부산대학교 도서관

Antimicrobial resistance (AMR) has become a major threat worldwide, especially in countries with inadequate sanitation and low antibiotic regulation. However, adequately prioritizing AMR interventions in such settings requires a quantification of the relative impacts of environmental, animal, and human sources in a One‐Health perspective. Here, we propose a stochastic quantitative risk assessment model for the different components at interplay in AMR selection and spread. The model computes the incidence of AMR colonization in humans from five different sources: water or food consumption, contacts with livestock, and interhuman contacts in hospitals or the community, and combines these incidences into a per‐year acquisition risk. Using data from the literature and Monte‐Carlo simulations, we apply the model to hypothetical Asian‐like settings, focusing on resistant bacteria that may cause infections in humans. In both scenarios A, illustrative of low‐income countries, and B, illustrative of high‐income countries, the overall individual risk of becoming colonized with resistant bacteria at least once per year is high. However, the average predicted incidence of colonization was lower in scenario B at 0.82 (CrI [0.13, 5.1]) acquisitions/person/year, versus 1.69 (CrI [0.66, 11.13]) acquisitions/person/year for scenario A. A high percentage of population with no access to improved water on premises and a high percentage of population involved in husbandry are shown to strongly increase the AMR acquisition risk. The One‐Health AMR risk assessment framework we developed may prove useful to policymakers throughout Asia, as it can easily be parameterized to realistically reproduce conditions in a given country, provided data are available. [ABSTRACT FROM AUTHOR]