Edward Calabrese has published a fascinating and terribly important paper in the University of Chicago Law Review titled US Risk Assessment Policy: A History of Deception that needs to be widely distributed and discussed. Here is the quoted introduction:

Strategies to limit the  general  public’s  exposure  to toxic  substances—via national standards such as community-based drinking water and air quality standards, food residue regulations, hazardous-waste siting decisions, or other strategies—are based on multiple factors including social, political, cultural, historical, economic, technological, as well as public health–related concerns. At the core of these decisions is the need for risk assessment estimates to be based on a sound foundation, using scientifically validated procedures and having high reliability. However, while it may be hard to believe, and even more difficult to accept, the foundation of our fundamental dose-response model—that is, the threshold dose-response— upon which all public health standards were originally based, and upon which we still highly depend, was never validated by the regulatory and scientific communities prior to its adoption by the FDA, EPA, OSHA, and other agencies in the United States and elsewhere in the world.

Calabrese’s paper is not a scientific work; it is a masterful work of scientific history, tracing the evolution of underlying assumptions about dose response. By explaining how the underlying basis for many regulations associated with both chemicals and radiation was never properly tested, Calabresse helps us understand why we have so many nonsensical regulations that seem to be based on solid mathematics.

Though only one dose response model is mentioned in the introduction, Calabrese actually discusses three available dose-response models: threshold, hormesis and Linear No-Threshold (LNT). He also explains how thoroughly testing available models shows that only one of the three – the one that is not currently being used by regulators – consistently makes predictions that align with observed scientific data.

Though there are many who reject her out of hand, Ayn Rand was perceptive enough about human behavior that her works have been on best seller lists for several decades. One of her frequent admonitions is that if something does not make sense, you need to check your premises. In my own educational journey, I have repeatedly experienced the fact that one can perform math perfectly, but obtain incorrect answers when using the wrong initial assumptions.

Since US voters agreed a long time ago to ask the government to help to protect our health and safety by issuing regulations, we should at least demand that our regulators reevaluate those rules in the face of better science and history that proves their fundamental assumptions were so wrong that they have led to harmfully inaccurate regulations.

In many discussions with professional colleagues regarding the LNT, I have run into people who generally agree that it is not correct, but they like the answers that it provides. They have also told me that they are unwilling to advocate for any changes unless we have something that can be used to replace it as the basis for regulation. Calabrese and his colleagues have shown that there is a better model that more accurately predicts health effects, both positive and negative.

The excuses continue to fall away. It is time to push regulators to change their modeling assumptions and then redo the math to produce regulation that truly protect our health and safety without putting us at risk of not beneficially using safe materials and processes because of poor assumptions about their risk profiles.