Anushia Ashokan1,2,3, Josh Hanson4,5, Ne Myo Aung5,6, Mar Mar Kyi5,6, Steven L Taylor1,2, Jocelyn M Choo1,2, Erin Flynn1,2, Fredrick Mobegi1,2, Morgyn S Warner3,7, Steve L Wesselingh8, Mark A Boyd3, Geraint B Rogers9,10. 1. Microbiome and Host Health, South Australia Health and Medical Research Institute, Adelaide, SA, 5001, Australia. 2. SAHMRI Microbiome Research Laboratory, Flinders University College of Medicine and Public Health, Adelaide, SA, Australia. 3. Faculty of Health and Medical Sciences, University of Adelaide, North Terrace, Adelaide, SA, Australia. 4. Cairns Hospital, Cairns, QLD, Australia. 5. Insein General Hospital, Insein, Yangon, Myanmar. 6. University of Medicine 2, Yangon, Myanmar. 7. South Australia (SA) Pathology, Adelaide, SA, Australia. 8. South Australia Health and Medical Research Institute, Adelaide, SA, Australia. 9. Microbiome and Host Health, South Australia Health and Medical Research Institute, Adelaide, SA, 5001, Australia. geraint.rogers@sahmri.com. 10. SAHMRI Microbiome Research Laboratory, Flinders University College of Medicine and Public Health, Adelaide, SA, Australia. geraint.rogers@sahmri.com.
Abstract
BACKGROUND: Antimicrobial resistance (AMR) represents a profound global health threat. Reducing AMR spread requires the identification of transmission pathways. The extent to which hospital wards represent a venue for substantial AMR transmission in low- and middle-income countries settings is poorly understood. METHODS: Rectal swabs were obtained from adult male inpatients in a "Nightingale" model general medicine ward in Yangon, Myanmar. Resistome characteristics were characterised by metagenomic sequencing. AMR gene carriage was related to inter-patient distance (representing inter-patient interaction) using distance-based linear models. Clinical predictors of AMR patterns were identified through univariate and multivariate regression. RESULTS: Resistome similarity showed a weak but significant positive correlation with inter-patient distance (r = 0.12, p = 0.04). Nineteen AMR determinants contributed significantly to this relationship, including those encoding β-lactamase activity (OXA-1, NDM-7; adjusted p < 0.003), trimethoprim resistance (dfrA14, adjusted p = 0.0495), and chloramphenicol resistance (catB3, adjusted p = 0.002). Clinical traits of co-located patients carrying specific AMR genes were not random. Specifically, AMR genes that contributed to distance-resistome relationships (OXA-1, catB3, dfrA14) mapped to tuberculosis patients, who were placed together according to ward policy. In contrast, patients with sepsis were not placed together, and carried AMR genes that were not spatially significant or consistent with shared antibiotic exposure. CONCLUSIONS: AMR dispersion patterns primarily reflect the placement of particular patients by their condition, rather than AMR transmission. The proportion of AMR determinants that varied with inter-patient distance was limited, suggesting that nosocomial transmission is a relatively minor contributor to population-level carriage.
BACKGROUND: Antimicrobial resistance (AMR) represents a profound global health threat. Reducing AMR spread requires the identification of transmission pathways. The extent to which hospital wards represent a venue for substantial AMR transmission in low- and middle-income countries settings is poorly understood. METHODS: Rectal swabs were obtained from adult male inpatients in a "Nightingale" model general medicine ward in Yangon, Myanmar. Resistome characteristics were characterised by metagenomic sequencing. AMR gene carriage was related to inter-patient distance (representing inter-patient interaction) using distance-based linear models. Clinical predictors of AMR patterns were identified through univariate and multivariate regression. RESULTS: Resistome similarity showed a weak but significant positive correlation with inter-patient distance (r = 0.12, p = 0.04). Nineteen AMR determinants contributed significantly to this relationship, including those encoding β-lactamase activity (OXA-1, NDM-7; adjusted p < 0.003), trimethoprim resistance (dfrA14, adjusted p = 0.0495), and chloramphenicol resistance (catB3, adjusted p = 0.002). Clinical traits of co-located patients carrying specific AMR genes were not random. Specifically, AMR genes that contributed to distance-resistome relationships (OXA-1, catB3, dfrA14) mapped to tuberculosis patients, who were placed together according to ward policy. In contrast, patients with sepsis were not placed together, and carried AMR genes that were not spatially significant or consistent with shared antibiotic exposure. CONCLUSIONS: AMR dispersion patterns primarily reflect the placement of particular patients by their condition, rather than AMR transmission. The proportion of AMR determinants that varied with inter-patient distance was limited, suggesting that nosocomial transmission is a relatively minor contributor to population-level carriage.
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