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The Cadmium Dietary Exposure Model (CDEM) utilizes national survey data on food cadmium concentrations and food consumption patterns to estimate dietary intakes in the U.S. population. The CDEM has been linked to a modification of the cadmium biokinetic model of Kjellström and Nordlberg (KNM) to derive predictions of kidney and urinary cadmium that reflect U.S. dietary cadmium intake and related variability. Variability in dietary cadmium intake was propagated through the KNM using a Monte Carlo approach. The model predicts a mean peak kidney cadmium burden of approximately 3.5 mg and a 5th-95th percentile range of 2.2-5.1 mg in males. The corresponding peak renal cortex cadmium concentration in males is 15 microg/g wet cortex (10-22, 5th-95th percentile). Predicted kidney cadmium levels in females were higher than males: 5.1 (3.3-7.6) mg total kidney, 29 (19-43) microg/g wet cortex. Predicted urinary cadmium in males and females agreed with empirical estimates based on the NHANES III, with females predicted and observed to excrete approximately twice the amount of cadmium in urine than males. An explanation for the higher urinary cadmium excretion in females is proposed that is consistent with the NHANES III data as well as experimental studies in humans and animals. Females may absorb a larger fraction of ingested dietary cadmium than males, and this difference may be the result of lower iron body stores in females compared to males. This would suggest that females may be at greater risk of developing cadmium toxicity than males. The predicted 5th-95th percentile values for peak kidney cadmium burden are approximately 60% of the peak kidney burden (8-11 mg) predicted for a chronic intake at the U.S. Environmental Protection Agency (EPA) chronic reference dose of 1 microg/kg-d.

Drinking water disinfection has effectively eliminated much of the morbidity and mortality associated with waterborne infectious diseases in the United States. Various disinfection processes, however, produce certain types and amounts of disinfection by-products (DBPs), including trihalomethanes (THM), haloacetic acids, haloacetonitriles, and bromate, among others. Human health risks from the ubiquitous exposure to complex mixtures of DBPs are of concern because existing epidemiologic and toxicologic studies suggest the existence of systemic or carcinogenic effects. Researchers from several organizations have developed a multiple-purpose design approach to this problem that combines efficient laboratory experimental designs with statistical models to provide data on critical research issues (e.g., estimation of human health risk from low-level DBP exposures, evaluation of additivity assumptions as useful for risk characterization, estimation of health risks from different drinking water treatment options). A series of THM experiments have been designed to study embryonic development, mortality and cancer in Japanese medaka (Oryzias latipes) and liver and kidney endpoints in female CD-1 mice. The studies are to provide dose-response data for specific mixtures of the 4 THMs, for the single chemicals, and for binary combinations. The dose-levels and mixing ratios for these experiments were selected to be useful for development and refinement of three different statistical methods: testing for departures from dose-additivity; development of an interactions-based hazard index; and use of proportional-response addition as a risk characterization method. Preliminary results suggest that dose-additivity is a reasonable risk assessment assumption for DBPs. The future of mixtures research will depend on such collaborative efforts that maximize the use of resources and focus on issues of high relevance to the risk assessment of human health.

Recent work indicates that the regression of toxicity data viewed as categories of pathological staging is useful for exploring the likely health risk at doses above a Reference Dose (RfD), which is an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Toxic effects, which may include both quantal and continuous data, are classified into ordered categories of total toxic severity (e.g., none, mild, adverse, severe). These severity categories are regressed on explanatory variables, such as dose or exposure duration, to estimate the probability of observing an adverse or severe effect. In this paper, categorical regression has been expanded to compare the likely risks across multiple chemicals when exposures are above their RfDs. Existing health risk data for diazinon, disulfoton, S-ethyl dipropylthiocarbamate, fenamiphos, and lindane were analyzed. As expected, the estimated risks of adverse effects above the RfD varied among the chemicals. For example, at 10-fold above the RfD these risks were modeled to be 0.002, 0.0001, 0.0007, 0.002, and 0.02, respectively. The results and impacts of this analysis indicate that categorical regression is a useful screening tool to analyze risks above the RfD for specific chemicals and suggest its application in evaluating comparative risks where multiple chemical exposures exist.

