Occupational risks and the value and modelling of a measurement of severity

In studies of occupational risks, severity, which is a component of the estimation of every risk, appears as a multifaceted entity assessable according to numerous criteria. A method of measuring the degree of severity of the consequences of potentially dangerous events would be of undeniable value to organisations seeking to improve their understanding of the complexity of such events. The need to control severity is highlighted by scientifically acquired improvements in the understanding of occupational risks, by certain new regulatory obligations in Europe, and by some requirements in the financial management of organisations. We put forward a statistical way of integrating several constituent elements of severity and hence of determining a relevant, synthetic, one-dimensional index. This is achieved by means of principal component analysis (PCA), which is used here to calculate a resultant severity, as in some physical measurements. We also investigate how severity may be statistically modelled, with the aim of contributing to the quantitative assessment of occupational risks. The choice of parametric models is detailed and illustrated by the search for a suitable model for workplace accidents in an organisational setting. The practical value of modelling severity is two-fold. First, one is able to study the distribution of the numerical values of severity over a continuum (a theoretically infinite numerical set) rather than through a limited number of arbitrarily defined categories. Second, with a generally applicable parametric model, one can estimate the law of probability of a measurement of severity in a particular situation, notably recent or new. Lastly, the statistical concept of risk curve is defined and discussed. The goal is to incorporate the severity component into the risk assessment in the form of a probability law, thus circumventing the difficulties associated with an analysis of scenarios.