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Journal of Racial and Ethnic Health
Disparities
ISSN 2197-3792
J. Racial and Ethnic Health Disparities
DOI 10.1007/s40615-020-00855-y
Socioeconomic and Racial Disparities in
Cancer Stage at Diagnosis, Tumor Size,
and Clinical Outcomes in a Large Cohort of
Women with Breast Cancer, 2007–2016
Dale Hardy & Daniel Y.Du
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Socioeconomic and Racial Disparities in Cancer Stage at Diagnosis,
Tumor Size, and Clinical Outcomes in a Large Cohort of Women
with Breast Cancer, 2007–2016
Dale Hardy
1
&Daniel Y. Du
2
Received: 7 April 2020 /Revised: 19 August 2020 /Accepted: 24 August 2020
#W. Montague Cobb-NMA Health Institute 2020
Abstract
Background Socioeconomic and treatment factors contribute to diagnosis of early-stage (local-stage) breast cancer, as well as
excess deaths among African American women.
Objectives We evaluated socioeconomic and treatment predictive factors for early-stage breast cancer among African American
women compared to Caucasian women. A secondary aim evaluated predictors and overall risks associated with all-cause and
breast cancer–specific mortality.
Methods We used retrospective cohort population-based study data from the Surveillance, Epidemiology, and End Results
(SEER) Program on 547,703 women aged ≥20 years diagnosed with breast cancer primary tumors from 2007 to 2016.
Statistical analysis used logistic regression to assess predictors of early-stage breast cancer and Cox proportional hazards
regression for mortality risks.
Results African American women were more likely to be diagnosed at advanced-stage, had larger tumor size at diagnosis, and
received less cancer-directed surgery, but more chemotherapy than Caucasian women. Insured women (> 50%) were more likely
to be diagnosed at early-stage and to have smaller tumors (p< 0.05). Education level, poverty level, and household income had no
impact on racial disparities or socioeconomic disparities in women diagnosed at early stage. We found increased risks for all-
cause mortality (hazard ratio = 1.18; 95% confidence interval, 1.16–1.21) and breast cancer–specific mortality (HR = 1.22; 95%
CI, 1.19–1.25) among African American women compared to Caucasian women after adjusting for demographic, socioeconom-
ic, and treatment factors.
Conclusions In this population-based study using the most recent SEER data, African American women with breast cancer
continued to exhibit higher all-cause mortality and breast cancer–specific mortality compared to Caucasian women.
Keywords Breast cancer .Early stage .Racial disparities .Treatment .Surgery .Chemotherapy .Race/ethnicity .African
American women
Introduction
Despite continual efforts to reduce the burden of inequalities
in socioeconomic levels and among ethnic minority races
compared to Caucasians, disparities in health care utilization
and clinical outcomes continue to persist [1–11]. Reports by
the Institute of Medicine [1] and other studies [2–11]have
consistently demonstrated disparities observed in cancer care
and treatment among ethnic minority populations compared to
Caucasians. Although outcomes on racial disparities in cancer
care utilization are relatively consistent, there are conflicting
reports on racial differences in survival even after adjustment
for dissimilarities in treatment [12–27]. Some studies showed
that racial disparities no longer existed after adjusting for treat-
ment rendered [12–16], while other studies demonstrated that
differences persisted in survival and mortality despite
adjusting for dissimilarities in health care and treatment
[17–27]. These inconsistent findings could be due to different
study designs, analytical methods, and biases and
*Dale Hardy
dhardy@msm.edu
1
Department of Internal Medicine, Morehouse School of Medicine,
Research Wing, Rm 339, 720 Westview Drive, Atlanta, GA 30310,
USA
2
Department of Natural Sciences, University of Houston,
Houston, TX 77030, USA
Journal of Racial and Ethnic Health Disparities
https://doi.org/10.1007/s40615-020-00855-y
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confounding in information and measurements [12–27].
Moreover, many previous reports did not have information
on treatment received [17–19]. Because health insurance and
access to health care are fundamentally important for early
detection of cancer and timely treatment [11,28–32], it is
critical to incorporate this information in the analysis. The
aim of this study was to evaluate socioeconomic and treatment
predictive factors associated with early-stage breast cancer
among African American compared to Caucasian women. A
secondary aim evaluated risk factors and overall risks associ-
ated with all-cause mortality and breast cancer–specific mor-
tality. In this study, we used the most recent data from the
National Cancer Institute’s Surveillance, Epidemiology, and
End Results (SEER) data released in November 2018. These
data sources enabled us to examine socioeconomic and racial
disparities in cancer stage at diagnosis, as well as tumor size
and clinical outcomes on survival in a large cohort of African
American and Caucasian women with breast cancer. Our
study findings may help inform interventions and policies
for cancer prevention and control.
Patients and Methods
Data Sources
We used data from the National Cancer Institute’s SEER
Public Use Data Set released in November 2018 [34]. SEER
data is an authoritative source of data on cancer incidence and
has high accuracy and completeness [33]. The SEER program
supports population-based tumor registries in nine areas (San
Francisco/Oakland, San Jose-Monterey, Los Angeles, Greater
California, Detroit, Seattle, Atlanta, Rural Georgia, and
Greater Georgia) and 8 states (Connecticut, Iowa, New
Mexico, Utah, Hawaii, Louisiana, Kentucky, and New
Jersey), covering 28% of the US population. The registries
ascertain all newly diagnosed (incident) breast cancer cases
from multiple reporting sources such as hospitals, outpatient
clinics, laboratories, private medical practitioners, nursing/
convalescent homes/hospices, autopsy reports, and death cer-
tificates. The SEER public use data set includes information
on types of cancer-directed surgical procedures received, re-
ceipt of radiation therapy, and chemotherapy. These treat-
ments were provided in the first course of therapy (within
4 months of initial therapy after diagnosis).
Information on SEER data also includes data on health
insurance coverage at the time of cancer diagnosis from
2007 through 2016, as well as information on tumor stage at
diagnosis, tumor location and size, lymph node and distant
organ metastases, histologic type, tumor grade, and demo-
graphic characteristics such as age, race/ethnicity, and marital
status. Additional variables included socioeconomic variables
such as education level, poverty level, and household income
at the county level. Our research received approval from
SEER after signing the SEER Research Data Agreement.
We received additional approval for using data on treatment
information for radiation therapy and chemotherapy. The
study was considered exempt for Institutional Review Board
(IRB) review because it did not involve any patient contact
and only contained review of de-identified SEER Public Use
Data.
