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.
Keywords: Acquisition; Asia; Monte-Carlo simulation; One-Health; antibiotic resistance; exposure assessment; health risk assessment; transmission summary for social media.
© 2020 Society for Risk Analysis.