Occupations and breast cancer risk among Chinese women in urban Shanghai

AMERICAN JOURNAL OF INDUSTRIAL MEDICINE 42:296–308 (2002)

Occupations and Breast Cancer Risk AmongChinese Women in Urban Shanghai

Kathleen M. Gardner, MT (ASCP), MSPH,1 Xiao Ou Shu, MD, PhD,1� Fan Jin, MD,2

Qi Dai, MD, PhD,1 Zhixian Ruan, BS,2 Shirley J. Thompson, PhD,3 James R. Hussey, PhD,3

Yu Tang Gao, MD,2 and Wei Zheng, MD, PhD1

Background Although, an elevated risk of breast cancer has been suggested for a numberof occupations, many earlier studies were limited by selection biases, the incompleteassessment of job histories, and the inability to control for confounding.Methods We examined the relationship between occupational history and breast cancerrisk using data from a population-based case-control study of 1,458 cases and 1,556 age-matched controls (90% response rate) conducted in Shanghai, China. Unconditionallogistic regression models were used to derive odds ratios (ORs) and 95% confidenceintervals (95% CIs) of breast cancer risk associated with occupations and duration ofemployment adjusting for non-occupational risk factors.Results The following occupations were found to be associated with an increased riskof breast cancer: laboratory technicians (OR 9.94, 95% CI 1.20–82.37), telephone andtelegraph operators (OR 4.63, 95% CI 1.85–11.59), leather and fur processors (OR 3.25,95% CI 1.11–9.53), and glass-manufacturing workers (OR 2.08, 95% CI 1.14–3.82).A dose–response pattern for years of employment was observed for leather and fur pro-cessors (P¼ 0.02) and glass-manufacturing workers (P¼ 0.01). Stratified analyses alsorevealed dose–response relationships between the risk of breast cancer and years ofemployment as inspector and product analysts among pre-menopausal women (P¼ 0.02),and as farmers among post-menopausal women (P¼ 0.04).Conclusions This study found that several occupations are associated with an increasedrisk of breast cancer among women. Studies examining various occupational exposures inthese high-risk occupations are warranted to identify carcinogens that may play a role inthe increasedbreastcancerrisk. Am. J. Ind. Med. 42:296–308, 2002.�2002Wiley-Liss, Inc.

KEY WORDS: breast cancer; occupations; employment; cancer; occupationalexposures

INTRODUCTION

Breast cancer incidence varies from nation to nation

[Parkin and Muir, 1992; Nab et al., 1994]. Although, China

has one of the lowest incidence rates of this malignancy in the

world, Shanghai, the largest industrial city on the east coast of

China, experienced a 40% increase in breast cancer incidence

from 1972 to 1989 [Jin et al., 1993a], with an 80–100% in-

crease among women between the ages of 35 and 44 [Jin et al.,

1993b]. Screening practices have remained the same in China

and, therefore, cannot be considered major contributors to

this increase in breast cancer [Jin et al., 1993b]. The increase

� 2002Wiley-Liss, Inc.

1Department of Medicine, and Vanderbilt-Ingram Cancer Center, Vanderbilt University,Nashville,Tennessee 37232-8300

2Department of Epidemiology, Shanghai Cancer Institute, Shanghai 200032, People’sRepublic of China

3Norman J. Arnold School of Public Health, University of South Carolina, South Carolina29203

Contract grant sponsor: USPHS; Contract grant number: R01CA64277.*Correspondence to: Xiao Ou Shu,Vanderbilt University Medical Center, Health Services

Research, 6th floor, Medical Center East, Nashville,TN 37232-8300.E-mail: [email protected]

Accepted10 June 2002DOI10.1002/ajim.10112. Published online inWiley InterScience

(www.interscience.wiley.com)

coincides with a decrease in age at menarche, an increase

in age at menopause [Jin et al., 1993a], and a delayed onset of

pregnancy [Jin et al., 1993b] among successive birth cohorts.

However, menstrual and reproductive factors can only ex-

plain 40–50% of the breast cancer within this population

[Gao et al., 2000]. During this same period, China has also

experienced rapid advancements in industrialization. It is

possible that occupational exposures resulting from this surge

of industrialization [He, 1998] may have contributed, in part,

to the increase in breast cancer incidence in China.

Over the last decade, a number of epidemiologic studies

have explored the association between occupations and breast

cancer. Possible increased risk of breast cancer has been

suggested for health care workers [Doebbert et al., 1988;

Sankila et al., 1990; Belli et al., 1992; Bulbulyan et al.,

1992; Rubin et al., 1993; Zheng et al., 1993; Morton, 1995;

Schenck, 1997; Petralia et al., 1998a, 1999a; Pollan and

Gustavsson, 1999], teachers [Threlfall et al., 1985; Rubin

et al., 1993; Schenck, 1997; Petralia et al., 1998a; Pollan and

Gustavsson, 1999; Simpson et al., 1999], clerical workers

[Threlfall et al., 1985; Ewertz, 1988; Bulbulyan et al., 1992;

Rubin et al., 1993; Zheng et al., 1993; Calle et al., 1998;

Pollan and Gustavsson, 1999; Simpson et al., 1999],

agricultural workers (farmers) [Petralia et al., 1998b; Pollan

and Gustavsson, 1999; Settimi et al., 1999], and rubber and

plastic manufacturers [Chiazze and Ference, 1981; Cantor

et al., 1995; Petralia et al., 1998a]. However, evidence on

the relationship between occupations and breast cancer risk

remains inadequate, and conflicting results from previous

studies may be due to methodological limitations. Many

studies were based on mortality data and, thus, may suffer

from survival biases [Chiazze and Ference, 1981; Katz, 1983;

Threlfall et al., 1985; Doebbert et al., 1988; Wong, 1990;

Bulbulyan et al., 1992; Rubin et al., 1993; Kogevinas et al.,

1994; Wong et al., 1994; Sala-Serra et al., 1996; Calle et al.,

1998; Burnett et al., 1999; Petralia et al., 1999a]. Other

studies obtained limited job histories [Katz, 1983; Threlfall

et al., 1985; Doebbert et al., 1988] and/or lacked information

on other breast cancer risk factors [Cantor et al., 1995] and,

thus, may have suffered from misclassification errors in ex-

posure assessment and/or confounding effects. Some case-

control studies were also limited by low response rates

[Doody et al., 1995; Petralia et al., 1998b, 1999b]. Low

employment rates for women in some study populations

could further jeopardize results of past studies.

