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Updated: Jan 8, 2026

Author Spotlight: Understanding and Detecting Environmental Antimicrobial Resistance by Combining Culture-Based Techniques and Genomics
Published on: July 19, 2024
Antimicrobial resistance and environmental-lifestyle exposures: A canonical analysis of national mortality indicators
1Ministry of Health, Zonguldak Provincial Health Directorate, Department of Public Health Services, Zonguldak, 67100, Türkiye; Health Management Program, Graduate School of Health Sciences, Üsküdar University, İstanbul, 34664, Türkiye.
Abstract:
Antimicrobial resistance (AMR) poses a growing global health and development threat, with environmental and lifestyle exposures increasingly recognised as underexplored contributors to resistance dynamics. This ecological study examines associations between AMR-associated and AMR-attributable mortality and three exposure indicators: death rates from air pollution, dietary risks, and tobacco use across 46 OECD and G20 countries. Canonical correlation analysis was employed to examine latent multivariate relationships between age-standardised mortality rates for AMR and environmental-lifestyle exposures. Data from 2019 were selected to minimize confounding from the COVID-19 pandemic. Findings revealed a dominant canonical construct (Canonical Variate 1 [CV1]) significantly linking mortality rates of environmental-lifestyle exposures to AMR outcomes (r = 0.87, p < 0.001, shared variance = 75.7 %). Air pollution-attributable mortality showed the strongest association with AMR-associated and AMR-attributable mortality (r = 0.860 and r = 0.858), followed by dietary- and tobacco-attributable mortality. CV1 explained 47.3 % of variance in AMR outcomes and 75.5 % in predictors. Canonical scores varied significantly across country groups, with G20 nations showing higher alignment with the AMR-related exposure profile than OECD counterparts. The results underscore the importance of integrating modifiable environmental and lifestyle exposures into AMR surveillance. By extending beyond clinical misuse, this approach supports cross-sectoral mitigation strategies aligned with One Health principles. However, using mortality rates as proxy exposures introduces potential shared-system confounding, ecological fallacy, and circularity, limiting causal interpretation. To strengthen causal inference and inform targeted interventions, longitudinal cohort studies are needed to clarify temporal and mechanistic links between exposure pathways and resistance-related mortality.
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