1352 Articles | JNCI Vol. 100, Issue 19 | October 1, 2008
One-fifth of the world’s women live in China. Incidence rates of
breast cancer in most of China are currently low compared with
those in Western countries. For example, the age-standardized
incidence rate of breast cancer in the rural county of Qidong is 12.8
per 100 000 women, which is approximately one-tenth that of white
women in the United States ( 1 ). However, Shanghai ( 2 ), Hong
Kong ( 3 ), Japan ( 4 ), and Singapore ( 5 ) have recently experienced rapid
increases in breast cancer incidence rates, and breast cancer is now the
most common cancer among women in these regions. Furthermore,
breast cancer incidence among Asian-American women is increasing
( 6 ): Rates in Japanese-American women have surpassed the
age-specific incidence rates in white US women ( 2 ). These trends
have been attributed to strong cohort effects that have arisen because
of shifts in risk factor profiles of younger women ( 3 – 5 , 7 ).
Established risk factors for breast cancer in women include
older age, a family history of breast cancer, greater height, adult
weight gain, high birth weight, alcohol intake, high mammo-
graphic density, postmenopausal hormone use, and certain repro-
ductive factors, including earlier menarche, later age at fi rst
pregnancy, less breastfeeding, lower parity, and longer interval
between births ( 8 ). Higher body mass index (BMI) is associated
with a reduced risk of breast cancer in premenopausal women
and an increased risk of breast cancer in postmenopausal women
( 8 ). The hypothesis that all of these risk factors are likely to be
Affiliations of authors: Department of Epidemiology, Harvard School of
Public Health, Boston, MA (EL, GAC); Division of Engineering and Applied
Sciences, California Institute of Technology Pasadena, CA (DS); Channing
Laboratory, Department of Medicine, Brigham and Women ’ s Hospital and
Harvard Medical School, Boston, MA (BAR); Department of Government and
Society of Fellows, Harvard University, Boston MA (KL); Institute of Child
Health, University College London, London, UK (TH); Institute of Population
Sciences, Zheijiang University, Hangzhou, People’s Republic of China (JDQ);
Department of Epidemiology, Shanghai Cancer Institute, Shanghai, People’s
Republic of China (YTG); Department of Medicine, Vanderbilt University,
Nashville, TN (WZ); Department of Surgery and Alvin J. Siteman Cancer
Center, Washington University School of Medicine, St Louis, MO (GAC) .
Correspondence to: Eleni Linos, MD, DrPH, Department of Dermatology,
Stanford University School of Medicine, 900 Blake Wilbur Drive Stanford, CA
94305 (email: firstname.lastname@example.org ).
See “Funding” and “Notes” following “References.”
© The Author 2008. Published by Oxford University Press. All rights reserved.
For Permissions, please e-mail: email@example.com.
Effects of Reproductive and Demographic Changes
on Breast Cancer Incidence in China: A Modeling
Eleni Linos , Demetri Spanos , Bernard A. Rosner , Katerina Linos , Therese Hesketh , Jian Ding Qu , †
Yu-Tang Gao , Wei Zheng , Graham A. Colditz
Background Breast cancer incidence is currently low in China. However, the distribution of reproductive and lifestyle
risk factors for breast cancer among Chinese women is changing rapidly. We quantified the expected
effect of changes in breast cancer risk factors on future rates of breast cancer in China.
Methods We first validated and calibrated the Rosner – Colditz log-incidence breast cancer model in Chinese women
who participated in the Shanghai Women’s Health Study cohort (N = 74 942). We then applied the cali-
brated model to a representative sample of Chinese women who were aged 35 – 49 years in 2001 using
data from the Chinese National Family Planning and Reproductive Health Survey (NFPRHS, N = 17 078) to
predict the age-specific and cumulative breast cancer incidence among all Chinese women of this age
group. We evaluated the relative impact of changes in modifiable risk factors, including alcohol intake,
parity, postmenopausal hormone use, and adult weight gain, on cumulative incidence of breast cancer.
Results Breast cancer incidence in China is expected to increase substantially from current rates, estimated at
10 – 60 cases per 100 000 women, to more than 100 new cases per 100 000 women aged 55 – 69 years by
2021. We predicted 2.5 million cases of breast cancer by 2021 among Chinese women who were 35 – 49
years old in 2001. Modest reductions in hormone and alcohol use, and weight maintenance could prevent
270 000 of these cases.
Conclusions China is on the cusp of a breast cancer epidemic. Although some risk factors associated with economic
development are largely unavoidable, the substantial predicted increase in new cases of breast cancer
calls for urgent incorporation of this disease in future health care infrastructure planning.
J Natl Cancer Inst 2008;100: 1352 – 1360
JNCI | Articles 1353
common to women of all ethnicities, including Chinese women, is
supported by data from an international case – control study ( 9 ) and
a multiethnic cohort ( 10 ), which demonstrated that, despite varia-
tions in the overall absolute rates of breast cancer, the associations
between these factors and the risk of breast cancer were similar
across different ethnic groups. For example, the benefi cial effects
of earlier fi rst birth, higher parity, and later age at menarche and
the detrimental effects of family history, adult weight gain, greater
height, and use of hormone replacement therapy have been con-
fi rmed for Chinese women living in Singapore ( 11 ) and Shanghai
( 12 , 13 ). Furthermore, breast tumors of Asian women have molecu-
lar and genetic characteristics that are similar to those of white
women ( 14 ).
The distribution of risk factors for breast cancer among
Chinese women has changed substantially in parallel with China’s
economic and social development. Secular trends in physical
growth among Chinese girls have been reported, including
increases in height ( 15 ) and in the prevalence of overweight and
obesity ( 16 ). Furthermore, increases in the number of Chinese
women in the workforce as well as decreases in fertility have drasti-
cally changed the underlying distributions of reproductive factors
associated with the risk of breast cancer. For example, the average
birth rate among Chinese women dropped from 5.9 births per
woman in 1970 to 2.9 births per woman in 1979 and then to 1.7
births per woman in 2004 ( 17 ). Meanwhile, the average age at
menarche decreased from 16.5 years in the 1940s to 13.9 years in
the 1980s ( 18 ).