Study Population
Our initial sample included 559,113 African American and
Caucasian women who were diagnosed with incident breast
cancer at age 20 or older between 2007 and 2016 in 17 SEER
registries. Patients of other race/ethnicity were not included in
the study. We studied cases diagnosed withbreastcancerfrom
2007 through 2016 because health insurance status informa-
tion was only available during these years. From our initial
sample (559,113 women), we excluded women with missing
information on tumor stage (n= 11,285) and socioeconomic
variables (education level, poverty level, and household in-
come) at the county level (n= 125). Our final sample for
analysis included 547,703 women of which 66,703 (11.6%)
were African American women and 481,000 (87%) were
Caucasian women.
Study Variables
Race/Ethnicity and Patient Characteristics
Tumor and patient characteristics included tumor size (cate-
gorized as < 1.0, 1.0–< 2.0, 2.0–< 3.0, 3.0–< 4.0, ≥4.0 cm,
and unknown tumor size); histological stage (local, regional,
or distant stage); tumor grade (well, moderately, poorly dif-
ferentiated, undifferentiated, or unknown); hormone receptor
status (estrogen or progesterone receptor positive, negative, or
unknown); age at diagnosis (< 45, 45–54, 55–64, 65–74, 75–
79, ≥80 years); marital status (married, unmarried, and un-
known marital status); race/ethnicity (African American and
Caucasians women); time period (2007 to 2016); and geo-
graphic areas (17 SEER registries) [34].
Health Insurance and Socioeconomic Status (SES)
Table 2gives a summary of the following variables in the first
column. Health Insurance at an individual level available from
2007 to 2017 was coded as uninsured, any Medicaid, insured,
insured but without specifics, and unknown. There were three
socioeconomic variables at the county level available in the
SEER*Stat program including education level, poverty level,
and household income. Counties were divided into quartiles
based on percentages of the patients in the given county with
the specific criteria for each of these 3 variables. Education
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level divided into quartiles for patients with less than high
school education was coded as follows: first quartile (2.1–<
9.8%), second (9.8–< 12.8%), third (12.8–<17.8%), and
fourth quartile (≥17.8%). For people living under poverty
levels, the counties’quartiles were defined as 4.0–<10.7%,
10.7–< 14.0%, 14.0–17.9%, and ≥17.9% percentage. For
household income, counties were classified based on the me-
dian annual household income grouped into quartiles:
≥$68,650, $57,580–< 68,650, $49,000–< 57,580, and
< $49,000. Education, poverty, and household income vari-
ables were first recoded to ensure that lower values represent-
ed lower quartiles of socioeconomic status.
Treatment for Breast Cancer
Cancer-directed surgery was coded as 10–89 in SEER data
and included total mastectomy and breast-conserving surgery
that was defined as receiving segmental mastectomy, lumpec-
tomy, quadrantectomy, tylectomy, wedge resection, nipple
resection, excisional biopsy, or partial mastectomy unspeci-
fied [34]. SEER records radiation therapy provided within
4 months after diagnosis that was coded as either yes or no/
unknown. Radiation therapy included beam radiation, radio-
active implants, brachytherapy, radioisotopes or other radia-
tion [33]. Chemotherapy was also coded as either yes or no/
unknown. Treatments were coded separately, but it was un-
known if radiation therapy or chemotherapy was given before
surgery or as adjuvant therapy after surgery, or stand-alone
treatment.
Survival and Mortality Variables
The vital status (dead or alive), cause of death, date of death,
and survival time in months from the date of diagnosis to the
date of death or the date of last follow-up (December 31, 2016)
were available from the SEER*Stat data [34]. All-cause mor-
tality was defined as death from any cause that was the under-
lying cause of death indicated in the SEER registries. Patients
who were alive at the last date of follow-up were censored.
Breast cancer–specific mortality was defined as breast cancer
as the underlying cause of death. In this specific analysis, pa-
tients who died of causes other than breast cancer or who were
still alive at the date of last follow-up were censored.
Statistical Analysis
Differences in the distribution of baseline characteristics among
the two race/ethnic groups were tested using Pearson’schi-
square statistic. Logistic regression models were used to assess
the odds ratio of being diagnosed at early tumor stage (local
stage), having smaller tumor size, and receiving cancer-directed
treatment (surgery, radiation therapy, and chemotherapy) after
adjusting for other risk factors and confounders, including age,
marital status, tumor grade, hormone receptor status, year of
diagnosis, SEER geographic areas, and socioeconomic factors
(health insurance, education level, poverty level, and household
income at the county level). Kaplan-Meier curves for survival
curves and log-rank tests were conducted between comparison
groups. Cox proportional hazard regression model was used in
survival analysis using the PHREG procedure available in the
SAS system (Cary, NC: SAS Institute, Inc.). The proportional-
ity assumption was satisfied when the log-log Kaplan-Meier
curves for survival functions by race/ethnicity or socioeconom-
ic status were parallel and did not intersect. In these Cox pro-
portional hazard regression analyses, four models were ana-
lyzed; however only the third and fourth models were present-
ed. The first model was an unadjusted model on the hazard ratio
of mortality between African American and Caucasian women.
The second model adjusted for patient demographic character-
istics (including age and marital status), tumor factors (such as
tumor stage, tumor grade, tumor size, and hormone receptor
status), year of diagnosis, and SEER geographic areas. The
third model adjusted for health insurance, education level, pov-
erty level, and household income, in addition to the factors in
the second model. The fourth model adjusted additionally for
cancer-directed treatment received (surgery, radiation therapy,
and chemotherapy).
Results
Table 1presents the distribution of demographic characteris-
tics, tumor characteristics, and treatment between the two
race/ethnic groups of women diagnosed with breast cancer
at age ≥20 over the past 10 years from 2007 to 2016.
Compared to Caucasian women, a higher percentage of
African American women were diagnosed with breast cancer
at younger ages, were unmarried at diagnosis, were diagnosed
at advanced stage, had larger tumor size, had less differentiat-
ed grade, and had negative hormone receptor status. These
differences were statistically significant at p<0.001.
