Suchergebnisse

Prediction of noncancer toxicologic outcomes in rodent bioassays of 37 chemicals from the National Toxicology Program was evaluated. Using the nonneoplastic lesions noted by NTP pathologists, we evaluate both agreement in toxic lesions across experiments and the predictive value of the presence (or absence) of a lesion in one group for other groups. We compare lesions between mice and rats, male mice and male rats, and female mice and female rats in both short‐term and long‐term bioassays. We also examine whether lesions found in a specific organ in a short‐term test are also found in the long‐term test of the same chemical. We find agreement (concordance) across species for specific lesions, as evaluated by the Kappa statistic, ranging from 0.58 (for concordance of nasal lesions between female mice and rats in long‐term studies) to −0.14 (lung lesions between mice and rats in long‐term studies). Predictive values are limited by the relatively small numbers of observations of each type of lesion. Positive predictive values range from 100% to 0%. Comparing the lesions found in short‐term tests to those found in long‐term tests resulted in Kappa statistic values from 0.76 (spleen lesions in male rats) to −0.61 (lung lesions in female mice). Positive predictive values of short‐term tests for long‐term tests range from 70% to 0%. Overall, there is considerable uncertainty in predicting the site of toxic lesions in different species exposed to the same chemical and from short‐term to long‐term tests of the same chemical.

Pollutant release and transfer registers (PRTRs) are becoming a popular measure for addressing industrial pollution in many countries. PRTRs require reporting of emissions from specific industrial sectors and making the information publicly available. This article suggests a framework for comparing PRTRs in order to determine whether they attain their declared goals and which factors, if any, influence their effectiveness. The challenges to such a comparison can be divided into three groups. The first refers to changes that are directly linked to the characteristics of PRTRs: both the changes within a specific system over time and variations among different systems. The second refers to parameters that affect the outcomes of the systems without being directly a part of them. The third involves the relations between the emissions reported to the PRTRs and the associated environmental risk. We suggest an approach that relies onrelative comparison,commensurate with the unique characteristics of each PRTR, that compares their actual outcomes. Such an approach is necessary both due to significant variations among current PRTRs as well as for following the unique policy objectives that are manifested in different PRTRs. Application of thiscomparative approachin the United States, England, Canada, and Australia demonstrates significant differences in PRTR systems across countries and suggests that the mere presence of a PRTR may not lead to reduced industrial emissions. The analysis also demonstrates that emission reductions do not correlate with reductions in risk‐related measures. The article proposes several simple modifications to the composition of current PRTR databases that may facilitate more accurate analysis of results and effective oversight of implementation.

AbstractIn this article, we discuss four vexing problems in risk‐based decision making that John Evans has addressed over the last nearly 40 years and has perennially challenged the two of us and others to think about. We tackle the role in decision making of potential thresholds in dose–response functions, how the lack of health reference values for many chemicals may distort risk management, the challenge of model uncertainty for risk characterization, and the yet‐untapped potential for value‐of‐information analysis to enhance public health decision making. Our theme is that work remains to be done on each of these, but that some of that work would merely involve listening to ideas that John has already offered.

The goal of this study was to systematically evaluate the choices made in deriving a chronic oral noncancer human health reference value (HHRV) for a given chemical by different organizations, specifically those from the U.S. Environmental Protection Agency, Health Canada, RIVM (the Netherlands), and the U.S. Agency for Toxic Substances and Disease Registry. This analysis presents a methodological approach for comparing both the HHRVs and the specific choices made in the process of deriving an HHRV across these organizations. Overall, across the 96 unique chemicals and 171 two‐way organizational comparisons, the HHRV agreed approximately 26% of the time. A qualitative method for identifying the primary factors influencing these HHRV differences was also developed, using arrays of HHRVs across organizations for the same chemical. The primary factors identified were disagreement on the critical or principal study and differential application of the total uncertainty factor across organizations. Of the cases where the total UF was the primary factor influencing HHRV disagreement, the database UF had the greatest influence.

Environmental and public health organizations, including the World Health Organization (WHO) and the U.S. Environmental Protection Agency (USEPA), develop human health reference values (HHRV) that set "safe" levels of exposure to noncarcinogens. Here, we systematically analyze chronic HHRVs from four organizations: USEPA, Health Canada, RIVM (the Netherlands), and the U.S. Agency for Toxic Substances and Disease Registry. This study is an extension of our earlier work and both closely examines the choices made in setting HHRVs and presents a quantitative method for identifying the primary factors influencing HHRV agreement or disagreement.(1) We evaluated 171 organizational comparisons, developing a quantitative method for identifying the factors to which HHRV agreement (that is, when both organizations considering the same data set the identical HHRV values) is most sensitive. To conduct this analysis, a Bayesian belief network was built using expert judgment, including the specific science policy choices analysis made in the context of setting an HHRV. Based on a sensitivity of findings analysis, HHRV agreement is most sensitive to the point of departure value, followed by the total uncertainty factor (UF), critical study, critical effect, animal model, and point of departure approach. This analysis also considered the specific impacts of individual UFs, with the database UF and the subchronic‐to‐chronic UF being identified as primary factors impacting the total UF differences observed across organizations. The sensitivity of findings analysis results were strengthened and confirmed by frequency analyses evaluating which choices most often disagreed when the HHRV and the total UF disagreed.