Quantitatively evaluating the cross-sectoral and one health impact of interventions: A scoping review and case study of antimicrobial resistance
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Naylor, N.R., Lines, J., Waage, J., Wieland, B. and Knight, G.M. 2020. Quantitatively evaluating the cross-sectoral and one health impact of interventions: A scoping review and case study of antimicrobial resistance. One Health 11: 100194.
Permanent link to cite or share this item: https://hdl.handle.net/10568/110224
Background: Current frameworks evaluating One Health (OH) interventions focus on intervention-design and -implementation. Cross-sectoral impact evaluations are needed to more effectively tackle OH-issues, such as antimicrobial resistance (AMR). We aimed to describe quantitative evaluation methods for interventions related to OH and cross-sectoral issues, to propose an explicit approach for evaluating such interventions, and to apply this approach to AMR. Methods: A scoping review was performed using WebofScience, EconLit, PubMed and gray literature. Quantitative evaluations of interventions that had an impact across two or more of the human, animal and environment sectors were included. Information on the interventions, methods and outcome measures found was narratively summarised. The information from this review informed the construction of a new approach to OH-related intervention evaluation, which then was applied to the field of AMR. Results: The review included 90 studies: 73 individual evaluations (from 72 papers) and 18 reviews, with a range of statistical modelling (n = 13 studies), mathematical modelling (n = 53) and index-creation/preference-ranking (n = 14) methods discussed. The literature highlighted the need to (I) establish stakeholder objectives, (II) establish quantifiable outcomes that feed into those objectives, (III) establish agents and compartments that affect these outcomes and (IV) select appropriate methods (described in this review) accordingly. Based on this, an evaluation model for AMR was conceptualised; a decision-tree of intervention options, a compartmental-microeconomic model across sectors and a general-equilibrium (macroeconomic) model are linked. The outcomes of this multi-level model (including cost-utility and Gross Domestic Product impact) can then feed into multi-criteria-decision analyses that weigh respective impact estimates alongside other chosen outcome estimates (for example equity or uncertainty). Conclusion: In conclusion, stakeholder objectives are key in establishing which evaluation methods (and associated outcome measures) should be used for OH-related interventions. The stated multi-level approach also allows for sub-systems to be modelled in succession, where resources are constrained.
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