In this study, we evaluated the distribution of demographic and
reproductive factors associated with breast cancer risk in China
and quantifi ed the impact of changes in these factors on future
breast cancer incidence rates. Specifi cally, we validated the
Rosner – Colditz log-incidence breast cancer model ( 8 , 19 ) in
Chinese women who participated in the Shanghai Women’s
Health Study (SWHS) ( 20 ). We then applied the model to Chinese
national survey data to predict future trends in the incidence of
breast cancer in China. We conducted a sensitivity analysis to
assess the relative impact of population-level changes in adult
weight gain, postmenopausal hormone therapy use, alcohol use,
and parity on breast cancer incidence to identify possible strategies
Study Population and Methods
Our study population consisted of women who participated in the
Chinese National Family Planning and Reproductive Health
Survey in 2001 (NFPRHS 2001). The Chinese National Family
Planning Commission surveyed a representative and random sam-
ple of premenopausal women aged 15 – 49 years living in 31 prov-
inces in China. Data were collected on 39 585 women from 346
cities and counties and from 1041 villages. A total of 74% of the
women who were surveyed lived in rural areas, and the overall
response rate was 98.3%. Participants in the NFPRHS 2001 were
assured of the anonymity and confidentiality of their responses to
increase the reliability of the information collected. The dataset for
this study population has been described previously ( 21 ); for this
study, we analyzed relevant reproductive data. Because only a small
proportion of women in China give birth after age 35, the number
of children born to a woman by age 35 approximates her lifetime
parity. To ensure that we would have complete information on
reproductive history, we restricted our analysis to women who were
aged 35 years or older before the survey was administered in 2001
(N = 17 078).
Breast Cancer Model and Validation
We used the Rosner – Colditz log-incidence breast cancer model,
which was developed in 1996 and updated in 2000 using data from
the Nurses ’ Health Study cohort ( 22 ). The model is presented in
the Appendix and has previously been described in detail ( 8 , 19 ).
Briefly, it incorporates the effects of various risk factors, including
history of benign breast disease, family history of breast cancer,
parity, age at first birth, age at and type of menopause (surgical vs
natural), use of postmenopausal hormones, alcohol use, height, and
weight, to estimate the incidence of breast cancer during a woman’s
lifetime. The Rosner – Colditz model has been validated in white
CONTEXT AND CAVEATS
Although the incidence of breast cancer is currently low in most of
China compared with Western countries, it has increased dramati-
cally over the last several decades in several cities in China and in
other Asian populations, making breast cancer the most common
cancer among women in these regions.
The Rosner – Colditz log-incidence breast cancer model was vali-
dated in Chinese women who participated in the Shanghai
Women’s Health Study and then applied to Chinese national sur-
vey data to predict future trends in the incidence of breast cancer
in China associated with changes in demographic and reproductive
Breast cancer incidence in China is expected to increase substan-
tially from the current estimated rate of 10 – 60 cases per 100 000
women to more than 100 cases per 100 000 women aged 55 – 69
years by 2021. Modeling predicts 2.5 million cases of breast cancer
by 2021 among Chinese women who were 35 – 49 years old in 2001.
Modest reductions in hormone and alcohol use, and weight main-
tenance are predicted to prevent approximately 10% of these
The substantial predicted increase in breast cancer cases in China
focuses attention on the adequacy of national infrastructure for
breast cancer therapy, the possible benefits of population-based
screening for breast cancer, and possible prevention strategies.
Underreporting of the number of children a woman had given birth
to in the national survey was possible. The limited availability of
individual-level data for many of the demographic variables neces-
sitated the use of various assumptions and imputations in the
model, which may have resulted in uncertainty in some projected
estimates. The model may slightly overestimate breast cancer inci-
dence at older ages because it does not account for competing risks.
From the Editors
1354 Articles | JNCI Vol. 100, Issue 19 | October 1, 2008
American women ( 23 ), for whom the observed incidence of breast
cancer was comparable to the expected incidence based on rates
for US women obtained from the Surveillance, Epidemiology, and
End Results (SEER) database [expected-to-observed ratio (E/O) =
1.0; 95% confidence interval (CI) = 0.98 to 1.03 ( 24 )]. To our
knowledge, this model has not yet been validated in an Asian
We examined the validity of this model in Chinese women by
using data from the SWHS. The SWHS is a cohort of 74 942 adult
Chinese women from urban communities who were recruited
between 1997 and 2000 and are interviewed in person every
2 years to collect personal and health-related information ( 20 ). We
applied the Rosner – Colditz model to the Shanghai cohort to pre-
dict the expected annual incidence of breast cancer for each cohort
member. We calculated the cumulative incidence rates and
expected number of cases of breast cancer in the entire cohort for
each year from the year of recruitment up to 2004.
To assess the goodness of fi t of our model within this group of
Chinese women, we compared the expected and observed number
of breast cancer cases in the overall cohort and within 5-year age
groups. Based on the age-specifi c incidence rates from the SEER
database, the expected number of cases (E SEER ) was 2.79 times
higher (95% CI = 2.55 to 3.04 times higher) than the observed
number of cases, whereas after adjusting for risk factors using the
Rosner – Colditz model, the expected number of cases (E model )
approached the observed number of cases. Specifi cally, after apply-
ing US incidence rates to the number of Shanghai women in each
age group, we expected 1357 cases of breast cancer in the Shanghai
cohort. The observed number of cases was 487. After adjusting for
all the risk factors using the Rosner – Colditz model, the expected
number of cases was 696. Therefore, the model accounted for 76%
of the discrepancy between United States and Chinese incidence
rates (ie, difference between the model and the observed Chinese
rates [1357 ? 696 = 661] divided by the difference between the
United States and the observed Chinese rates [1357 ? 487 = 870],
or 661/870 = 0.759).
