Breast cancer stage at diagnosis and area-based socioeconomic status: a multicenter 10-year retrospective clinical epidemiological study in China.

Abstract

Although socioeconomic status (SES) has been focused on as a key determinant of cancer stage at diagosis in western countries, there has been no systemic study on the relationship of SES and breast cancer stage at diagnosis in China.
The medical charts of 4,211 eligible breast cancer patients from 7 areas across China who were diagnosed between 1999 and 2008 were reviewed. Four area-based socioeconomic indicators were used to calculate area-based SES by cluster analysis. The associations between area-based SES and stage at diagnosis were analyzed by trend chi-square tests. Binary logistic regression was performed to estimate odds ratios for individual demographic characteristics' effects on cancer stages, stratified by area-based SES.
The individual demographic and pathologic characteristics of breast cancer cases were significantly different among the seven areas studied. More breast cancer cases in low SES areas (25.5%) were diagnosed later (stages III & IV) than those in high (20.4%) or highest (14.8%) SES areas (χ² for trend = 80.79, P < 0.001). When area-based SES is controlled for, in high SES areas, cases with less education were more likely to be diagnosed at later stages compared with more educated cases. In low SES areas, working women appeared to be diagnosed at earlier breast cancer stages than were homemakers (OR: 0.18-0.26).
In China, women in low SES areas are more likely to be diagnosed at later breast cancer stages than those in high SES areas.

Full text

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RESEARCH ARTICLEOpen Access
Breast cancer stage at diagnosis and area-based
socioeconomic status: a multicenter 10-year
retrospective clinical epidemiological study in
China
Qiong Wang1,2, Jing Li2, Shan Zheng3, Jia-Yuan Li1*, Yi Pang1, Rong Huang1,2, Bao-Ning Zhang4, Bin Zhang4,5,
Hong-Jian Yang6, Xiao-Ming Xie7, Zhong-Hua Tang8, Hui Li9, Jian-Jun He10, Jin-Hu Fan2*and You-Lin Qiao2
Abstract
Background: Although socioeconomic status (SES) has been focused on as a key determinant of cancer stage at
diagosis in western countries, there has been no systemic study on the relationship of SES and breast cancer stage
at diagnosis in China.
Methods: The medical charts of 4,211 eligible breast cancer patients from 7 areas across China who were
diagnosed between 1999 and 2008 were reviewed. Four area-based socioeconomic indicators were used to
calculate area-based SES by cluster analysis. The associations between area-based SES and stage at diagnosis were
analyzed by trend chi-square tests. Binary logistic regression was performed to estimate odds ratios for individual
demographic characteristics’ effects on cancer stages, stratified by area-based SES.
Results: The individual demographic and pathologic characteristics of breast cancer cases were significantly
different among the seven areas studied. More breast cancer cases in low SES areas (25.5%) were diagnosed later
(stages III & IV) than those in high (20.4%) or highest (14.8%) SES areas (c2for trend = 80.79, P < 0.001). When area-
based SES is controlled for, in high SES areas, cases with less education were more likely to be diagnosed at later
stages compared with more educated cases. In low SES areas, working women appeared to be diagnosed at
earlier breast cancer stages than were homemakers (OR: 0.18-0.26).
Conclusions: In China, women in low SES areas are more likely to be diagnosed at later breast cancer stages than
those in high SES areas.
Keywords: Breast cancer, Stage at diagnosis, Area-based socioeconomic status, Nation-wide, Multi-center, Retro-
spective study
Background
Breast cancer is by far the most common cancer among
women both in developed and developing regions, with
an estimated 1.38 million new cancer cases diagnosed
worldwide in 2008 (23% of all cancers) [1]. In recent
years, both incidence of and mortality from breast
cancer have declined in the United States; between 1999
and 2006, incidence rates decreased by 2.0% per year,
and mortality decreased by 1.9% annually between 1998
and 2006 [2]. However, the incidence of breast cancer is
steadily rising in developing countries [3-6]. In China,
the incidence of breast cancer rose from 126,227 cases
in 2002 [7] to over 169,000 in 2008 [1].
Preventive screening for breast cancer can increase the
number of individuals diagnosed at an early stages and
reduce mortality. In China, the main methods of breast
cancer screening are clinical breast examination (CBE),
mammography,high-frequency ultrasound,and
* Correspondence: lijiayuan73@163.com; fjhjjj@sohu.com
1Department of Epidemiology, West China School of Public Health, Sichuan
University, 16 Ren Min Nan Lu, Chengdu, Sichuan 610041, China
2Department of Cancer Epidemiology, Cancer Institute & Hospital, Chinese
Academy of Medical Sciences & Peking Union Medical College, 17 South
Panjiayuan Lane, Beijing 100021, China
Full list of author information is available at the end of the article
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© 2012 Wang et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.

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magnetic resonance imaging (MRI). However, due to the
lack of large population-based screening evaluations in
China, there is no consistent evidence indicating which
screening method or combination of methods is best for
the country. To date, nationwide breast cancer screening
has never been implemented routinely in China, and in
places where routine breast cancer screening exists, its
usage varies with area-based socioeconomic status (SES)
[8,9]. Individuals living in poorer areas are less likely to
seek cancer screening compared with individuals living
in wealthier areas [10]. Studies over the past several dec-
ades have indicated that individuals living in less devel-
oped areas often had worse general health compared
with individuals living in relatively developed areas
[11-17]. The majority of studies investigating associa-
tions between SES and breast cancer stage at diagnosis
have also documented socioeconomic and geographic
disparities, with higher incidence of late stage breast
cancer in lower income areas [18-24]. Regional dispari-
ties in SES are significant in China, where economic
development in eastern cities generally started earlier
and has been faster than that in the country’s interior
and in rural areas [25].
