Evidence to date reveals an inconsistent association between parental and subject occupation and brain tumors (1-4). Paternal exposures in hydrocarbon-associated occupations, the petroleum industries, and paint exposures have been associated with brain cancer (5). Maternal exposures have received less attention, but some studies have yielded suggestive results linking occupational exposures to pesticides and solvents to childhood brain tumors (6, 7).

We previously reported an association between maternal employment in electronic parts manufacturing and textile industries and brain tumor from a population-based case-control study from 74 cases and 170 controls (7). In this report, we present the results from the larger, completed case-control study to assess the association between parental and subject occupation and brain tumor risk.

Study Design

Details regarding the design of our study (202 cases and 501 controls) have been previously described (7-9). Briefly, this is a population-based case-control study conducted in the metropolitan area of Kaohsiung, an industrialized city in southern Taiwan with a large petroleum industry. Subjects were recruited between November 1997 and December 2005. Incident cases were identified and recruited through (a) a rapid case ascertainment system set up in three large referral hospitals in Kaohsiung and (b) screening the Cancer Registry File from the Taiwan Department of Health. All cases were reviewed by a pathologist in Kaohsiung Medical University Hospital. Eligible cases included all pathologically confirmed incident primary brain cases (benign and malignant: International Classification of Diseases-9 codes 191-192, 194.3-194.4, and 225), ages 0 to 29 years, and currently residing in the study area. Eighty-four percent of brain tumor cases initially reviewed were enrolled in the study. Controls were cancer-free residents of the study area, selected randomly from the population registry data based on the personal identification number system of the Taiwanese government. Each case was matched with three controls on age (±1 year) and sex. Of those initially contacted, 61% of prospective controls agreed to participate in the study. In-person interviews were conducted with the parents and/or subjects to collect information regarding the descriptive characteristics, medical history, as well as environmental, occupational, and behavioral factors. The histology and location of the brain tumors in our cases are presented in Table 1. Demographic information on the study population is displayed in Table 2.

Table 1.

Histology and location of 202 brain tumor cases in subjects 0 to 29 y in Kaohsiung, Taiwan (1997-2005)

CharacteristicNo. of cases (%)
Histology of tumor (%)  
    Glioma 75 (37) 
        Pilocytic astrocytoma 10 
        Astrocytoma, nonpilocytic 48 
        Oligodendroglioma 
        Ependymoma 
    Medulloblastoma 20 (10) 
    Adenoma 52 (26) 
    Craniopharyngioma 10 (5) 
    Nerve sheath tumor 7 (3) 
    Meningioma 9 (4) 
    Germ cell tumor or teratoma 15 (8) 
    Other 8 (4) 
    Missing histology 6 (3) 
Location of tumor  
    Cerebrum 72 (36) 
    Posterior fossa 31 (15) 
    Sella turcica 71 (35) 
    Meninges 24 (12) 
    Cranial nerve 4 (2) 
CharacteristicNo. of cases (%)
Histology of tumor (%)  
    Glioma 75 (37) 
        Pilocytic astrocytoma 10 
        Astrocytoma, nonpilocytic 48 
        Oligodendroglioma 
        Ependymoma 
    Medulloblastoma 20 (10) 
    Adenoma 52 (26) 
    Craniopharyngioma 10 (5) 
    Nerve sheath tumor 7 (3) 
    Meningioma 9 (4) 
    Germ cell tumor or teratoma 15 (8) 
    Other 8 (4) 
    Missing histology 6 (3) 
Location of tumor  
    Cerebrum 72 (36) 
    Posterior fossa 31 (15) 
    Sella turcica 71 (35) 
    Meninges 24 (12) 
    Cranial nerve 4 (2) 
Table 2.

Characteristics of cases and controls

CharacteristicCases (n = 202)Controls (n = 501)
Sex (%)   
    Male 105 (52) 260 (52) 
    Female 97 (48) 241 (48) 
Age (%)   
    Under 5 y 32 (16) 80 (16) 
    5 to 9 25 (12) 59 (12) 
    10 to 14 22 (11) 62 (12) 
    15 to 19 34 (17) 88 (18) 
    20 and over 89 (44) 212 (42) 
History of smoking (%)   
    Ever 29 (14) 48 (10) 
    Never 172 (86) 452 (90) 
Smoker in household (%)   
    Yes 126 (62) 292 (58) 
    No 76 (38) 209 (42) 
X-ray (%)   
    Yes 44 (22) 103 (21) 
    No 158 (78) 398 (79) 
Petroleum/petrochemical plant within 1 km of home (%)   
    Yes 19 (9) 51 (10) 
    No 183 (91) 450 (90) 
Parental occupational history available 127 (63) 309 (62) 
CharacteristicCases (n = 202)Controls (n = 501)
Sex (%)   
    Male 105 (52) 260 (52) 
    Female 97 (48) 241 (48) 
Age (%)   
    Under 5 y 32 (16) 80 (16) 
    5 to 9 25 (12) 59 (12) 
    10 to 14 22 (11) 62 (12) 
    15 to 19 34 (17) 88 (18) 
    20 and over 89 (44) 212 (42) 
History of smoking (%)   
    Ever 29 (14) 48 (10) 
    Never 172 (86) 452 (90) 
Smoker in household (%)   
    Yes 126 (62) 292 (58) 
    No 76 (38) 209 (42) 
X-ray (%)   
    Yes 44 (22) 103 (21) 
    No 158 (78) 398 (79) 
Petroleum/petrochemical plant within 1 km of home (%)   
    Yes 19 (9) 51 (10) 
    No 183 (91) 450 (90) 
Parental occupational history available 127 (63) 309 (62) 

Job Title Classification

Details of the job title classification system have been previously described (7). Each job was assigned two four-digit codes, one for occupation and one for industry, by project staff in Taiwan familiar with the Taiwanese occupational and industrial coding system. Codes followed the Taiwanese government's standardized classification system for occupations and industries, which is adapted from the systems used by the International Labor Organization (10). The classification system groups occupations and industries by a four-digit code, the first digit designating the broadest group and additional digits representing progressively more specific categories. Odds ratios were calculated for each occupation or industry group, first using one-digit codes, and then subsequently using two-, three-, and four-digit codes to investigate more specific classifications. We list the major (one-digit) occupation and industry categories of our study population in Table 3.

