Use of a toxicokinetic model in the analysis of cancer mortality in relation to the estimated absorbed dose of dioxin (2,3,7,8-tetrachlorodibenzo-p-dioxin, TCDD).

We performed an analysis of All cancer and Lung cancer mortality in relation to estimated absorbed dose of dioxin (2,3,7,8-tetrachlorodibenzo-p-dioxin, TCDD) in the cohort of chemical workers at 12 US plants assembled by the US National Institute for Occupational Safety and Health (NIOSH) (n = 5172). Estimates of cumulative exposure to TCDD were based on a minimal physiologic toxicokinetic model (MPTK) that accounts for inter- and intra-individual variations in body mass index (BMI) over time. Population-level parameters related to liver elimination and background (input or concentration) of TCDD were estimated from separate data with repeated measures of serum TCDD (US Air Force Health Study). An occupational TCDD input parameter was estimated based on one-point-in-time TCDD data available for a subset (n = 253) of the NIOSH cohort. Model-based time-dependent cumulative dose estimates (area under the curve (AUC) of the lipid-adjusted serum TCDD concentration over time) were obtained for members of the full cohort with recorded body height and weight (n = 4049), as this information is required by the MPTK model to compute dose. Missing-value problems arose in the estimation of the occupational input parameter (n = 42) and in TCDD-dose calculation in the full cohort (n = 886) and they were handled with multiple imputation methods. Risk-regression analyses were based on Cox log-linear models including age at entry, year of entry and duration of employment as categorical covariates in addition to the logarithm of cumulative TCDD dose in ppt-years. Risk sets were stratified on birth cohort. Estimates of the unlagged exposure coefficient in these models were 0.1249 [95% confidence interval (CI) 0.0144, 0.2354] for All cancer and 0.2158 (95% CI 0.02376, 0.4078) for lung cancer. A 10-year lag produced an increase in the estimate for all cancer (0.1539, 95% CI 0.0387, 0.2691), whereas, the estimate for lung cancer was not affected much (0.2125, 95% CI 0.0138, 0.4112). At a dose level of 100 times the background the estimates obtained with a 10-year lag translate into a relative risk of 2.03 (95% CI 1.19-3.45) for all cancer and of 2.66 (95% CI 1.07-6.64) for lung cancer. Higher estimates of the exposure coefficients were obtained after imputation of missing values. This increase in risk seemed due to the inclusion of short-term workers, who may exhibit a higher mortality for reasons other than dioxin exposure.

Uncertainty in estimating exposure using a toxicokinetic model. The example of 2,3,7,8-tetrachlorodibenzo-p-dioxin.

This paper deals with sources of uncertainty in the use of a minimal physiological toxicokinetic model to obtain dose estimates for a dose-response analysis of cancer in an occupational cohort. Toxicokinetic models make it possible to construct exposure parameters that are more closely related to the individual dose than traditional measures of exposures to toxic agents. However, the process introduces a wide array of sources of uncertainty. Selecting a model structure to describe the kinetics of a toxic agent implies necessarily making simplifications and assumptions that influence the range of applicability of the model. Once a model has been selected, the value of certain model parameters (constants) must be assigned, for example, from anthropometric data. The question then arises of how sensitive the model predictions are to variations in the values of these constants. Other model parameters, typically those describing the kinetics of the agent, are next estimated from actual data. There may be limitations in the data concerning, for example, sparseness (too few observations per subject) or missing values. The methods used for parameter estimation carry their own set of assumptions that need to be appropriate to the situation at hand. In summary, the dioxin example is used to characterize the sources of uncertainty at different levels, such as model structure, methods and data used for parameter estimation, estimation of occupational exposure, and imputation of missing values in exposure indices derived from the kinetic model.