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No Association Between Residential Exposure to Petrochemicals and Brain Tumor Risk

¹ Occupational Health Program and ² Exposure, Epidemiology, and Risk Program, Department of Environmental Health, ³ Department of Biostatistics, and ⁴ Department of Epidemiology, Harvard School of Public Health; ⁵ Pulmonary and Critical Care Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; ⁶ Department of Geography, National Changhua University of Education, Changhua, Taiwan, Republic of China; and ⁷ School of Public Health, Kaohsiung Medical University, Kaohsiung, Taiwan, Republic of China

Requests for reprints: David C. Christiani, Occupational Health Program, Department of Environmental Health, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115. Phone: 617-432-3323; Fax: 617-432-3441. E-mail: dchristi{at}hsph.harvard.edu

	Introduction

Top Introduction Materials and Methods Results Discussion Appendix References

 
Evidence to date reveals an inconsistent association between petrochemical exposure and brain tumors (1-5). Community concerns have been raised regarding air pollution caused by the petrochemical industry in southern Taiwan, whereas water pollution has been less addressed because of the central reservoir water supply system. Air monitoring of Kaohsiung conducted by the Taiwanese Environmental Protection Administration (available from January 1993; http://taqm.epa.gov.tw/emc/default.aspx?pid=b0601&cid=b0601) has shown poorer air quality as compared with the average levels in Taiwan (Table 1). However, these pollutants are not spread by the petrochemical industry alone, showing the need for an improved exposure assessment method. Thus, we developed a procedure using geographic information system tools to assign individual residential exposure estimates by accounting for subject mobility, length of stay at each residence, distance to relevant petrochemical plant(s), monthly prevailing wind direction, and multiple petrochemical plant pollution sources (6).

	Materials and Methods

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Study design
Details regarding the design of our study (cases, n = 143; controls, n = 364) were described previously (6, 7). Briefly, this is a population-based case-control study conducted in the metropolitan area of Kaohsiung, southern Taiwan, excluding the rural areas to avoid the potential confounding effects from pesticide exposure. The study area is 657.1 km². There are four petrochemical industrial complexes in the study area, producing various products from upstream to downstream petrochemicals. Subjects were recruited between November 1997 and June 2003. Eligible cases included all pathologically confirmed incident primary brain tumor 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. Controls were cancer-free current 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. In-person interviews were conducted with the parents and/or subjects to collect information regarding descriptive characteristics, residential history, medical history, as well as environmental, occupational, and behavioral factors.

Exposure assessment
Details of the exposure assessment have been described previously (6). Briefly, all long-term residences of each study subject were recorded and then geocoded by a commercial company (MapAsia, Hong Kong). Cases and controls were comparable in address data quality as assessed by geocoding results (non-geocodable addresses: 7% for cases and 9% for controls). Based on previous studies (8-10), we defined potentially exposed areas as areas located within a 3 km radius from the geographic centroid of any of the four petrochemical complexes. Additionally, our group developed a procedure using geographic information system software (ArcInfo, Redlands, CA) to estimate residential petrochemical exposures on an individual level. The exposure estimates from our approach—which we called the exposure opportunity score (EOS)—accounts for subject mobility, length of stay at each residence, distance to petrochemical plant(s), monthly prevailing wind direction, and multiple petrochemical pollution sources. The EOS was log-transformed for normality and was entered in the conditional logistic regression model to calculate the odds ratios (OR) and 95% confidence intervals (CI).

	Results

Top Introduction Materials and Methods Results Discussion Appendix References

 
There were no differences in the proportions of exposed individuals according to disease status. Also, the distributions of log-transformed EOS among the exposed individuals did not vary by disease status. No association was seen between the log-transformed EOS and risk of brain tumors (adjusted OR, 0.96; 95% CI, 0.79-1.17; Table 2). Age-stratified analyses did not materially change our results. The inclusion of occupational petrochemical exposure of the subjects themselves and/or of their parents did not change our conclusion (data not shown). Subgroup analysis of glioma (the most predominant histologic type, accounting for 35% of cases) did not reveal significant associations between residential petrochemical exposures and glioma.

	Discussion

Top Introduction Materials and Methods Results Discussion Appendix References

Recall bias—differential recall on exposure among cases and controls—is a major concern in case-control studies, as cases may recall exposure more thoroughly than controls (12). In our study, however, the proportions of non-geocodable residences did not vary by disease status (7% for cases and 9% for controls). Because we used geocoded addresses as the basis of exposure assessment, recall bias is not a likely threat to the validity of our study.

Several factors may have contributed to the null association found in this study. First, brain tumor itself is a broad term describing diverse histologic types (benign and malignant) and each of them could have a different etiology. Our sample size was not large enough to assess all subtypes of brain neoplasms. Also, although our approach has significantly reduced some commonly challenged uncertainties in estimating personal exposure, there are still possible sources of exposure misclassification—such as not accounting for indoor pollution sources and not pinpointing specific chemical agents. These sources of exposure misclassification are nondifferential by disease status and would attenuate our OR estimates towards the null. Despite these limitations, our data do not support such an association.

	Appendix

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Members of the Kaohsiung Brain Tumor Research Group: Kaohsiung Medical University, Chung-Ho Memorial Hospital (Shen-Long Howng, Ann-Shung Lieu, Aij-Lie Kwan); Kaohsiung Chang Gung Memorial Hospital (Jih-Tsun Ho, Teng-Yuan Shih, Tao-Chen Lee, Min-Hsiung Cheng, Wu-Fu Chen, Kuo-Sheng Hung, Shun-Sheng Chen); Kaohsiung Veterans General Hospital (Yuk-Keung Lo, Shu-Shong Hsu); E-Da Hospital, I-Shou University (Han-Jung Chen, Kang Lu, Cheng-Loong Liang, Po-Chou Liliang).

	Acknowledgments

 
We thank Chien-Chin Chou for questionnaire data verification, Janna Frelich for data management, and Lia Shimada for technical assistance.

	Footnotes

 
Grant support: NIH grants ES09723 (D.C. Christiani) 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.

Note: The members of the Kaohsiung Brain Tumor Research Group are listed in the Appendix.

Received 8/ 2/05; revised 8/29/05; accepted 9/29/05.

	References

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