Breast cancer survival rates decline with delayed diag-
nosis. The Surveillance Epidemiology and End Results
(SEER) showed that 98.3% of women treated for early-
stage breast disease survive five or more years, while only
83.5% of women diagnosed with regional breast cancer
and 23.3% of those with distant breast cancer survive
beyond five years of the initial diagnosis [2]. Thus, as
improvement in breast cancer prognosis would have a
significant impact on countries’ health budgets, and SES
disparities result in different breast cancer outcomes,
strategies are urgently needed that target higher-risk
areas and resonate with higher-risk population sub-
groups. Breast cancer prevention and control has been
prioritized by China’s Ministry of Health, but so far,
there has been no systemic study of the relationship
between SES and breast cancer stage in the country. In
this study, based on the Nationwide Multicenter 10-year
(1999-2008) Retrospective Clinical Epidemiological Study
of Breast Cancer in China, directed by the Cancer Hospi-
tal/Institute, Chinese Academy of Medical Sciences
(CICAMS), we explored the effects of both area level fac-
tors, namely SES, and individual demographic character-
istics on breast cancer stage at diagnosis.
Methods
Hospital selection, case sampling, and data collection
Hospital selection, case sampling, and data collection
methods have been previously described in detail [26].
Briefly, all of China was stratified into seven geographic
areas (north, northeast, central, south, east, northwest,
and southwest). Then, purposive sampling was used to
choose one tertiary public cancer hospital in each region
with the following characteristics. Firstly, participant
hospitals were the leading public cancer hospitals and
regional referral centers providing pathology diagnosis,
surgery, radiotherapy, medical oncology, and routine fol-
low-up care for patients with breast cancer. Secondly,
their patient source had to include the entire study
region. Finally, since patients tend to seek better medical
service in big cities, the hospitals were each located in a
major city. The participant hospitals were the Cancer
Institute & Hospital, Chinese Academy of Medical
Sciences (in Beijing city, north China), Liaoning Cancer
Hospital (in Liaoning Province, northeast China), Sec-
ond Xiangya Hospital, Central South University (in
Hunan Province, central China), Sun Yat-Sen University
Cancer Center (in Guangdong Province, south China),
Zhejiang Cancer Hospital (in Zhejiang Province, east
China), First Affiliated Hospital of Xi’an Jiaotong Uni-
versity (in Shannxi Province, northwest China), and
Sichuan Cancer Hospital (in Sichuan Province, south-
west China).
The selected hospitals provided us with the medical
records of breast cancer patients diagnosed between
1999 and 2008. For each year in each hospital, one
month was randomly selected, and all inpatient cases for
that month were reviewed (January and February were
excluded from the random selection to eliminate any
confounding effects of China’s largest annual holiday).
The hospital records were reviewed in each local hospi-
tal by local clerks who had been trained systematically
in Beijing. Then, standard case report forms (CRF)
designed by CICAMS [26] was used to extract the pri-
mary medical reports of every qualified patient, includ-
ing general information, risk factors, diagnostic imaging
tests, therapy models, and pathologic characteristics.
The reliability and validity of CRF had been assessed by
proceeding a pilot study and CRF was regarded as reli-
able and valid. After collection, the raw variables were
coded and sorted into different categories for analysis.
Cancer stage at diagnosis was categorized into six
groups by archiaters referring to pathologic reports and
using the American Joint Committee on Cancer (AJCC)
TNM System (0, I, II, III, IV, and unknown stage or not
applicable). The cases admitted between 1999 and 2002
were staged using the fifth version (revised in 1997)
[27], and the cases from 2003-2008 were staged with the
sixth version (revised in 2002) [28]. If individuals were
diagnosed with more than one primary breast tumor at
the same time, we included only the record of the
tumor at the most advanced stage. We excluded tumors
with an unknown or not applicable stage, and grouped
the breast cancer stages of the other cases into two cate-
gories: “early” (stages 0 & I) or “non-early” (stages II &
III & IV).
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The study protocol was approved by the Cancer Foun-
dation of China Institutional Review Board and data
were stripped of all personal identifiers, per the board’s
approved procedures.
Area-based socioeconomic indicators
Area-based SES measures derived from census data have
been shown to be related to various health outcomes
independent of individual SES [11,12,22,29,30]. Because
there is a lack of accepted SES measures in China, we
referred to the socioeconomic measures of “percent of
the population living below poverty line” and “percent
of the population ≥ 25 years of age without a high
school diploma” in the United States [31] to devise our
own socioeconomic indicators (SEIs) reflecting the eco-
nomic and education status of the seven areas. These
are “GDP per capita,” “percentage of health-service
expenditure in the regional/provincial public affairs gen-
eral budget (HSE percentage),” “ratio of urban to rural
population (PU/PR ratio),” and “percentage of illiteracy
in females aged 15 and over (FPI percentage).” The SEIs
of each district or province with a selected hospital were
obtained from annual reports issued by the National
Bureau of Statistics of the People’s Republic of China
from 1999 to 2008, except that data on the PU/PR ratio
was available only from 2005 to 2008. These SEIs were
used to represent the SES of each area.
Data Analysis
Descriptive statistics were used to summarize the demo-
graphic and pathologic characteristics of the study
population. The mean and standard deviation of quanti-
tative variables were calculated, and the differences
among the seven areas were analyzed with one-way
ANOVA. Differences in distribution of variables were
examined using Mantel-Haenszel chi-square tests or
Fisher’s exact tests.
The single SEI values among the seven areas were
compared using one-way ANOVA followed by the Stu-
dent-Newman-Keuls (SNK) test, a post hoc tests that
measures significance among multiple groups. Those
areas between which there were no statistically signifi-
cant differences were combined, forming ordinal cate-
gorical levels of SEI status. Trend chi-square tests were
used to examine associations between stage at diagno-
sis and the SEIs status. Additionally, area-based SES
was calculated by cluster analysis, an exploratory data
analysis tool which sorts objects into groups such that
the degree of association between two objects is maxi-
mal if they belong to the same group and minimal
otherwise. During cluster analysis, the k-means cluster-
ing was applied with k = 3, so that the seven regions
were classified into three levels based on four SEIs.
Binary logistic regression was performed stratified by
area-based SES to further explore the association
between individual demographic characteristics and
breast cancer stage at diagnosis (non-early stage vs.
early stage) in SES subgroups. SPSS statistical software
version 17.0 was used to analyze the data. Statistical
significance was assessed by two-tailed tests with an a
level of 0.05.