The recently completed Shanghai Breast Cancer Study

was designed to overcome many limitations of earlier studies.

This study was conducted in a population where virtually all

women work and hold relatively stable jobs over time. In this

report, we examined the relationship between occupations

and breast cancer risk focusing first on the suggestive high-

risk occupations (healthcare workers, teachers, clerical

workers, farmers, and rubber and plastic manufacturers) that

have been linked to breast cancer in earlier studies. We then

evaluated the association of breast cancer risk with other

occupations.

MATERIALS AND METHODS

The Shanghai Breast Cancer Study, a population-based

case-control study, was conducted between August 1996 and

March 1998 among Chinese women between the ages of 25

and 64 who were permanent residents of Shanghai with no

former history of cancer, and who were willing to undergo an

in-person interview. Relevant institutional review boards

approved the use of human subjects in this study and a written

consent was obtained from all study participants. Newly

diagnosed, pathologically confirmed breast cancer cases, as

defined by ICD-9 code 174, were identified through both a

rapid case ascertainment system and the population-based

Shanghai Cancer Registry. Potentially eligible controls were

randomly selected from female residents of the general popu-

lation using the Shanghai Resident Registry, a registry for all

permanent residents of Shanghai, and frequency matched to

cases by age (5 years). The number of controls in each age-

specific stratum was pre-determined using the age distribu-

tion of the 1990–1993 incident breast cancer cases reported

to the Shanghai Cancer Registry. Controls were considered

eligible for the study only if they were still residing at the

address recorded in the residential registry at the time of

interview.

Information was collected from participants during an

in-person interview using a structured questionnaire. The

questionnaire obtained data on demographics, physical acti-

vity, diet, smoking, drinking, history of benign breast disease,

family history of breast cancer, weight history, detailed

menstrual, and reproductive history, and an in-depth

occupational history. Occupational history included up to

four occupations, held for a minimum of 3 years, with cor-

responding starting and stopping dates and occupational

physical assessment. Additional occupational information

included ever working on a farm and a detailed history of

pesticide exposure.

Job titles were coded by a three level hierarchy coding

system [National Bureau of Statistics, 1982]. The 3-digit level

corresponds to 301 specific occupational titles, the 2-digit

level corresponds to 63 occupational categories and the 1-digit

level corresponds to seven general occupational categories

[National Bureau of Statistics, 1982]. Based on published

literature, we selected five occupations (healthcare workers,

teachers, clerical workers, agricultural workers, and rubber

and plastic workers) as high-risk groups and performed an

in-depth evaluation of these occupations in relation to breast

cancer. A systematic evaluation of all other occupational

groups was also conducted by using the corresponding 1- or

2-digit codes.

To measure the association of occupations and breast

cancer risk, unconditional logistic regression analyses were

Occupations and Breast Cancer Risk 297

used to calculate adjusted odds ratios (ORs) and 95%

confidence intervals (95% CIs) for all women combined and

stratified by menopausal status. All variables that have been

found to be associated with a high risk of breast cancer in

this study were considered as potential confounders, and

adjusted for in the logistic regression models. These include

age, education level, age at menarche, age at menopause/pre-

menopause, age at first live birth/nulliparity, family history

of breast cancer, history of fibroadenoma, body mass index

(kg/m2) (BMI), waist-to-hip ratio (WHR), height and leisure

physical activity. Age was coded as a continuous variable.

All others were treated as categorical variables. Ever having

held a specific occupation and the duration of that occupation

provided two measures by which occupations were inves-

tigated in this study. Duration of the occupation was dicho-

tomized as � 10 years vs. > 10 years of employment in a

particular occupation. To determine whether there was a

dose–response relationship for duration of employment in a

suggestive high-risk occupation, a test for trend was per-

formed by assigning the score 0 for never having held that

occupation, 1 for having held the occupation for � 10 years,

and 2 for having held that occupation for > 10 years, and

then, entering this score into the logistic model as a con-

tinuous variable. Since menopausal status was missing for

6 cases and 4 controls, these subjects were excluded from

multivariate logistic regression analysis. All analyses were

performed using PC-SAS, version 8.0, (SAS Institutes, Inc.,

Cary, NC), and all tests were two-sided.

RESULTS

The participation rate of subjects was acceptable. Of the

1,602 eligible cases, 109 (6.8%) chose not to participate, 17

(1.1%) could not be located, and 17 (1.1%) died before

interview, resulting in a 91.1% response rate. One additional

case had to be eliminated from this occupational study due to

incomplete occupational information. Of the 1,724 eligible

controls, 166 (9.6%) chose not to participate, and 2 (0.1%)

died before interview, resulting in a 90.3% response rate.

Table I compares cases and controls on the distribution

of demographic, menstrual, and reproductive factors among

all women, pre- and post-menopausal women. In general,

cases were slightly older, more educated, younger at menar-

che, older at first live birth, taller, heavier, had a higher WHR,

and were more likely to have a family history of breast cancer

and a history of fibroadenoma than controls. Among pre-

menopausal women, compared with controls, cases were

older, older at first live birth, older at menopause, had a higher

WHR, and were more likely to have a family history of breast

cancer and a history of fibroadenoma. Among post-

menopausal women, cases were taller, heavier, more edu-

cated, younger at menarche, older at first live birth, and were

less likely to engage in leisure physical activity, and more

likely to have a history of fibroadenoma than controls.