Categorical regression is a mathematical tool that can be adapted to estimate potential health risk from chemical exposures. By regressing ordered categories of toxic severity or pathological staging on exposure dose, this method can estimate the likelihood of observing any of the categories of severity at any dose level. Depending on the nature of the available data, these estimates can take the form of incidence rates for any of the categories in an exposed population or the probability of a new study conducted at a specified dose level being classified as one of the categories. Categorical regression is illustrated using toxicity data on aldicarb. For aldicarb, the data fall into three different groups: human clinical studies, dietary exposures in experimental animals, and accidental human exposure by contaminated crops. The U.S. EPA has assessed this literature and developed a reference dose (RfD) of 0.001 mg/kg-day. The results of applying categorical regression to data from human clinical studies suggests a maximum likelihood risk estimate of adverse effects of 0.008% at a 10-fold higher dose than the RfD when blood cholinesterase inhibition is not considered as an adverse effect. When blood cholinesterase inhibition of 20% or more is considered as an adverse effect, a maximum likelihood risk estimate of adverse effects is 0.1% at a dose 10-fold higher than the RfD.

Published studies in which rats were exposed to CdCl2 in standard chow or drinking water were analyzed to compare the relative bioavailability of cadmium from the two media. Relative bioavailability was assessed from estimates of the rate of accumulation of cadmium in kidney cortex or liver. Data were grouped into tiers based on study design and reporting of data: Tier 1, identical experimental protocols and dosage can be estimated; Tier 2, very similar or identical protocols and dosage can be estimated; Tier 3, protocols may differ and dosage can be estimated; and Tier 4, protocols may differ and dosages cannot be estimated (but concentration of cadmium in food or water is reported). Tiers were nested, such that Tier 4 contained all relevant studies; Tier 3 included data sets from Tiers 1 and 2; and Tier 2 included the data set from Tier 1. Data within Tiers 1, 2, and 3 were subjected to a linear regression analysis with dosage as the independent variable and tissue accumulation rate as the dependent variable to determine whether bioavailability of cadmium was significantly different based on medium of administration. The results of this analysis show the following: (1) In rats receiving food and drinking water ad libitum, the bioavailability of cadmium in drinking water is not significantly different (P > 0.05) from the bioavailability of cadmium in food when dosages are less than 4 mg/kg body wt/day. (2) Cadmium decreases food and water consumption; therefore, assessments of relative bioavailability should be made based on actual dosage rather than exposure levels. (3) Diet composition and status of the gastrointestinal tract are probably a more important determinant of the bioavailability of cadmium than is the exposure medium. (4) Studies of the effect of total diet composition on bioavailability of cadmium may be more relevant than are studies of the effect of the exposure medium. It is concluded from this analysis that the bioavailability of cadmium in food is not different from that in water when diet is provided ad libitum. Therefore, we recommend that distinct RfDs for cadmium in food and drinking water should not be based on the assumption that the bioavailability of cadmium in drinking water is greater than that of cadmium in food.

Quantitative estimates of cancer risk generally involve low-dose extrapolation based on an exponential dose-response model for dichotomous response data. Frequently more than one data set is available. If a careful analysis of the biological issues indicates that more than one of the available data sets could be used in the quantitative estimate of cancer risk, it is reasonable to think of combining the data. Before combining data, however, it would be prudent to test whether the data sets are compatible with a common dose-response model. If they are not, it could be concluded that an underlying biological factor is responsible. If they are statistically compatible, the decision to combine data sets based on biological issues would be reinforced. A statistical test based on the generalized likelihood ratio method is proposed for evaluating the compatibility of different data sets with a common dose-response model. This method of constructing a statistical test and the associated asymptotic theory is consistent with the approach used by GLOBAL86 (R. B. Howe, K. S. Crump, and C. Van Landingham, GLOBAL86: A Computer Program to Extrapolate Quantal Animal Toxicity Data to Low Doses, K. S. Crump & Co., Ruston, LA, 1986) for estimating the confidence limits that are used as a basis for quantitative estimates.