Prediction and Evaluation of Dietary Arsenic Exposure Estimates Using the SHEDS Model Jianping Xue, Valerie Zartarian, Hal ilk Ozkaynak U.S.EPA/GRD National Exposure Research Laboratory, RTP, NC, USA Presented at ISEA 2006, Paris, France 6 Q % I Introduction Method Epidemiological studies reveal that arsenic can cause human cancers and many other adverse health effects. Ingestion of food especially seafood is a primary route for human arsenic exposure. Foods from endemic areas have higher arsenic concentrations. Sources of arsenic are plant or animal intake and/or contamination from food processing. An accurate probabilistic human exposure model for arsenic ingestion can help reduce uncertainty in arsenic risk assessments. Table 1 As residue concentration (mg/kg) from selected food items k, pan-cooked 2.336 5.643 p50 p5 p25 p75 p95 10.430 1 0.430 rice infant cereal, instant, prepared witl" carrots, strained/junior chicken, fried (breast, leg, and thigh) he chicken, fried (breast, leg, and thigh), fa granola cereal oatrino cereal 0.024 0H20 0.023 1) Collect and process available As residue and food consumption data • Continuing survey of food intake by individuals (CSFII) 1994-96/98 by USDA • Recipe files from OPP • Total diet survey (TDS) by FDA 2) Apply EPA/ORD's Stochastic Human Exposure and Dose Simulation (SHEDS) dietary module using As data • Process As residue data • Match CSFII data with residues by food items • Match CSFII data with residues by food commodities • Process measurements below limit of detection (LOD) • Apply Processing factors • Calculate food residue intake 3) Conduct variability and uncertainty runs with SHEDS • Randomly draw subjects from CSFII • Randomly draw As residue • Repeat runs for stability • Bootstrap various portions of CSFII • Bootstrap various portion of residues Table 2 Contribution of Major food categories to As dietary exposure 0.400 7.6923 0.086 73.077 0!083 76.923 79.3M> 16.7% 4.0% 84.7% 10.0% 5.4% 62^3J/o 35.5% 2.2% 66:7% 29.9% 3.4% | CSFII | | Residue | 1 food Hem in „„l 1 Rtc* |no |1— .„.l 1 | Modified Matched PatH | | Food Intake Calculation"] Figure 3. Uncertainty of daily dietary exposure of As with 1/8 for As residue and 1/30 CSFII bootstrap 1.0 118.6 1.6 130.6 4.2 147.3 Uncertainty Percentile SHEDS dietary module flowchart Figure 1. Table 3 As dietaiy intake from SHEDS evaluated with NHEXAS duplicate data Figure 2. Dietary Model evaluation* Table 4 Uncertainty Analyses on CSFII and As residue data n mean p50 p5 p25 p75 p95 NHEXAS MODEL 5th by Mean MODEL 50th by Mean MODEL 95th by Mean MODEL 5th by Mean MODEL 50th by Mean MODEL 95th by Mean Results 156 0.185 0.300 0.095 0.019 0.049 0.174 Fill in no-detects with zero 154 0.080 0.305 0.000 0.000 0.000 0.031 154 0.152 0.406 0.009 0.000 0.000 0.066 154 0.260 1.168 0.007 0.000 0.000 0.055 Fill in no-detects with 1/2 LOD when As residue mean > 154 0.128 0.399 0.051 0.004 0.023 0.110 154 0.192 0.561 0.052 0.004 0.024 0.115 154 0.300 1.293 0.062 0.009 0.029 0.128 Uncertainly Ratio (95th vs 5 th) Bootstrap 50th 95th 99th CSFII 1/20 bootstrap 1.19 1.93 3.28 As 1 iA and CSFII 1/10 bootstrap 1.20 1.66 2.43 As 1 iA and CSFII 1 /20 bootstrap 1.24 2.03 3.40 CSFII 1/8 bootstrap 1.14 1.51 2.14 As 1 /8 bootstrap 1.20 1.31 1.73 As 1 /8 and CSFII 1/10 bootstrap 1.26 1.69 2.52 As 1 /8 and CSFII 1 /20 bootstrap 1.30 1.99 3.87 As 1 /8 and CSFII 1/30 bootstrap 1.39 2.22 4.47 Conclusions Seafood has 100% detection rate with high As residue concentrations; Rice also has high As residues (see Table 1) Asian has higher As intake from dietary than other races (Table 2) Seafood is dominant contributor; for Asian, rice accounts for 22% high percentile and 35% for whole (T able 2) 200 repeated runs with SHEDS dietary matched with region, race, age, and gender show good fit with NHEXAS data (Figure 2 and Table 3) Model uncertainty ranged from 1.5 to 5 times for 50th, 95th and 99th by using ratio of 95th to 5th percentiles. The higher percentile, the higher uncertainty and uncertainty is more sensitive to CSFII (Figure 3 and Table 4) Seafood and rice are major sources of dietary exposure to As SHEDS dietary model performs well in evaluation of model against duplicate dietary survey Uncertainty analyses indicated dietary survey data has more uncertainty References: J Xue et al, A probabilistic arsenic exposure assessment for children who contact chromated copper arsenate (CCA)-treated playests And decks, part2: sensitivity and uncertainty analyses; Risk Analysis,2006, Vol. 26, No. 2 :533-541 P. Barry Ryan et al, Analysis of dietary intake of selected metals in the NHEXAS-Maryland investigation, Environmental Health Perspectives, 2001, Vol. 109, No. 2: 122-128 The United States Environmental Protection Agency through its Office of Research and Development funded and managed the research described here. It has been subj ected to Agency review and approved for publication. ------- |