Results
SEIs, individual demographic and pathologic
characteristics by area
A total of 4,211 eligible cases were included in our
study. In general, the differences in SEIs among the
seven areas were significant (P < 0.001) (Table 1). Dur-
ing the period studied, the mean of GDP per capita
across the seven areas was 13,048 ± 5,567 yuan per per-
son, ranging from 8,050 ± 3,599 in the southwest to
36,146 ± 17,217 in northern China. More than 70% of
breast cancer patients were 40-69 years old when diag-
nosed. Cases in the north (42.1%), northeast (32.8%),
and south (29.8%) were more likely to be overweight at
diagnosis (BMI ≥ 25.00 kg/m2) than were those in other
areas (3.9%-18.8%). Women in eastern, northwestern,
and southwestern China were mainly occupied in man-
ual work (80.4%, 54.0%, and 62.7%, respectively), while
more women were homemakers in central China
(13.2%) than in other areas (average across all areas =
4.0%). Information on patients’ education levels was
generally unavailable in north, northeast, and southwest
China. Among the other four areas, we found that
women in central China had higher education levels,
with 27.5% receiving higher education (university and
above). The vast majority of cases in all seven areas
were married. All the above demographic characteristics
of breast cancer cases were significantly different among
the seven areas (Table 2, P < 0.001).
Invasive ductal carcinoma was the dominant patholo-
gic subtype, ranging from 70.1% of cases in the north-
east to 92.4% in central China. Among 3555 cases that
were tested for estrogen receptor (ER) and progester-
one receptor (PR), 3534 had test results included in
their records. About half of these (47.6%) were ER +
and PR+, and 32.0% were ER- and PR-. In northwest
China, only 282 (58.4%) cases were tested for ER/PR
and 38.7% of them were ER + and PR+; both of these
rates were far lower than in the other areas. Human
epidermal growth factor receptor 2 (Her-2) informa-
tion was available for 2849 patients, the majority of
whom (2113, 65.0%) were Her-2 negative. The north
had a high testing rate (609/641, 95.0%) and the high-
est positive rate (286/609, 47.0%) for Her-2. All of the
pathologic characteristics of breast cancer cases were
significantly different across the seven areas (Table 3,
P < 0.001).
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SEIs, area-based SES, and breast cancer stage at diagnosis
For the analysis of factors affecting breast cancer stage
at diagnosis, we excluded 717 cases with an unknown or
not applicable stage, so that the final number of cases in
the following analyses was 3494.
We found an inverse relationship between breast can-
cer stage and both GDP per capita and ratio of urban to
rural population, while percentage of illiteracy in females
aged 15 and over was positively related to breast cancer
stage (P for trend < 0.001) (Table 4). However, there
was no significant trend correlation between percentage
of health-service expenditure in the regional/provincial
public affairs general budget and breast cancer stage (c2
for trend = 3.307, P = 0.081). The area-based SES based
on the four SEIs was negatively correlated with breast
cancer stage: 14.8% (95/641) of cases in the highest SES
areas were diagnosed at stages III or IV, while 20.4%
(417/2024) of cases in high SES areas and 25.5% (389/
1528) in low SES areas were diagnosed at these late
stages (c2for trend = 80.79, P < 0.001). Patients in Beij-
ing and Guangzhou, where routine breast cancer screen-
ing has been implemented for several years [32,33], were
more likely to be diagnosed at stages 0 or I (27.9% and
17.5%) than were patients in other areas (Table 3).
Association between individual demographic
characteristics and breast cancer stage at diagnosis
stratified by area-based SES
With the exception of percentage of health-service
expenditure in the regional/provincial public affairs
general budget, each of the SEIs was associated with
breast cancer stage (Table 4). To further explore the
association between individual demographic character-
istics and breast cancer stage at diagnosis, we used
binary logistic regression and implemented stratified
analysis by area-based SES (Table 5). In north China,
the only area categorized as highest SES, education
information was unavailable for 50.3% of the cases, so
we were unable to analyze the effect of individual
demographic characteristics in this subgroup. In high
SES areas, education appeared to influence the cancer
stage at diagnosis. Compared with the patients who
had attended university, those only attended middle
and/or high school had about a three-fold higher risk
of being diagnosed at a late stage. In low SES areas,
working women (manual, white-collar, or other) were
less likely to be diagnosed at late breast cancer stages
than were homemakers (OR: 0.18-0.66). Other demo-
graphic characteristics, including age at diagnosis and
marital status, were not associated with breast cancer
stage at diagnosis.
Discussion
Increasing social inequalities in health in China, coupled
with growing inequalities in income and wealth, have
focused attention on social class as a key determinant of
population health. Our study focused on breast cancer
stage at diagnosis as an indicator of health care access
and quality, and we found a significant relationship
between cancer stage and area-based SES. To our
Table 1 SEIs of seven regions
Variable by
level (Range)
Total Beijing
(North)
Liaoning
(Northeast)
Hunan
(Central)
Guangdong
(South)
Zhejiang
(East)
Shannxi
(Northwest)
Sichuan
(Southwest)
F (P-
value)
SNK*
(No. of
subgroups)
SEI
GDP per
capita(1)
13048 ±
5567
(8050-
36146)
4.47 ±
0.43
(3.18-6.51)
0.80 ±
0.04
(0.54-5.39)
15.17 ±
2.98
(7.20-
17.74)
36146 ±
17217a
17200 ±
6762b
9223 ±
4019c
21292 ±
9165b
23832 ±
10331b
8840 ±
4615c
8050 ±
3599c
12.69
(<
0.001)
3
HSE
percentage(2)
6.51 ± 0.51a3.18 ± 0.41c
3.41 ±
0.74c
4.27 ± 0.45b
5.71 ± 0.71a
3.87 ± 0.75c
4.56 ± 0.66b
38.69
(<
0.001)
1210
(<
0.001)
23.02
(<
0.001)
3
PU/PR ratio(3)
5.39 ± 0.22a1.45 ± 0.03c
0.66 ±
0.06d
1.67 ± 0.09b
1.32 ± 0.04c
0.66 ± 0.06d
0.54 ± 0.05d
4
FPI
percentage(4)
7.21 ± 1.63a7.20 ± 1.60a
11.31 ±
2.60b
9.89 ± 2.57b
17.30 ±
3.07c
15.86 ±
4.30c
17.74 ±
3.31c
3
*: The single SEI values among the seven areas were compared using one-way ANOVA followed by the Student-Newman-Keuls (SNK) test, a post hoc test for significant
differences among multiple groups. The areas between which there were no statistically significant differences were combined to form subgroups. a, b, c, and d
represent different subgroups for each variable; if two or more regions are followed by the same letter, there is no significant difference between them.
