Challenges in Defining Thresholds for Health Effects: Some Considerations for Asbestos and Silica

Julie Goodman ^1* Lorenz Rhomberg ¹ Samuel Cohen ² Kenneth A. Mundt ³ Bruce Case ⁴ Igor Burstyn ⁵ Michael Becich ⁶ Graham Gibbs ⁷

• ¹ Gradient, Boston, United States
• ² University of Nebraska Medical Center, Omaha, Nebraska, United States
• ³ University of Massachusetts Amherst, Amherst, Massachusetts, United States
• ⁴ McGill University, Montreal, Quebec, Canada
• ⁵ Drexel University, Philadelphia, Pennsylvania, United States
• ⁶ School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
• ⁷ Private Consultant in Epidemiology and Occupational Health, Eastbourne, United Kingdom

This paper summarizes several presentations in the Thresholds in Epidemiology and Risk Assessment session at the Monticello III conference. These presentations described evidence regarding thresholds for particles, including asbestos and silica, and cancer (e.g., mesothelioma) and non-cancer (e.g., silicosis) endpoints. In the case of exposure to various types of particles and malignancy, it is clear that even though a linear non-threshold model has often been assumed, experimental and theoretical support for thresholds exist (e.g., through particle clearance, repair mechanisms, and various other aspects of the carcinogenic process). For mesothelioma and exposure to elongate mineral particles (EMPs), there remains controversy concerning the epidemiological demonstration of thresholds. However, using data from the Québec mining cohort studies, it was shown that a "practical" threshold exists for chrysotile exposure and mesothelioma. It was also noted that, in such evaluations, measurement error in diagnosis and exposure assessment needs to be incorporated into risk analyses. Researchers were also encouraged to use biobanks that collect specimens and data on mesothelioma to more precisely define cases of mesothelioma and possible variants for cases of all ages, and trends that may help define background rates and distinguish those mesotheliomas related to EMP exposures from those that are not, as well as other factors that support or define thresholds. New statistical approaches have been developed for identifying and quantifying exposure thresholds, an example of which is described for respirable crystalline silica (RCS) exposure and silicosis risk. Finally, the application of Artificial Intelligence (AI) to considering the multiple factors influencing risk and thresholds may prove useful.