Table 2presents the distribution of health insurance and
socioeconomic status by race/ethnicity. A larger percentage
of African American women with breast cancer were unin-
sured or on Medicaid and were in the lowest quartiles (3rd
and 4th quartiles) of socioeconomic status (education level,
poverty level, and household income atthe county level) com-
pared to Caucasians. For example, 74.7% of African
American women vs. 86% of Caucasians women were in-
sured, while 69.6% of African American women vs. 46.7%
Caucasian women were in the last two lowest quartiles of
socioeconomic status.
Table 3presents the percentage of patients diagnosed with
early tumor stage (local stage), having tumor size < 1 cm, and
receiving cancer-directed treatment (surgery, radiation thera-
py, and chemotherapy) by race/ethnicity, health insurance,
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and other socioeconomic factors. A higher percentage of
Caucasian women were diagnosed at an early stage, had
smaller tumor size at diagnosis, and received cancer-directed
surgery than African American women. However, a higher
percentage of African American women with breast cancer
received less radiation therapy, but more chemotherapy than
Caucasian women. In addition, a higher percentage of women
with breast cancer were diagnosed at an early stage and re-
ceived cancer-directed surgery. Moreover, a higher percent-
age of women without health insurance or who had Medicaid
coverage received chemotherapy. This was likely due to
detection of late-stage tumor at diagnosis. In addition, there
were statistically significant differences in early-stage diagno-
sis, tumor size, and treatment by socioeconomic status factors
(education level, poverty level, and household income) at the
county level.
Table 4presents the adjusted odds ratio of having been diag-
nosed at early tumor stage (local stage), having tumor size <
1 cm, and receiving cancer-directed treatment (surgery, radiation
therapy, and chemotherapy) by race/ethnicity, health insurance,
and other socioeconomic factors after adjusting for differences
in age, marital status, other tumor characteristics, year of
Table 1 Comparison of patient
and tumor characteristics between
African American and Caucasian
and women diagnosed with breast
cancer, 2007–2016
Patient and tumor African Americans Caucasians pvalue
Characteristics n%n%
Age (years) <0.001
<45 (20–44) 9419 14.1 46,865 9.7
45–54 15,957 23.9 96,876 20.1
55–64 18,223 27.3 123,113 25.6
65–74 13,489 20.2 115,857 24.1
75–84 7186 10.8 71,263 14.8
≥85 2429 3.6 27,026 5.6
Marital Status <0.001
Married 22,373 33.5 267,486 55.6
Unmarried 40,531 60.8 189,404 39.1
Unknown 3799 5.7 24,110 5.0
Tumor stage <0.001
Local stage 37,631 56.4 314,183 65.8
Regional stage 23,235 34.8 137,661 28.6
Distant stage 5837 8.8 26,787 5.6
Tumor size (cm) <0.001
< 1.0 10,262 15.4 101,283 21.1
1.0–< 2.0 19,103 28.6 163,395 34.0
2.0–< 3.0 13,023 19.5 88,957 18.5
3.0–< 4.0 7378 11.1 41,538 8.6
≥4.0 13,709 20.6 67,769 14.1
Unknown size 3228 4.8 18,058 3.8
Tumor grade <0.001
Well differentiated (I) 9054 13.6 109,317 22.7
Moderately differentiated(II) 23,232 34.8 202,340 40.1
Poorly differentiated (III) 28,694 43.0 136,706 28.4
Undifferentiated (IV) 380 0.6 2303 0.5
Unknown 5343 8.0 30,334 6.3
Hormone receptor status < 0.001
Positive 46,217 69.3 392,978 81.7
Negative 17,775 26.7 70,362 14.6
Unknown 2711 4.1 17,660 3.7
Total 66,703 100.0 481,000 100.0
pvalues for proportions of categorical variables among patient and tumor characteristics were calculated using
Pearson’s chi-square tests of hypothesis for independence
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diagnosis, and SEER geographic areas. Similar to our findings
in Table 3, African American women were more likely to be
diagnosed at advanced stage, more likely to have larger tumor
size at diagnosis, and more likely to receive chemotherapy, but
less likely to receive cancer-directed surgery than Caucasian
women. In addition, insured women were more likely to receive
cancer-directed surgery, but were less likely to receive chemo-
therapy as compared to uninsured patients, after adjusting for
age, marital status, tumor stage, tumor grade, hormone receptor
status, year of diagnosis, and SEER geographic areas. There
were no significant differences in early-stage diagnosis for so-
cioeconomic factors (education level, poverty level, and house-
hold income at the county level). However, there were varying
effects of socioeconomic factors for tumor size < 1 cm and treat-
ment by the quartiles of education level, poverty level, and
household income at the county level. For example, patients in
the lowest quartile (4th quartile) compared to those in the highest
quartile (1st quartile) of education were 8% less likely to have a
smaller tumor size < 1 cm (odds ratio = 0.92; 95% confidence
interval, 0.88–0.95), unlike those in the 2nd quartile of education
whowere7%morelikelytohaveatumorsize<1cm(OR=
1.07; 95% CI, 1.05–1.10).
