Residential proximity to industrial facilities and risk of non-Hodgkin lymphoma.

ABSTRACT

Industrial pollution has been suspected as a cause of non-Hodgkin lymphoma (NHL), based on associations with chemical exposures in occupational studies. We conducted a case-control study of NHL in four SEER regions of the United States, in which residential locations of 864 cases and 684 controls during the 10 years before recruitment were used to characterize proximity to industrial facilities reporting chemical releases to the Environmental Protection Agency's Toxics Release Inventory (TRI). For each of 15 types of industry (by 2-digit SIC code), we evaluated the risk of NHL associated with having lived within 2 miles of a facility, the distance to the nearest facility (miles categories of < or =0.5, >0.5-1.0, >1.0-2.0, >2 [referent]), and the duration of residence within 2miles (years categories of 10, 1-9, 0 [referent]), using logistic regression. Increased risk of NHL was observed in relation to lumber and wood products facilities (SIC 24) for the shortest distance of residential proximity (< or =0.5 mile odds ratio [OR]=2.2, 95% confidence interval [CI] 0.4-11.8) or the longest duration (10 years OR=1.9, 95% CI 0.8-4.8); the association with lumber facilities was more apparent for diffuse large B-cell lymphoma (lived within 2 miles OR=1.7, 95% CI 1.0-3.0) than for follicular lymphoma (OR=1.1, 95% CI 0.5-2.2). We also observed elevated ORs for the chemical (SIC 28, 10 years OR=1.5, 95% CI 1.1-2.0), petroleum (SIC 29, 10 years OR=1.9, 95% CI 1.0-3.6), rubber/miscellaneous plastics products (SIC 30, < or =0.5mile OR=2.7, 95% CI 1.0-7.4), and primary metal (SIC 33, lived within 2miles OR=1.3, 95% CI 1.0-1.6) industries; however, patterns of risk were inconsistent between distance and duration metrics. This study does not provide strong evidence that living near manufacturing industries increases NHL risk. However, future studies designed to include greater numbers of persons living near specific types of industries, along with fate-transport modeling of chemical releases, would be informative.