Limitations of quantitative risk assessment using aggregate exposure and risk models

Abstract

Chapter 4 showed that risk matrices can assign small risks to high-risk categories and larger risks to lower-risk categories, defeating the intent of the classification system. Do other methods necessarily do better? This chapter shows that careless use of quantitative risk assessment concepts can also lead to worse-than-useless risk comparisons and recommendations. This happens if causal drivers of risk (such as agespecific failure rates, detailed exposures, or individual dose-response relations) are ignored in favor of potentially meaningless aggregate quantities (such as “average annual frequency,” “aggregate exposure,” or “population exposure-response ratio,” respectively). A lesson from Chapter 4 was that risk matrices cannot correctly compare some risks. The main lesson from this chapter is milder. Care must be taken in using quantitative risk concepts to make sure that they correctly represent causal relations among actions, exposures, and probable consequences. Otherwise, they may give rise to meaningless or misleading numbers and predictions.