The overall E/O using the Rosner – Colditz model was 1.43
(95% CI = 1.31 to 1.56), which suggests that such a model devel-
oped in white American women overestimates breast cancer inci-
dence in Chinese women in Shanghai by approximately 40%.
This overestimate could be due to several differences between the
predominantly white American women in whom the model was
developed, and the Asian women to whom it was applied, includ-
ing the lower average height and BMI of the Asian women.
Differences in physical activity, dietary factors ( 25 ), breastfeeding,
or variations in the underlying genetic predisposition and endog-
enous hormone levels may also explain the discrepancy ( 26 , 27 ).
Furthermore, the fact that mammographic screening rates are
very low in China and relatively high in the United States makes
the difference in the numbers of detected cancers even more
To correct for this overestimation, we calibrated the log-
incidence model by adjusting the intercept by 0.357 (the natural
logarithm of 1.43). After adjustment, the E/O was 1.00 (95% CI =
0.91 to 1.09) and showed adequate validity given that the 95% CIs
included 1 in all age groups ( Table 1 ). The goal of this calibration
was to obtain a model that would provide conservative, lower limit
predictions of breast cancer incidence.
We applied the adjusted Rosner – Colditz model to the NFPRHS
dataset to predict annual breast cancer incidence for each woman
surveyed. For our primary modeling analysis, we used individual-
level data from the NFPRHS dataset as well as data from
population-representative health surveys, the published literature,
and the SWHS cohort. Individual-level data included each woman’s
age from 2001 to 2021, age at first birth, parity, and the interval
between births. Age at menarche, in 10-year age groups, according
to year of birth was imputed from aggregate data in the 1997
NFPRHS (J. D. Qu MD, personal communication ). The aggre-
gate-level estimates from other representative sources were imputed
into the prediction model and randomly assigned to the 17 078
women in the NFPRHS dataset.
BMI and height were assigned according to distributions in the
SWHS cohort and the China Health and Nutrition Survey. In
addition, on the basis of mean values from the SWHS, we assumed
that 17% of women would ever develop benign breast disease, 2%
would have had a positive family history of breast cancer, 3%
would have used postmenopausal hormone therapy for 2 years
after menopause, and 1% would have had an oopherectomy at age
Table 1 . Expected number of breast cancers and ratios of expected to observed numbers of breast cancer cases (E/O ratios) by
age group, Shanghai Women’s Health Study cohort *
Age group, y No. of person-yearsObserved no. of cases E SEER †
Before model calibrationAfter model calibration ‡
E model E model /O (95% CI)E model ‡ E model ‡ /O (95% CI)
40 – 44
45 – 49
50 – 54
55 – 59
60 – 64
65 – 69
70 – 74
75 – 79
4572581.29 (0.96 to 1.72)
1.39 (1.15 to 1.68)
1.37 (1.12 to 1.68)
1.44 (1.12 to 1.84)
1.38 (1.08 to 1.77)
1.61 (1.27 to 2.03)
1.55 (1.15 to 2.10)
1.33 (0.43 to 4.12)
410.91 (0.68 to 1.22)
0.97 (0.80 to 1.17)
0.96 (0.78 to 1.18)
1.02 (0.80 to 1.31)
0.97 (0.76 to 1.24)
1.13 (0.89 to 1.42)
1.07 (0.79 to 1.45)
1.00 (0.32 to 3.10)
* SEER = Surveillance, Epidemiology, and End Results (Program) database; CI = confidence interval.
† Expected number of cases of breast cancer in each age group after standardization to US SEER rates over the same time period.
‡ Calibration was used to address model overprediction. Model intercept reduced by the natural logarithm of 1.43 (ie, 0.357).
JNCI | Articles 1355
45. We estimated the average weekly alcohol intake to be 14 g
(about one glass of wine or beer) and randomly applied this value
to 10% of the population based on data from other studies in
China and Hong Kong that showed that approximately 10% of
women in this age group drink alcohol (T. Hesketh MD, PhD and
G. Leung MD , personal communication). Because all of the
women surveyed were premenopausal, we assumed that meno-
pause would occur at age 49 years, which has been well docu-
mented as the mean age at menopause for women of many
ethnicities, including Chinese women ( 18 ). We examined the
robustness of our estimates to the choice of these imputed values
in a sensitivity analysis (see below).
Time- and age-specifi c incidence rates, cumulative incidence
rates, and the expected number of cases were calculated for 17 078
NFPRHS participants who were aged 35 – 49 years at baseline in
2001. Our predictions of breast cancer risk extend 20 years forward
in time, up to 2021, when the NFRPHS participants would be
55 – 69 years old. We used information from the China 2000
Census ( 27 ) to approximate the population distribution of Chinese
women in 2001. Our estimates of cumulative incidence of breast
cancer derived from the representative sample of Chinese women
(ie, the NFPRHS participants) were applied to the total number of
Chinese women aged 35 – 49 years [N = 130 304 574 ( 28 )] to obtain
overall predictions for this age group across China.
To assess the range of our predictions according to different
assumptions about demographic changes in China, we recalculated
the predicted age-specific and cumulative incidence rates, as well as
the expected numbers of cases, according to different assumptions
about patterns of weight gain, hormone replacement therapy use,
alcohol use, age at menopause, and parity (see Appendix).
Our projection analysis included 17 078 women aged 35 – 49 years
who participated in the NFPRHS. The mean age of these women
at the NFPRHS interview was 41 years (SD = 4.6 years). The mean
birth rate in the overall NFPRHS was 1.94 (SD = 0.6; 2.11 in rural
areas and 1.43 in urban areas); among women 35 – 49 years old, it
was 2.10 (SD = 0.6). The mean age at first birth was 24 years
(SD = 2.9 years; median age at first birth = 26 years, range = 14 – 39
Projected age- and birth cohort – specifi c breast cancer inci-
dence rates up to the year 2021 are shown in Table 2 and Figure 1 .
