Citations

Abstract Context: Environmental exposure to herbicides has been hypothesized to contribute to the long-term increase in non-Hodgkin lymphoma (NHL). Objective: To estimate the effects of residential herbicide exposure on NHL risk. Design: Population-based case-control study. Setting: Iowa and metropolitan Detroit, Los Angeles, and Seattle, 1998 to 2000. Participants: NHL patients ages 20 to 74 years and unaffected residents identified by random digit dialing and Medicare eligibility files. Main Outcome Measures: Computer-assisted personal interviews (1,321 cases, 1,057 controls) elicited data on herbicide use at each home occupied since 1970. Levels of 2,4-dichlorophenoxy-acetic acid and dicamba were measured in dust taken from used vacuum cleaner bags in the current home (679 cases, 510 controls who had owned at least half of their carpets for ≥5 years). Results: Herbicide use on the lawn or garden was similar among cases and controls (adjusted relative risk, 1.02; 95% confidence interval, 0.84-1.23). Estimated risk did not increase with greater duration, frequency, or total number of applications of herbicides to the lawn, the garden, or to both combined. Risk was not elevated for respondents who applied the herbicides themselves and not for those exposed during the 1970s, 1980s, or 1990s. We detected 2,4-dichlorophenoxy-acetic acid equally often in homes of cases and controls (78%). We found dicamba in homes of 15% of cases and 20% of controls. We also found no elevation in risk among the respondents who had the highest dust levels and highest self-reported exposures. We found no consistent patterns for specific histologies. Conclusions: We found no detectable excess associated with residential exposures. Residential herbicide exposures are unlikely to explain the long-term increase in NHL.

Abstract Background: The International Lymphoma Epidemiology Consortium (InterLymph) provides an opportunity to analyze the relationship between cigarette smoking and non-Hodgkin lymphoma with sufficient statistical power to consider non-Hodgkin lymphoma subtype. The results from previous studies of this relationship have been inconsistent, likely due to the small sample sizes that arose from stratification by disease subtype. To clarify the role of cigarette smoking in the etiology of non-Hodgkin lymphoma, we conducted a pooled analysis of original patient data from nine case-control studies of non-Hodgkin lymphoma conducted in the United States, Europe, and Australia. Methods: Original data were obtained from each study and uniformly coded. Risk estimates from fixed-effects and two-stage random-effects models were compared to determine the impact of interstudy heterogeneity. Odds ratios (OR) and 95% confidence intervals (95% CI) were derived from unconditional logistic regression models, controlling for study center, age, sex, and race. Results: In our pooled study population of 6,594 cases and 8,892 controls, smoking was associated with slightly increased risk estimates (OR, 1.07; 95% CI, 1.00-1.15). Stratification by non-Hodgkin lymphoma subtype revealed that the most consistent association between cigarette smoking and non-Hodgkin lymphoma was observed among follicular lymphomas (n = 1452). Compared with nonsmokers, current smokers had a higher OR for follicular lymphoma (1.31; 95% CI, 1.12-1.52) than former smokers (1.06; 95% CI, 0.93-1.22). Current heavy smoking (≥36 pack-years) was associated with a 45% increased OR for follicular lymphoma (1.45; 95% CI, 1.15-1.82) compared with nonsmokers. Conclusions: Cigarette smoking may increase the risk of developing follicular lymphoma but does not seem to affect risk of the other non-Hodgkin lymphoma subtypes we examined. Future research is needed to determine the biological mechanism responsible for our subtype-specific results.

Abstract A possible role for SV40, a macaque polyomavirus, in non-Hodgkin lymphoma (NHL) in humans was raised recently by the reported detection of SV40 DNA in tumor tissue. Animals with SV40-induced tumors frequently produce high-level antibodies against T antigen, the SV40 oncoprotein. In this study, we assessed whether SV40 T antibody measured in humans supported a relationship between SV40 and NHL. Subjects were sampled from a U.S. population-based case-control study of NHL, according to presence of antibodies against capsids of SV40 and BK, a related human polyomavirus (n = 85 cases, n = 95 controls). T antibody was measured by enzyme immunoassay. We also evaluated serum specimens from SV40-infected and SV40-uninfected macaques (n = 19 and n = 8, respectively), SV40-uninfected hamsters (n = 5), and hamsters with SV40-induced tumors (n = 10). Hamsters with SV40-induced tumors all produced robust SV40 T antibody [median absorbance, 0.99), whereas SV40-uninfected hamsters and macaques had much lower levels (median absorbance, 0.05 and 0.04, respectively). NHL cases, controls, and SV40-infected macaques resembled these latter two groups, generally showing only low-level T antibody (median absorbance, 0.03, 0.04, and 0.04, respectively). Overall, only five cases (6%) and five controls (5%) had T antibody responses classified as seropositive (odds ratio, 1.2; 95% confidence interval, 0.3-4.6). Interestingly, all 10 humans with T antibody responses also showed antibody responses to BK capsid. We found no association between the presence of T antibody and NHL, arguing against SV40 as a cause of NHL. Infrequent and low-level T antibody responses among humans could represent cross-reactivity to BK virus T antigen.

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.