Prioritizing zoonotic diseases: differences in perspectives between human and animal health professionals in North America.
Zoonoses pose a significant burden of illness in North America. Zoonoses represent an additional threat to public health because the natural reservoirs are often animals, particularly wildlife, thus eluding control efforts such as quarantine, vaccination and social distancing. As there are limited resources available, it is necessary to prioritize diseases in order to allocate resources to those posing the greatest public health threat. Many studies have attempted to prioritize zoonoses, but challenges exist. This study uses a quantitative approach, conjoint analysis (CA), to overcome some limitations of traditional disease prioritization exercises. We used CA to conduct a zoonoses prioritization study involving a range of human and animal health professionals across North America; these included epidemiologists, public health practitioners, research scientists, physicians, veterinarians, laboratory technicians and nurses. A total of 699 human health professionals (HHP) and 585 animal health professionals (AHP) participated in this study. We used CA to prioritize 62 zoonotic diseases using 21 criteria. Our findings suggest CA can be used to produce reasonable criteria scores for disease prioritization. The fitted models were satisfactory for both groups with a slightly better fit for AHP compared to HHP (84.4% certainty fit versus 83.6%). Human-related criteria were more influential for HHP in their decision to prioritize zoonoses, while animal-related criteria were more influential for AHP resulting in different disease priority lists. While the differences were not statistically significant, a difference of one or two ranks could be considered important for some individuals. A potential solution to address the varying opinions is discussed. The scientific framework for disease prioritization presented can be revised on a regular basis by updating disease criteria to reflect diseases as they evolve over time; such a framework is of value allowing diseases of highest impact to be identified routinely for resource allocation.