The need for One Health systems-thinking approaches to understand multiscale dissemination of antimicrobial resistance

TY - JOUR

T1 - The need for One Health systems-thinking approaches to understand multiscale dissemination of antimicrobial resistance

AU - Arnold, Kathryn E.

AU - Laing, Gabrielle

AU - McMahon, Barry J.

AU - Fanning, Séamus

AU - Stekel, Dov J.

AU - Pahl, Ole

AU - Coyne, Lucy

AU - Latham, Sophia M.

AU - McIntyre, K. Marie

N1 - Funding Information: We would like to thank everyone who participated in the workshop on which this Personal View was based. This study was generously funded by the N8 Research Partnership, Engineering and Physical Sciences Research Council, and the University of York. The funding sources were not involved in the writing of this paper. Publisher Copyright: © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

PY - 2024/2/1

Y1 - 2024/2/1

N2 - Although the effects of antimicrobial resistance (AMR) are most obvious at clinical treatment failure, AMR evolution, transmission, and dispersal happen largely in environmental settings, for example within farms, waterways, livestock, and wildlife. We argue that systems-thinking, One Health approaches are crucial for tackling AMR, by understanding and predicting how anthropogenic activities interact within environmental subsystems, to drive AMR emergence and transmission. Innovative computational methods integrating big data streams (eg, from clinical, agricultural, and environmental monitoring) will accelerate our understanding of AMR, supporting decision making. There are challenges to accessing, integrating, synthesising, and interpreting such complex, multidimensional, heterogeneous datasets, including the lack of specific metrics to quantify anthropogenic AMR. Moreover, data confidentiality, geopolitical and cultural variation, surveillance gaps, and science funding cause biases, uncertainty, and gaps in AMR data and metadata. Combining systems-thinking with modelling will allow exploration, scaling-up, and extrapolation of existing data. This combination will provide vital understanding of the dynamic movement and transmission of AMR within and among environmental subsystems, and its effects across the greater system. Consequently, strategies for slowing down AMR dissemination can be modelled and compared for efficacy and cost-effectiveness.

AB - Although the effects of antimicrobial resistance (AMR) are most obvious at clinical treatment failure, AMR evolution, transmission, and dispersal happen largely in environmental settings, for example within farms, waterways, livestock, and wildlife. We argue that systems-thinking, One Health approaches are crucial for tackling AMR, by understanding and predicting how anthropogenic activities interact within environmental subsystems, to drive AMR emergence and transmission. Innovative computational methods integrating big data streams (eg, from clinical, agricultural, and environmental monitoring) will accelerate our understanding of AMR, supporting decision making. There are challenges to accessing, integrating, synthesising, and interpreting such complex, multidimensional, heterogeneous datasets, including the lack of specific metrics to quantify anthropogenic AMR. Moreover, data confidentiality, geopolitical and cultural variation, surveillance gaps, and science funding cause biases, uncertainty, and gaps in AMR data and metadata. Combining systems-thinking with modelling will allow exploration, scaling-up, and extrapolation of existing data. This combination will provide vital understanding of the dynamic movement and transmission of AMR within and among environmental subsystems, and its effects across the greater system. Consequently, strategies for slowing down AMR dissemination can be modelled and compared for efficacy and cost-effectiveness.

UR - http://www.scopus.com/inward/record.url?scp=85184514009&partnerID=8YFLogxK

U2 - 10.1016/S2542-5196(23)00278-4

DO - 10.1016/S2542-5196(23)00278-4

M3 - Review article

C2 - 38331529

SN - 2542-5196

VL - 8

SP - e124-e133

JO - The Lancet Planetary Health

JF - The Lancet Planetary Health

IS - 2

ER -