(1): yuan per person per year (mean ± SD, 1999-2008)
(2): percentage of health-services expenditure in the regional/provincial public affairs general budget (%) (mean ± SD, 1999-2008)
(3): ratio of urban to rural population (mean ± SD, 2005-2008) (data is available in the National Bureau of Statistics of the People’s Republic of China only for 2005 to 2008)
(4): percentage of illiteracy among women aged 15 and over (%) (mean ± SD, 1999-2008)
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knowledge, ours is the first study to tie SES to health
status within a developing country.
In this nationwide multicenter study of breast cancer
in China, area-based SES was categorized into three
levels: highest, high, and low. The Beijing district in
north China was the only highest SES area, and also had
the highest GDP per capita, percentage of health-service
expenditure in the regional/provincial public affairs gen-
eral budget, and ratio of urban to rural population, and
the lowest percentage of illiteracy in females aged 15
and over. Northeast, south, and east China were high
SES areas, while central, northwest, and southwest
China were low SES areas. This classification accords
with the pattern of economic growth and regional
inequality in China during the reform era [25]. We
found that cases living in low SES areas were more
likely to be diagnosed at a later breast cancer stage than
were those in high SES areas.
Table 2 Individual demographic characteristics by region
Total Beijing
(North)
Total N =
641
N (%)
Liaoning
(Northeast)
Total N =
832
N (%)
Hunan
(Central)
Total N =
546
N (%)
Guangdong
(South)
Total N =
604
N (%)
Zhejiang
(East)
Total N =
606
N (%)
Shannxi
(Northwest)
Total N =
483
N (%)
Sichuan
(Southwest)
Total N =
499
N (%)
Statistical
value
P-
value
Demographic
Characteristic
Total N =
4211
N (%)
Age at diagnosis
(years)
Mean ± SD48.68 ±
10.47
919 (21.8)
3126 (74.2)
166 (3.9)
50.45 ±
10.92
113 (17.6)
489 (76.3)
39 (6.1)
48.88 ±
9.95
161 (19.4)
645 (77.5)
26 (3.1)
48.25 ±
10.34
133 (24.4)
387 (70.9)
26 (4.8)
47.82 ±
10.94
155 (25.7)
429 (71.0)
20 (3.3)
47.35 ±
9.58
147 (24.3)
443 (73.1)
16 (2.6)
50.10 ±
11.01
89 (18.4)
369 (76.4)
25 (5.2)
47.86 ±
10.32
121 (24.2)
364 (72.9)
14 (2.8)
7.63*<
0.001
≤ 40
40-69
≥ 70
Body Mass Index
(kg/m2)(1)
Mean ± SD23.35 ±
2.89
3287 (78.1)
921 (21.9)
24.65 ±
3.46
371 (57.9)
270 (42.1)
24.01 ±
3.08
559 (67.2)
273 (32.8)
21.72 ±
1.85
520 (95.2)
26 (4.8)
23.25 ± 3.47 22.28 ±
2.86
492 (81.2)
114 (18.8)
23.25 ± 1.29 23.25 ± 1.8267.42*<
0.001
≤ 24.99
≥ 25.00
422 (70.2)
180 (29.8)
464 (96.1)
19 (3.9)
460 (92.2)
39 (7.8)
Occupation
Homemaker 173 (4.0)17 (2.7)2 (0.2) 72 (13.2)39 (6.5) 11 (1.8)14 (2.9) 18 (3.6) 6.52**<
0.001
Manual worker
White-collar
worker
Other
Unknown
Education
None
1893 (45.0)
1028 (24.4)
230 (35.9)
203 (31.7)
258 (31.1)
174 (20.9)
185 (33.9)
168 (30.8)
159 (26.3)
167 (27.6)
487 (80.4)
61 (10.1)
261 (54.0)
153 (31.7)
313 (62.7)
102 (20.4)
529 (12.6)
588 (14.0)
70 (10.9)
121 (18.9)
99 (11.9)
299 (35.9)
114 (20.9)
7 (1.2)
162 (26.8)
77 (12.8)
40 (6.6)
7 (1.1)
19 (3.9)
36 (7.5)
25 (5.0)
41 (8.2)
186 (4.4) 5 (0.8) 1 (0.1)0 (0.0) 41 (6.8)124 (20.5) 14 (2.9)1 (0.2) 4.63***<
0.001
Primary school
Middle school
High school
University and
above
Unknown
Marital Status
Single
462 (11.0)
606 (14.4)
441 (10.5)
396 (9.4)
3 (0.5)
0 (0.00)
0 (0.00)
4 (0.6)
0 (0.0)
0 (0.0)
2 (0.2)
23 (2.8)
125 (22.9)
147 (26.9)
122 (22.3)
150 (27.5)
116 (19.2)
172 (28.5)
143 (23.7)
112 (18.5)
173 (28.5)
165 (27.2)
56 (9.2)
29 (4.8)
39 (8.1)
115 (23.8)
116 (24.0)
76 (15.7)
6 (1.2)
7 (1.4)
2 (0.4)
2 (0.4)
2120 (50.3)629 (98.1) 806 (96.9) 2 (0.4)20 (3.3)59 (9.8) 123 (25.5)481 (96.4)
51 (1.2) 6 (0.9)9 (1.1)4 (0.7)17 (2.8) 5 (0.8) 3 (0.6)7 (1.4) 56.43**<
0.001
Married
Widowed/
Divorced
Unknown
4090 (97.1)
52 (1.2)
620 (96.7)
12 (1.9)
821 (98.7)
0 (0.0)
525 (96.2)
17 (3.1)
586 (97.0)
0 (0.0)
589 (97.2)
7 (1.2)
474 (98.1)
5 (1.0)
475 (95.2)
11 (2.2)
18 (0.5) 3 (0.5) 2 (0.2)- 1 (0.2) 5 (0.8) 1 (0.2)6 (1.2)
*: one-way ANOVA
**: Chi-square test
***: Fisher’s exact test
(1): Missing BMI values were replaced with the mean from available data, 23.35 kg/m2
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Exactly how area-based socioeconomic conditions
influence the stage at which an individual is diagnosed
with breast cancer is complex. One explanatory factor is
that individuals residing in areas with high GDP per