Finally, we investigated the risk of all-cause mortality as
well as breast cancer–specific mortality using four different
statistical models for African American women compared to
Caucasian women (Table 5). For all-cause mortality, in model
1 (not presented), the unadjusted hazard ratio (HR) showed a
1.50-fold increased risk (HR = 1.50; 95% CI, 1.48–1.53) in
African American women as compared to Caucasian women
with breast cancer. However, this effect estimate decreased in
model 2 (not presented) to 1.27-fold increased risk (HR =
1.27; 95% CI, 1.25–1.30) for all-cause mortality after
adjusting for patient and tumor characteristics as well as year
of diagnosis, and SEER geographic areas. In model 3 in
Table 5, we further adjusted for health insurance and other
socioeconomic factors (education level, poverty level, and
household income at the county level), and the hazard ratio
was reduced to 1.22-fold increased risk (HR = 1.22; 95% CI,
1.19–1.24) for all-cause mortalityfor African American wom-
en as compared to Caucasian women. Model 4 in Table 5
further adjusted for cancer-directed treatment rendered (sur-
gery, radiation therapy, and chemotherapy), and the hazard
ratio for all-cause mortality was reduced to 1.18-fold in-
creased risk (HR = 1.18; 95% CI, 1.16–1.21) for all-cause
Table 2 Comparison of
socioeconomic factors between
African American and Caucasian
women diagnosed with breast
cancer, 2007–2016
Socioeconomic Factors African Americans Caucasians pvalue
N%n%
Health insurance <0.001
Uninsured 2180 3.3 6556 1.4
Any Medicaid 13,301 19.9 46,028 9.6
Insured 49,828 74.7 417,880 86.9
Insured with no specifics 9685 14.5 66,201 13.8
Unknown 1394 21. 10,536 2.2
Education (% with less than high school) at county level < 0.001
1st quartile (2.1–< 9.8%) 9894 14.8 126,198 26.2
2nd quartile (9.8–< 12.8%) 17,984 27.0 120,094 25.0
3rd quartile (12.8–< 17.8%) 22,789 34.2 113,748 23.7
4th quartile (≥17.8%) 16,036 24.0 120,960 25.2
Poverty (% with poverty line) at county level < 0.001
1st quartile (4.0–< 10.7%) 8085 12.1 127,361 26.5
2nd quartile (10.7–< 14.0%) 12,216 18.3 129,123 26.8
3rd quartile (14.0–17.9%) 18,913 28.4 118,644 24.7
4th quartile (≥17.9%) 27,489 41.2 105,872 22.0
Household income ($) at county level < 0.001
1st quartile ($19,340–< $49,000) 25,189 37.8 111,713 23.2
2nd quartile ($49,000–< $57,580) 18,039 27.0 117,426 24.4
3rd quartile ($57,580–< $68,650) 13,451 20.2 125,192 26.0
4th quartile (≥$68,650) 10,024 15.0 126,669 26.3
Total 66,703 100.0 481,000 100.0
pvalues for proportions of categorical variables for patient and tumor characteristics were calculated using
Pearson’s chi-square tests of hypothesis for independence
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mortality, and remained significantly higher in African
American women compared to in Caucasian women. We then
investigated the risk of breast cancer–specific mortality for
African American women compared to Caucasian women.
Model 1 (not presented) had an unadjusted hazard ratio of
1.83-fold increased risk (HR = 1.83; 95% CI, 1.79–1.88) in
African American women compared to Caucasian women.
Model 2 (not presented) had a substantially reduced hazard
ratio (HR = 1.29; 95% CI, 126–1.32) after adjusting for pa-
tient and tumor characteristics as well as year of diagnosis, and
SEER geographic areas in African American women com-
pared to Caucasian women. Model 3 and model 4 in Table 5
showed a small decrease in risk for breast cancer–specific
mortality for African American women compared to
Caucasian women, after adjusting for health insurance and
other socioeconomic status factors at the county level (HR =
1.25; 95% CI, 1.22–1.28) and treatment rendered (HR = 1.22;
95% CI, 1.19–1.25).
Among all women with breast cancer in 2007–2016 with the
last follow-up of December 31, 2016, mean survival time was
49.4 months for Caucasian women and 45.2 months for
African American women with breast cancer. Figure 1presents
the Kaplan-Meier survival curves for overall survival and
breast cancer–specific survival, in which survival rates were
higher in Caucasian women than in African American women
with breast cancer (log-rank tests were significant at p<0.001).
Discussion
In this study, we examined socioeconomic and treatment pre-
dictive factors for early-stage breast cancer among African
American women compared to Caucasian women. In addi-
tion, we evaluated predictors and overall risks associated with
all-cause mortality and breast cancer–specific mortality. We
found that more Caucasian women were diagnosed at early
stage compared to African American women. African
American women had larger tumors at diagnosis and received
less cancer-directed surgery but received more chemotherapy
treatment than Caucasian women. Being insured was impor-
tant for being diagnosed at early stage with smaller tumors.
For patients diagnosed at early stage, education level, poverty
Table 3 Percentage of patients who were diagnosed at early stage, small tumor size, and treatment received including race/ethnicity and socioeconomic
factors in women with breast cancer
Percent (%)
Race and socioeconomic factors Early-stage diagnosis Tumor size < 1 cm Cancer-directed surgery Radiation therapy Chemotherapy
Race (%)
African Americans 56.4* 15.4* 86.6* 46.0* 50.1*
Caucasians 65.8 21.1 91.6 47.7 37.5
Health insurance (%)
Uninsured 47.6* 11.3* 78.2* 42.9* 55.9*
Any Medicaid 52.6 12.8 85.6 42.4 48.2
Insured 66.8 21.8 92.3 49.2 38.5
Insured with no specifics 64.8 20.1 89.2 45.3 34.2
Unknown 65.6 19.1 76.3 31.8 27.0
Education (% with less than high school) at county level
1st quartile (2.1–< 9.8%) 66.5* 22.5* 91.6* 52.3* 38.2*
2nd quartile (9.8–< 12.8%) 65.9 21.7 91.2 50.5 38.7
3rd quartile (12.8–< 17.8%) 63.7 19.7 90.7 46.7 39.9
4th quartile (≥17.8%) 62.5 17.6 90.4 40.4 39.1
Poverty (% with poverty line) at county level
1st quartile (4.0–< 10.7%) 67.1* 22.8* 91.8* 51.9* 37.9*
2nd quartile (10.7–< 14.0%) 65.4 21.1 91.2 48.7 38.5
3rd quartile (14.0–17.9%) 63.6 19.2 90.2 42.9 38.0
4th quartile (≥17.9%) 62.6 18.4 90.6 46.4 41.7
Household income ($) at county level (%)
1st quartile ($19,340–< 49,000) 63.2* 19.1* 90.9* 47.2* 41.0*
2nd quartile ($49,000–< 57,580) 63.4 18.9 90.5 43.2 38.8
3rd quartile ($57,580–< 68,650) 65.1 21.3 91.2 50.2 39.1
4th quartile (≥$68,650) 67.0 22.1 91.2 49.1 37.2
*Indicates significance at p< 0.05 from chi-square crude comparisons by race, insurance, education, poverty, and income
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level, and household income had no impact on racial dispar-
ities or socioeconomic health disparities in breast cancer be-
tween African American and Caucasian women. There were
significant disparities in mortality between African American
women compared to Caucasian women with breast cancer.
However, these differences were reduced substantially after
adjusting for patient and tumor characteristics, health insur-
ance, socioeconomic factors, and treatment received.
Nevertheless, African American women had increased risks
for all-cause mortality and breast cancer–specific mortality
compared to Caucasian women.