These breast cancer incidence rates ranged from 39.0 per 100 000
women for women who were born between 1962 and 1966 and
were 35 – 39 years old in 2001 to 132.3 per 100 000 women for
women who were born between 1952 and 1956 and would be 65 –
69 years old in 2021. The age-specifi c incidence rates were higher
among more recent birth cohorts; for example, the incidence of
breast cancer in women aged 55 – 59 years is higher in women born
between 1962 and 1966 (109.8 per 100 000) than in women born
between 1952 and 1956 (102.6 per 100 000). On the basis of the
cumulative incidence rates, the model predicted that 146 cases of
breast cancer would occur within this sample of 17 078 women by
2011 and that 326 cases would occur by 2021. When we applied
the model to the entire Chinese population of women of the same
age distribution in 2000 as our sample population (N = 130 304 574)
( 28 ), the predicted number of cases of breast cancer by 2011 was
approximately 1.1 million. The predicted number of breast cancer
cases increased to nearly 2.5 million by 2021 ( Table 2 ).
We examined the dependence of these breast cancer incidence pre-
dictions on variations in modifiable risk factors by changing our
Table 2 . Predicted breast cancer incidence among NFPRHS participants by year of birth *
Year of birth
1952 – 19561957 – 1961 1962 – 1966 All (1952 – 1966)
Age-specific incidence per 100 000 women in 2001 – 2021
Age group in 2001 – 2021, y
35 – 39
40 – 44
45 – 49
50 – 54
55 – 59
60 – 64
65 – 69
Cumulative incidence per 100 000 women
Population size and number of cases
N = 5495
N = 40 838 382
NFPRHS population (2001)
Chinese population †
N = 4940N = 6643N = 17 078
N = 39 465 761
N = 50 000 431
N = 130 304 574
1 114 591
2 492 250
* — = not applicable; NFPRHS = National Family Planning and Reproductive Health Survey.
† Chinese population data from the 2000 census were used to approximate the number of women in each age group in 2001.
1356 Articles | JNCI Vol. 100, Issue 19 | October 1, 2008
assumptions about the frequency of postmenopausal hormone use,
alcohol use, and weight gain ( Figure 2 ). These estimates reflect theo-
retical changes in risk factors that we applied to the general Chinese
population. Compared with an estimated cumulative incidence of
1912.7 cases per 100 000 women from the base case model, a model
that incorporated concurrent increases in the extent of postmeno-
pausal hormone use, alcohol consumption, and weight gain predicted
an additional 76 cases of breast cancer per 100 000 women by 2021.
This increase corresponds to approximately 100 000 additional cases
of breast cancer in Chinese women who will be aged 55 – 69 years in
2021. A reduction in parity, from the national average of 2.1 births
per woman to 1.1 births per woman (which is similar to birth rates in
several developed Asian and European countries), would account for
an additional 200 000 cases in this subgroup of Chinese women.
Conversely, a model that incorporated a stable weight in adulthood
resulted in 140 fewer cases per 100 000 women, or a total of 190 000
fewer cases. A model that included low alcohol consumption, stable
weight and infrequent use of postmenopausal hormones indicated
possible prevention of 270 000 new cases of breast cancer overall in
this subset of women across China by 2021. Under these conserva-
tive assumptions, the cumulative incidence of breast cancer was pre-
dicted to be 1704 cases per 100 000 women by 2021, corresponding
to a total of 2.2 million incident cases across China by 2021 .
Breast cancer incidence has increased dramatically over the past 30
years in several cities in China and in other Asian populations. Our
modeling results show that this trend is likely to continue across
China. On the basis of our modeling results, we predict that the
nationwide incidence of breast cancer among women who will be 55 –
69 years in 2021 will increase from the current rate, which has been
estimated at 10 – 60 cases per 100 000 women ( 2 , 29 ). Applying these
rates to the 130 million Chinese women in this age group, we estimate
nearly 2.5 million cases of breast cancer from 2001 to 2021 among
women who were 35 – 49 years old in 2000. Even under very conserva-
tive assumptions of low alcohol use, no postmenopausal hormone use,
and no adult weight gain, we predict that the cumulative incidence of
breast cancer will increase to at least 2.2 million new cases of breast
cancer among this subgroup of women across China over the 20-year
period from 2001 to 2021. These results point to an emerging epi-
demic of breast cancer in China, where breast cancer incidence rates
are approaching those in Western nations, in which breast cancer is the
most commonly diagnosed female cancer. Our findings raise impor-
tant issues regarding future health care infrastructure needs, the role of
breast cancer screening programs, and possible prevention strategies.
The strengths of this study include our use of a validated and
calibrated model of breast cancer and a representative population
that included both rural and urban Chinese women. The valida-
tion used regional data from Shanghai (SWHS), and the predic-
tion modeling was performed using representative national survey
data (NFPRHS). To our knowledge, this is the fi rst quantitative
Figure 1 . Predicted age-specifi c breast cancer incidence according to
year of birth.
Figure 2 . Sensitivity analysis of age-specifi c
cumulative incidence of breast cancer per
100 000 women by 2021 according to differ-
ent population assumptions. PMH = post-
menopausal hormone use.
JNCI | Articles 1357
analysis of the impact of demographic change on breast cancer
trends in a developing country.
Nonetheless, our analysis has several limitations. First, although
the NFPRHS dataset we used is based on a representative sample
of all Chinese women of reproductive age, some women who
responded to the survey may have deliberately underreported the
number of children they had given birth to in order to avoid the
consequences of violating China’s one-child family policy. An
underestimate in a protective factor such as parity would result in
an overestimation of the number of projected cases of breast can-
cer. However, NFPRHS participants were ensured anonymity and
confi dentiality in an attempt to reduce such underreporting.