Table II presents the ORs of breast cancer risk associated

with ever being employed in the five primary occupations of

interest and by the duration of employment. The five primary

occupational groups of interest were generally not related to

the risk of breast cancer. However, upon examining 3-digit

occupations among health care workers, we found that labo-

ratory technicians (consisting of 7 cases and 1 control) had a

tenfold increased risk of breast cancer (ORADJ, 9.94, 95% CI

1.20–82.37) (data not shown in table). No significant in-

creased risks were found for other health care occupations

examined, including doctors, nurses, pharmacists, and public

health workers (data not shown). A suggestive increased risk

of 19% was present for women ever being employed as cleri-

cal workers (ORADJ, 1.19, 95% CI 0.95–1.47). Women who

worked as clerical workers for � 10 years had a 48% incre-

ased risk of breast cancer (ORADJ, 1.48, 95% CI 1.08–2.03).

This elevated risk was mainly confined to post-menopausal

women (ORADJ, 2.07, 95% CI 1.21–3.81). Women who

remained in the job as clerical workers for > 10 years were

not at increased risk, whether examining all women or women

by menopausal status. Women working as farmers for over

10 years were twice as likely to develop breast cancer

(ORADJ, 2.08, 95% CI 1.15–3.74). This increased risk was

seen among both pre-menopausal (ORADJ, 1.72, 95% CI

0.87–3.41) and post-menopausal women (ORADJ, 2.75, 95%

CI 0.78–9.76), although, the point estimates did not reach

statistical significance. Stratified analyses also revealed a

dose–response relationship between the risk of breast cancer

and years of employment as farmers among post-menopausal

women (P¼ 0.04).

Table III shows breast cancer risks for women working

in all 1- and 2-digit occupations. Statistically elevated risks

of breast cancer were found in the following occupations:

postal and communication workers (ORADJ, 2.98, 95% CI

1.50–5.92), leather and fur processors (ORADJ, 3.25, 95% CI

1.11–9.53), and glass-manufacturing workers (ORADJ, 2.08,

95% CI 1.14–3.82). A reduced risk of breast cancer was

found among lawyers (ORADJ, 0.10, 95% CI 0.01–0.87) and

commercial workers (ORADJ, 0.80, 95% CI 0.63–1.00).

After stratifying by menopausal status, an increased

risk of breast cancer among postal and communication

workers was observed for both pre- (ORADJ, 2.47, 95% CI

1.05–5.82) and post-menopausal women (ORADJ, 4.03, 95%

CI 1.26–12.90). The occupation responsible for the in-

creased risk among postal and communication workers was

telephone and telegraph operators (ORADJ, 4.63, 95% CI,

1.85–11.59) (data not shown in table). A statistically signi-

ficant increased risk of breast cancer was not observed among

leather and fur processors, after stratification by meno-

pausal status, due to small numbers. Glass-manufacturing

workers were 2.7 times more likely to develop pre-meno-

pausal breast cancer (ORADJ, 2.70, 95% CI 1.20–6.05), and

had a 39% increased, but non-significant risk of devel-

oping post-menopausal breast cancer (ORADJ, 1.39, 95% CI

298 Gardner et al.

TABLE I. Comparison of Cases and Controls on Breast Cancer Risk Factors forAllWomen,Pre- and Post-MenopausalWomen in Shanghai*

All women Pre-menopausal women Post-menopausal women

Case (1458)(%)

Control (1556)(%) P value

Case (951)(%)


Case (501)(%)


Age (years)25^34 2.8 5.3 4.3 8.2 0.2 0.235^44 35.9 36.4 53.5 55.6 2.4 2.745^54 38.8 33.4 41.6 36.0 33.3 28.855^64 22.6 24.9 <0.01 0.5 0.3 <0.01 64.1 68.3 0.47

EducationNo formal education 3.6 5.5 0.5 0.4 9.6 14.4Elementary 8.5 8.4 3.2 2.5 18.4 18.5Middleþ high 74.3 75.4 84.7 88.0 54.5 53.6Prof.þ college 13.6 10.7 0.01 11.6 9.1 0.23 17.6 13.5 0.05

Age atmenarche� 12 9.4 8.4 8.4 8.8 11.3 7.813^16 80.1 77.2 82.8 79.8 74.7 72.8� 17 10.5 14.4 <0.01 8.8 11.4 0.13 14.0 19.4 0.02

Age at first birth/nulliparity< 30 73.8 80.3 70.7 76.3 79.8 87.530^34 17.0 13.2 19.6 16.5 12.2 7.5� 35 4.1 2.6 4.5 3.1 3.4 1.6Nulliparity 5.1 3.9 <0.01 5.2 4.1 0.04 4.6 3.4 <0.01

Age atmenopause/pre-menopause<45 5.8 7.9 16.8 21.745^47 6.6 6.2 19.2 17.148^50 12.0 13.7 34.7 37.9> 50 10.1 8.4 29.3 23.3Pre-menopause 65.5 63.8 0.05 0.04

Family history of breast cancerYes 3.7 2.4 3.5 1.9 4.2 3.4No 96.3 97.6 0.04 96.5 98.1 0.03 95.8 96.6 0.49

History of fibroadenomaYes 9.6 5.0 10.0 5.5 9.0 4.3No 90.4 95.0 < 0.01 90.0 94.5 < 0.01 91.0 95.7 < 0.01

BMI (kg/m2)< 20.70 20.3 24.4 24.3 28.4 12.8 17.420.71^22.79 23.9 25.4 26.7 28.3 19.0 20.122.80^25.10 26.8 25.3 26.7 23.9 26.8 27.9> 25.10 29.0 24.9 < 0.01 22.3 19.4 0.08 41.6 34.5 0.05

WHR� 0.764 19.5 25.0 22.9 30.2 13.2 15.80.765^0.800 25.3 26.8 28.2 28.2 20.0 24.00.801^0.835 25.0 23.0 23.8 21.9 27.3 24.7> 0.835 30.2 25.3 < 0.01 25.1 19.6 < 0.01 39.5 35.4 0.16

Height (cm)� 155.0 23.4 26.8 19.4 19.8 30.3 39.1155.1^159.0 30.2 27.5 28.5 27.0 33.7 28.5159.1^162.0 21.0 23.0 22.5 24.9 18.2 19.8> 162.0 25.4 22.7 0.03 29.6 28.3 0.59 17.8 12.6 < 0.01

(Continued )


0.52–3.71). Risk of pre-menopausal breast cancer was also

elevated among inspectors and product analysts (ORADJ,

1.49, 95% CI 1.06–2.08), and other service workers (ORADJ,

9.40, 95% CI 1.09–81.03). Risk of post-menopausal breast

cancer was elevated, but not statistically significant among

wood preparation workers (ORADJ, 3.63, 95% CI 0.93–

14.18).