capita are likely to have more personal income com-
pared with those in low GDP per capita areas. In the
United States, lack of ability to pay for screening ser-
vices is often implicated as the reason why individuals
Table 3 Individual pathologic characteristics by region
Total Beijing
(North)
Total N =
641
N (%)
Liaoning
(Northeast)
Total N =
832
N (%)
Hunan
(Central)
Total N =
546
N (%)
Guangdong
(South)
Total N =
604
N (%)
Zhejiang
(East)
Total N =
606
N (%)
Shannxi
(Northwest)
Total N =
483
N (%)
Sichuan
(Southwest)
Total N =
499
N (%)
c2
P-
value
Pathological
Characteristics
Total N =
4211
N (%)
Type of Postoperative Pathological Diagnosis(1)
Carcinoma in Situ
(CIS)
Invasive Ductal
Carcinoma
Other Invasive
Carcinoma
Other
Unavailable
Pathological stage(2)
Stage 0
143 (3.4)61 (9.5) 22 (2.6)17 (3.1) 19 (3.1)7 (1.2) 5 (1.0)12 (2.4)169.1<
0.001
3471 (82.4)528 (82.4)583 (70.1) 504 (92.4)528 (87.4) 523 (86.3)406 (84.1)399 (80.0)
385 (9.1) 47 (7.3) 87 (10.5) 21 (3.8) 29 (4.8) 59 (9.7)70 (14.5)72 (14.4)
15 (0.4)
197 (4.7)
5 (0.8)
-
0 (0.0)
140 (16.8)
1 (0.2)
3 (0.5)
4 (0.7)
24 (4.0)
0 (0.0)
17 (2.8)
2 (0.4)
-
3 (0.6)
13 (2.6))
39 (0.9) 7 (1.1)13 (1.6) 6 (1.1) 8 (1.3)4 (0.7) 0 (0.0)1 (0.2)450.5<
0.001
Stage I
Stage II
Stage III
Stage IV
Unavailable
ER/PR test
Performed
663 (15.7)
1891 (44.9)
788 (18.7)
113 (2.7)
717 (17.1)
172 (26.8)
280 (43.7)
93 (14.5)
2 (0.3)
87 (13.6)
131 (15.7)
323 (38.8)
119 (14.3)
4 (0.5)
242 (29.1)
73 (13.4)
400 (73.3)
53 (9.7)
3 (0.5)
11 (2.0)
98 (16.2)
243 (40.2)
117 (19.4)
20 (3.4)
118 (19.5)
95 (15.7)
239 (39.4)
152 (25.1)
5 (0.8)
111 (18.3)
77 (15.9)
170 (35.2)
127 (26.3)
56 (11.6)
53 (11.0)
17 (3.4)
236 (47.3)
127 (25.5)
23 (4.6)
95 (19.0)
3555 (84.4) 616 (96.1)711 (85.5)535 (98.0)511 (84.6) 462 (76.2)282 (58.4) 438 (87.8) 483.2<
0.001
Not performed
Unknown
Her-2 test
Performed
471 (11.2)
185 (4.4)
24 (3.7)
1 (0.2)
85 (10.2)
36 (4.3)
8 (1.5)
3 (0.5)
54 (8.9)
39 (6.5)
67 (11.1)
77 (12.7)
192 (39.8)
9 (1.8)
41 (8.2)
20 (4.0)
3251 (77.2)609 (95.0)693 (83.3) 533 (97.6) 469 (77.6)424 (70.0) 241 (49.9)282 (56.5) 640.2<
0.001
Not performed
Unknown
ER/PR status(3)
ER+&PR+
762 (18.1)
198 (4.7)
31 (4.8)
1 (0.2)
98 (11.8)
41 (4.9)
8 (1.5)
5 (0.9)
99 (16.4)
36 (6.0)
98 (16.1)
84 (13.9)
227 (47.0)
15 (3.1)
201 (40.3)
16 (3.2)
1691 (47.6)373 (60.6) 318 (44.7)244 (45.6) 241 (47.2)221 (47.8)109 (38.7)185 (42.2) 121.0<
0.001
ER+&PR-
ER-&PR+
ER-&PR-
Unavailable
Her-2 status(4)
Her-2 +
337 (9.5)
367 (10.3)
1139 (32.0)
21 (0.6)
44 (7.1)
47 (7.6)
146 (23.7)
6 (1.0)
79 (11.1)
108 (15.2)
206 (29.0)
-
54 (10.1)
32 (6.0)
203 (37.9)
2 (0.4)
37 (7.2)
75 (14.7)
152 (29.7)
6 (1.2)
41 (8.9)
34 (7.4)
165 (35.7)
1 (0.2)
31 (11.0)
37 (13.1)
101 (35.8)
4 (1.4)
51 (11.6)
34 (7.8)
166 (37.9)
2 (0.5)
736 (22.6) 286 (47.0)36 (5.2) 156 (29.3)90 (19.2)68 (16.0)29 (12.0)71 (25.2)339.6<
0.001
Her-2 -
Uncertain/
unavailable
2113 (65.0)
402 (12.4)
299 (49.1)
24 (3.9)
545 (78.6)
112 (16.2)
314 (58.9)
63(11.8)
337 (71.9)
42 (9.1)
317 (74.8)
39 (9.2)
181 (75.1)
31 (12.9)
120 (42.6)
91 (32.2)
(1): carcinoma in situ: ductal carcinoma in situ, lobular carcinoma in situ, microinvasive ductal carcinoma, and Paget’s disease invasive ductal carcinoma: pure
invasive ductal carcinoma and mixed invasive ductal carcinoma other invasive carcinomas: invasive labular carcinoma, tubular carcinoma, medullary carcinoma,
mucin carcinoma, and other invasive carcinomas
(2): according to the AJCC/UICC TNM system
(3): ER/PR status was based on all breast cancer cases tested for ER/PR
(4): Her-2 status was based on all breast cancer cases tested for Her-2
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with low incomes, or those living in the poorest areas,
have lower screening rates and are more likely to be
diagnosed with cancer at a late stage [34]. In the present
study, we observed that the patients from hospitals
located in Beijing and Guangdong were diagnosed at an
earlier stage than others. This may partly be explained
by the routine breast cancer screening programs that
have been implemented in these two cities for several
years [32,33]. However, the screening programs in a city
may not reflect the screening situation of the whole
region. To evaluate the effect of routine screening on
early diagnosis, a comprehensive prospective study is
needed to compare districts with and without screening
programs.