Second, the limited availability of individual-level data for many
of the demographic variables necessitated the use of various
assumptions and imputations in the model, which may have
resulted in uncertainty in the projected estimates, especially when
predicting breast cancer after menopause. In particular, we
assumed that the distributions of BMI, height, hormone therapy
use, age at menopause, and benign breast disease in Chinese
women living in China were similar to those of Chinese women
living in Shanghai. We also assumed that these imputed variables
were not correlated with each other. Third, our model does not
account for competing risks and may, therefore, slightly overesti-
mate breast cancer incidence at older ages. However, as the popu-
lation of China ages, chronic diseases, including breast cancer, are
likely to become more prevalent. Despite these limitations, we
believe that our results refl ect the best possible estimates given the
available data and our results send an important public health
message that warrants immediate action regarding health care
planning in China. Additional studies that include representative
individual-level data on breast cancer risk factors and breast
cancer incidence are needed to provide more comprehensive
and accurate estimates of breast cancer burden.
One important consideration in the interpretation of our fi nd-
ings is whether a model that was developed in white American
women is valid when used to predict breast cancer in Chinese women.
By using information on risk factors that are common to women
of all ethnicities, we found that the Rosner – Colditz model was able
to explain as much as 76% of the difference in rates between
American and Chinese women. Nonetheless, the model overesti-
mated Chinese breast cancer incidence rates by approximately
40%. This overestimate may be due to differences between white
American women and Chinese women in diet, genetics, body
shape, or other breast cancer risk factors or may refl ect systematic
differences in how breast cancer screening is recorded on cancer
records. Regardless of whether the model itself or quality of the
data used to validate it account for this discrepancy, our estimates,
after calibrating down by 40%, are conservative.
There are several reasons that the true burden of breast cancer
may be even higher than our estimates. Our predictions apply to a
small fraction of the Chinese female population (those aged 35 – 49
years in 2001, 21% of the female population); thus, the overall
number of breast cancer cases that will develop by 2021 is likely to
be far more than what we have estimated. Younger women (ie,
those born after 1967) were not included in this analysis because
we wanted to ensure that the reproductive information was accu-
rate. However, because breast cancer risk factors are more preva-
lent in this younger group, our calculations may underestimate the
true breast cancer incidence rates across all Chinese women. In
addition , the predictions from the sensitivity analyses could be
substantially higher if concurrent changes were to occur in multi-
ple risk factors that interact with each other. Nonetheless, our
fi ndings are consistent with trends of increasing breast cancer inci-
dence rates predicted in an earlier study ( 29 ) and observed in other,
more developed Asian regions ( 3 , 4 , 30 , 31 ) that have undergone
temporal shifts in reproductive and lifestyle risk factors (toward
lower fertility, increased weight, more frequent use of postmeno-
pausal hormones, and higher alcohol consumption).
Our sensitivity analyses allowed us to model the effects of
changes in modifi able risk factors on predicted breast cancer inci-
dence rates. Modest increases in the use of postmenopausal hor-
mones and alcohol and in weight were predicted to cause an
additional 100 000 new cases of breast cancer by 2021. Although
increases in the prevalence of these behavioral risk factors may
contribute to the future burden of disease, these risk factors also
provide targets for public health interventions: Moderation in these
three factors could prevent 270 000 new cases of breast cancer in
among women aged 35 – 49 years in 2001. In addition to preventing
premenopausal breast cancer, avoidance of weight gain through
physical activity and good nutrition is likely to benefi t overall
health by preventing diabetes and cardiovascular disease, which are
established health care priorities in China ( 32 , 33 ). Moreover,
among Western women who drink alcohol, suffi cient dietary folate
has been shown to mitigate the excess risk of breast cancer associ-
ated with alcohol consumption and, hence, a potential prevention
effort could lie in emphasizing increasing folic acid intake. The
recent declines in US breast cancer rates that have been attributed
to decreases in use of hormone therapy ( 34 , 35 ) have also been
noted in Asian women ( 36 ), making reductions in hormone use a
realistic health care goal for breast cancer prevention in China.
Given the parallel shifts in the underlying breast cancer risk
factors that have been noted across the developing world, our fi nd-
ings may have implications for many other developing countries
worldwide. For example, our results raise questions about the
adequacy of national infrastructure for breast cancer therapy and
the possible benefi ts of population-based screening for breast can-
cer. Although mammography has been benefi cial in reducing the
health burden of malignant breast cancers in Western countries
( 37 ), cost-effectiveness analyses in Hong Kong suggest that mam-
mography may not be cost-effective for Asian populations ( 38 , 39 ).
Further research on the regional patterns of breast cancer inci-
dence, including possible differences between urban and rural
areas, is needed to guide screening policy .
Our fi ndings highlight the likely consequences of recent
changes in reproductive and other lifestyle patterns on breast can-
cer incidence in China and, perhaps, other developing countries.
The decline in fertility in China is part of a worldwide trend
toward having fewer children that is associated with economic and
social development and is not thought to be attributable to any
single policy measure ( 21 ). Breast cancer prevention is only one of
many considerations that should infl uence population planning;
economic development, environmental sustainability, and cultural
priorities are also critical considerations. However, not all propos-
als to achieve economic and demographic goals have equivalent
1358 Articles | JNCI Vol. 100, Issue 19 | October 1, 2008
health effects. To prevent breast cancer, fertility policies should
emphasize early, rather than late, childbearing and shorter, rather
than longer, spacing between births and breast-feeding.
In summary, we predict a substantial increase in breast cancer
incidence in China over the next two decades. The underlying
causes of this emerging epidemic, namely shifting reproductive
trends and growth and lifestyle changes that are associated with
economic development, are applicable to many developing nations
worldwide. Our fi ndings may have implications for both health
care planning within China and across the developing world.