Additional analyses were conducted for occupations that

showed a high-risk of breast cancer based on our preliminary

analyses to evaluate the occupational association by the dura-

tion of employment (Table IV). A positive dose–response

pattern associated with duration of employment was observed

for postal and communication workers (P for trend< 0.01),

leather and fur processors (P for trend¼ 0.02), and glass-

manufacturing workers (P for trend¼ 0.01). Indeed, a signi-

ficant relationship was observed between breast cancer risk

and employment for> 10 years as postal and communication

workers (ORADJ, 3.42, 95% CI 1.33–8.81), leather and fur

processors (ORADJ, 10.51, 95% CI 1.27–86.80), and glass-

manufacturing workers (ORADJ, 2.71, 95% CI 1.09–6.77).

A dose–response relationship was also observed for pre-

menopausal breast cancer among glass-manufacturing

workers (P for trend¼ 0.02) (3.37, 95% CI 0.89–12.73

for > 10 years of employment), and inspector and product

analysts (P for trend¼ 0.02) (ORADJ, 1.61, 95% CI 1.04–

2.49 for > 10 years of employment). Risk of post-meno-

pausal breast cancer also increased with the duration of

employment among postal and communication workers

(P for trend¼ 0.03) (ORADJ, 4.53, 95% CI 0.92–22.42 for

> 10 years of employment).

DISCUSSION

Healthcare workers [Doebbert et al., 1988; Belli et al.,

1992], teachers [Threlfall et al., 1985], clerical workers

[Threlfall et al., 1985; Ewertz, 1988; Bulbulyan et al., 1992;

Rubin et al., 1993; Zheng et al., 1993; Calle et al., 1998;

Pollan and Gustavsson, 1999; Simpson et al., 1999], agri-

cultural workers [Pollan and Gustavsson, 1999; Band et al.,

2000], and rubber and plastic manufacturers [Chiazze and

Ference, 1981] have previously been found to have a higher

risk of breast cancer. While we did not find health care

workers to be at increased risk of breast cancer as a whole,

analysis on individual health care occupations revealed a

significant tenfold increased risk of breast cancer among

laboratory technicians, consistent with several earlier studies

[Belli et al., 1992; Dosemeci et al., 1992; Andersen et al.,

1999]. Suggested potential exposures that may contribute to

this increased risk among health care workers are infectious

agents, sterilizing agents, ionizing radiation, cytostatic

drugs, and other toxic chemicals in addition to a high level

of stress [Katz, 1983; Sankila et al., 1990].

In this study, we also found that women employed as

farmers for over 10 years were two times more likely to

develop breast cancer than women never employed as far-

mers. Although, it has been suggested that women working

with pesticides may be at increased risk of breast cancer

due to the proposed estrogenic effects of these chemicals

[Labreche and Goldberg, 1997], earlier studies have gene-

rally not found a significantly increased risk of breast cancer

among farmers [Petralia et al., 1998b; Settimi et al., 1999].

Some of these studies were apparently limited by lack of sta-

tistical power to detect an association by menopausal status

[Petralia et al., 1998b; Settimi et al., 1999] and duration of

employment [Settimi et al., 1999]. In a recent population-

based case-control study, crop farmers, a subgroup of far-

mers, were found to have a tenfold increased risk of breast

cancer after adjusting for other risk factors [Band et al., 2000].

Interestingly, the vast majority of women who have ever

worked on farms in our study were crop farmers. Studies

stemming from large cohorts of women farmers will be

needed to confirm this association.

Women ever employed in leather and fur processing had

over a threefold increased risk of breast cancer as compared

to women never employed in this occupation. Moreover,

women employed for more than 10 years had over a tenfold

increased risk for breast cancer, although, the estimate was

based on only 7 cases and 1 control. Previous studies have

TABLE I. (Continued )

All women Pre-menopausal women Post-menopausal women

Case (1458)(%)


Case (951)(%)


Case (501)(%)


Leisure physical activityYes 18.7 25.2 14.0 15.5 27.5 42.6No 81.3 74.8 < 0.01 86.0 84.5 0.36 72.5 57.4 < 0.01

*Subjectswithmissingvalueswereexcludedfromanalysis.

300 Gardner et al.

TABLE

II.AdjustedORsofBreastCancerAssociatedWithaprioriH

igh-RiskOccupationsbyEverEm

ployedandbyD

urationofEmployment(�10and>

10Years)intheOccupationofInterestforAllW

omen,Pre-

andPost-MenopausalW

omeninShanghai*

, **

Allwom

enPre-menopausal

Post-menopausal

Cases

(1452)

Controls

(1552)

ORa

Trendtest

( Pvalue)

Cases

(951)

Controls

(990)

ORb

Trendtest

( Pvalue)

Cases

(501)

Controls

(562)

ORa

Trendtest

( Pvalue)

Medical&publichealthw

orkerc

7778

0.94(0.67^

1.13)

3543

0.74

(0.48^

1.24)

4235

1.19(0.72^

1.97)

�10yearsofemployment

1017

0.57

(0.26^

1.28)

511

0.44(0.15^1.30)

56

0.82

(0.24^

2.80)

>10yearsofemployment

6761

1.04(0.72^

1.51)

0.93

3032

0.89

(0.53^

1.50)

0.44

3729

1.27(0.74

^2.20)

0.43

Teacherc

127

111

1.06(0.79^

1.42)

5046

0.89

(0.58^

1.38)

7765

1.20(0.79^

1.81)


3133

0.92(0.55^

1.54)

1821

0.83

(0.43^

1.60)

1312

1.15(0.49^

2.70)


9678

1.12(0.79^

1.58)

0.60

3225

0.94(0.53^

1.67)

