Use of human data in quantitative risk assessment of carcinogens: Impact on epidemiologic practice and the regulatory process

Abstract Epidemiologic data are increasingly being used to assess cancer risk from chemicals as their value is recognized and as more and better studies become available. Weight-of-evidence approaches are now available for classifying the experimental and epidemiological evidence regarding human carcinogenicity. When the human data are extensive and of good quality, they should be given substantial weight in assessing risk. Both the positive and the negative epidemiologic data should be used in a quantitative risk assessment (QRA), because only then can an unbiased risk assessment be derived. Good-quality epidemiological studies are those with sound methodology, lack of bias, long enough follow-up times to observe a carcinogenic response, adequate exposure information, and dose-response information. Before a lack of carcinogenicity can be inferred, it is essential that the exposures be of substantial duration and intensity, and that the number of exposed persons be reasonably large. Epidemiologists need to give more attention to exposure assessment, because lack of quantitative exposure information is often the limiting factor that prevents the use of epidemiologic data in QRA. Development of methods to estimate historical workplace exposure intensities from surrogate industrial hygiene variables should receive high research priority, since they have the potential to increase the usefulness in QRA of many epidemiologic studies that have limited exposure information. Several frequently used surrogates for exposure measurements have limitations or pitfalls in their use. In particular, the use of “ever/never” exposed has a large potential to produce falsely negative results by means of a “dilution” effect, especially in the common case where the exposure distribution is skewed. Duration of exposure (rather than duration × intensity) may also give misleading results. There is little information to suggest that synergistic exposures to multiple toxicants in an industrial environment are likely to invalidate QRAs, probably because few studies have identified a group of workers with major workplace exposures to multiple carcinogens that cause the same type of cancer. Most of the interactive effects which have been identified to date are between smoking and some occupational carcinogen, so this possibility needs careful evaluation for smoking-related diseases. It is important to evaluate dose-response gradients in a QRA to obtain maximum precision and accuracy in the resulting risk coefficient. The analysis should take into account an appropriate cancer induction period. Various methods to account for cancer induction times are compared; those that incorporate a lag period or model the induction-time distribution are superior to other methods. A joint analysis of available epidemiologic data sets is valuable, because it has the potential to provide a more stable central risk estimate and narrower confidence bounds than any individual study. When the raw data or detailed tabulations in an appropriate standard format are supplied, joint analyses can be much more useful than when only the published results are available, because various potentially confounding factors can then be evaluated and dose-response relationships can be examined in parallel. Studies should not be excluded on the grounds of study size, since a joint analysis will weight each study according to its precision. Confidence intervals from epidemiologic studies are useful in evaluating the compatibility of the human data with risk estimates which have been derived from animal data. Incompatibilities between the two should be given careful consideration before standards are set. The biomathematical models for deriving QRAs, which have been applied primarily to animal bioassay data, are being used with human data as well. Comparative studies indicate that all the models have technical weaknesses in their performance, to which the user should be alert. To be more applicable to epidemiologic data, some models have been broadened to incorporate factors such as variability in dose rates over time, but most still need further development to handle epidemiologic risk factors and confounder variables. More attention needs to be paid to the lifetime risk projections that are overlaid on the mathematical models. Constant relative risks for the remaining lifetime have usually been assumed in the regulatory agencies' QRAs, but the epidemiologic data for a number of carcinogenic agents indicate that the relative risk diminishes within a few decades after an exposure. Failure to incorporate this information into the models can cause serious overestimates of lifetime risk.