Prioritization, resource allocation and utilization of decision support tools in animal health: Results of qualitative interviews with experts - Amenu, Kebede; Daborn, C.; Huntington, B.; Knight-Jones, Theodore J.D.; Rushton, J.; Grace, Delia (Elsevier, 2024)
A follow up to an online questionnaire survey (in a kind of a sequential study design), qualitative assessment was made on the views of selected animal health experts on disease prioritization methods, resource allocation and use of decision-support tools. This was done through in-depth interviews with experts working for national or international organizations and sectors. A semi-structured question guide was formulated based on the information generated in the online questionnaire and a systematic content analysis of animal and human health manuals for disease prioritization and resource allocation. In-depth, one-on-one, online interviews on the process of disease prioritization, animal health decision-making, types of prioritization tools and aspects of improvements in the tools were conducted during March and April 2022 with 20 expert informants. Prioritization approaches reported by experts were either single criterion-based or multiple criteria-based. Experts appreciated the single-criterion-based approach (quantitative) for its objectivity in contrast to multicriteria prioritization approaches which were criticized for their subjectivity. Interviews with the experts revealed a perceived lack of quality and reliable data to inform disease prioritization, especially in smallholder livestock production systems. It was found that outputs of disease prioritization exercises do not generally directly influence resource allocation in animal health and highlighted the paucity of funding for animal health compared to other agricultural sectors. The experts considered that the available decision-support tools in animal health need improvement in terms of data visualization for interpretation, management decision making and advocacy. Further recommendations include minimizing subjective biases by increasing the availability and quality of data and improving the translation of disease prioritization outputs into actions and the resources to deliver those actions.