0.70

6453

1.21(0.78^

1.89)

0.39

Clerical&relatedworkerd

219

192

1.19(0.95^

1.47)

141

131

1.06(0.81^

1.39)

7761

1.32(0.90^

1.94)


103

761.48(1.08^2.03)

6957

1.28(0.87^

1.86)

3419

2.07

(1.21^3.81)


116

116

1.00(0.76^

1.32)

0.40

7274

0.91(0.64^

1.29)

0.97

4342

1.01(0.63^

1.62)

0.46

Farmerc

262

250

1.09(0.89^

1.34)

224

217

0.99(0.79^

1.24)

3832

1.65(0.97

^2.81)


228

229

1.02(0.82^

1.26)

198

202

0.94(0.74

^1.19)

3027

1.54(0.86^

2.76)


3420

2.08

(1.15^3.74)

0.13

2615

1.72(0.87^

3.41)

0.68

84

2.75

(0.78^

9.76)

0.04

Rubber&plasticproductm

akerc

3125

1.39(0.80^

2.40)

2316

1.51(0.78^

2.94)

89

1.12(0.41

^3.11)


1210

1.37(0.58^

3.23)

76

1.22(0.40^

3.73)

54

1.60(0.40^

6.45)


1915

1.40(0.69^

2.84)

0.26

1610

1.70(0.75^

3.85)

0.19

35

0.74

(0.16^3.41)

0.98

*Referencegroupconsistedofwom

enneverhavingtheoccupationofinterestcorrespondingtothegroupofwom

enbeingexamined.

**Excludedfromanalysis

durationofemploymentonw

omenwithincompleteinformationonlengthofemployment.

a Adjustedforage,education,ageatmenarche,everhavingalive

birth,ageatfirstlivebirth,menopausalstatus,ageatmenopause,familyhistoryofbreastcancer,historyoffibroadenom

a,WHR,BMI,height,andleisurephysicalactivity.

b Adjustedforallabovepotentialconfoundersexceptageatmenopause.

c Two-digitoccupations.

d One-digitoccupations.

301

TABLE

III.

AdjustedORsofBreastCancerAssociatedWithOne-Digitand

Two-DigitCodedOccupationsHeldfor3

orMoreYearsforAllW

omen,Pre-and

Post-MenopausalW

omeninShanghai*

Allwom

enPre-menopausal

Post-menopausal

Case(1458)

Control(1556)

ORADJa(95%

CI)

Case(951)

Control(990)

ORADJb(95%

CI)

Case(501)

Control(562)

ORADJa(95%

CI)

Professionalc

443

442

0.90(0.75^

1.10)

254

263

0.82

(0.65^

1.03)

187

176

1.14(0.82^

1.59)

Scientific

researcher

97

1.10(0.39^

3.09)

64

1.26(0.34^

4.70)

33

0.94(0.17^5.13)

Industrialtechnician

3840

0.79

(0.48^

1.30)

1616

0.82

(0.39^

1.74)

2224

0.80

(0.40^

1.61)

Administratorsinscience

1720

0.87

(0.44^

1.70)

1614

1.11(0.53^

2.34)

15

0.22

(0.03^

1.94)

Aircraft&shipengineer

52

2.05

(0.38^

11.19)

00

NA5

22.35

(0.41

^13.34)

Medical&publichealthworker

7778

0.94(0.67^

1.31)

3543

0.77

(0.48^

1.24)

4235

1.19(0.72^

1.97)

Econom

ists&

financialplanner

178

182

0.95(0.75^

1.19)

132

137

0.90(0.68^

1.17)

4545

1.11(0.70^

1.76)

Lawyer

18

0.10(0.01

^0.87)

04

NA1

40.21

(0.02^

2.16)

Teacher

127

111

1.06(0.79^

1.42)

5046

0.89

(0.58^

1.38)

7765

1.20(0.79^

1.81)

Professionalartist

146

2.34

(0.80^

6.79)

73

1.84(0.46^

7.33)

62

3.42

(0.61^

19.22)

Culturalworker

1833

0.56

(0.31^

1.01)

1421

0.63

(0.31^

1.28)

412

0.36

(0.11^1.17)

Leaderc

9190

1.03(0.75^

1.40)

5649

1.05(0.70^

1.59)

3441

1.01(0.61^

1.67)

Leaderparty/massorganization

4028

1.47(0.88^

2.46)

2616

1.47(0.77^

2.83)

1312

1.56(0.67^

3.61)

Leadertown/streetcommittee

1318

0.75

(0.36^

1.57)

35

0.59

(0.14^2.52)

1013

0.92(0.39^

2.17)

Leaderbusinessorganization

4951

0.94(0.62^

1.43)

3230

0.97(0.57^

1.63)

1721

0.88

(0.44^

1.78)

Clerical&relatedworkerc

219

192

1.19(0.95^

1.47)

141

131

1.06(0.81^

1.39)

7761

1.32(0.90^

1.94)

Administrativeclerk

161

152

1.08(0.85^

1.38)

105

100

1.04(0.77^

1.40)

5552

1.03(0.67^

1.59)

Politicalandsecuritypersonnel

4039

0.96(0.60^

1.52)

2828

0.87

(0.50^

1.51)

1211

1.09(0.46^

2.62)

Postal/com

municationw

orker

3112

2.98

(1.50^5.9

2)18

82.47

(1.05^5.82)

134

4.03

(1.26^12.90)

Otherclerical&relatedworker

34

0.72

(0.16^3.34)

13

0.38

(0.04^

3.71)

21

1.78(0.15^21.44)

Commercialworkerc

162

203

0.80

(0.63^

1.00)

121

153

0.79

(0.61^

1.04)

3949

0.80

(0.50^

1.27)

Salesmanandshopassistant

143

164

0.91(0.71^

1.16)

104

126

0.87

(0.65^

1.15)

3737

1.05(0.64^

1.75)

Service

workerc

225

262

0.95(0.78^

1.17)

155

167

1.01(0.79^

1.30)

6994

0.87

(0.60^

1.27)

Publicservice

worker

170

189

1.00(0.79^

1.26)

