Abstract
Microorganisms’ ability to endure the effects of antimicrobials they were previously susceptible to, namely antimicrobial resistance (AMR), is an ongoing threat to public health. The Integrated Assessment Model for Antimicrobial Resistance (iAM.AMR) is a research initiative at the Public Health Agency of Canada that conceptualizes pathways that resistant enteric bacteria take along the farm-to-fork continuum. One of the model’s aims is to predict the prevalence of resistant enteric disease in Canada and to estimate its subsequent burden of illness (BOI) and economic impact. The iAM.AMR is presently able to predict the risk of human exposure to resistant bacteria, and the purpose of this practicum was to incorporate data that connect risk of exposure to risk of subsequent illness.
Measures of association and related data were extracted from studies identified in a previously-conducted scoping review of human factors that influence extended-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae colonization and/or infection in humans. Only data from studies whose outcome was resistant bacteria carriage/colonization were extracted during the practicum. Factors and corresponding measures of association were organized into multiple levels of categories using Excel and R software.
A total of 1369 eligible factors from 10 factor themes were extracted and categorized from 124 observational studies. A relatively broad range of odds ratios above and below 1 were observed across the different themes and subcategories, demonstrating how more studies and/or a more disaggregated analysis is required to better determine which factors are protective for ESBL colonization, and which factors increase risk.
This analysis is a first step towards better understanding which human factors influence colonization of ESBL-producing Enterobacteriaceae after exposure. Once factors for ESBL infection and factors for subsequent BOI are identified and integrated into the iAM.AMR, the model will be able to predict the risk of illness after exposure to resistant bacteria. This can be repeated for other types of resistant bacteria, and thus expand the iAM.AMR’s capability to predict BOI for a range of different types of enteric AMR. With this ability, the iAM.AMR will have the potential to inform policies and public health measures that aim to prevent the emergence and spread of resistant enteric bacteria.