Weight maintenance and avoidance of alcohol and postmeno-
pausal hormones may form targets for public health interventions
and be used to mitigate the future burden of this disease.
Description of the Log-Incidence Model of Breast Cancer
We fi t our log-incidence model of breast cancer to incident cases of invasive breast
cancer identifi ed during follow-up of the Nurses ’ Health Study cohort. The
approach to model fi tting was to assume that incidence at time t ( It ) is proportional
to the number of cell divisions ( Ct ) accumulated throughout life up to age t, that is
I t = kC t .
The cumulative number of breast cell divisions is calculated as follows:
C C x
Thus, λ i = C i +1/ C i represents the rate of increase of breast cell divisions
from age i to age i + 1. Log (λ i ) is assumed to be a linear function of risk factors
that are relevant at age i . The set of relevant risk factors and their magnitude
may vary according to the stage of reproductive life. The overall model is
324 14 2
where t = age; t o = age at menarche; t m = age at menopause; t * = min (age, age
at menopause); m t = 1 if postmenopausal at age t , 0 otherwise; s t = parity at age t ;
t i = age at i th birth, i = 1, … , s ; b
greater than or equal to i at age t , = 0 otherwise; m A = 1 if natural menopause, = 0
otherwise; m B = 1 if bilateral oophorectomy, = 0 otherwise; bbd = 1 if benign breast
disease = yes, = 0 otherwise; fhx = 1 if family history of breast cancer in mother or
sister = yes, = 0 otherwise; pmh A = number of years on oral estrogen; pmh B =
number of years on oral estrogen and progesterone; pmh C = number of years on
other types of postmenopausal hormones; pmh cur, t = 1 if current user of postmeno-
pausal hormones at age t , = 0 otherwise; pmh past, t = 1 if past user of postmenopausal
hormones at age t , = 0 otherwise; BMI j = body mass index at age j (kg/m 2 ); alc j =
alcohol consumption (grams) at age j ; h = height (inches).
The terms for BMI, height, and alcohol in relation to menopause and use of
hormones are summarized below:
t t b b
1 if parity is
21 824 4
(. ) ,
where β0 represents the rate of increase in incidence prior to menopause among
nulliparous women with no benign breast disease and no family history; β 1 and
β 2 represent modifi cation to the rate of increase in incidence for parous women
according to the number and precise spacing of births; γ 1 and γ 2 represent rates
of increase in incidence after menopause according to type of menopause among
women without benign breast disease not currently using postmenopausal hor-
mones; δ 1 , δ 2 and δ 3 represent modifi cations to the rate of increase in incidence
after menopause among women currently using postmenopausal hormones
according to the duration of the specifi c types of postmenopausal hormones
used; δ 4 and δ 5 represent the immediate effect of starting and stopping post-
menopausal hormone use on rates of increase in incidence after menopause; and
f represents the effect of family history of breast cancer on the number of breast
cell divisions at birth (ie, C o ).
β 3 represents the effect of BMI either before menopause or after menopause
on breast cancer incidence for a woman who is currently on postmenopausal
hormones. β * 3 represents the effect of BMI after menopause on breast cancer
incidence while not on postmenopausal hormones. β 4 and β 3 are the effects of
height defi ned similarly to β 3 and β* 3 and β 5 ,β * 5 and β ** 5 represent effects of alcohol
before menopause, after menopause on postmenopausal hormones, and after
menopause not on postmenopausal hormones, respectively. The rationale for the
separate terms is the fi nding in exploratory analyses and from the literature ( 40 )
that effects of BMI and possibly height and alcohol on breast cancer incidence are
different before and after menopause and that the effect of BMI on breast cancer
incidence after menopause differs according to whether a woman is or is not cur-
rently on postmenopausal hormones ( 41 ). α 1 , α 2 , α 3 , and α 4 represent modifi cations
to 1) the number of breast cell divisions at birth, 2) the rates of increase in the
number of cell divisions after birth, but before menarche, 3) the rates of increase
in the number of cell divisions after menarche, but before menopause, and 4) the
rates of increase in the number of cell divisions after menopause among women
with benign breast disease, respectively. The rationale for these extra terms
involving benign breast disease is that the relative risk for benign breast disease
varies according to age, with the relative risk being strongest among younger
women and diminishing over time.
The general rationale for a log-incidence model of a specifi c cancer is that the
number of precancerous cells increases multiplicatively with time, whereas the risk
factor profi le from birth to current age (eg, 35 years) differentially affects the rate
of increase in incidence. Specifi cally, for the breast cancer incidence model
described above, the number of precancerous cells is assumed to increase annually
at the rate of (β0) prior to menopause for nulliparous women; at the rate of exp(β0+
β1s) prior to menopause for parous women with parity = s , and so forth. Finally,
the number of precancerous cells increases immediately after the fi rst birth
by exp[β2( t 1 - t 0 )] The incidence rate of breast cancer is, therefore, assumed to be
approximately proportional to the number of precancerous cells.
Cumulative incidence was calculated according to the following formula:
where r i is the incidence rate for each year. Because of the lag in recruitment from
1997 to 2000 and loss to follow-up, individual women were followed for different
lengths of time. We therefore calculated each woman’s expected cumulative inci-
dence for the length of time she was actually followed in the cohort. By summing
over the predicted cumulative incidences for each woman, we obtained the
expected number of cases in the entire cohort. We further subdivided the follow-up
time into 5-year age groups and calculated the average incidence rates within
each age group, thus estimating the age-specifi c incidence rates predicted in this
population. The sum of the cumulative incidences for each 5-year age group
yielded the expected number of cases within each age group. Confi dence
JNCI | Articles 1359
intervals for E/O ratio described in validation section were calculated according
to the following formula:
E/O exp ±1.96 √ 1/O .
