A Unified Probabilistic Framework for Cancer Risk Management

Cancer risk assessments in the regulatory realm are often deterministic. Probabilistic approaches that allow characterizing and propagating uncertainty and variability are better suited to predict the socioeconomic impacts of regulating carcinogens. In this article, I present a unified framework for cancer risk management consisting of (i) a probabilistic exposure model that takes into account variability in individual exposure to the substance of concern; (ii) a probabilistic dose-response model that accounts for differences in individual cancer susceptibility; (iii) an impact assessment model that quantifies individuals' excess lifetime cancer risk; and (iv) a welfare model that values changes in disability-adjusted life expectancy based on workers' willingness-to-pay and aggregates individual valuations across the population at risk. I illustrate the framework with data on occupational exposure to hexavalent chromium in France. In a cohort of 10,000 synthetic workers, about one third of the exposed benefit from the introduction of a binding occupational exposure limit (BOEL). Limiting hexavalent chromium exposure to the BOEL reduces the statistical worker's excess lifetime risk of fatal and nonfatal lung cancer by 4.7E-3 and 1.5E-3, respectively. At cohort level, the risk reduction corresponds to 738.4 full and 30.7 disability-adjusted life years saved. The expected welfare gain of introducing the BOEL is close to €30 million. A major advantage of the framework is its ability to visualize uncertainty and variability inherent to cancer risk assessment. Notwithstanding some implementation challenges, the framework provides a transparent characterization of regulatory impacts that supports informed risk management decisions.