Other studies have investigated early-stage breast
cancer–specific mortality in African American compared
to Caucasian women [35–41]. Komenaka et al. [35]report-
ed that in their study of mostly uninsured women, the effect
of race on survival was no longer significant after adjusting
for demographic and treatment factors. Yedjou et al. [39]
reported in their review that the disparity in mortality for
African American women compared to Caucasian women
may be due to many clinical and non-clinical risk factors,
such as lack of health insurance, barriers to early detection
and screening, more advanced stage of disease at diagnosis,
and lack of access to better cancer treatment. In our study,
we found that African American women were diagnosed at
later stages and subsequently had larger-sized tumors than
Caucasian women. Furthermore, after we controlled for
confounders and risk factors, there were significant differ-
ences in early stage at diagnosis, and varying effects for
predictors of tumor size < 1 cm and treatment, education
level, poverty level, and household income at the county
level. In addition, African American women with breast
cancer received less cancer-directed surgery but were more
likely to receive chemotherapy than their Caucasian coun-
terparts after adjusting for risk factors.
Table 4 Multiple regression of being diagnosed at early stage, having small tumor size, and receiving treatment by race and socioeconomic factors in
women with breast cancer
Odds ratio (95% confidence interval)*
Early-stage diagnosis Tumor size < 1 cm Cancer-directed surgery Radiation therapy Chemotherapy
Race/ethnicity
Caucasians 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
African Americans 0.85 (0.84–0.87) 0.88 (0.86–0.90) 0.85 (0.83–0.88) 1.02 (1.00–1.04) 1.26 (1.24–1.29)
Health insurance
Uninsured 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
Any Medicaid 1.11 (1.05–1.16) 1.16 (1.08–1.25) 1.50 (1.40–1.61) 0.98 (0.93–1.03) 0.84 (0.80–0.88)
Insured 1.54 (1.47–1.61) 1.82 (1.70–1.95) 1.96 (1.83–2.09) 1.04 (0.99–1.09) 0.68 (0.64–0.71)
Insured with no specifics 1.51 (1.44–1.59) 1.68 (1.56–1.80) 1.42 (1.32–1.52) 0.94 (0.90–0.98) 0.53 (0.50–0.55)
Unknown 2.18 (2.04–2.33) 1.60 (1.47–1.74) 0.75 (0.69–0.82) 0.65 (0.61–0.69) 0.39 (0.36–0.42)
Education (% with less than high school) at county level
1st quartile (2.1–< 9.8%) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
2nd quartile (9.8–< 12.8%) 0.98 (0.95–1.00) 1.07 (1.05–1.10) 1.08 (1.04–1.13) 1.03 (1.00–1.05) 0.99 (0.97–1.02)
3rd quartile (12.8–< 17.8%) 0.97 (0.94–1.00) 0.97 (0.95–1.01) 1.09 (1.03–1.14) 0.89 (0.87–0.92) 1.05 (1.02–1.08)
4th quartile (≥17.8%) 0.98 (0.95–1.02) 0.92 (0.88–0.95) 1.17 (1.10–1.24) 0.84 (0.82–0.87) 1.06 (1.03–1.10)
Poverty (% with poverty line) at county level
1st quartile (4.0–< 10.7%) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
2nd quartile (10.7–< 14.0%) 1.01 (0.99–1.04) 0.95 (0.92–0.97) 0.91 (0.87–0.95) 0.92 (0.90–0.94) 0.94 (0.92–0.96)
3rd quartile (14.0–17.9%) 0.98 (0.96–1.02) 0.99 (0.96–1.03) 0.90 (0.84–0.95) 1.01 (0.98–1.04) 0.91 (0.88–0.95)
4th quartile (≥17.9%) 0.98 (0.94–1.02) 0.99 (0.96–1.04) 0.94 (0.87–1.01) 1.00 (0.97–1.04) 0.94 (0.90–0.98)
Household income ($) at county level
1st quartile ($19,340–<49,000) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
2nd quartile ($49,000–< 57,580) 1.01 (0.96–1.06) 0.95 (0.90–1.00) 1.11 (1.02–1.21) 1.00 (0.97–1.05) 0.99 (0.94–1.03)
3rd quartile ($57,580–< 68,650) 0.99 (0.95–1.03) 0.93 (0.89–0.97) 1.15 (1.07–1.24) 1.03 (0.99–1.07) 1.07 (1.02–1.11)
4th quartile (≥$68,650) 0.97 (0.95–1.00) 0.99 (0.96–1.02) 1.12 (1.06–1.17) 1.08 (1.05–1.10) 1.04 (1.01–1.07)
Italicized values indicate p<0.05
*Odds ratios from multiple logistic regressions models were adjusted for age, marital status, tumor stage, tumor size, tumor grade, hormone receptor
status, SEER areas, year of diagnosis and socioeconomic factors (health insurance, education level, poverty level, and household income at the county
level)
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Table 5 Racial disparities and effects of socioeconomic factors on mortality in women with breast cancer
Hazard ratio (95% confidence interval) of mortality
Race/ethnicity and other factors All-cause mortality Breast cancer–specific mortality
Model 3* Model 4
&
Model 3* Model 4
&
Race/ethnicity
Caucasians 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
African Americans 1.22 (1.19–1.24) 1.18 (1.16–1.21) 1.25 (1.22–1.28) 1.22 (1.19–1.25)
Age (years)
<45 (20–44) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
45–54 1.04 (1.01–1.07) 1.02 (0.99–1.05) 1.03 (1.00–1.07) 1.01 (0.98–1.04)
55–64 1.37 (1.34–1.41) 1.31 (1.27–1.35) 1.19 (1.15–1.23) 1.13 (1.09–1.17)
65–74 2.16 (2.10–2.22) 1.97 (1.92–2.03) 1.43 (1.38–1.48) 1.32 (1.27–1.36)
75–84 4.35 (4.23–4.47) 3.67 (3.57–3.78) 2.20 (2.12–2.27) 1.90 (1.84–1.97)
≥85 8.92 (8.65–9.19) 6.48 (6.28–6.68) 3.82 (3.68–3.98) 2.90 (2.78–3.03)
Marital status
Married 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
Unmarried 1.30 (1.28–1.32) 1.26 (1.25–1.28) 1.20 (1.18–1.22) 1.17 (1.15–1.19)
Unknown 1.23 (1.19–1.27) 1.14 (1.11–1.18) 1.16 (1.11–1.21) 1.09 (1.04–1.13)
Tumor stage
Local stage 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
Regional stage 1.62 (1.60–
1.65) 1.72 (1.69–1.74) 2.69 (2.63–2.76) 2.77 (2.70–2.84)
Distant stage 7.49 (7.34–9.64) 5.02 (4.91–5.13) 15.63 (15.22–16.05) 9.83 (9.54–10.13)
Tumor size (cm)
< 1.0 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
1.0–<2.0 1.20 (1.14–1.20) 1.19 (1.16–1.22) 1.33 (1.27–1.39) 1.37 (1.31–1.43)
2.0–<3.0 1.44 (1.41–1.48) 1.46 (1.42–1.50) 1.91 (1.82–1.99) 1.96 (1.87–2.05)
3.0–<4.0 1.77 (1.72–1.82) 1.72 (1.67–1.77) 2.50 (2.38–2.62) 2.48 (2.36–2.59)
4.0 + 2.13 (2.07–2.18) 2.06 (2.01–2.11) 3.20 (3.07–3.34) 3.14 (3.01–3.28)
Unknown size 2.45 (2.37–2.53) 1.90 (1.84–1.96) 3.64 (3.47–3.82) 2.88 (2.75–3.03)
Tumor grade
Well differentiated (I) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
Moderately differentiated (II) 1.13 (1.11–1.16) 1.15 (1.12–1.17) 1.63 (1.57–1.69) 1.64 (1.58–1.71)
Poorly differentiated (III) 1.45 (1.42–1.49) 1.54 (1.51–1.58) 2.47 (2.37–2.57) 2.61 (2.50–2.71)
