Health Assessment Document for Diesel Emissions - NSCEP | US ...
Health Assessment Document for Diesel Emissions - NSCEP | US ...
Health Assessment Document for Diesel Emissions - NSCEP | US ...
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1 be varied (<strong>for</strong> example, by including a background term or a threshold term), the number of<br />
2 model runs <strong>for</strong> a database as complete as that <strong>for</strong> diesel exhaust can become unmanageable. In<br />
3 addition to this large number of endpoints and models, several dose or exposure terms could be<br />
4 used (e.g., <strong>for</strong> rats one could use the exposure concentration, the exposure concentration x time,<br />
·5 the duration-average exposure concentration, the lung burden estimated by a deposition model, or<br />
6 the human equivalent concentration based on a deposition and Clearance model). Clearly, the<br />
7 number of possible model runs expands geometrically, and some decisions must be made to limit<br />
8 and focus the extent of_the analyses.<br />
9 In these analyses, the dose metric used <strong>for</strong> rat data was the human equivalent continuous<br />
10 exposure concentration based on the Yu and Yoon (1990) deposition/clearance model. For mice,<br />
11 the duration-averaged exposure concentration was used. The 41 data sets were modeled using<br />
12 the polynomial model dichotomous or continuous data (Howe, 1990a, 1990b ), based on extra<br />
13 risk, and including or excluding a threshold term. The inclusion of a threshold term improved<br />
14 model fit substantially only in cases with low-dose. groups showing no response over controls. In<br />
15 most cases a background term was also estimated, and in some analyses with dichotomous data it<br />
16 was omitted. As expected, the inclusion of a background term improved model fit <strong>for</strong><br />
17 dichotomous data only when there was a nonzero response in the controls. Based on the results<br />
18 using the polynomial model, 12 data sets were selected <strong>for</strong> analysis with the Wei bull model<br />
19 (Howe, 1990c, 1990d) to determine the sensitivitY of the BMC estimate to the choice of model.<br />
20 The availability of different dose metrics and different models <strong>for</strong> different endpoints introduces<br />
21 additional difficulty in determining the most appropriate BMC <strong>for</strong> deriving the RfC. This<br />
22 difficulty is lessened by the fact that the rat data <strong>for</strong> BMC analysis are the most extensive, they<br />
23 include several studies <strong>for</strong> which both LOAEL and NOAEL are identified, and they are also the<br />
24 data <strong>for</strong> which the deposition and clearance model is available. As discussed previously, the data<br />
25 from other species do not suggest large differences in species sensitivity at low concentrations.<br />
26 Perhaps the most critical decision in the BMC approach is the level of response defined<br />
27 as the benchmark response. As discussed by EPA (1995b ), there is an emerging consensus that a<br />
28 BMR of 0.05 or 0.1 is probably appropriate <strong>for</strong> dichotomous data <strong>for</strong> most endpoints. There is<br />
29 no clear consensus about the appropriate choice ofBMR or on how to select a BMR <strong>for</strong> a<br />
30 continuous effect. This dilemma is the main reason <strong>for</strong> the appeal of models such as that<br />
31 presented by Crump (1995) or by Gaylor and Slikker (1990), which derive a BMC in terms of a<br />
32 probability statement. However, both of those models require that some magnitude of response<br />
33 be selected to delineate between "responders" and "nonresponders," so the issue remains as to<br />
34 how one can consistently define a response lev.el <strong>for</strong> the myriad endpoints found in the<br />
35 toxicological literature. One approach is to work backward from the way the BMC will be used<br />
2/1/98 6-18 DRAFT --DO NOT CITE OR QUOTE