Expanding the risk assessment methodology for work-related health: a technique for incorporating multivariate curvilinear effects

Abstract

Although there is conceptual and empirical evidence that supports the existence of possible curvilinear relationships between job characteristics and health outcomes, risk assessments usually rely on linear estimation approaches. However, this approach may not be conducive to good risk management practice. Where curvilinear effects exist, it is possible for there to be too much of a beneficial work characteristic, or too little of one that is harmful. If that is the case then there will be an optimum level for health and well-being. This study explores a new risk estimation technique that can accommodate multivariate curvilinear relationships. The partial derivatives technique can provide stronger predictive utility, incorporate synergistic effects of predictors, and is supported by conceptual work and empirical evidence. To illustrate these ideas, a risk assessment was conducted on a sample of 354 police officers in Greece. Multivariate polynomial regression analyses indicated that a number of job characteristics salient to participants’ experiences were related to outcomes curvilinearly. A risk index (Ri) was derived from a range of values that represent the slopes of all possible lines tangential to the curve that describes the relationship between predictor and outcome. This technique may help to refine and extend current models of risk assessment for work-related health and stimulate new interest in research into risk assessment methodology