111

118

1.03(0.77^

1.37)

5870

1.00(0.67^

1.50)

Cook

5576

0.82

(0.57^

1.18)

4253

0.84

(0.55^

1.28)

1323

0.78

(0.37^

1.63)

Agriculturalworkerc

273

264

1.09(0.89^

1.33)

233

226

1.00(0.80^

1.25)

4037

1.44(0.87^

2.41)

Farmer

262

250

1.09(0.89^

1.34)

224

217

0.99(0.79^

1.24)

3832

1.65(0.97

^2.81)

Forester

76

1.29(0.43^

3.90)

64

1.18(0.49^

6.37)

12

0.35

(0.03^

4.44)

Production&

relatedworkerc

871

946

1.06(0.90^

1.25)

596

601

1.12(0.92^

1.37)

272

343

0.90(0.67^

1.21)

Metalrefiningw

orker

2831

1.00(0.59^

1.72)

1615

1.16(0.56^

2.42)

1216

0.71

(0.31^

1.61)

Chem

icalprocessors&related

2324

0.96(0.53^

1.73)

1218

0.69

(0.32^

1.47)

116

1.60(0.57^

4.49)

Rubber&plasticproductm

aker

3125

1.39(0.80^

2.40)

2316

1.51(0.78^

2.94)

89

1.12(0.41

^3.11)

Textileworker

150

199

0.82

(0.65^

1.03)

102

129

0.84

(0.63^

1.12)

4770

0.83

(0.55^

1.26)

Leatherandfurprocessor

125

3.25

(1.11^9.53)

84

2.98

(0.87^

10.17)

31

6.37

(0.63^

64.16)

Tailor

7284

0.90(0.65^

1.26)

5152

1.00(0.66^

1.51)

2132

0.78

(0.42^

1.42)

(Con

tinue

d)

302

TABLE

III.

(Continued)

Allwom

enPre-menopausal

Post-menopausal

Case(1458)

Control(1556)

ORADJa(95%

CI)

Case(951)

Control(990)

ORADJb(95%

CI)

Case(501)

Control(562)

ORADJa(95%

CI)

Foodandbeverageprocessor

2017

1.32(0.68^

2.57)

1211

1.17(0.50^

2.73)

86

1.39(0.45^

4.26)

TobaccoP

roductmaker

31

4.59

(0.46^

4.71)

20

NA1

11.39(0.08^

23.53)

WoodPreparationW

orker

1312

1.21(0.54^

2.68)

59

0.61

(0.20^

1.86)

83

3.63

(0.93^

14.18)

Paperandpaperproductmaker

1012

0.89

(0.37^

2.12)

88

0.92(0.34^

2.50)

24

0.89

(0.15^5.39)

Printersandrelatedworker

2227

0.93(0.52^

1.66)

1217

0.77

(0.36^

1.66)

1010

1.32(0.52^

3.36)

Stonecuttersandcarver

11

1.39(0.09^

22.73)

01

NA1

0NA

Blacksmithsand

toolmaker

144

155

1.03(0.80^

1.32)

9290

1.03(0.75^

1.41)

164

1.02(0.68^

1.55)

Machineryfitterand

assembler

5768

0.96(0.66^

1.39)

3543

0.86

(0.54^

1.38)

2225

1.09(0.59^

2.03)

Electricalfitter/relatedworker

7494

0.92(0.67^

1.27)

5156

0.98(0.66^

1.47)

2338

0.72

(0.41

^1.26)

Plum

ber&

welder

4042

1.14(0.72^

1.79)

2925

1.27(0.73^

2.23)

1117

0.80

(0.36^

1.79)

Glass-manufacturingw

orker

3018

2.08

(1.14^3.82)

209

2.70

(1.20^6.0

5)10

91.39(0.52^

3.71)

Painter

1813

1.46(0.69^

3.07)

118

1.16(0.46^

2.95)

74

1.56(0.44^

5.55)

Otherproduction/relatedworker

3639

0.97(0.60^

1.55)

2425

0.89

(0.50^

1.60)

1214

0.98(0.43^

2.23)

Constructionw

orker

1020

0.65

(0.30^

1.43)

412

0.41(0.13^1.30)

68

1.12(0.37^

3.40)

Engine&Equipm

entOperator

78

1.13(0.40^

3.19)

54

1.53(0.40^

5.91)

24

0.58

(0.10^3.35)

Materialequipmentoperator

3936

1.25(0.78^

2.02)

3024

1.43(0.81^

2.52)

912

0.87(0.35^

2.18)

Transportationequipment

2827

1.10(0.63^

1.92)

2114

1.44(0.71^

2.89)

713

0.59

(0.22^

1.60)

Inspector&

productanalyst

134

122

1.24(0.94^

1.60)

9668

1.49(1.06^2.08)

3853

0.82

(0.51^

1.30)

Othertransportationw

orker

108

131

0.89

(0.67^

1.17)

7784

0.91(0.65^

1.27)

3147

0.82

(0.50^

1.34)



enbeingexamined.





c One-digitoccupations.

303

TABLE

IV.AdjustedORsofBreastCancerAssociatedWithSuggestiveH

igh-RiskOccupationsbyEverEm

ployedandbyD

urationofEmployment(�10and>10Years)WithintheO

ccupationofInterestforAll

Wom

en,Pre-and

Post-MenopausalW

omeninShanghai* Al

lwom

enPre-menopausal

Post-menopausal

Case

(1452)

Control

(1552)

ORa

Trendtest

( Pvalue)

Case

(951)

Control

(990)

ORb

Trendtest

( Pvalue)

Case

(501)

Control

(562)

ORa

Trendtest

( Pvalue)

Postal/Com

municationw

orkerc

3112

2.98

(1.50^5.9

2)18

82.47

(1.05^5.82)

134

4.03

(1.26^12.90)


136

2.54

(0.94^

6.86)

84

2.34

(0.68^

8.04)

52

3.05

(0.64^

19.17)


186

3.42

(1.33^8.81)

<0.01

104

2.60

(0.80^

8.48)