Sensitivity Analysis Assumptions
1. Stable weight: no changes in weight from age 18 years onward; mean
BMI = 23 kg/m 2 .
2. Average weight gain: 0.1 annual increase in BMI from BMI 20 kg/m 2 at
age 18 ( 42 ).
3. Above-average weight gain: 0.2 annual increase in BMI from BMI of 20 kg/m 2
at age 18.
4. Low alcohol use: 2% of women drinking an average of 2 g of alcohol per
5. High alcohol use: 40% of women drinking an average of 2 g of alcohol per
6. No postmenopausal hormone use: 0% of women using hormones.
7. High postmenopausal hormone use: 6% of women using hormone therapy
(2% estrogen and progesterone, 2% estrogen only, 2% other hormone
therapy) for 2 years following menopause.
8. Fertility rate: 1.1 births per woman.
9. High alcohol use, above-average weight gain, and high postmenopausal
hormone use (ie, assumptions 3, 5, and 7).
10. Stable weight, low alcohol use, and no postmenopausal hormone uses
(ie, assumptions 1, 4, and 6).
1. Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.
cancer.gov) . SEER*Stat Database: Incidence - SEER 9 Regs Limited-Use , Nov
2007 Sub (1973 – 2005), National Cancer Institute, DCCPS, Surveillance
Research Program, Cancer Statistics Branch, released April 2008, based
on the November 2007 submission. http://seer.cancer.gov/registries/
2. Parkin DM , Whelan SL , Ferlay J , Storm H. Cancer Incidence in Five
Continents, Vol. I to VIII . Lyon : IARC Cancer Base 2005 ; 7 .
3. Leung GM , Thach TQ , Lam TH , et al . Trends in breast cancer incidence
in Hong Kong between 1973 and 1999: an age-period-cohort analysis . Br
J Cancer . 2002 ; 87 ( 9 ): 982 – 988 .
4. Minami Y , Tsubono Y , Nishino Y , Ohuchi N , Shibuya D , Hisamichi S .
The increase of female breast cancer incidence in Japan: emergence of
birth cohort effect . Int J Cancer . 2004 ; 108 ( 6 ): 901 – 906 .
5. Seow A , Duffy SW , McGee MA , Lee J , Lee HP . Breast cancer in Singapore:
trends in incidence 1968 – 1992 . Int J Epidemiol . 1996 ; 25 ( 1 ): 40 – 45 .
6. Deapen D , Liu L , Perkins C , Bernstein L , Ross RK . Rapidly rising breast
cancer incidence rates among Asian-American women . Int J Cancer . 2002 ;
99 ( 5 ): 747 – 750 .
7. Chia KS , Reilly M , Tan CS , et al . Profound changes in breast cancer
incidence may refl ect changes into a Westernized lifestyle: a comparative
population-based study in Singapore and Sweden . Int J Cancer . 2005 ; 113 ( 2 ):
302 – 306 .
8. Colditz GA , Rosner B . Cumulative risk of breast cancer to age 70 years
according to risk factor status: data from the Nurses ’ Health Study . Am J
Epidemiol . 2000 ; 152 ( 10 ): 950 – 964 .
9. Pathak DR , Whittemore AS . Combined effects of body size, parity, and
menstrual events on breast cancer incidence in seven countries . Am J
Epidemiol . 1992 ; 135 ( 2 ): 153 – 168 .
10. Pike MC , Kolonel LN , Henderson BE , et al . Breast cancer in a multieth-
nic cohort in Hawaii and Los Angeles: risk factor-adjusted incidence in
Japanese equals and in Hawaiians exceeds that in whites . Cancer Epidemiol
Biomarkers Prev . 2002 ; 11 ( 9 ): 795 – 800 .
11. Ng EH , Gao F , Ji CY , Ho GH , Soo KC . Risk factors for breast carcinoma
in Singaporean Chinese women: the role of central obesity . Cancer . 1997 ;
80 ( 4 ): 725 – 731 .
12. Gao YT , Shu XO , Dai Q , et al . Association of menstrual and reproductive
factors with breast cancer risk: results from the Shanghai Breast Cancer
Study . Int J Cancer . 2000 ; 87 ( 2 ): 295 – 300 .
13. Shu XO , Jin F , Dai Q , et al . Association of body size and fat distribution
with risk of breast cancer among Chinese women . Int J Cancer . 2001 ; 94 ( 3 ):
449 – 455 .
14. Yu K , Lee CH , Tan PH , Tan P . Conservation of breast cancer molecular
subtypes and transcriptional patterns of tumor progression across distinct
ethnic populations . Clin Cancer Res. 2004 ; 10 ( 16 ): 5508 – 5517 .
15. Ji CY , Ohsawa S , Kasai N . Secular changes in the stature, weight and age
at maximum growth increments of urban Chinese girls from the 1950s to
1985 . Am J Hum Biol. 1995 ; 7 : 473 – 484 .
16. Wu Y . Overweight and obesity in China . BMJ . 2006 ; 333 ( 7564 ): 362 – 363 .
17. Hesketh T , Lu L , Xing ZW . The effect of China’s one-child family policy
after 25 years . N Engl J Med . 2005 ; 353 ( 11 ): 1171 – 1176 .
18. Chen J , Campbell C , Li J , Peto R . Diet, Life-style, and Mortality in China:
A Study of the Characteristics of 65 Chinese Counties . Oxford, UK : Oxford
University Press ; 1991 .
19. Rosner B , Colditz GA . Nurses ’ health study: log-incidence mathematical
model of breast cancer incidence . J Natl Cancer Inst . 1996 ; 88 ( 6 ):
359 – 364 .
20. Zheng W , Chow WH , Yang G , et al . The Shanghai Women’s Health
Study: rationale, study design, and baseline characteristics . Am J Epidemiol .
2005 ; 162 ( 11 ): 1123 – 1131 .