Undifferentiated (IV) 1.49 (1.39–1.59) 1.55 (1.44–
1.66) 2.43 (2.22–2.67) 2.53 (2.31–2.78)
Unknown 1.40 (1.36–1.44) 1.21 (1.18–1.24) 2.27 (2.17–2.37) 1.92 (1.83–2.01)
Hormone receptor status
Positive 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
Negative 1.52 (1.50–1.55) 1.62 (1.59–1.65) 1.81 (1.77–1.85) 1.91 (1.86–1.95)
Unknown 1.60 (1.56–1.64) 1.47 (1.43–1.51) 1.78 (1.72–1.85) 1.67 (1.62–1.73)
Health insurance
Uninsured 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
Any Medicaid 0.91 (0.87–0.95) 0.95 (0.90–0.99) 0.89 (0.84–0.94) 0.93 (0.88–0.98)
Insured/insured with no specifics 0.61 (0.59–0.64) 0.67 (0.64–0.70) 0.67 (0.64–0.71) 0.74 (0.70–0.78)
Unknown 0.73 (0.69–0.78) 0.71 (0.67–0.75) 0.84 (0.78–0.90) 0.83 (0.77–0.89)
Education (% < high school)
1st quartile (2.1–< 9.8%) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
2nd quartile (9.8–< 12.8%) 0.99 (0.96–1.01) 1.00 (0.97–1.02) 1.00 (0.97–1.04) 1.02 (0.98–1.05)
3rd quartile (12.8–< 17.8%) 0.99 (0.96–1.01) 0.99 (0.96–1.02) 0.99 (0.95–1.03) 1.00 (0.96–1.04)
4th quartile (≥17.8%) 1.03 (0.99–1.07) 1.05 (1.02–1.09) 1.05 (1.00–1.11) 1.08 (1.03–1.14)
J. Racial and Ethnic Health Disparities
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Having health insurance was associated with a better like-
lihood of being diagnosed at early stage or to be diagnosed
with a small tumor size < 1 cm, or to receive cancer-directed
surgery, but was associated with less likelihood of receiving
chemotherapy. Green et al. [41] found in their meta-analysis
that African American women were more likely to have de-
lays in treatment over 90 days and were more than likely to
discontinue chemotherapy, which may explain in part the in-
creased risk of mortality.
There were several strengths in this study. A major
strength was the large, retrospective population-based sam-
ple size from 17 SEER geographic areas representing 28%
of the US population. Another unique strength is that we
had sufficient sample size to stratify by race/ethnicity, treat-
ment, and socioeconomic factors. However, our study also
had several limitations. Information on radiation therapy
and chemotherapy was coded yes or no/unknown.
Treatments were coded separately; however, it was un-
known if radiation therapy or chemotherapy was given be-
fore surgery or as adjuvant therapy after surgery, or stand-
alone treatment. Although it is possible to know if multiple
treatments (radiation, chemotherapy, and surgery) were re-
ceived per individual; it may be beyond the scope of this
study’s main purpose. In addition, those who did not receive
radiation therapy and chemotherapy might have received
the therapy elsewhere from private clinics outside the reg-
istries; hence, caution should be kept in mind in interpreting
these results. However, several validation studies on these
therapies demonstrated that other sources such as Medicare
or medical chart reviews only enhanced a small percentage
of patients receiving these therapies [42,43]. No evidence
so far showed that the above underreporting of radiation
therapy and chemotherapy was differential by race/
ethnicity and socioeconomic factors, and therefore might
have a minimal effect on the findings and conclusions of
this study. Another important limitation is that we could
not include comorbid conditions for confounder adjustment
in our study and therefore could not assess this effect in our
study. Comorbid conditions may have an important impact
on surgery, chemotherapy, or radiation treatments as well as
on overall mortality. Furthermore, the assessment of socio-
economic status (education, poverty level, and household
income) effects was at the county level but did not reflect
the individual patient level; hence, there could be an eco-
logical bias. However, having health insurance as another
assessment of socioeconomic status was at an individual
Table 5 (continued)
Hazard ratio (95% confidence interval) of mortality
Race/ethnicity and other factors All-cause mortality Breast cancer–specific mortality
Model 3* Model 4
&
Model 3* Model 4
&
Poverty (%) at county level
1st quartile (4.0–< 10.7%) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
2nd quartile (10.7–< 14.0%) 1.03 (1.00–1.05) 1.01 (0.99–1.04) 1.03 (0.99–1.06) 1.01 (0.97–1.04)
3rd quartile (14.0–17.9%) 1.03 (0.9–1.06) 1.01 (0.98–1.05) 1.04 (0.99–1.09) 1.02 (0.97–1.07)
4th quartile (≥17.9%) 1.03 (0.98–1.07) 1.01 (0.97–1.06) 1.02 (0.96–1.08) 1.01 (0.95–1.07)
Income at county level
4th quartile (≥$68,650) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
1st quartile ($19,340–< 49,000) 1.09 (1.04–1.14) 1.10 (1.05–1.16) 1.08 (1.01–1.15) 1.10 (1.03–1.17)
2nd quartile ($49,000–< 57,580) 1.10 (1.05–1.14) 1.11 (1.07–1.16) 1.12 (1.06–1.18) 1.13 (1.07–1.20)
3rd quartile ($57,580–< 68,650) 1.04 (1.01–1.07) 1.05 (1.02–1.08) 1.04 (1.00–1.09) 1.06 (1.02–1.10)
Treatment
Surgery (yes vs no/uk) 0.41 (0.40–0.41) 0.36 (0.35–0.37)
Radiation therapy (yes vs no/uk) 0.75 (0.74–0.76) 0.82 (0.81–0.84)
Chemotherapy (yes vs no/uk) 0.79 (0.78–0.81) 0.86 (0.84–0.88)
uk, unknown
Italicized values indicate p<0.05
*Model 3: Hazard ratio adjusted forage, marital status, tumorcharacteristics, SEER areas, yearof diagnosis, and socioeconomic status (health insurance,
education level, poverty level, and household income at county level);
&
Model 4: Hazard ratio further adjusted for treatment received (cancer-directed surgery, radiation therapy, and chemotherapy) in addition to factors
above
Note: In model 3, there are no treatment estimates because these variables were not included in this part of the analysis
J. Racial and Ethnic Health Disparities
Author's personal copy
level, and was found to have a greater impact on stage at
diagnosis and treatment received. Finally, other factors such
as quality of treatment, timeliness of treatment, and patient
adherence were not measured in this study and could have
contributed to the observed results. These factors, in addi-
tion to other factors, could be measured in future studies to
further explore the underlying cause of the observed dispar-
ity between race/ethnic groups.