0.05

82

4.53

(0.92^

22.42)

0.03

Leather&

furprocessorc

125

3.25

(1.11^9.53)

84

2.98

(0.87^

10.17)

31

NA�10yearsofemployment

54

1.46(0.36^

5.96)

43

1.93(0.42^

8.95)

01

NA>10yearsofemployment

71

10.51(1.27^86.8)

0.02

41

6.18(0.67^

56.89)

0.06

30

NANA

Woodpreparationw

orkerc

1312

1.21(0.54^

2.69)

59

0.61

(0.20^

1.86)

83

3.63

(0.93^

14.18)


56

0.78

(0.23^

2.59)

25

0.39

(0.7^2.03)

31

3.14(0.30^

33.12)


86

1.71(0.58^

5.02)

0.46

34

0.95(0.20^

4.39)

0.56

52

3.89

(0.74

^20.57)

0.07

Glass-manufacturingw

orkerc

3018

2.08

(1.14^3.82)

209

2.70

(1.20^6.0

5)10

91.39(0.52^

3.71)


1411

1.67(0.74

^3.77)

116

2.34

(0.84^

6.50)

35

0.73

(0.16^3.42)


167

2.71

(1.09^6.77)

0.01

93

3.37

(0.89^

12.73)

0.02

74

2.21

(0.59^

8.26)

0.34

Inspector&

productanalystc

134

122

1.24(0.94^

1.60)

9668

1.49(1.06^2.08)

3853

0.82

(0.51^

1.30)


5750

1.32(0.88^

1.98)

3731

1.33(0.81^

2.19)

2018

1.17(0.59^

2.32)


7772

1.16(0.82^

1.63)

0.21

5937

1.61(1.04^2.49)

0.02

1835

0.61

(0.33^

1.14)

0.21



enbeingexamined.





c 2-digitoccupations.

304

generally found a non-significant elevated risk of breast

cancer among leather and tanning workers [Bulbulyan et al.,

1992; Goldberg and Labreche, 1996; Weiderpass et al., 1999],

although a retrospective cohort study in Shanghai failed to

find any association [Petralia et al., 1998a]. Possible sources

of exposure that might increase the risk of breast cancer

among these workers would include benzene and chlorinated

solvents [Goldberg and Labreche, 1996; Labreche and

Goldberg, 1997], and high levels of exposure to leather dust

[Weiderpass et al., 1999].

A twofold increased risk for breast cancer was found for

glass-manufacturing workers consisting of glass workers,

potters, enamelware, and porcelain workers. The few studies

that have examined the relationship between glass-manu-

facturing workers and breast cancer only found a 40–50%

non-significant increased risk for breast cancer [Petralia et al.,

1998a; Pollan and Gustavsson, 1999]. Yet, a recent study

found a significant fourfold increased risk of breast cancer

among pre-menopausal women working in material proces-

sing (mine, rubber, metal, glass, or clay) [Band et al., 2000].

Sources of exposure to various chemicals and industrial

dusts have been suggested in this line of work for pancreatic

cancer [Ji et al., 1999] and lung cancer [Jahn et al., 1999].

More recently, studies have demonstrated that dust in cera-

mic manufacturing industry contains pesticides (dioxins)

[Ferrario and Byrne, 2002]. The firing process itself may

further emit dioxins into the workplace [Ferrario and Byrne,

2002], thereby possibly exposing glass-manufacturing work-

ers to these proposed mammary carcinogens by dermal

exposure and inhalation.

Breast cancer risk was also elevated among pre-

menopausal inspectors and analysts and post-menopausal

wood preparation workers. These elevated risks would not

have been detected if the data had not been stratified by

menopausal status. However, numbers become rather small

when stratifying by menopausal status resulting in instability

and a loss of power. Yet, since employment for > 10 years in

these two occupations further increased the risk of breast

cancer, these findings are further substantiated. Sources of

carcinogenic exposure in these potentially high-risk occupa-

tions might include a multitude of chemicals, including

solvents and remnants of pesticides, in addition to high levels

of exposure to wood dusts in the wood preparation workers.

In this study, we also found that women in postal and

communication occupations were three times more likely to

develop breast cancer as compared to women never work-

ing in these occupations and risk increased with duration of

employment. Postal and communication workers consist

of only three occupations: (1) post office administrators,

(2) telephone and telegraph operators, and (3) other postal

and communication workers. Examining each of these speci-

fic occupations revealed that the increased risk seen in postal

and communication workers was due to a fourfold significant

increased risk among telephone and telegraph operators.

Aside from the Shanghai retrospective cohort study that

found only a non-significant increased risk of breast cancer

among postal and communication workers [Petralia et al.,

1998a], no other studies have addressed the risk of this group

in relation to breast cancer. Among telephone and telegraph

operators, many studies have found a significantly incre-

ased risk of breast cancer [Morton, 1995; Brinton and

Devesa, 1996; Goldberg and Labreche, 1996; Tynes et al.,

1996; Pollan and Gustavsson, 1999; Simpson et al., 1999].

Other studies only found a non-significant increased risk

[Bulbulyan et al., 1992; Calle et al., 1998; Petralia et al.,

1998a] or no increased risk of breast cancer [Band et al.,

2000]. The hypothesized source of exposure for telephone

and telegraph operators is their exposure to electromagnetic

fields (EMFs) [Goldberg and Labreche, 1996]. The pineal

gland, responsible for secreting melatonin, undergoes a re-

duction in the secretion of this hormone due to exposure from

EMFs, thereby, allowing an increase in the production of

estrogen increasing cellular proliferation of the breast [Cohen

et al., 1978; Stevens, 1987; Gammon and John, 1993; Caplan

et al., 2000]. As recently noted in a review on breast cancer

and EMFs, experimental data and epidemiological studies

focusing on this relationship further substantiates a possible

biologically plausible association between EMFs and breast

cancer risk [Caplan et al., 2000]. Moreover, a meta-analysis

of 24 epidemiologic studies examining the relationship

between EMFs and breast cancer among women resulted in a

pooled estimated relative risk (RR 1.12, 95% CI 1.09–1.15)

with study risk estimates ranging from 0.6 to 1.64 [Erren,

2001]. Not finding an increased risk among electrical workers

in this study, however, suggests that the underlying cause of

breast cancer among telephone and telegraph operators may

be a result of an exposure other than EMFs.

