Epidemiologic application of a dosimetric model of dust overload.

Evidence from laboratory animals suggests that certain rates of deposition of dust can slow the normal clearance of the lungs by macrophages. If this occurs in humans, then estimates of exposure or dose for epidemiologic models may be improved by explicity incorporating this "overload" phenomenon into lung dose estimates. Using a model of dust overload, the authors estimated the lung dust dose for a group of workers exposed to silicon carbide dust. From a knowledge of underlying biologic mechanisms and using goodness of fit tests, the authors identified parameters for the dosimetric model that yielded dose estimates which fit the epidemiologic data well. The dosimetric estimates also compare favorably with cumulative exposure as risk predictors of radiographic evidence of pulmonary fibrosis. The evidence for overload is not strong, however, since the data are also consistent with other clearance patterns including very slow linear one-compartment clearance. Nevertheless, in some data sets, use of a dosimetric model instead of cumulative exposure to dust may reduce misclassification in exposure and improve fit to epidemiologic data. Furthermore, evidence of a better fit would provide valuable information about biologic mechanisms or aspects of dose such as the effect of short-term, high-intensity exposures. These in turn might provide information about prevention strategies such as standard setting.