In summary, African American women tend to be diagnosed
at later stages of breast cancer than Caucasian women. Health
insurance was significantly associated with a higher likelihood
of having an early-stage diagnosis and smaller size tumors <
1 cm and receiving cancer-directed surgery, whereas education
level, poverty level, and household income did not have a sig-
nificant impact on the risk of being diagnosed with early-stage
breast cancer among African American and Caucasian women.
However, African American women with breast cancer remain
at increased risk for all-cause mortality and breast cancer–
specific mortality compared to Caucasian women.
Acknowledgments We acknowledge the efforts of the National Cancer
Institute in the creation of the SEER cancer registry data for public use.
We also acknowledge Dr. Xianglin L. Du for his guidance in this study.
Code Availability Code has been addressed in the manuscript and is
given upon request.
Authors’Contributions Dale Hardy and Daniel Du contributed to the
study conception and design.
Dale Hardy and Daniel Du wrote the manuscript.
DSH and DD interpreted the data analysis results.
DSH performed the data analysis.
Data Availability The data is available from SEER.
a. Overall survival.
b. Breast cancer specific survival. -
Fig. 1 Kaplan-Meier survival
curves for overall survival and
breast cancer–specific survival for
African American (Black) and
Caucasian (White) women with
breast cancer. aOverall survival.
bBreast cancer–specific survival.
The log-rank tests were signifi-
cant at p< 0.001 between two
groups
J. Racial and Ethnic Health Disparities
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Compliance with Ethical Standards
Conflict of Interest The authors declare that they have no conflict of
interest.
Ethics Approval Because SEER Public Use Data are de-identified,
existing data without any patient contact and did not involve human
subject participants research, making the study exempt for the
Institutional Review Board (IRB) review.
Consent to Participate See statement above.
Consent for Publication We give our consent to have this manuscript
published.
References
1. Institute of Medicine. Unequal treatment: confronting racial and
ethnic disparities in health care. Washington, DC: National
Academy Press; 2002.
2. Shavers VL, Brown ML. Racial and ethnic disparities in the receipt
of cancer treatment. J Natl Cancer Inst. 2002;94:334–57.
3. Mandelblatt JS, Kerner JF, Hadley J, Hwang YT, Eggert L,
Johnson LE, et al. Variations in breast carcinoma treatment in older
Medicare beneficiaries: is it black or white? Cancer. 2002;95:1401–
14.
4. Bach PB, Pham HH, Schrag D, Tate RC, Hargraves JL. Primary
care physicians who treat blacks and whites. N Engl J Med.
2004;351:575–84.
5. Ward E, Jemal A, Cokkinides V, Singh GK, Cardinez C, Ghafoor
A, et al. Cancer disparities by race/ethnicity and socioeconomic
status. CA Cancer J Clin. 2004;54:78–93.
6. Daly B, Olopade OI. A perfect storm: how tumor biology, geno-
mics, and health care delivery patterns collide to create a racial
survival disparity in breast cancer and proposed interventions for
change. CA Cancer J Clin. 2015;65:221–38.
7. Iqbal J, Ginsburg O, Rochon PA, Sun P, Narod SA. Differences in
breast cancer stage at diagnosis and cancer-specific survival by race
and ethnicity in the United States. JAMA. 2015;313:165–73.
8. Daly B, Olopade OI. Race, ethnicity, and the diagnosis of breast
cancer. JAMA. 2015;313:141–2.
9. Amini A, Jones BL, Yeh N, Guntupalli SR, Kavanagh BD, Karam
SD, et al. Disparities in disease presentation in the four screenable
cancers according to health insurance status. Public Health.
2016;138:50–6.
10. Jemal A, Lin CC, Davidoff AJ, Han X. Changes in insurance cov-
erage and stage at diagnosis among nonelderly patients with cancer
after the affordable care act. J Clin Oncol. 2017;35:3906–15.
11. Ko NY, Hong S, Winn RA, Calip GS. Association of Insurance
status and racial disparities with the detection of early-stage breast
cancer. JAMA Oncol. 2020;6:385. https://doi.org/10.1001/
jamaoncol.2019.5672. [Epub ahead of print].
12. Eley JW, Hill HA, Chen VW, Austin DF, Wesley MN, Muss HB,
et al. Racial differences in survival from breast cancer: results of the
National Cancer Institute Black/White Cancer Survival Study.
JAMA. 1994;272:947–54.
13. Howard DL, Penchansky R,Brown MB. Disaggregating the effects
of race on breast cancer survival. Fam Med. 1998;30:228–35.
14. El-Tamer MB, Homel P, Wait RB. Is race a poor prognostic factor
in breast cancer? J Am Coll Surg. 1999;189:41–5.
