerning how the research
community, the American health care system, and society
as a whole will move toward providing remedies for this
unacceptable reality [of disparities in health]’’ . It is
our hope that the analyses presented in the current report
will help move this pursuit ahead.
Cancer Foundation (grant #05-2010-075) for its generous support of
this research and other efforts to eliminate disparities in breast cancer
mortality in Chicago.
The authors would like to thank the Avon Breast
1. Ries, L., Eisner, M., & Kosary, M. (2003). SEER Cancer Statistics
Review, 1975–2000. Bethesda, MD: National Cancer Institute.
2. Hirschman, J., Whitman, S., & Ansell, D. (2007). The black:
White disparity in breast cancer mortality: The example of Chi-
cago. Cancer Causes and Control, 18, 323–333.
3. Brawley, O. W. (2002). Disaggregating the effects of race and
poverty on breast cancer outcomes. Journal of the National
Cancer Institute, 94, 471–473.
4. Olopade, O. I., Fackenthal, J. D., Dunston, G., et al. (2002).
Breast cancer genetics in African Americans. Cancer, 97,
5. Cunningham, J. E., & Butler, W. M. (2004). Racial disparities in
female breast cancer in South Carolina: Clinical evidence for a
biological basis. Breast Cancer Research and Treatment, 88,
6. Woodward, A. W., Huang, E. H., McNeese, M. D., et al. (2006).
African-American race is associated with a poorer overall sur-
vival rate for breast cancer patients treated with mastectomy and
Doxorubicin-based chemotherapy. Cancer, 107, 2662–2668.
7. Carey, L. A., Perou, C. M., Livasy, C. A., et al. (2006). Race,
breast cancer subtypes and survival in the Carolina breast cancer
study. JAMA, 295, 2492–2502.
8. Albain, K. S., Unger, J. M., Crowley, J. J., Coltman, C. A., &
randomized clinical trials patients of the Southwest Oncology
Group. Journal of the National Cancer Institute, 101, 984–992.
9. Anderson, R. N., Minino, A. M., Hoyert, D. L., & Rosenberg, H.
M. (2001). Comparability of cause of death between ICD-9 and
ICD-10: Preliminary estimates. National Vital Statistics Reports,
10. U.S. Census Bureau. (2005). American Community Survey.
11. Keppel, K. G., Pearcy, J. N., & Klein, R. J. (2010). Measuring
progress in healthy people 2010. Healthy People 2010 Statical
Notes, 2004(25), 1–16.
12. Kim, H. J., Fay, M. P., Feuer, E. J., & Midthune, D. N. (2000).
Permutations tests for joinpoint regression with applications to
cancer rates. Statistics in Medicine, 19, 335–351. (correction,
13. Jones, B. A., Patterson, E. A., & Calvocoressi, L. (2003).
Mammography screening in African American women: Evalu-
ating the research. Cancer, 97(1 Suppl), 981–985.
14. Berry, D. A., Cronin, K. A., Plevritis, S. K., et al. (2005). Effect
of screening and adjuvant therapy on mortality from breast can-
cer. New England Journal of Medicine, 353, 1784–1792.
15. Chu, Q. D., Smith, M. H., Williams, M., et al. (2009). Race/
ethnicity has no effect on outcome for breast cancer patients
treated at an academic center with a public hospital. Cancer
Epidemiology, Biomarkers and Prevention, 18(8), 2157–2161.
16. Tehranifar, P., Neugut, A. I., Phelen, J. C., Link, B. G., Liao, Y.,
Desai, M., et al. (2009). Medical advances and racial/ethnic
J Community Health
disparities in cancer survival. Cancer Epidemiology, Biomarkers
and Prevention, 18(10), 2701–2708.
17. Centers for Disease Control and Prevention. (2005). Breast can-
cer screening and socioeconomic status—35 metropolitan areas,
2000 and 2002. MMWR. Morbidity and Mortality Weekly Report,
18. Whitman, S., Shah, A. M., Silva, A., & Ansell, D. (2007).
Mammography screening in six diverse communities in Chicago-
a population study. Cancer Detection and Prevention, 31,
19. Smith-Bindman, R., Miglioretti, D. L., Lurie, N., et al. (2006).
Does utilization of screening mammography explain racial and
ethnic differences in breast cancer? Annals of Internal Medicine,
20. Kagay, C. R., Quale, C., & Smith-Bindman, R. (2006). Screening
mammography in the American elderly. American Journal of
Preventive Medicine, 31, 142–149.
