WHY DOSE-RESPONSE RELATIONSHIPS ARE OFTEN NONLINEAR AND SOME CONSEQUENCES

Given the dependence of many risk assessments on the assumption of lin earity of dose-response relationships in human populations, we analyze the circumstances likely to lead to non-linearity and test our hypoth esis of the high prevalence of non-linearity by examination of recent literature. Methods. The analaysis of Bross, based on whether irradiat ed cells die or can manifest malignancy, leads him to generalize that if a single exposure can have one of two (or more) countervailing outc omes, non-linearity of dose-response will result. We list four other c ommon mechanisms which would have similar effects: symptom-stimulated withdrawal from exposures to respiratory irritants; certain aspects of the ''healthy worker effect'' (especially its obverse-withdrawal from the work force due to illness); selected consequences of the competin g risks of long-term disease; and shift in relative strength among mul tiple independent variables. We then examine recent literature to see how often reports of linear, monotonic, and non-monotonic dose-respons e relationships occur and discuss the likelihood of countervailing alt ernate outcomes in selected examples. Results. Non-linear and linear r elationships are about equally frequent. Under circumstances where cou ntervailing outcomes are probable, dose-response relationships should be non-linear and often are. These conditions may also lead to non-lin ear difference equations, which may manifest ''chaotic'' attributes. C onclusions. Regulations and policies cannot be routinely derived on th e basis of extrapolating linear dose-response relationships for human populations. Although our analysis is oriented principally to epidemio logy, similar considerations apply to toxicological studies as well.