Table 4 Incorporated SEIs, area-based SES, and breast cancer stage at diagnosis
Variable by
level
Mean ± SDIncluded Provinces Total Stage
0&I
N (%)
Stage II Stage
III&IV
N (%)
Unavailable Chi-square for
trend
P-
value
N (%) N (%)
GDP per capita
(1)
Highest36146 ±
17217
20774 ±
9006
8704 ± 3986
Beijing641 179
(27.9)
349
(17.1)
174
(11.4)
280
(43.7)
805
(39.4)
806
(52.7)
95 (14.8)87 (13.6) 80.79<
0.001
High Liaoning, Guangdong,
Zhejiang
Hunan, Shannxi, Sichuan
2024 417 (20.4) 471 (23.1)
Low 1528389 (25.5)159 (10.4)
HSE percentage
(2)
Highest6.11 ± 0.73 Beijing, Zhejiang1247 278
(22.3)
124
(11.2)
300
(16.1)
519
(41.6)
479
(43.5)
893
(48.0)
252 (20.2)198 (15.9)3.307 0.081
High 4.42 ± 0.57 Guangdong, Sichuan1103287 (26.0) 213 (19.3)
Low 3.49 ± 0.70 Liaoning, Hunan, Shannxi 1861362 (19.5) 306 (16.4)
PU/PR ratio(3)
Highest 5.39 ± 0.22Beijing641 179
(27.9)
106
(17.5)
243
(16.9)
174
(11.4)
280
(43.7)
243
(40.2)
562
(39.1)
806
(52.7)
95 (14.8)87 (13.6)72.00<
0.001
Higher 1.67 ± 0.09Guangdong 604 137 (22.7)118 (19.6)
High 1.39 ± 0.08Liaoning, Zhejiang 1438280 (19.5) 353 (24.5)
Low0.62 ± 0.08 Hunan, Shannxi, Sichuan 1528389 (25.5)159 (10.4)
FPI percentage
(4)
Highest16.97 ± 3.55Zhejiang, Shannxi, Sichuan 1588 194
(12.2)
185
(16.1)
323
(21.9)
645
(40.6)
643
(55.9)
603
(40.9)
490 (30.9)259 (16.3) 135.83<
0.001
High10.60 ± 2.62 Hunan, Guangdong1150 193 (16.8)129 (11.2)
Low 7.21 ± 1.57Beijing, Liaoning, 1473218 (14.8) 329 (22.4)
Area-based SES
(5)
Highest- Beijing 641179
(27.9)
349
(17.1)
174
(11.4)
280
(43.7)
805
(39.4)
806
(52.7)
95 (14.8) 87 (13.6)80.79<
0.001
High- Liaoning, Guangdong,
Zhejiang
Hunan, Shannxi, Sichuan
2024417 (20.4) 471 (23.1)
Low- 1528 389 (25.5) 159 (10.4)
(1): GDP per capita: yuan per person per year (mean ± SD, 1999-2008)
(2): percentage of health-services expenditure in the regional/province public affairs general budget (%) (mean ± SD, 1999-2008)
(3): ratio of urban to rural population (mean ± SD, 2005-2008) (data is available in the National Bureau of Statistics of the People’s Republic of China only for 2005
to 2008)
(4): percentage of illiteracy among women aged 15 and over (%) (mean ± SD, 1999-2008)
(5): Area-based SES is a composite measure synthesized by cluster analysis of the four SEIs
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Local governments with a high percentage of health-
service expenditure in their regional/provincial public
affairs general budgets (HSE percentage) may provide
better medical services, giving residents more access to
health care such as breast cancer screening, diagnosis,
and treatment, which in turn results in earlier diagnosis.
Barrya and Breen found that residence in economically
and socially distressed or medically underserved neigh-
borhoods tends to increase the likelihood of late-stage
cancer diagnoses [19]. However, we observed no clear
trend association between HSE percentage and breast
cancer stage at diagnosis. HSE percentage may not be a
direct indicator of the level of medical service of an
area, since HSE percentage may be inconsistent with an
area’s economic development. For instance, while north-
east China had high GDP per capita, it was the lowest
HSE percentage of the areas studied.
In contrast, we found an association between earlier
breast cancer stage at diagnosis and higher ratio of
urban to rural population (PU/PR ratio). In China, there
is wide SES gap between cities and rural areas, and so
urban/rural status is highly correlated with SES. A
higher PU/PR ratio hints that more individuals in the
area live in cities, and thus may have more access to
medical services and ability to afford medical insurance
than do residents of rural areas. This would in turn
make them more likely to seek prompt medical care for
any health problems. These differences may contribute
to disparities in breast cancer stage at diagnosis. Several
studies have shown that patients with health insurance
are less likely to be diagnosed at a late stage of breast
cancer [22,35], an effect similar to that found for other
cancers, such as colorectal cancer [34,36].