21. Rauscher, G. H., Johnson, T. P., Cho, T. I., & Walk, J. A. (2008).
Accuracy of self-reported cancer screening histories: A meta-
analysis. Cancer Epidemiology, Biomarkers and Prevention, 17,
22. Olivotto, I. A., Gomi, A., Bancej, C., et al. (2002). Influence of
delay to diagnosis on prognostic indicators of screen-detected
breast carcinoma. Cancer, 94, 2143–2150.
23. American College of Radiology (ACR). (2003). Breast imaging
reporting and data system (BI-RADS) (4th ed.). Reston, VA:
American College of Radiology.
24. Rosenberg, R. D., Yankaskas, B. C., Abraham, L. A., et al.
(2006). Performance benchmarks for screening mammography.
Radiology, 24, 55–66.
25. May, D. S., Lee, N. C., Richardson, L. C., Giustozzi, A. G., &
Bobo, J. K. (2000). Mammography and breast cancer detection by
race and hispanic ethnicity: Results from a national program
(United States). Cancer Causes and Control, 11, 697–705.
26. Wang, H., Karesen, R., Hervik, A., & Thoresen, S. O. (2001).
Mammography screening in Norway: Results from the first
screening round in four countries and cost-effectiveness of a
modeled nationwide screening. Cancer Causes and Control, 12,
27. Sickles, E. A., Wolverton, D. E., & Dee, K. E. (2002). Perfor-
mance parameters for screening and diagnostic mammography:
Specialist and general radiologists. Radiology, 224, 861–869.
28. Ryerson, A. B., Bernard, V. B., & Major, A. C. (2005). National
breast and cervical cancer early detection program: 1991–2002
national report. Atlanta, GA: Centers for Disease Control and
Prevention, National Center for Chronic Disease Prevention and
Health Promotion, Division of Cancer Prevention and Control.
29. Moss, M., & Steinhauer, J. (2002). Mammogram clinic’s flaws
highlight gaps in US rules. The New York Times, A1.
30. Centers for Disease Control and Prevention. (2004). Breast can-
cer-screening data for assessing quality of services—New York,
2000–2003. MMWR. Morbidity and Mortality Weekly Report,
31. Miglioretti, D. L., Smith-Bindman, R., Abraham, L., et al. (2007).
Radiologist characteristics associated with interpretive perfor-
mance of diagnostic mammography. Journal of National Cancer
Institute, 99, 1854–1863.
32. Jones, B. A., Kasl, S. V., Culler, C. S., et al. (2001). Is variation
in quality of mammographic services race linked?. Journal of
Health Care for the Poor and Underserved, 12, 113–126.
33. Smith-Bindman, R., Chu, P., Miglioretti, D. L., et al. (2005).
Physician predictors of mammographic accuracy. Journal of the
National Cancer Institute, 97, 358–367.
34. Institute of Medicine and National Research Council. (2005).
Improving breast imaging quality standards. Washington, DC:
The National Academies Press. See especially Chapter 2.
35. Barlow, W. E., Chi, C., Carney, P. A., et al. (2002). Accuracy of
screening mammography interpretation by characteristics of
radiologists. Journal of the National Cancer Institute, 94,
36. Miglioretti, D. L., Smith-Bindman, R., Abraham, R., et al. (2007).
Radiologist characteristics associated with interpretive perfor-
mance of diagnostic mammography. Journal of the National
Cancer Institute, 99, 1854–1863.
37. Hirschman, J., Whitman, S., Ansell, D., Grabler, P., Allgood, K.
mammography quality. Chicago, IL: Sinai Urban Health Institute.
http://www.suhichicago.org/files/publications/G.pdf. Accessed 2
38. Kerner, J. F., Yedidia, M., Padget, D., et al. (2003). Realizing the
promise of breast cancer screening: Clinical follow-up after
abnormal screening among black women. Preventive Medicine,
39. Elmore, J. G., Nakano, C. Y., Linden, H. M., Reisch, L. M.,
Ayanian, J. A., & Larson, E. B. (2005). Racial inequities in the
timing of breast cancer detection, diagnosis, and initiation of
treatment. Medical Care, 43(2), 141–148.
40. Jones, B. A., Reams, K., Calvocoressi, L., Dailey, A., Kasl, S. V.,
& Liston, N. M. (2007). Adequacy of communicating results
from screening mammograms to African American and white
women. American Journal of Public Health, 97(3), 531–538.
