

Articles
by
Kenny S. Crump 
Total Records (
2 ) for
Kenny S. Crump 





Kenny S. Crump
,
Chao Chen
,
John F. Fox
,
Cynthia Van Landingham
and
Ravi Subramaniam


Conolly et al. (2003, 2004) developed biologically motivated models of formaldehyde carcinogenicity in F344 rats and humans based on a twostage clonal expansion model of cancer. Based on the human model, Conolly et al. (2004) claimed that cancer risks associated with inhaled formaldehyde are deminimis at relevant human exposure levels. However, they did not conduct a sensitivity analysis to evaluate the robustness of this conclusion. Here, we present a limited sensitivity analysis of the formaldehyde human model. We show that when the control animals from the National Toxicology Program (NTP) studies are replaced with control animals only from NTP inhalation studies, estimates of human risk are increased by 50fold. When only concurrent control rats are used, the model does not provide any upper bound (UB) to human risk. No data went into the model on the effect of formaldehyde on the division rates and death rates of initiated cells. We show that slight numerical perturbations to the Conolly et al. assumptions regarding these rates can be made that are equally consistent with the underlying data used to construct the model, but produce estimates of human risk ranging anywhere from negative up to 10 000 times higher than those deemed by Conolly et al. to be ‘conservative’. Thus, we conclude that estimates of human risk by Conolly et al. (2004) are extremely sensitive to modeling assumptions. This calls into question the basis for the Conolly et al. claim of de minimis human risk and suggests caution in using the model to derive human exposure standards for formaldehyde. 




Kenny S. Crump
and
D. wayne Berman


Mounting evidence that long asbestos fibers (e.g. >20 or even 40 μm) pose the greatest cancer risk underscores the need for accurate measurement of concentrations of such fibers. These fiber lengths are of the same order of magnitude as the size of openings in the grids (typically ≈90 μm per side) used to analyze asbestos samples by transmission electron microscopy. This means that a substantial proportion of long fibers will cross the edge of a grid opening (GO) and therefore not be completely visible. Counting rules generally deal with such fibers by assigning a length equal to twice the visible length. Using both theoretical and simulation methods, we show that this doubling rule introduces bias into estimates of fiber concentrations and the amount of bias increases with fiber length. We investigate an alternative counting rule that counts only fibers that lie completely within a GO and weights those fibers by the reciprocal of the probability that a fiber of that length lies totally within a GO. This approach does not have the bias inherent in the doubling rule and is essentially unbiased if the stopping rule specifies a fixed number of GOs to be scanned. However, a stopping rule based on successively scanning GOs until a fixed number of fibers have been counted will introduce bias into any counting method, although this bias may typically not be large enough to be of practical concern. We recommend use of the weighted approach as a supplement to use of the doubling rule when estimating concentrations of long fibers, irrespective of the stopping rule employed. 