An increased risk of breast cancer was also found among

clerical workers, most notably, among the subgroup of cleri-

cal workers consisting of postal and communication workers

(as mentioned above). However, the patterns of association

for job duration differed for clerical workers and the subgroup

of postal and communication workers. The increased risk for

clerical workers existed for those employed � 10 years, and

the increased risk among postal and communication workers

existed for those employed > 10 years. Analysis of clerical

workers excluding postal and communication workers did

not alter the results for clerical workers. When analysis was

restricted to women who had worked exclusively as clerical

workers, only pre-menopausal women had worked � 10

years. These women had an elevated risk of breast cancer

(ORADJ, 6.55, 95% CI 0.72–59.89 based on 5 cases and 1

control). The perplexing increased risk among women

working as clerical workers for � 10 years could be due to

an unidentified risk factor or may very well be a chance

finding. A possible explanation for the observed increased

risk of post-menopausal breast cancer among short-term

clerical workers could be that women previously employed


in high-risk occupations might have been transferred to

clerical positions due to declining health.

Several occupations found to be associated with an

increased risk of breast cancer in this study may stem from

occupational exposures that may be classified as endocrine

disrupters, exogenous chemicals that alter endocrine func-

tion [McGlynn, 2001]. Xenoestrogens, one class of endocrine

disrupters, consist of exogenous chemicals with estrogenic

properties, such as aromatic and halogenated solvents and

organochlorines that are lipophilic and can build up in the

fatty tissue of the breast [Labreche and Goldberg, 1997].

These weak estrogens may increase the risk of breast cancer

by increasing cellular proliferation [Davis et al., 1993]. Some

xenoestrogens have proven to be mammary carcinogens in

animals [Labreche and Goldberg, 1997]. Solvents, such as

polycyclic aromatic hydrocarbons (PAH) (e.g., benzene,

toluene, xylene) are potential carcinogens that may behave

as xenoestrogens. Exposures to these solvents may occur in

many of the occupations found to be associated with an in-

creased risk of breast cancer in our study, including laboratory

workers, leather and fur processors, glass-manufacturing

workers, and inspector and product analysts. Women work-

ing in agriculture may also be exposed to xenoestrogens by

way of certain organochlorine pesticides [Morton, 1995],

such as DDT [Soto et al., 1995], although, the current litera-

ture examining the relationship between pesticides and breast

cancer has been inconsistent [Aronson et al., 2000]. Large

cohort studies of women examining the appropriate biomar-

kers of carcinogens and/or xenoestrogens are needed to

advance the current knowledge.

Caution needs to be taken when making conclusions

from this study due to the multiple comparisons conducted.

To minimize Type I error, we gave more emphasis to positive

associations with a priori hypothesis and/or a dose–

response. On the other hand, small numbers for various

occupations may jeopardize finding a significant association

even if one is present. This may be especially true when

examining the association between occupations and breast

cancer by menopausal status. Another potential drawback to

this study could be uncontrolled confounding that may exist

from environmental exposures outside the workplace.

Exposures in-utero, infancy, childhood, and adolescence

may very well be important missing links to the mystery of

this multifactorial disease. An additional limitation is that

occupations and occupational groups rather than the

occupational exposures were evaluated in this study.

Masking of associations can occur since occupational titles

and occupational groups are crude surrogates for occupa-

tional exposures. Although cases of this study are only

slightly older than controls (mean ages, 49.7 for cases and

47.3 for controls), age differences may vary widely within

certain occupations leading to unequal exposure opportunity.

However, we found no significant age differences between

cases and controls within occupations where significant

associations were observed. Finally, the genetic make-up of

certain subgroups of the study population may make them

more prone to exposures to carcinogens and/or endocrine

disruptors in the workplace that may influence their risk of

breast cancer. Not taking into consideration genetic suscept-

ibility factors may hinder finding some associations between

occupations and breast cancer.

Our study has many strength. Not only is this a

population-based case-control study that uses incident cases,

the detailed collection of reproductive, menstrual, and life-

style factors allows for full adjustment for traditional breast

cancer risk factors. High response rates for both cases and

controls minimized selection bias. Plus, the lower back-

ground risk of breast cancer among Chinese women may

provide a window of opportunity to examine the association

between occupations and breast cancer with less impact from

other breast cancer risk factors. Moreover, the occupational

stability that exists in the Chinese population [Zheng et al.,

1993] enhances the ability to find occupations that may pre-

dispose women to breast cancer. In this study population,

93% of study participants reported having no more than

three occupations. Our in-depth assessment of occupational

histories reduced the exposure misclassification that may

have occurred in other studies that had information on only

one occupation. Plus the average duration of employment for

our study participants was 24 years. Errors in recall may be

minimized in this study population, since the Chinese are

more apt to remain in the same occupation [Zheng et al.,

1993]. Another real advantage of this study is that it takes

into account a thorough occupational history, whereas

many previous studies did not. In our study, 1 year or less

elapsed between jobs for 91% of women, demonstrating

that we have captured a relatively complete occupational

history for the vast majority of women. Moreover, examining

the occupations for women by menopausal status in addition

to examining the occupations for all women provided

leverage for finding occupations that may increase the risk

of breast cancer in pre- or post-menopausal women only.

In summary, this study provides preliminary insights

into the associations between occupations and the risk of

breast cancer among Chinese women from urban Shanghai.

Future studies are needed to examine the association between

breast cancer risk and specific occupational exposures in

these suspected high-risk occupations to identify the res-

ponsible mammary carcinogen(s) in order to develop further

preventive measures for breast cancer.

ACKNOWLEDGMENTS

We thank Dr. Wanquing Wen for his expert assistance in

SAS programming and Dr. Martha Shrubsole for her valuable

comments on the manuscript.

306 Gardner et al.

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Occupations and breast cancer risk among Chinese women in urban Shanghai