Risk Estimation with Epidemiologic Data When Response Attenuates at High-Exposure Levels

• Type of Material: Article
• Authors: Kyle Steenland, Emory University; Ryan Seals, Emory University; Mitchel Klein, Emory University; Jennifer Jinot, U.S. Environmental Protection Agency; Henry D. Kahn, U.S. Environmental Protection Agency
• Language: English
• Date: 2011-01-10
• Publisher: National Institute of Environmental Health Sciences (NIEHS)
• Publication Version: Final Published Version
• Copyright Statement: Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely.
• Final Published Version (URL): http://ehp.niehs.nih.gov/1002521/
• Title of Journal or Parent Work: Environmental Health Perspectives
• ISSN: 0091-6765
• Volume: 119
• Issue: 6
• Start Page: 831
• End Page: 837
• Grant/Funding Information: This study was supported by the U.S. Environmental Protection Agency.
• Keywords: risk assessment; ethylene oxide; statistical models
• Research Categories: Health Sciences, Epidemiology; Health Sciences, Public Health

Abstract

Background: In occupational studies, which are commonly used for risk assessment for environmental settings, estimated exposure–response relationships often attenuate at high exposures. Relative risk (RR) models with transformed (e.g., log- or square root–transformed) exposures can provide a good fit to such data, but resulting exposure–response curves that are supralinear in the low-dose region may overestimate low-dose risks. Conversely, a model of untransformed (linear) exposure may underestimate risks attributable to exposures in the low-dose region. Methods: We examined several models, seeking simple parametric models that fit attenuating exposure–response data well. We have illustrated the use of both log-linear and linear RR models using cohort study data on breast cancer and exposure to ethylene oxide. Results: Linear RR models fit the data better than do corresponding log-linear models. Among linear RR models, linear (untransformed), log-transformed, square root–transformed, linear-exponential, and two-piece linear exposure models all fit the data reasonably well. However, the slopes of the predicted exposure–response relations were very different in the low-exposure range, which resulted in different estimates of the exposure concentration associated with a 1% lifetime excess risk (0.0400, 0.00005, 0.0016, 0.0113, and 0.0100 ppm, respectively). The linear (in exposure) model underestimated the categorical exposure–response in the low-dose region, whereas log-transformed and square root–transformed exposure models overestimated it. Conclusion: Although a number of models may fit attenuating data well, models that assume linear or nearly linear exposure–response relations in the low-dose region of interest may be preferred by risk assessors, because they do not depend on the choice of a point of departure for linear low-dose extrapolation and are relatively easy to interpret.