In this study, area-based education was measured as
percentage of illiteracy in females aged 15 and over (FPI
percentage), emphasizing education of females. We
found a trend association between FPI percentage and
stage at diagnosis: the higher the FPI percentage in an
area, the later the stage of breast cancer at which
women in the area were diagnosed. After stratified
Table 5 Multivariable logistic regression for association between individual demographic characteristics and breast
cancer stage at diagnosis (stage 0 & I, stage II, III & IV), stratified by area-based SES*
VariableHigh SES areas
OR (95%CI)
Low SES areas
Wald (P) OR (95%CI) Wald (P)
Age at Diagnosis (Years)
≤ 40
40-69
≥ 70
Body Mass Index (kg/m2)
≤ 24.99
≥ 25.00
Occupation
Homemaker
Manual worker
White-collar worker
Other
Education
University and above
High school
Middle school
Primary school
None
Marital Status
Married
Single
Widowed/Divorced
ER/PR status
ER+&PR+
ER+&PR-
ER-&PR+
ER-&PR-
1.00 1.00
0.94 (0.29-3.09)
1.02 (0.70-1.51)
0.01 (0.92)
0.02 (0.90)
1.18 (0.43-3.23)
1.40 (0.85-2.29)
0.10 (0.75)
1.75 (0.19)
1.00 1.00
1.37 (0.92-2.06)2.39 (0.12) 1.09 (0.40-12.97)0.03 (0.86)
1.00 1.00
0.81 (0.33-2.02)
0.77 (0.33-1.80)
1.21 (0.45-3.25)
0.20 (0.65)
0.37 (0.54)
0.14 (0.70)
0.18 (0.05-0.64)
0.26 (0.08-0.88)
0.18 (0.05-0.65)
6.99 (0.01)
4.66 (0.03)
6.85 (0.01)
1.001.00
2.89 (1.30-6.44)
2.77 (132-5.79)
1.87 (0.99-3.64)
1.64 (0.87-3.08)
6.76 (0.01)
7.27 (0.01)
3.43 (0.06)
2.34 (0.13)
0.42 (0.07-2.36)
0.97 (0.42-2.21)
0.98 (0.47-2.05)
0.74 (0.40-1.36)
0.97 (0.32)
0.01 (0.94)
0.01 (0.96)
0.93 (0.34)
1.001.00
6.27 (0.81-48.76)
1.18 (0.14-10.06)
3.08 (0.08)
0.02 (0.88)
1.01 (0.11-9.51)
0.49 (0.15-1.62)
0.001 (0.99)
1.38 (0.24)
1.001.00
0.97 (0.50-1.90)
1.57 (0.83-2.95)
1.04 (0.69-1.56)
0.01 (0.93)
1.96 (0.16)
0.04 (0.85)
1.10 (0.54-2.23)
0.73 (0.36-1.51)
1.31 (0.81-2.11)
0.07 (0.79)
0.70 (0.40)
1.19 (0.28)
*: Education information for 50.3% of cases in the highest SES area was unknown, so we did not analyze the effect of individual demographic characteristics in
this subgroup
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analysis by area-based SES, individual education level
appeared to influence the stage at diagnosis in high SES
areas, while in low SES areas, working women were less
likely to be diagnosed at late breast cancer stages than
were homemakers. These results highlight the impor-
tance of education in high SES areas. Education may be
a proxy variable representing individual knowledge and
behaviors toward prevention and screening of breast
cancer. Women with lower education level may also
have higher risk of alcohol-related death and diseases
[37]. Low education itself may also create barriers to
receiving recommended screening, since low health lit-
eracy, low general literacy, and language barriers impact
an individual’s ability to navigate the medical service
system, understand screening options and recommenda-
tions, and communicate with healthcare professionals
[38]. Working women in low SES areas were more likely
to be diagnosed at early stages, which may result from
their high breast cancer screening attendance relative to
that of homemakers. The results from our previous
study [39] and a study by Damiani et al. [40] show that
education and occupation were positively associated
with breast cancer screening attendance, which in turn
may influence breast cancer stage at diagnosis.
However, after adjusting for area-based SES, we found
no significant associations between other individual
demographic characteristics and breast cancer stage at
diagnosis. Thus, area-based SES appeared to be the
main, underlying factor influencing breast cancer stage
at diagnosis. Baquet et al. similarly found that SES pre-
dicted the likelihood of a group’s access to education,
certain occupations, and health insurance, as well as
income level and living conditions, all of which are asso-
ciated with a person’s chances of being diagnosed with
late stage disease and of surviving cancer [41]. More-
over, Singh et al. [42] and Launay et al. [43] found that
at every stage of diagnosis, breast cancer patients from
lower-income areas had lower 5-year relative survival
rates did than did those from higher-income areas. The
presence of additional illnesses and treatment disparities
may contribute to these differences [44,45]. Compared
with less developed areas, more developed areas possess
better detection techniques, and their cases have the
ability to pay more for more accurate tests and more
effective therapy. For instance, we found that in Beijing,
the highest SES area, more than 95% of cases were
tested for ER/PR/Her-2. In most of the low SES areas,
however, only about half of cases were tested for Her-2.
Even so, this pattern was in agreement with the guide-
lines of the Breast Health Global Initiative (BHGI),
which contend that Her-2 measurement is problematic
in limited-resource settings due to the high cost of
immunohistochemical analysis, fluorescence in situ
hybridization, and trastuzumab therapy, and recommend
introducing this test only at the maximal-resource level
[46].