Table 3.

Selected industrial and occupational categories of study population

PersonIndustry or occupationCases, controls with job
Subject Agriculture, forestry, fishing and animal husbandry 17, 69 
 Electricity, gas and water 20, 42 
 Trade and eating-drinking places 15, 40 
 Social, personal and related community services 28, 51 
 Professional 12, 24 
 Technicians and associate professionals 17, 44 
 Clerks 14, 33 
 Craft and related trades workers 20, 23 
Mother Electricity, gas and water 48, 139 
 Trade and eating-drinking places 24, 60 
 Social, personal and related community services 40, 63 
 Professionals 11, 29 
 Technicians and associate professionals 20, 54 
 Clerks 18, 53 
 Service workers and shop and market sales workers 32, 61 
 Craft and related trades workers 22, 49 
 Plant and machine operators and assemblers 18, 44 
Father Agriculture, forestry, fishing and animal husbandry 74, 200 
 Electricity, gas and water 21, 55 
 Construction 10, 14 
 Trade and eating-drinking places 12, 28 
 Finance, insurance, and real estate 11, 8 
 Servicemen 55, 139 
 Technicians and associate professionals 12, 25 
 Service workers and shop and market sales workers 14, 26 
 Craft and related trades workers 22, 66 
 Plant and machine operators and assemblers 11, 24 
PersonIndustry or occupationCases, controls with job
Subject Agriculture, forestry, fishing and animal husbandry 17, 69 
 Electricity, gas and water 20, 42 
 Trade and eating-drinking places 15, 40 
 Social, personal and related community services 28, 51 
 Professional 12, 24 
 Technicians and associate professionals 17, 44 
 Clerks 14, 33 
 Craft and related trades workers 20, 23 
Mother Electricity, gas and water 48, 139 
 Trade and eating-drinking places 24, 60 
 Social, personal and related community services 40, 63 
 Professionals 11, 29 
 Technicians and associate professionals 20, 54 
 Clerks 18, 53 
 Service workers and shop and market sales workers 32, 61 
 Craft and related trades workers 22, 49 
 Plant and machine operators and assemblers 18, 44 
Father Agriculture, forestry, fishing and animal husbandry 74, 200 
 Electricity, gas and water 21, 55 
 Construction 10, 14 
 Trade and eating-drinking places 12, 28 
 Finance, insurance, and real estate 11, 8 
 Servicemen 55, 139 
 Technicians and associate professionals 12, 25 
 Service workers and shop and market sales workers 14, 26 
 Craft and related trades workers 22, 66 
 Plant and machine operators and assemblers 11, 24 

In our analysis, all persons in the subgroup who held jobs, but did not have the occupation or industry title in question, were used as the referent groups. Paternal, maternal, and subject data were analyzed separately. Odds ratios were adjusted using conditional logistic regression for subject smoking (ever smoked or not), parental smoking (either parent ever smoked or not), and exposure to medical radiation (ever exposed or not). These covariates were chosen to form a parsimonious analysis and to provide comparability with our previously published analysis.

No association was seen between parental or subjects' occupation or industry classification and brain tumor. Age-adjusted analyses did not change our results. Subgroup analysis of glioma, the predominant histologic type, did not reveal significant associations between brain tumor and either occupation or industry classification. Because of the location of our study (in a city with a large petrochemical processing industry), we also examined (as a group) those parental and subject occupations and industries that were likely to involve exposures to petrochemicals. For these jobs considered as a group, we found no significant association with brain tumors.

We did not detect any association between parental or subjects' occupational history and the risk of brain tumors in our population-based case-control study which used questionnaires to determine occupational and industrial classifications. The question of whether parents' exposure to carcinogens in the workplace could increase the risk of brain cancer in the children is yet unresolved. Recent reviews of the studies concerning the relation of parents' occupations and brain tumors have suggested that there are elevated risks of brain tumors in children for fathers who work in industries involving paper and pulp, pesticide solvents, painting, printing, and graphic arts, and for mothers with exposure to fertilizers and pesticides during pregnancy (2, 4, 5). Our study, set in an industrial city with a large petrochemical and petroleum industry, was not able to show an association.

Our study has several major strengths including (a) the choice of controls from a population registry, (b) high participation rate of cases and controls, and (c) tumor pathology confirmed by a pathologist.

The limitations of our study include recall bias—differential recall on exposure among cases and controls (11). Our ability to obtain occupational histories, however, did not vary by disease status, and it is possible that parents are more likely to accurately remember jobs than exposures of possibly shorter duration such as dietary intake or alcohol use.

Several factors may have contributed to the null association found in this study. Brain tumor is a broad term describing diverse histologic types and each of them could have different etiologic factors contributing to development. Our sample size was not large enough to assess all subtypes separately, but we were able to examine the largest subtype, glioma, independently.

No potential conflicts of interest were disclosed.

Grant support: T32 MH073122 (M. Mazumdar), ES09723 and ES00002 (D.C. Christiani), and the Brain Tumor Society (D.C. Christiani).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

We thank Chu-Ling Yu and Robbie Ali for their assistance and Janna Frelich for data management.

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