21. Ding QJ , Hesketh T . Family size, fertility preferences, and sex ratio
in China in the era of the one child family policy: results from national
family planning and reproductive health survey . BMJ . 2006 ; 333 ( 7564 ):
371 – 373 .
22. Colditz GA . The nurses ’ health study: a cohort of US women followed
since 1976 . J Am Med Womens Assoc. 1995 ; 50 ( 2 ): 40 – 44 .
23. Rockhill B , Byrne C , Rosner B , Louie MM , Colditz G . Breast cancer risk
prediction with a log-incidence model: evaluation of accuracy . J Clin
Epidemiol . 2003 ; 56 ( 9 ): 856 – 861 .
24. Rosner B , Colditz GA , Iglehart JD , Hankinson SE . Risk prediction
models with incomplete data with application to prediction of estrogen
receptor-positive breast cancer: prospective data from the Nurses' Health
Study . Breast Cancer Res. 2008 ; 10 ( 4 ): R55 .
25. Cui X , Dai Q , Tseng M , Shu XO , Gao YT , Zheng W . Dietary patterns
and breast cancer risk in the shanghai breast cancer study . Cancer Epidemiol
Biomarkers Prev . 2007 ; 16 ( 7 ): 1443 – 1448 .
26. Bernstein L , Yuan JM , Ross RK , et al . Serum hormone levels in pre-
menopausal Chinese women in Shanghai and white women in Los
Angeles: results from two breast cancer case-control studies . Cancer Causes
Control . 1990 ; 1 ( 1 ): 51 – 58 .
27. Setiawan VW , Haiman CA , Stanczyk FZ , Le Marchand L , Henderson
BE . Racial/ethnic differences in postmenopausal endogenous hormones:
the multiethnic cohort study . Cancer Epidemiol Biomarkers Prev . 2006 ; 15 ( 10 ):
1849 – 1855 .
28. International Data Base, U.S. Census Bureau . China 2000 Census . www.
census.gov/ipc/www/idbnew.html. Accessed September 31, 2007 .
29. Yang L , Parkin DM , Ferlay J , Li L , Chen Y . Estimates of cancer incidence
in China for 2000 and projections for 2005 . Cancer Epidemiol Biomarkers
Prev . 2005 ; 14 ( 1 ): 243 – 250 .
30. Wong IO , Cowling BJ , Schooling CM , Leung GM . Age-period-cohort
projections of breast cancer incidence in a rapidly transitioning Chinese
population . Int J Cancer . 2007 ; 121 ( 7 ): 1556 – 1563 .
31. Shen YC , Chang CJ , Hsu C , Cheng CC , Chiu CF , Cheng AL . Signifi cant
difference in the trends of female breast cancer incidence between
Taiwanese and Caucasian Americans: implications from age-period-
cohort analysis . Cancer Epidemiol Biomarkers Prev . 2005 ; 14 ( 8 ): 1986 – 1990 .
32. He J , Gu D , Wu X , et al . Major causes of death among men and women
in China . N Engl J Med . 2005 ; 353 ( 11 ): 1124 – 1134 .
33. Wang L , Kong L , Wu F , Bai Y , Burton R . Preventing chronic diseases in
China . Lancet . 2005 ; 366 ( 9499 ): 1821 – 1824 .
34. Clarke CA , Glaser SL , Uratsu CS , Selby JV , Kushi LH , Herrinton LJ .
Recent declines in hormone therapy utilization and breast cancer
incidence: clinical and population-based evidence . J Clin Oncol . 2006 ;
24 ( 33 ): e49 – e50 .
35. Ravdin PM , Cronin KA , Howlader N , et al . The decrease in breast-cancer
incidence in 2003 in the United States . N Engl J Med . 2007 ; 356 ( 16 ):
1670 – 1674 .
1360 Articles | JNCI Vol. 100, Issue 19 | October 1, 2008
36. Leung KY , Ling M , Tang GW . Use of hormone replacement therapy in
the Hong Kong public health sector after the Women’s Health Initiative
trial . Maturitas . 2005 ; 52 ( 3 – 4 ): 277 – 285 .
37. Nystrom L , Andersson I , Bjurstam N , Frisell J , Nordenskjold B , Rutqvist
LE . Long-term effects of mammography screening: updated overview of
the Swedish randomised trials . Lancet . 2002 ; 359 ( 9310 ): 909 – 919 .
38. Woo PP , Kim JJ , Leung GM . What is the most cost-effective population-
based cancer screening program for Chinese women? J Clin Oncol . 2007 ;
25 ( 6 ): 617 – 624 .
39. Wong IO , Kuntz KM , Cowling BJ , Lam CL , Leung GM . Cost effective-
ness of mammography screening for Chinese women . Cancer . 2007 ; 110 ( 4 ):
885 – 895 .
40. Hunter DJ , Willett WC . Diet, body size and breast cancer . Epidemiol Rev.
1993 ; 15 ( 1 ): 110 – 132 .
41. Huang Z , Hankinson SE , Colditz GA , et al . Dual effects of weight and
weight gain on breast cancer risk . J Am Med Assoc. 1997 ; 278 : 1407 – 1411 .
42. Wang H , Du S , Zhai F , Popkin BM . Trends in the distribution of body
mass index among Chinese adults, aged 20 – 45 years (1989 – 2000) . Int J
Obes (Lond) . 2007 ; 31 ( 2 ): 272 – 278 .
Funding for this work was provided by the National Institutes of Health grant
R25 CA098566. The study sponsor had no role in study design; in the collec-
tion, analysis, or interpretation of data; in the writing of the report; or in the
decision to submit the paper for publication.
We would like to thank Dr Wanquin Wen, Dr Gabriel Leung, and Dr Irene
Wong for their help in data collection.
Manuscript received January 22 , 2008 ; revised July 8 , 2008 ; accepted July
30 , 2008 .