Lubin, Jay H.

Abstract Polychlorinated biphenyls (PCB) have been suspected as possible contributors to increasing non-Hodgkin's lymphoma incidence during the latter half of the 20th century based on their toxicologic properties and provocative epidemiologic reports. We investigated PCBs and other organochlorines and risk of non-Hodgkin's lymphoma in a population-based case-control study in the United States. Congeners of PCBs (including coplanar congeners), dioxins, furans and pesticides or pesticide metabolites were measured in plasma of 100 untreated cases and 100 control subjects. We used a multiple imputation procedure to fill in missing values of levels determined to be below the detection limits. Risks of non-Hodgkin's lymphoma associated with each analyte were estimated using conditional logistic regression for the continuous measure, exposure quartiles, trend across quartile categories, and exposures above the 95th percentile. Certain PCB congeners were associated with increased risk of non-Hodgkin's lymphoma, including coplanar PCBs 156, 180, and 194, with odds ratios for the highest versus lowest quartile ranging from 2.7 to 3.5, and significant trends. Each of the furan congeners was associated with risk of non-Hodgkin's lymphoma, as were total furans, with 3.5-fold increased risk for the highest versus lowest quartile and a significant trend across quartiles (P = 0.006). The toxic equivalency quotient (TEQ), a summed metric that weights congeners by their dioxin-like potency, was associated with non-Hodgkin's lymphoma, with 35% increased risk per 10 TEQ pg/g lipid (95% confidence interval, 1.02-1.79). Our results add to existing literature, which suggests that exposure to organochlorines contributes to non-Hodgkin's lymphoma risk; these risks were most apparent for certain PCBs and furans.

Abstract Quantitative measurements of environmental factors greatly improve the quality of epidemiologic studies but can pose challenges because of the presence of upper or lower detection limits or interfering compounds, which do not allow for precise measured values. We consider the regression of an environmental measurement (dependent variable) on several covariates (independent variables). Various strategies are commonly employed to impute values for interval-measured data, including assignment of one-half the detection limit to nondetected values or of “fill-in” values randomly selected from an appropriate distribution. On the basis of a limited simulation study, we found that the former approach can be biased unless the percentage of measurements below detection limits is small (5–10%). The fill-in approach generally produces unbiased parameter estimates but may produce biased variance estimates and thereby distort inference when 30% or more of the data are below detection limits. Truncated data methods (e.g., Tobit regression) and multiple imputation offer two unbiased approaches for analyzing measurement data with detection limits. If interest resides solely on regression parameters, then Tobit regression can be used. If individualized values for measurements below detection limits are needed for additional analysis, such as relative risk regression or graphical display, then multiple imputation produces unbiased estimates and nominal confidence intervals unless the proportion of missing data is extreme. We illustrate various approaches using measurements of pesticide residues in carpet dust in control subjects from a case–control study of non-Hodgkin lymphoma.