The present study is the first geographically epide-
miologic study of breast cancer in China. Although we
only included women with breast cancer attended to
these 7 regional referral hospitals, we expect the effect
of SES and later stage of diagnosis will be much stron-
ger than we found in this study (if we included those
women attended at local hospitals, such county hospi-
tals). Our findings thus provide a baseline for under-
standingthe country’s
characteristics. Our evaluation of associations between
breast cancer stage and area-based SES, as well as indi-
vidual demographic characteristics stratified by area-
based SES, may help to identify high risk areas and the
most important and controllable regional and indivi-
dual factors influencing breast cancer stage at diagno-
sis. However, this was an ecological study, so we must
recognize the possibility of ecological fallacy. In addi-
tion to the individual-level variables examined in this
study, it would be useful to examine the effects of
other variables, such as access to breast cancer screen-
ing services, alcohol use, family history of breast can-
cer, and lack of exercise, as they may affect breast
cancer stage at diagnosis by influencing women’s atti-
tudes and behaviour to breast cancer prevention and
screening. However, their effects could not be analyzed
in this study due to the high proportion of missing
data; we plan to test the effects of these variables on
breast cancer stage in a future study. Another limita-
tion is that we lacked data on individual SES, which
was likely strongly correlated with both education level
and area-based SES. Although our method of calculat-
ing area-based SES may not have assessed actual socio-
economic conditions with complete accuracy, our
results for area-based SES accord with the pattern of
economic growth and regional inequality in China dur-
ing the reform era [25]. Thus, we think this indicator
has some validity. However, a more comprehensive,
validated area-based SES measurement is needed that
takes into consideration factors such as concentration
of poverty, health insurance coverage, proportion of
the population with blue collar jobs, unemployment
rate, median household income, and median value of
owner-occupied houses [47]. Finally, area-based SES
was measured at a large geographic scale in our study;
it would be instructive to study area-based SES differ-
ences among smaller geographic areas in a future
study.
patientand tumor
Conclusion
In summary, the effects of area-based SES are complex
and multidimensional. Women in lower SES areas may
be more likely to ignore symptoms for a variety of
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economic, social, and cultural reasons. Additionally,
women in lower SES areas tend to be less educated and
may not understand disease progression, and so may
not seek treatment until breast cancer has developed
into a late stage. WHO’s national cancer control pro-
grammes suggest that research to determine the most
cost-effective cancer control strategies is especially rele-
vant in developing countries, perhaps even more so than
in industrialized countries. However, the available
research data for effective and efficient cancer control
decision-making are extremely limited in these coun-
tries. Our results suggest that in China and other devel-
oping countries with similar socioeconomic conditions,
breast cancer control programs should focus on ensur-
ing adequate access to screening and improving breast
cancer awareness and education. This would facilitate
diagnosis at an early stage, particularly for populations
living in socioeconomically disadvantaged areas, and
would in turn decrease breast cancer mortality and
improve surviving patients’ quality of life.
Abbreviations
SES: Area-based socioeconomic status; SEIs: Socioeconomic indicators; SEER:
Surveillance Epidemiology and End Results; HSE: percentage Percentage of
health-service expenditure in the regional/provincial public affairs general
budget; PU/PR: ratio Ratio of urban to rural population; FPI: percentage
Percentage of illiteracy in females aged 15 and over; CICAMS: Chinese
Academy of Medical Sciences; AJCC: American Joint Committee on Cancer;
ER: Estrogen receptor; PR: Progestin receptor; Her-2: Human epidermal
growth factor receptor 2; CRF: Case report form; CBE: Clinical breast
examination; MRI: Magnetic resonance imaging; BHGI: Breast Health Global
Initiative.
Acknowledgements
We thank the Cancer Institute of the Chinese Academy of Medical Sciences
(CICAMS) with providing their expertise in the development of the study.
We also thank the local investigators from Beijing, Liaoning (Shenyang),
Hunan (Changsha), Guangdong (Guangzhou), Zhejiang (Hangzhou), Shannxi
(Xi’an), and Sichuan (Chengdu) for data collection and assisting us complete
the project successfully. The authors also thank Pfizer for funding the
research and Shawna Williams for editing this text.
Author details
1Department of Epidemiology, West China School of Public Health, Sichuan
University, 16 Ren Min Nan Lu, Chengdu, Sichuan 610041, China.
2Department of Cancer Epidemiology, Cancer Institute & Hospital, Chinese
Academy of Medical Sciences & Peking Union Medical College, 17 South
Panjiayuan Lane, Beijing 100021, China.3Department of Pathology, Cancer
Institute & Hospital, Chinese Academy of Medical Sciences & Peking Union
Medical College, 17 South Panjiayuan Lane, Beijing 100021, China.4Center of
Breast Disease, Cancer Institute & Hospital, Chinese Academy of Medical
Sciences & Peking Union Medical College, 17 South Panjiayuan Lane, Beijing
100021, China.5Department of Breast Surgery, Liaoning Cancer Hospital, 44
Xiaoyanhe Road, Dadong District, Shenyang 110042, China.6Department of
Breast Surgery, Zhejiang Cancer Hospital, 38 Banshanqiao Guanji Road,
Hangzhou 310022, China.7Department of Breast Oncology, Sun Yat-Sen
University Cancer Center, 651 Dongfeng East, Gungzhou 510060, China.
8Department of Breast-thyroid Surgery, Xiangya Sencod Hospital, Central
South University, 139 Renminzhonglu, Changsha 410011, China.9Department
of Breast Surgery, the Second People’s Hospital of Sichuan Province,
Chengdu 610041, China.10Department of Oncosurgery, the First Affiliated
Hospital of Medical College, Xi’an Jiao Tong University, 277 Yanta West Road,
Xi’an 710061, China.
Authors’ contributions
QW helped to analyze, and interpret the data. She also drafted the initial
manuscript. JL helped to design the study and interpret the data. SZ helped
design the CRF to collect the data. JYL was the local PI who helped with
data collection and performed critical revisions of the manuscript. JHF, YP,
and RH helped with the data management and analysis. BNZ helped to
design the study and is the clinical PI of the study. BZ, HJY, XMX, ZHT, HL,
and JJH were the local PIs who helped with data collection. YLQ was the PI
of this study and assisted in designing the study and performed critical
revisions of the manuscript. All authors read and approved the final
manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 20 November 2011 Accepted: 29 March 2012
Published: 29 March 2012
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Cite this article as: Wang et al.: Breast cancer stage at diagnosis and
area-based socioeconomic status: a multicenter 10-year retrospective
clinical epidemiological study in China. BMC Cancer 2012 12:122.
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