Non-Hodgkin lymphoma and organochlorine exposure among studies. Our study is the first to use prospectively sampled adipose tissue in expo- sure assessment. Adipose tissue is the principal storage medium for lipophilic organochlorines in the human body (Anderson 1985) and has been regarded by various authors (Allam and Lucena 2001; Hardell et al. 1996; Quintana et al. 2004) as the preferred indicator of human exposure because it represents cumula- tive internal exposure. Blood samples, which have been used to assess exposure in four pre- vious studies (Cantor et al. 2003; De Roos et al. 2005; Hardell et al. 2001; Spinelli et al. 2007), are more easily obtained and less costly but may represent more recent exposure than levels in adipose samples collected at the same time (Archibeque-Engle et al. 1997). The limited sample size is a weak- ness of this study, which limited the power to detect statistically significant interactions with sex and prevented analyses of association for specific NHL subtypes. A better statisti- cal power would have been achieved with a larger sample size (e.g., by selecting a larger subcohort), but organochlorine analysis costs were a constraint. Further, the age distribution between the cases and subcohort was quite dif- ferent, which is expected as the incidence rate for NHL increases with age, and cases were generally older than the subcohort members. In our analyses, however, we used age as the time scale, which is recommended to avoid bias when analyzing the effect of covariates that correlate with age (Thiebaut and Benichou 2004). Another limitation of this study in rela- tion to some of the pesticides is the number of samples with concentrations below the LOD. It can be argued that the exclusion of these samples in the present study could introduce bias. Thus, we investigated the sensitivity of our results related to assignment of organochlorine levels below the LOD to the lowest exposure group if the LOD for the actual sample was below the 25th percentile (the cutoff between the two lower exposure categories). We found our results robust in these analyses. Retaining samples below the LOD, for example, by – imputing LOD/√2 as their value, is advisable only in cases in which < 5–10% of samples are below LOD (Lubin et al. 2004). This strategy is problematic in the present study because individual LODs depend on the amount of adipose tissue in that particular sample, such that LOD varied by up to two orders of mag- nitude between samples below LOD for the same organochlorine. Thus, imputing these LODs might mask or reverse true associations. Table 3. IRRs for NHL and 95% CI in association with adipose tissue concentrations of PCBs. IRR (95% CI) IRR (95% CI) n (cases/ subcohort) 53/50 63/62 58/56 34/38 25/27 233/233 62/53 51/63 54/59 45/35 23/26 235/236 32/49 42/41 53/44 24/27 20/16 171/177 53/49 44/69 74/63 41/34 26/28 238/243 56/55 57/61 56/73 42/28 28/27 239/244 57/61 47/41 69/78 42/32 23/31 238/243 Model 1b 1.00 0.97 (0.57, 1.66) 1.00 (0.59, 1.71) 0.78 (0.42, 1.45) 0.79 (0.40, 1.55) 0.92 (0.72, 1.17) 1.00 0.56 (0.33, 0.96) 0.64 (0.38, 1.09) 0.83 (0.46, 1.50) 0.57 (0.28, 1.15) 0.95 (0.75, 1.20) 1.00 1.63 (0.88, 3.02) 1.59 (0.87, 2.91) 1.20 (0.58, 2.48) 1.45 (0.61, 3.43) 1.10 (0.83, 1.44) 1.00 0.66 (0.38, 1.13) 1.05 (0.63, 1.75) 1.25 (0.67, 2.31) 0.68 (0.34, 1.34) 0.99 (0.78, 1.25) 1.00 0.87 (0.52, 1.47) 0.64 (0.38, 1.07) 1.38 (0.75, 2.53) 0.79 (0.39, 1.57) 0.94 (0.75, 1.19) 1.00 1.09 (0.63, 1.89) 0.79 (0.48, 1.30) 1.16 (0.64, 2.08) 0.64 (0.32, 1.29) 0.89 (0.68, 1.18) Model 2c 1.00 0.88 (0.50, 1.56) 0.96 (0.55, 1.65) 0.67 (0.34, 1.31) 0.72 (0.36, 1.44) 0.88 (0.68, 1.14) 1.00 0.59 (0.34, 1.02) 0.68 (0.40, 1.16) 0.94 (0.51, 1.75) 0.66 (0.31, 1.37) 1.01 (0.79, 1.29) 1.00 1.60 (0.85, 3.01) 1.56 (0.84, 2.89) 1.20 (0.58, 2.49) 1.42 (0.59, 3.40) 1.09 (0.83, 1.43) 1.00 0.66 (0.38, 1.14) 1.04 (0.62, 1.74) 1.25 (0.67, 2.33) 0.68 (0.34, 1.36) 0.99 (0.78, 1.26) 1.00 0.88 (0.52, 1.50) 0.67 (0.40, 1.12) 1.50 (0.81, 2.78) 0.85 (0.42, 1.73) 0.97 (0.77, 1.23) 1.00 1.19 (0.68, 2.09) 0.93 (0.54, 1.59) 1.46 (0.75, 2.83) 0.80 (0.38, 1.69) 0.98 (0.72, 1.33) n (cases/ subcohort) 53/60 55/52 61/54 49/48 21/31 239/245 59/53 35/54 69/60 40/34 23/26 226/227 61/58 49/62 62/60 44/33 22/27 238/240 43/47 62/61 58/47 36/44 25/28 224/227 62/59 55/66 57/62 42/32 23/26 239/245 60/58 55/63 59/65 42/32 23/27 239/245 Model 1b 1.00 0.94 (0.56, 1.67) 1.04 (0.62, 1.83) 0.98 (0.50, 1.59) 0.54 (0.27, 1.08) 0.90 (0.75, 1.08) 1.00 0.59 (0.34, 1.05) 0.92 (0.55, 1.53) 1.03 (0.56, 1.90) 0.66 (0.33, 1.31) 0.87 (0.70, 1.08) 1.00 0.68 (0.40, 1.15) 0.90 (0.54, 1.51) 1.12 (0.60, 2.07) 0.62 (0.30, 1.27) 0.88 (0.71, 1.11) 1.00 0.91 (0.52, 1.59) 1.10 (0.62, 1.97) 0.69 (0.37, 1.29) 0.73 (0.34, 1.56) 0.85 (0.64, 1.13) 1.00 0.74 (0.45, 1.24) 0.78 (0.47, 1.30) 1.08 (0.59, 1.95) 0.65 (0.32, 1.33) 0.95 (0.76, 1.19) 1.00 0.77 (0.46, 1.30) 0.77 (0.47, 1.29) 1.15 (0.64, 2.09) 0.62 (0.31, 1.26) 0.95 (0.76, 1.18) Compound/concentrationa (μg/kg lipids) PCB-118 10–25 25–34 34–48 48–62 62–150 Linear estimate per IQRd PCB-156 13–28 28–34 34–41 41–50 50–88 Linear estimate per IQRd PCB-99 9–20 20–27 27–37 37–47 47–110 Linear estimate per IQRd PCB-138 29–100 100–140 140–180 180–230 230–380 Linear estimate per IQRd PCB-153 98–240 240–300 300–370 370–430 430–730 Linear estimate per IQRd PCB-170 37–87 87–100 100–130 130–150 150–230 Linear estimate per IQRd aThe cutoffs between exposure groups were: 25th, 50th, 75th, and 90th percentiles. bAge as time scale; model stratified according to sex. cAge as time scale; model stratified according to sex and adjusted for BMI (continuous variable). dLinear estimate per IQR range based on PCB concentrations analyzed as continuous variables. eSum of PCB congeners. fIQRΣPCB = 373 μg/kg. gSum of suggested immunotoxic PCB congeners 138, 153, and 180. hIQRΣPCBimmune = 260 μg/kg. Environmental Health Perspectives • volume 120 | number 1 | January 2012 Compound/concentrationa (μg/kg lipids) PCB-180 71–170 170–200 200–240 240–290 290–480 Linear estimate per IQRd PCB-183 6–19 19–24 24–31 31–39 39–65 Linear estimate per IQRd PCB-187 17–46 46–56 56–68 68–84 84–140 Linear estimate per IQRd PCB-201 6–15 15–19 19–23 23–28 28–45 Linear estimate per IQRd ΣPCBe 150–770 770–939 939–1,143 1,143–1,351 1,351–2,157 Linear estimate per IQRf ΣPCBimmuneg 150–530 530–650 650–790 790–930 930–1,490 Linear estimate per IQRh Model 2c 1.00 1.03 (0.60, 1.77) 1.19 (0.69, 2.05) 1.09 (0.59, 2.01) 0.69 (0.32, 1.46) 0.99 (0.77, 1.27) 1.00 0.58 (0.32, 1.03) 0.91 (0.54, 1.51) 1.03 (0.56, 1.90) 0.68 (0.34, 1.37) 0.88 (0.70, 1.10) 1.00 0.69 (0.40, 1.17) 0.97 (0.57, 1.64) 1.30 (0.68, 2.47) 0.69 (0.33, 1.44) 0.92 (0.73, 1.15) 1.00 0.98 (0.56, 1.73) 1.20 (0.66, 2.21) 0.82 (0.41, 1.67) 0.88 (0.38, 2.03) 0.93 (0.68, 1.28) 1.00 0.74 (0.44, 1.24) 0.81 (0.48, 1.35) 1.15 (0.63, 2.11) 0.71 (0.34, 1.45) 0.99 (0.79, 1.25) 1.00 0.79 (0.47, 1.34) 0.82 (0.49, 1.38) 1.29 (0.70, 2.38) 0.68 (0.33, 1.41) 0.99 (0.78, 1.24) 109