41. Smedley, B. D., Stih, A. Y., & Nelson, A. R. (Eds.). (2003).
Unequal treatment: Confronting racial and ethnic disparities in
health care. Washington, DC: The National Academies Press.
42. Epstein, A. M., Ayanian, J. Z., Keogh, J. H., et al. (2000). Racial
disparities in access to renal transplantation. New England
Journal of Medicine, 343, 1537–1544.
43. Trivedi, A. N., Zaslavsky, A. M., Schneider, E. C., & Ayanian, J.
Z. (2006). Relationship between quality of care and racial dis-
parities in medicare health plans. JAMA, 296, 1998–2004.
44. Gross, C. P., Smith, B. D., Wolf, E., & Andersen, M. (2008).
Racial disparities in cancer therapy: Did the gap narrow between
1992 and 2002? Cancer, 112, 900–908.
45. Li, C. I., Malone, K. E., & Daling, J. R. (2003). Differences in
breast cancer stage, treatment, and survival by race and ethnicity.
Archives of Internal Medicine, 163, 49–56.
46. Gorin, S. S., Heck, J. E., Cheng, B., et al. (2006). Delays in breast
cancer diagnosis and treatment by racial/ethnic group. Archives of
Internal Medicine, 166, 2244–2252.
47. Schleinitz, M. D., DePalo, D., Blume, J., & Stein, M. (2006). Can
differences in breast cancer utilities explain disparities in breast
cancer care? Journal of General Internal Medicine, 21(12),
48. Breen, N., Wesley, M. N., Merrill, R. M., et al. (1999). The
relationship of socio-economic status and access to minimum
expected therapy among female breast cancer patients in the
National Cancer Institute Black-White cancer survival study.
Ethnicity and Disease, 9, 111–125.
49. Institute of Medicine. (2006). Committee on Quality of Health
Care in America. Crossing the quality chasm: A new health
system for the 21st Century. Washington, DC: National Academy
50. Ansell, D., Grabler, P., Whitman, S., et al. (2009). A community
effort to reduce the black/white breast cancer mortality disparity
in Chicago. Cancer Causes and Control. Published online: 18
51. Chlebowski, R. T., Chen, Z., Anderson, G. L., et al. (2005).
Ethnicity and breast cancer: Factors influencing differences in
incidence and outcome. Journal of the National Cancer Institute,
52. Curtis, E., Quale, C., & Haggstrom, D. (2008). Racial and ethnic
differences in breast cancer survival. Cancer, 112, 171–180.
J Community Health
53. Freeman, H. P. (1998). The meaning of race in science–consid-
erations for cancer research. Cancer, 82, 219–225.
54. Hunt, L. M., & Megyesi, M. S. (2008). The ambiguous meanings
of the racial/ethnic categories routinely used in human genetics
research. Social Science and Medicine, 66, 349–361.
55. Duster, T. (2003). Backdoor to eugenics (2nd ed.). New York,
NY: Routledge Press.
56. Allen, T. W. (1997). The Invention of the White Race. New York,
NY: Verso Press.
57. Kaufman, J. S., Cooper, R. S., & McGhee, D. L. (1997). Socio-
economic status and health in blacks and whites: The problem of
residual confounding and the resiliency of race. Epidemiology, 8,
58. Krieger, N., Chen, J. T., & Waterman, P. D. (2010). Decline in
US breast cancer rates after the women’s health initiative:
Socioeconomic and racial/ethnic differentials. American Journal
of Public Health, 100, S132–S139.
59. McClintock, M. K., Conzen, S. D., Gehlert, S., Masi, C., & Ol-
opade, F. (2005). Mammary cancer and social interactions:
identifying multiple environments that regulate gene expression
throughout the life span. Journals of Gerontology. Series B,
Psychological Sciences and Social Sciences, 60, 32–41.
60. Sankar, P. S., Cho, M. K., Condit, C. M., et al. (2004). Genetic
research and health disparities. JAMA, 291, 2985–2989.
61. Cooper, R. S., Kaufman, J. S., & Ward, R. (2003). Race and
62. Jones, J. (1993). Bad blood. New York, NY: Free Press.
63. Brawley, O. W., & Freeman, H. P. (1999). Race and outcomes: Is
this the end of the beginning for minority health research?
Journal of the National Cancer Institute, 9, 1433–1440.
J Community Health