Identifying and measuring stakeholder preferences for disease prioritisation: A case study of the pig industry in Australia

We describe stakeholder preference modelling using a combination of new and recently developed techniques to elicit criterion weights to incorporate into a multi-criteria decision analysis framework to prioritise exotic diseases for the pig industry in Australia. Australian pig producers were requested to rank disease scenarios comprising nine criteria in an online questionnaire. Parallel coordinate plots were used to visualise stakeholder preferences, which aided identification of two diverse groups of stakeholders – one group prioritised diseases with impacts on livestock, and the other group placed more importance on diseases with zoonotic impacts. Probabilistic inversion was used to derive weights for the criteria to reflectthe values of each ofthese groups, modelling their choice using a weighted sum value function.Validation of weights against stakeholders’ rankings for scenarios based on real diseases showed that the elicited criterion weights for the group who prioritised diseases with livestock impacts were a good reflection of their values, indicating that the producers were able to consistently infer impacts from the disease information in the scenarios presented to them. The highest weighted criteria for this group were attack rate and length of clinical disease in pigs, and market loss to the pig industry. The values of the stakeholders who prioritised zoonotic diseases were less well reflected by validation, indicating either that the criteria were inadequate to consistently describe zoonotic impacts, the weighted sum model did not describe stakeholder choice, or that preference modelling for zoonotic diseases should be undertaken separately from livestock diseases. Limitations of this study included sampling bias, as the group participating were not necessarily representative of all pig producers in Australia, and response bias within this group. The method used to elicit criterion weights in this study ensured value trade-offs between a range of potential impacts, and that the weights were implicitly related to the scale of measurement of disease criteria. Validation of the results of the criterion weights against real diseases – a step rarely used in MCDA – added scientific rigour to the process. The study demonstrated that these are useful techniques for elicitation of criterion weights for disease prioritisation by stakeholders who are not disease experts. Preference modelling for zoonotic diseases needs further characterisation in this context.