A review of qualitative risk assessment in animal health: Suggestions for best practice - Horigan, Verity; Simons, Robin; Kavanagh, Kim; Kelly, Louise (Frontiers in Veterinary Science, 2023)
Qualitative risk assessment (QRA) can provide a decision support system, but in order for it to be objective and consistently applied it is necessary to standardise the approach as much as possible. This review considers how QRAs have historically been used for the benefit of animal health, what problems have been encountered during their development, and how these problems have been addressed. Four main elements were identified as having been the subject of some proposed standard methodology: (i) the description of risk levels, (ii) combining probabilities, (iii) accounting for multiple products/animals and (iv) uncertainty. These elements were addressed in different ways but were highlighted as being fundamental to improving the accuracy in estimating the risk and conveying the results to the risk manager with minimal ambiguity. The development of standardisation of methodology thus represents an important advance in animal health QRA. It can also be concluded that QRAs are useful for identifying important chains of events and critical control points along risk pathways which can be used to inform risk management programs. Further work needs to be done regarding an objective uniform methodology for deriving an overall uncertainty estimate. In line with this, several tools have been developed which attempt to use mathematical reasoning to incorporate uncertainty and improve the objectivity of the qualitative framework. Ensuring a robust objective methodology is used and that the reasons for differences in results, such as assumptions and uncertainty are clearly described to the customer is essential to maintain confidence in the QRA process. Overall, animal health QRAs have established their usefulness by providing a tool for rapid risk estimation which can be used to identify whether or not the risk exceeds a decision-making threshold above which action should be taken.