15. Yood MU, Johnson CC, Blount A, Abrams J, Wolman E,
McCarthy BD, et al. Race and differences in breast cancer survival
in a managed care population. J Natl Cancer Inst. 1999;91:1487–
91.
16. Bradley CJ, Given CW, Roberts C. Race, socioeconomic status,
and breast cancer treatment and survival. J Natl Cancer Inst.
2002;94:490–6.
17. Neale AV. Racial and marital status influences on 10 year survival
from breast cancer. J Clin Epidemiol. 1994;47:475–83.
18. Simon MS, Severson RK. Racial differences in survival of female
breast cancer in the Detroit metropolitan area. Cancer. 1996;77:
308–14.
19. Jatoi I, Becher H, Leake CR. Widening disparity in survival be-
tween white and African-American patients with breast carcinoma
treated in the U.S. Department of Defense Healthcare system.
Cancer. 2003;98:894–9.
20. Tammemagi CM, David N, Neslund-Dudas C, Feldkamp C,
Nathanson D. Comorbidity and survival disparities among black
and white patients with breast cancer. JAMA. 2005;294:1765–72.
21. Hershman D, McBride R, Jacobson JS, Lamerato L, Roberts K,
Grann VR, et al. Racial disparities in treatment and survival among
women with early-stage breast cancer. J Clin Oncol. 2005;23:
6639–46.
22. Field TS, Buist DS, Doubeni C, et al. Disparities and survival
among breast cancer patients. J Natl Cancer Inst Monogr.
2005;35:88–95.
23. Blackman DJ, Masi CM. Racial and ethnic disparities in breast
cancer mortality: are we doing enough to address the root causes?
J Clin Oncol. 2006;24:2170–8.
24. GrannV,TroxelAB,ZojwallaN,HershmanD,GliedSA,
Jacobson JS. Regional and racial disparities in breast cancer-
specific mortality. Soc Sci Med. 2006;62:337–47.
25. Newman LA, Griffith KA, Jatoi I, Simon MS, Crowe JP, Colditz
GA. Meta-analysis of survival in African American and white
American patients with breast cancer: ethnicity compared with so-
cioeconomic status. J Clin Oncol. 2006;24:1342–9.
26. Wojcik BE, Spinks MK, Optenberg SA. Breast carcinoma survival
analysis for African American and white women in an equal-access
health care system. Cancer. 1998;82:1310–8.
27. Roetzheim RG, Gonzalez EC, Ferrante JM, Pal N, van Durme DJ,
Krischer JP. Effects of health insurance and race on breast carcino-
ma treatments and outcomes. Cancer. 2000;89:2202–13.
28. Halpern MT, Bian J, Ward EM, Schrag NM, Chen AY. Insurance
status and stage of cancer at diagnosis among women with breast
cancer. Cancer. 2007;110:403–11.
29. Halpern MT, Ward EM, Pavluck AL, Schrag NM, Bian J, Chen
AY. Association of insurance status and ethnicity with cancer stage
at diagnosis for 12 cancer sites: a retrospective analysis. Lancet
Oncol. 2008;9:222–31.
30. Coburn N, Fulton J, Pearlman DN, Law C, DiPaolo B, Cady B.
Treatment variation by insurance status for breast cancer patients.
Breast J. 2008;14:128–34.
31. Ward E, Halpern M, Schrag N, Cokkinides V, DeSantis C, Bandi P,
et al. Association of insurance with cancer care utilization and out-
comes. CA Cancer J Clin. 2008;58:9–31.
32. DeSantis C, Jemal A, Ward E.Disparities in breast cancer prognos-
tic factors by race, insurance status, and education. Cancer Causes
Control. 2010;21:1445–50.
33. National Cancer Institute. Overview of the SEER Program: https://
seer.cancer.gov/about/overview.html, Accessed on March 7, 2020.
34. SEER Research Data 1975–2016 when Using SEER*Stat:
Surveillance, Epidemiology, and End Results (SEER) Program
(www.seer.cancer.gov) SEER*Stat Database: incidence - SEER 9
Regs Research Data, Nov 2018 Sub (1975–2016) <Katrina/Rita
Population Adjustment> −Linked To County Attributes - Total
U.S., 1969–2017 Counties, National Cancer Institute, DCCPS,
Surveillance Research Program, released April 2019, based on
the November 2018 submission.
J. Racial and Ethnic Health Disparities
Author's personal copy
35. Komenaka IK, Martinez ME, Pennington RE Jr, Hsu CH, Clare SE,
Thompson PA, et al. Race and ethnicity and breast cancer outcomes
in an underinsured population. J Natl Cancer Inst. 2010;102:1178–
87.
36. Hunt BR, Whitman S, Hurlbert MS. Increasing Black:White dis-
parities in breast cancer mortality in the 50 largest cities in the
United States. Cancer Epidemiol. 2014;38:118–23.
37. Hunt BR. Breast cancer prevalence and mortality among hispanic
subgroups in the United States, 2009-2013. J Cancer Epidemiol.
2016;2016:8784040.
38. DeSantis CE, Ma J, Goding Sauer A, Newman LA, Jemal A. Breast
cancer statistics, 2017, racial disparity in mortality by state. CA
Cancer J Clin. 2017;67:439–48.
39. Yedjou CG, Tchounwou PB, Payton M, Miele L, Fonseca DD,
Lowe L, et al. Assessing the racial and ethnic disparities in breast
cancer mortality in the United States. Int J Environ Res Public
Health. 2017;14:486.
40. Reeder-Hayes KE, Anderson BO. Breast cancerdisparities at home
and abroad: a review of the challenges and opportunities for system-
level change. Clin Cancer Res. 2017;23:2655–64.
41. Green AK, Aviki EM, Matsoukas K, Patil S, Korenstein D, Blinder
V. Racial disparities in chemotherapy administration for early-stage
breast cancer: a systematic reviewand meta-analysis. Breast Cancer
Res Treat. 2018;172:247–63.
42. Du XL, Key CR, Dickie L, Darling R, Delclos GL, Waller K, et al.
Information on chemotherapy and hormone therapy from tumor
registry had moderate agreement with chart reviews. J Clin
Epidemiol. 2006;59:53–60.
43. Noone AM, Lund JL, Mariotto A, Cronin K, McNeel T, Deapen D,
et al. Comparison of SEER treatment data with Medicare claims.
Med Care. 2016;54:e55–64.
Publisher’sNoteSpringer Nature remains neutral with regard to jurisdic-
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