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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Global modelling of food-system factors influencing antimicrobial-resistance exposure risk: implications for health promotion and nutrition policy

Authors: Saber, Amr;

Global modelling of food-system factors influencing antimicrobial-resistance exposure risk: implications for health promotion and nutrition policy

Abstract

Background: Antimicrobial resistance (AMR) increasingly intersects with dietary patterns and food-system safety. Within a One Health frame, we examined how national diet composition, dietary industrialisation, and economic capacity relate to food-borne AMR exposure potential. Methods: We assembled harmonised country-year data from FAO Food Balance Sheets and World Bank Development Indicators for 180 countries from 2010–2019. We defined a reproducible AMR Risk Proxy combining animal-source food intake and GDP per capita, and modelled associations with a Processing Index (alcohol, oils and fats, sugars, sweeteners), animal-source intake, and GDP per capita using OLS with heteroskedasticity-robust standard errors. Prespecified stratified analyses compared low- versus high-income contexts. Results: Across 2,227 observations, higher animal-source food intake was positively associated with the AMR Risk Proxy, while GDP per capita showed a protective association; the Processing Index had a smaller positive association. In income-stratified models, processing intensity related to higher exposure potential in low-income settings but attenuated in high-income settings, consistent with stronger food-safety governance. Conclusion: Food-system determinants—dietary patterns, processing intensity, and economic capacity—are linked to potential food-borne AMR exposure. Integrating balanced, plant-rich diet promotion with investments in food-safety infrastructure and hygiene regulation can advance community-level AMR prevention. The AMR Risk Proxy offers a practical indicator for monitoring and evaluating health-promotion and nutrition-policy interventions.

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average